Microsoft Research https://www.microsoft.com/en-us/research Fri, 30 Sep 2016 03:08:35 +0000 en-US hourly 1 https://wordpress.org/?v=4.5.4 Boosting fitness and workplace performance looks to machine learning for answers https://www.microsoft.com/en-us/research/boosting-fitness-and-workplace-performance-looks-to-machine-learning-for-answers/ Fri, 16 Sep 2016 18:32:57 +0000 https://www.microsoft.com/en-us/research/?p=293258 By Miran Lee, Principal Research Program Manager, Microsoft Research A field study exploring the usability of integrating fitness equipment into a workstation environment has researchers looking to tap next-generation machine learning innovations to address the seemingly elusive challenge of burning calories without ever leaving the desk. Elliptical trainers small enough to fit under a desk […]

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By Miran Lee, Principal Research Program Manager, Microsoft Research

A field study exploring the usability of integrating fitness equipment into a workstation environment has researchers looking to tap next-generation machine learning innovations to address the seemingly elusive challenge of burning calories without ever leaving the desk.

Elliptical trainers small enough to fit under a desk may seem like a perfect way to stay fit and be productive at work. But until recently no one had really studied the viability of successfully doing both at the same time.

Suspecting that it’s much harder than it looks, Uichin Lee, an associate professor at the Korea Advanced Institute of Science and Technology (KAIST), teamed up with Microsoft researchers in Asia to gauge how office workers integrated elliptical trainers into their workflow.

The research, Exploring User Experiences of Active Workstations: A Case Study of Under Desk Elliptical Trainers was presented September 15 in a session during the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016) in Heidelberg Germany.

The user experience (UX) study tracked 13 office workers over the course of a month and found they typically only used the elliptical trainers when performing simple office tasks or taking breaks. Some mostly used them as footrests or just to fidget. Although acknowledging the potential for improved health, participants noted any overall benefit comes at a cost of some leg room and “a fair amount of noise.”

But what if active workstations could be configured to fully track a user’s biometrics, workflow and other data streams to predict optimal times to work on any given task?

“Our next mission is to devise a novel persuasive computing system that can recommend the opportune moments for active workstation usage,” said Lee, who co-authored the paper with KAIST colleagues Woohyeok Choi and Aejin Song in collaboration with Darren Edge and Masaaki Fukumoto of Microsoft Research Asia.

For recommendation algorithm design, Lee and his team will consider multidimensional data, including computer usage, individual physiological/physical states and environmental sensor data. Owing to its wide variety of available sensing data, Microsoft Band 2 will be used to collect physiological/physical data such as heart rate, galvanic skin response, skin temperature and 3-axis linear/rotational acceleration. The team will continue to use Microsoft Azure to manage and analyze the multidimensional data for machine learning.

Lee, whose Interactive Computing Lab at KAIST covers topics like social computing systems and vehicular sensor networking, was surprised by the lack of UX research for active workstations. “This rapidly growing field has somehow received little attention from HCI [human-computer interaction] researchers,” Lee stated. “UX research will be beneficial to a broad range of stakeholders, including end users, researchers and device designers. Moreover, this will open up many other research opportunities in this field, such as active workstation design, workout intervention software design, and interactive workplace design.”

The project was the latest of several collaborative efforts with HCI researchers Edge and Fukumoto including the group swimming “exergame” SwimTrain.

To conduct the field study, researchers modified existing Stamina InMotion elliptical trainers to support usage tracking and goal setting features. A microcontroller was attached to the trainer to sense pedal spinning while an app displayed the total daily steps in real time with options for users to set daily activity goals. Participants were all graduate students employed in office environments with an average daily sedentary working time of 8.7 hours.

During the study, the team collected usage logs that showed “participants performed an average of 2,690.8 strides for an average 38.4 minutes on work days. Five participants failed to complete 3,000 strides per day, and the others took 43.0 minutes to meet their daily goals.”

under desk elliptical trainer

Researchers obtained direct feedback via weekly surveys and a final interview with each participant at the study’s conclusion. The interviews showed that work contexts such as the urgency of requests and their cognitive load were the main factors in choosing whether to work and pedal at the same time.

One participant commented: “I pedaled this machine while surfing the web or watching sports videos on YouTube during a rest […] I may pedal when doing familiar tasks. For example, when reading an academic paper in a familiar domain, pedaling did not disturb me. However, it was difficult to comprehend a novel or an unfamiliar paper while using this machine.”

Using the trainer to combat drowsiness was among the most popular reasons to use the trainer, a majority of participants indicated.

“After eating a meal, I felt sleepy and could not concentrate on work,” a participant indicated. “So I used the machine to stay awake.”

Learn More

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Microsoft Research brings summer school to Russia’s emergent tech hub https://www.microsoft.com/en-us/research/microsoft-research-brings-summer-school-russias-emergent-tech-hub/ Thu, 01 Sep 2016 20:44:25 +0000 https://www.microsoft.com/en-us/research/?p=286520 By John Kaiser, Writer, Microsoft Microsoft Research recently concluded  its eighth annual summer school in Kazan, Russia, challenging students to conceptualize and build new applications from the sensors and devices emerging from the Internet of Things (IoT). “Our summer schools are an opportunity to expose students to the latest technologies in computer science,” said Judith […]

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By John Kaiser, Writer, Microsoft

Microsoft Research recently concluded  its eighth annual summer school in Kazan, Russia, challenging students to conceptualize and build new applications from the sensors and devices emerging from the Internet of Things (IoT).

Microsoft summer school in Kazan, Russia, 2016

Students and speakers at the Microsoft summer school in Kazan, Russia, 2016

“Our summer schools are an opportunity to expose students to the latest technologies in computer science,” said Judith Bishop, director of computer science, who organized the weeklong program held in July. “In order to obtain, analyze and use information from sensors of all different kinds, knowledge of broad range of underlying technology is essential. The students were eager to learn from experts in IoT.”

The 60 mostly postgraduate students at the summer school were carefully selected from across Russia and the Commonwealth of Independent States. While lectures were going on focused on everything from Dependable Communications to Design and Machine Learning to Security, students formed groups and created useful IoT applications from Microsoft Band bracelets, SensorTag Dev Kits and other devices.

Russian Summer School students

Students working on their projects at the Microsoft Russian summer school

Some groups configured new applications for indoor spaces using sensors to control background lighting, to measure tiny fluctuations in the height of skyscrapers or to adjust the microclimate for indoor houseplants.

Others used sensors for educational purposes. Students hypothesized that learning to play the guitar might be easier if people received tactile feedback triggered whenever they made a mistake, such as strumming the wrong note. So they configured a bracelet to vibrate in response to user error. Conversely, a silent bracelet effectively signaled that the student was playing correctly.

Learn to Play a Guitar group at Microsoft Research Summer School

Judith Bishop seen with the Learn to Play a Guitar group

Other students aimed to build a wrist band solution to help people stay motivated during fitness routines, such as jogging.

According to one student’s account, “Originally we wanted to embed many lie detectors, which would work on the basis of an analysis of the skin temperature and other sensor readings.”

Eventually, they turned to behavioral economics to inspire a possible solution. In theory, a bracelet could be configured to automatically trigger a transfer of funds from the jogger’s account to a charity in response to missing a planned workout.

Kazan is one of Russia’s leading tech hubs and home to 44 academic institutions, contributing to a steady stream of talented computer science students in the region. Four years ago, a research park was created on the outskirts of Kazan, forming Innopolis University to focus entirely on IT and robotics research.

Learn More

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Summer school data science research could trigger real world changes https://www.microsoft.com/en-us/research/summer-school-data-science-research-trigger-real-world-changes/ Fri, 26 Aug 2016 16:00:31 +0000 https://www.microsoft.com/en-us/research/?p=283292 By John Kaiser, Writer, Microsoft Research Microsoft Research hosted its third annual Data Science Summer School in New York City as a diverse group of undergraduate students deployed some of the latest data crunching techniques on millions of rows of anonymized data in an effort to uncover useful information. “We’re really hoping to give them […]

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By John Kaiser, Writer, Microsoft Research

Microsoft Research hosted its third annual Data Science Summer School in New York City as a diverse group of undergraduate students deployed some of the latest data crunching techniques on millions of rows of anonymized data in an effort to uncover useful information.

“We’re really hoping to give them a flavor of solving a research problem that hasn’t yet been solved,” said Jake Hofman, one of several Microsoft Research instructors leading the intensive eight-week hands-on course that concluded in August. Coursework for the program is freely available on Github.

Data points to tweaking incentives at Airbnb

AirBnb

AirBnB team left to right: Shawndra Hill (MSR, mentor), Chris Riederer (Columbia, teaching assistant/mentor), Erica Ram (student), Louise Lai (student), Jacqueline Curran (student), Kaciny Calixte (student), Fernando Diaz (MSR, mentor), Amit Sharma (MSR, mentor)

This year marked the first time that student-led research relied on machine learning algorithms to predict actual outcomes. In a project called “Airbnb: Predicting Loyalty,” the students tapped decision tree learning techniques — “using decision trees to find patterns in the given data to predict on unseen data.” Most importantly, they were able to pinpoint how the company might tweak specific factors to encourage guests to book another stay or incentivize hosts to open up their home another time.

Students looked for patterns indicating a higher or lower probability of being a repeat customer.

“How does host loyalty interplay with guest loyalty?” asked summer school student Louise Lai, in describing one of the primary areas of focus for the Airbnb study group. “We’re looking at that interplay as something very new and very distinct for the sharing economy.”

For the Airbnb student project, Lai was joined by Kaciny Calixte, Jacqueline Curran and Erica Ram.

Explaining how “predictive models show that reviews and interaction between hosts and guests is of great importance,” the study concluded that “Airbnb could potentially boost return-rates of first time guests by providing them with incentives to stay at highly-rated properties.”

The project relied on two datasets collected by InsideAirbnb, which describes itself as an “independent, non-commercial set of tools and data” that allows anyone to “explore how Airbnb is really being used in cities around the world.”

In another sign of the maturing field of data science, this year marked the first time that student projects used pre-existing datasets without the need for modification.

“It does raise the bar for the types of questions that get asked. We’re seeing the tools improve, we’re seeing more and more interesting datasets out there.

And certainly Microsoft Azure’s point and click graphical interface makes it seem easy — if you know what to look for.

“What were’ trying to train our students on is more around what questions to ask and how to answer them,” Hofman added.

Taxi data points to carpooling to push to counter redundant trips

FareShare

Fare Share team left to right: Chris Riederer (Columbia, teaching assistant/mentor), Abraham Neuwirth (student), Jai Punjwani (student), Fatima Chebchoub (student), Marieme Toure (student), Ashton Anderson (MSR, mentor), Sid Sen (MSR, mentor), Jake Hofman (MSR, mentor)

The other student project, “Fare Share: Flow and Efficiency in NYC’s Taxi System,” tapped into what’s officially known as the “2013 Yellow Taxi Driver Set,” which contains anonymized driver IDs, trip time, distance, point of origin, destination and other information.

In the group’s final presentation, summer school student Jai Punjwani described the dataset this way: “Imagine if you have info on every single cab in New York City and you able to see where every single cab was going — who they were picking up, who they were dropping off, what they were doing afterward.”

Punjwani is now entering his junior year in computer science at Adelphi University, where he’s developed an Android app that enables students “to find each other and study at his university.” For the taxi data student project, Punjwani was joined by Abraham Neuwirth, Marieme Toure and Fatima Chebchoub.

The research project focused on a single month of data, which included more than 13 million rides, for an average of 420,000 trips per day, driven by over 32,000 different drivers.

Unlike a similar project in 2009 that yielded largely inconclusive results, this year’s study zeroed in on longer trips to specific destinations, revealing that large numbers of taxis ferried just a single passenger on various popular commute and transit routes. The study notes that on “weekday mornings around 7 a.m., there are roughly 25 redundant trips from Port Authority to Rockefeller Center that take place every five minutes for the duration of rush hour.”

The students concluded that a “taxi stand policy requiring people to wait no more than five minutes to carpool with another rider at these locations could improve the system by upwards of 5 percent, eliminating more than 650,000 trips. That translates into a potential savings to consumers of more than $8.5 million.”

It’s a good example of how data science can shine a light on efficiencies that would otherwise go unnoticed, and it shows how research could lead spur new policies.

“You could really improve the efficiency of the taxi system that could happen at almost zero cost,” Hofman noted.

There’s a reason it’s called “big data.” For their projects, students were faced with the tricky task of culling through millions of rows of data, discarding anomalies like multiple Airbnb listings or faulty geolocation taxi journey data such as an erroneous trip to Antarctica.

Microsoft Research Data Science Summer School

The program was launched in 2014 as part of a commitment to boost the diversity in computer science, encouraging “applications from women, minorities, people with disabilities and students from resource-limited colleges.” This year’s class included a woman who immigrated from Senegal and another who moved from Morocco.

In choosing applicants from more than 100 entries, Microsoft looks for candidates who have demonstrated a degree of passion around computer science from their undergraduate coursework and related activities.

Projects from earlier years drew on data from New York’s public school system, subway and fleet of shared bicycles, as well as stats compiled from ongoing police practices.

Summer 2016 Projects

Airbnb: Predicting Loyalty

Watch the talk or read the paper for more details. Source code is available on GitHub.

Fare Share: Flow and Efficiency in NYC’s Taxi System

Watch the talk or read the paper for more details. Source code is available on GitHub as well as an interactive map of travel patterns across neighborhoods.

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Nanotechnology comes to life with needle-based human interface devices https://www.microsoft.com/en-us/research/nanotechnology-comes-life-needle-based-human-interface-devices/ Wed, 24 Aug 2016 17:40:52 +0000 https://www.microsoft.com/en-us/research/?p=282761 By Noboru Kuno, Research Program Manager, Microsoft Research Researchers at Microsoft and Tokyo’s Keio University have developed systems that could allow people to use tiny, painless needles to do things like monitor medical conditions or receive information without looking at a screen. The research project, which explores the convergence of micro- and nanotechnology, wearable sensors […]

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By Noboru Kuno, Research Program Manager, Microsoft Research

Researchers at Microsoft and Tokyo’s Keio University have developed systems that could allow people to use tiny, painless needles to do things like monitor medical conditions or receive information without looking at a screen.

The research project, which explores the convergence of micro- and nanotechnology, wearable sensors and actuators for human computer interaction, was among the winning innovations on display at “Japan Korea Academic Day,” held recently in Tokyo.

The project, “Wearable Human Interface Devices Using Micro-Needles,” could spearhead new forms of medical self-monitoring and novel ways of receiving information without the need to look at a screen, a potential boon for distraction-free driving, immersive gaming and other activities.

Led by Norihisa Miki, associate professor at Keio University, the project was one of seven winners recognized for best exemplifying the unique and fascinating research results emerging from collaboration between Microsoft Research Asia and academic institutions. In particular, Miki’s project was lauded as a “great symbol of what Microsoft aims to inspire through collaboration with leading researchers and scientists in the Asia-Pacific region.”

A key finding for Miki came in 2015 when his team at Keio University’s Miki Laboratory discovered that the outermost layer of the skin (stratum corneum) could also serve as an effective interface. Soon after, Miki and his colleagues started developing “micro-needle-based human interface devices” with support from Microsoft Research.

These devices have great promise for a number of reasons.

For one, they’re unobtrusive: Limiting the needle length to the depth of the stratum corneum, a layer of 15-20 dead cells, the needles don’t go deep enough to cause any pain.

For another, they’re very energy-efficient: The needle-type electro-tactile displays transfer tactile information at a much lower voltage than conventional flat electro-tactile types.

Further, they’re highly accurate and adaptable: The needle-type electrodes for EEG can conduct high-quality measurements. This shape also was optimized for electrode density, mechanical strength and to ensure the needles wouldn’t reach pain points.

Norihisa Miki

Norihisa Miki, associate professor Keio University

With the popularity of wearable devices soaring, it’s becoming more vital to acquire data with high sensitivity and accuracy, and also to transfer information back to users with accuracy and low power consumption.

Candle-like EEG electrodes

Candle-like EEG electrodes

The team has also created flexible and wearable needle-type electro-tactile displays. These can be attached to the body and efficiently transfer information to users through tactile sensation. A device attached to the wrist could send tactile information and feedback to users, providing new ways for patients to capture critical medical information in the home such as measuring the effectiveness of medications.

arm+needle

The micro-needle devices are currently designed to be worn comfortably only for short periods. Research is now underway into form factors for longer term use, exploring other types of material along with different shapes and densities.

Although Miki’s team acknowledges that some users may be apprehensive about the notion of wearing anything with needles, they’re confident that user adoption won’t be hindered given the device’s actual comfort and valuable uses.

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Long-term collaboration takes aim at mobile browsing https://www.microsoft.com/en-us/research/long-term-collaboration-takes-aim-mobile-browsing/ Thu, 11 Aug 2016 16:00:50 +0000 https://www.microsoft.com/en-us/research/?p=274551 By Lily Sun, Research Program Manager, Microsoft Research Asia As mobile browsing continues to consume an ever larger share of Internet services, the stakes of improving the mobile user experience have never been greater. That’s one of the reasons that Microsoft Research Asia (MSRA) and Peking University are embarking on a joint project to raise the […]

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By Lily Sun, Research Program Manager, Microsoft Research Asia

As mobile browsing continues to consume an ever larger share of Internet services, the stakes of improving the mobile user experience have never been greater.

That’s one of the reasons that Microsoft Research Asia (MSRA) and Peking University are embarking on a joint project to raise the quality of user experience (QoE) for mobile browsers. Building on the success of earlier collaborations, MSRA researcher Yunxin Liu will once again join forces with Xuanzhe Liu, associate professor at the university’s School of Electronics Engineering and Computer Science.

The mobile experience

It’s common sentiment that the QoE of mobile web browsing is far from satisfying. Mobile browsers suffer from the redundant transfer of resources, which leads to duplicated data transmission, long page load time, and high energy drain.

Building a collaborative team

Representing their organizations, Yunxin and Xuanzhe have jointly conducted various in-depth studies covering mobile web performance, Android OS latency, human-computer interactions, and related technologies. After first winning a Microsoft Fellowship in 2007, Xuanzhe came to MSRA in 2013 as “Star-Track” Young Visiting Professor.

The two researchers co-authored a number of papers presented at top conferences such as the International World Wide Web Conference (WWW), and published in leading journals including IEEE Transactions on Mobile Computing and IEEE Transactions on Services Computing. Together they’ve fostered a strong, sustained relationship between the two organizations.

“In our collaborations, Microsoft Research, as a leader in operating system research and industry, is able to provide first-hand, real-world users’ requirements and industry resources, where we can explore valuable and interesting problems to tackle”, says Xuanzhe. “Furthermore, collaborating with top researchers in Microsoft Research greatly helps promote the rapid growth of my students. Working with Dr. Yunxin Liu and other colleagues from the System Research Group is really an invaluable opportunity.”

Xuanzhe Liu and Yunxin Liu

Xuanzhe Liu and Yunxin Liu

Solving the puzzle of web cache performance

The team’s focus on QoE issues in mobile web browsing has led to some key findings. Research papers published in IEEE Transactions on Mobile Computing and WWW showed mobile web browsing currently suffers from an imperfect cache mechanism. The studies — based on analyzing 1 month of version traces for more than 100 popular web apps — pinpoint three root causes for the weak mobile web cache performance:

  • Same content. The same resources have different URLs when requested at different times.
  • Heuristic expiration. The caching policy is not explicitly defined by the server and thus it depends on browsers to infer an expiration time.
  • Conservative expiration time. The expiration time is set to be too short.

at the IEEE International Conference on Web Service (ICWS). Analyzing the differences between native apps and web apps with the same functionality, their results helped inform how native apps package resources before delivering them to the user.

Building the dual proxy solution

The findings enabled the team to design and implement a dual-proxy system to optimize the QoE of mobile web browsing. Consisting of a remote proxy on the cloud/cloudlet and a local proxy on a client device, the system differs from traditional proxies that remain largely limited to simple request forwarding and cache lookup. The improved remote proxy can proactively crawl and render webpages from web servers. It can store all the downloaded resources in loading each webpage as well as build a resource loading graph for the webpage. As a result, proxies can now determine the resources required to load the webpage. And crucially, they can optimize the order in which they should load.

Now when a client requests a webpage (via the local proxy of the client), the remote proxy can immediately send all the pre-fetched resources of the webpage to the client in a batch and in the right order.

So far, the project is showing measurable success: An evaluation of 50 websites indicate average page load time is reduced by 43.1 percent and network data transmission is cut by 57.6 percent — while imposing marginal system overhead. Given these findings, the team is hopeful that their project will lead to improved QoE of mobile web browsing on a broader scale.

Learn More

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PhD Summer School brings top students to Cambridge https://www.microsoft.com/en-us/research/phd-summer-school-brings-top-students-cambridge/ Wed, 10 Aug 2016 16:00:41 +0000 https://www.microsoft.com/en-us/research/?p=271518 By Scarlet Schwiderski-Grosche, Senior Research Program Manager Pivoting from the Old World charm of High Tea to contemplating a dystopian AI-dominated future was among the many experiences facing more than 80 doctoral students at the PhD Summer School, held July 4–8 in Cambridge, England. Each year the Microsoft Research Cambridge Lab brings together tech luminaries and […]

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By Scarlet Schwiderski-Grosche, Senior Research Program Manager

Pivoting from the Old World charm of High Tea to contemplating a dystopian AI-dominated future was among the many experiences facing more than 80 doctoral students at the PhD Summer School, held July 4–8 in Cambridge, England.

PhD Summer School 2016 group photo

Each year the Microsoft Research Cambridge Lab brings together tech luminaries and researchers with PhD students from research institutions across the EMEA (Europe, Middle East, Africa) region to learn not only about the latest innovations in computing, but also to explore other parts of a successful researcher’s toolkit such as communicating their research or gaining a deeper insight into the social and economic impacts of advancing technology.

Diverse student body

Hailing from 36 research institutions spanning 10 countries in Europe, the Middle East and Africa, the students brought huge diversity to the Cambridge Lab, not just in terms of national origin and culture, but also in their research backgrounds, which extended beyond computer science and engineering into the realms of design and various natural and social sciences. The event’s attendees included recipients of Microsoft Research PhD Scholarships, along with students working with our EMEA Joint Research Centres, collaborating on Microsoft Azure for Research projects or otherwise partnering with Microsoft Research Cambridge.

Keynotes

Speaking on the “The Evolution of Innovation,” keynote speaker Hermann Hauser talked about the increasing speed of innovation and how computing has advanced through six waves beginning with the mainframe. Hauser, co-founder of Amadeus Capital, delighted the audience with an astute analysis of the innovations underlying each of the subsequent waves, culminating with the internet of things and machine learning. Hauser expressed his concern over the potential perils of AI and pointed to the new Centre for the Study of Existential Risk at University of Cambridge as he encouraged students to engage in the AI debate.

AI opportunities and concerns were reflected later in the week by Cambridge Lab Director Christopher Bishop. “New developments in machine learning, coupled with exponential growth in both data and processing power, suggest the time may be ripe to take the next steps towards this elusive goal,” Bishop said referring to the goal of creating a machine with human-level or super-human intelligence.

Talks

On the more practical side of machine learning and AI, Cambridge Lab researcher Katja Hofmann and her team in the Machine Intelligence & Perception group presented a talk and demo on Project Malmo. Malmo is a sophisticated AI experimentation platform built on top of Minecraft and designed to support fundamental research in artificial intelligence. Malmo received much attention recently after the code was released as open-source. In just the first week, thousands of users downloaded the open source software and more than 100,000 people viewed the project page.

Alison Noble, Professor in the Department for Biomedical Engineering at the University of Oxford, explained how her team uses machine learning in computer vision techniques to acquire and interpret ultrasound diagnostic data. Noble showed how her research is increasing both the usability and accuracy of ultrasound in the hands of non-experts, an important step as devices become cheaper, smaller and more portable.

Scientist Sara-Jane Dunn of the Cambridge Lab’s Biological Computation Group spoke about how cells could be “reprogramed” to induce an adult cell back to its stem cell like state. Such understanding could help develop better cell therapies and regenerative medicines.

Other Summer School talks centered on somewhat more practical issues such as optimizing the efficiency of datacenters and making computer programs and Internet communication more secure.

Demos

DemoFest AutoPixel

The Summer School really came to life at DemoFest where students learned about some of the most compelling developments from all five research groups at the Cambridge Lab. There were two health and well-being related demos: the Biological Computation Group demonstrated in-house experimental facilities that had recently become operational, illustrating how computational modelling, experimental protocols and lab automation will be used in this wet lab to advance the programming of biological systems. And, the Human Experiences & Design group presented Project Torino, a physical programming language inclusive of blind children.

Another demo was micro:bit, a pocket-sized, codeable computer that allows children to get creative with technology. Developed by the BBC and partners, including Microsoft, the project provides up to 1 million free micro:bits to every 11- or 12-year-old child across the UK. The demo featured a remote controlled car project from Lancaster University as well as a number of projects from Microsoft Research.

Applying for PhD scholarships

Microsoft Research PhD Scholarships support collaborative research projects between EMEA-based academics and researchers in the Cambridge Lab. Scholarships are awarded per a variety of criteria including how proposals relate to current focal areas of the Cambridge Lab. See the PhD Scholarship site for full details.

Prospective PhD supervisors submit applications via their academic institution. Applications are then peer reviewed and approximately 20 projects are selected for funding. PhD students are appointed to the selected projects and begin their research in the following academic year under the supervision of their academic supervisor and, with co-supervision from the Cambridge Lab researcher.

The online submission tool for the 2017 PhD Scholarship Program opens September 1 and submissions will close September 26, 2016.

Learn more

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Microsoft researchers enable secure data exchange in the cloud https://www.microsoft.com/en-us/research/microsoft-researchers-enable-secure-data-exchange-cloud/ Tue, 09 Aug 2016 16:00:56 +0000 https://www.microsoft.com/en-us/research/?p=270618 By John Roach, Writer, Microsoft Research In the future, machine learning algorithms may examine our genomes to determine our susceptibility to maladies such as heart disease and cancer. Between now and then, computer scientists need to train the algorithms on genetic data, bundles of which are increasingly stored encrypted and secure in the cloud along […]

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By John Roach, Writer, Microsoft Research

In the future, machine learning algorithms may examine our genomes to determine our susceptibility to maladies such as heart disease and cancer. Between now and then, computer scientists need to train the algorithms on genetic data, bundles of which are increasingly stored encrypted and secure in the cloud along with financial records, vacation photos and other bits and bytes of digitized information.

And there the data sits, full of potential but ultimately of little use to anyone but its owner.

That’s because encrypted data must first be decrypted before it can be used. But decrypted data is vulnerable to malicious attacks, which creates a tradeoff between data usability and security.

New research from Microsoft aims to unlock the full value of encrypted data by using the cloud itself to perform secure data trades between multiple willing parties in a way that provides users full control over how much information the exchange reveals.

Ran Gilad-Bachrach / Photography by Scott Eklund/Red Box Pictures

Ran Gilad-Bachrach / Photography by Scott Eklund/Red Box Pictures

“What we are trying to do is keep the data private and, at the same time, get the value out of it,” says Ran Gilad-Bachrach, a researcher in the Cryptography Research group within Microsoft’s research organization and co-author of a paper released in June that describes the protocol, or set of rules, for this system to securely exchange data.

Multiparty computation

The exchange is based on the idea of a secure multiparty computation, where two or more parties agree to evaluate their data in a way that one or more of the parties gets a result but none of the parties learns anything about the others’ data, except for what can be inferred from the result.

The multiparty computation is akin to a group of employees who want to know where their individual salary ranks in relation to the group as a whole, but none of them wants to reveal their pay to the group.

One way to solve this problem is for each individual to tell their salary in confidence to a trusted colleague. This colleague calculates the average salary and shares the result with the group. Each employee can determine where their pay falls without learning what any individual is paid. The trusted colleague conveniently forgets everything.

“This secure data exchange emulates that, but without the need for the trusted colleague,” says paper co-author Peter Rindal, a PhD candidate at Oregon State University who is in his second internship at Microsoft and an expert on secure multiparty computation.

The cloud, according to the researchers, is a key feature of the exchange. It transforms a computation technique used to resolve water cooler disputes over pay to a secure system to train algorithms, perform market research, conduct auctions and enable new business opportunities.

Exchange in action

Here’s how it works:

Data owners – hundreds, thousands of them – encrypt their data and send it to the cloud for storage. Think of them as relatively passive sellers in the exchange. When an active buyer – usually one entity – comes along and wants to make a transaction with some of the sellers, those sellers approve the transaction by sending the buyer keys to the data.

But since those keys can decrypt the data stored in the cloud, the cloud can’t directly share the stored data with the buyer, otherwise security and privacy would be compromised.Kim Laine

“Instead, we want to use the keys to decrypt the data inside a multiparty computation,” says paper co-author Kim Laine, a post-doctoral researcher also in the Cryptography Research group who studies how to compute on encrypted data. Doing so unencrypts the data for a computation “without actually revealing anything to anyone except the result” of the computation.

All of the computation is performed in the cloud, and the computation itself is encrypted in such a way that not even the cloud knows what is being computed, which protects any of the buyer’s data used in the computation such as a proprietary algorithm. If everything goes as expected, the cloud reveals the decrypted results to the interested parties.

Set up this way, according to the researchers, the data exchange is secure provided that the cloud itself follows the rules and nothing more.

Test driving data

Here’s another advantage to the system: It’s costly to purchase data, and researchers with limited budgets need to make sure it is worth it. The exchange, Gilad-Bachrach explains, offers a way for a buyer to “test drive” a portion of the sellers’ data and thus make an informed decision over whether to buy the keys to unlock the full dataset.

Consider researchers at a pharmaceutical company who are developing a machine learning model that combs through genomes to determine individuals’ risk of various diseases. To improve the model and further study it, the researchers are interested in buying access to a medical center’s bundle of anonymized patient genomes, but only if the bundle contains distinctly different data than what the researchers have already used.

“We call this ‘can we test drive your data’ because why would you buy anything without knowing what you are buying,” says Laine. “But the problem with data is you can’t just show it.”

The secure data exchange system allows the researchers to perform a statistical analysis on a portion of the medical center’s anonymized genetic data that reveals how much it differs from the data already used to build the disease-prediction algorithm. After this test drive, the researchers can decide whether to buy the keys to the full bundle.

“What we are trying to build,” Gilad-Bachrach says, “is a mechanism by which you can say, ‘Look, I am interested in your data, but I want to verify it is really what I need before I purchase it.’”

Real world applications

In another use of the exchange, a medical center could compare the outcomes of its treatment plan for pneumonia with the outcomes of treatment plans used at other medical centers without any one medical center revealing what treatment plan it uses. That avoids the risk of getting called out for using a less effective treatment.

Individuals could even use the exchange as a marketplace to sell researchers access to their encrypted genetic data for algorithm training. Ultimately, Laine notes, the researchers might develop an algorithm that uses the exchange to communicate to participants whether or not their genome contains a specific mutation related to a health concern such as heart disease or cancer.

“If you are a match,” notes Laine, “you can decide if you want to contact the research group.”

It’s a research project for now. But the team aims to publicly release the library, or tools, needed to implement the secure data exchange in the near future.

Related links

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Microsoft NLP researchers converge at ACL 2016, edging ever closer to human-like conversational experiences https://www.microsoft.com/en-us/research/microsoft-nlp-researchers-converge-acl-2016-edging-ever-closer-human-like-conversational-experiences/ Mon, 08 Aug 2016 16:01:03 +0000 https://www.microsoft.com/en-us/research/?p=272250 By Bill Dolan, Principal Researcher, Microsoft Research This year, the annual meeting of the Association for Computational Linguistics (ACL) will be held in Berlin, Germany, August 7-12, 2016, at Humboldt University. ACL is the premier conference on natural language processing (NLP) systems and computational linguistics. As a Gold sponsor, Microsoft is proud to have more […]

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By Bill Dolan, Principal Researcher, Microsoft Research

ACLThis year, the annual meeting of the Association for Computational Linguistics (ACL) will be held in Berlin, Germany, August 7-12, 2016, at Humboldt University. ACL is the premier conference on natural language processing (NLP) systems and computational linguistics.

As a Gold sponsor, Microsoft is proud to have more than 20 researchers attending and presenting at ACL. Along with my colleagues in the Natural Language Processing and Speech group, we’ll be  presenting our latest research aimed at allowing computers to manipulate human language in order to engage and assist users.

A major focus of our recent work is language generation: giving a “voice” to machine state, code and structured data, so that users can begin to use language to productively collaborate with their devices through a natural, flowing dialog in which the machine becomes an active conversational collaborator. One of our papers at ACL this year, for instance, demonstrates how we can generate plausible, commonsensical questions in response to a photograph; another explores how we can begin to “translate” between natural language and code.

We’ll also be talking about the latest work in our long-term effort aimed at training neural conversational models from huge volumes of naturally-occurring human conversations, learning how to generate natural-sounding dialog from scratch at each point in a conversation. As natural language dialog becomes an increasingly important direction in interface design, this data-driven approach — pioneered by Microsoft Research —  is emerging as a key research area. One important challenge involves imbuing game/virtual reality characters and personal agents with distinctive personalities, so that their dynamically generated responses sound as if they were produced by a specific, real intelligence. At ACL 2016, we’ll be presenting the first published work on data-driven persona modeling, “A Persona-Based Neural Conversation Model.” This work demonstrates how state-of-the-art neural modeling techniques train conversational agents that “sound like” a specific character. What’s more, the work shows how these techniques permit the persona to adjust its language use to match the linguistic behavior of the person it is talking with; a subtle yet crucial phenomenon that is characteristic of natural human conversation.

Our ultimate goal is to be able to tap the profile of an arbitrary person and generate conversations that accurately emulate that individual’s persona in terms of linguistic response behavior and other salient characteristics. As the paper states, “this would dramatically change the ways in which we interact with dialog agents of all kinds, opening up rich new possibilities for user interfaces. Given a sufficiently large training corpus in which a sufficiently rich variety of speakers is represented, this objective does not seem too far-fetched.”

In addition, Microsoft Research is pleased to cosponsor the 1st Workshop on Representation Learning for NLP, which will discuss recent advances in “vector space models of meaning, compositionality and the application of deep neural networks and spectral methods to NLP” as well as explore future research directions.

If you are going to ACL 2016, please chat with our researchers and scientists about the projects and opportunities at Microsoft that involve solving interesting AI, ML and NLP problems for billions of users. To learn more about our research being presented at ACL 2016, see the sections below for Tutorials, Workshops, Accepted papers and Microsoft attendees.

Conference details

Tutorials

Workshops

Accepted papers

Microsoft attendees

Learn more

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Our open source commitment: The proof is in the projects https://www.microsoft.com/en-us/research/open-source-commitment-proof-projects/ Wed, 27 Jul 2016 22:36:18 +0000 https://www.microsoft.com/en-us/research/?p=266109 By Miran Lee, Principal Research Program Manager & Winnie Cui, Senior Research Program Manager, Microsoft Research Asia Openness allows innovation to evolve in unforeseen, novel and exciting ways, and sometimes even provides solutions that no one ever imagined were possible. Getting more done with crowdsourcing One such innovation is GeoMission (geo-location-based mission), a crowdsourcing platform developed […]

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By Miran Lee, Principal Research Program Manager & Winnie Cui, Senior Research Program Manager, Microsoft Research Asia

Openness allows innovation to evolve in unforeseen, novel and exciting ways, and sometimes even provides solutions that no one ever imagined were possible.

Getting more done with crowdsourcing

One such innovation is GeoMission (geo-location-based mission), a crowdsourcing platform developed by MSRA and a team of researchers from the Hong Kong University of Science and Technology (HKUST). GeoMission lets users share and accept tasks based on where they are located.

Users submit location-based requests via GeoMission apps, which then push questions to other users near the target location (as long as they meet any additional criteria in the request.)

The project owner Professor Lei Chen from HKUST is introducing GeoMission to audience

The project owner Professor Lei Chen from HKUST is introducing GeoMission to audience

Developed for IOS and Android clients, the GeoMission server platform allows users to initiate requests by audio, video, photo or plain old texting.

All of GeoMission’s source code is hosted on GitHub, providing some critical benefits for a research-based project — like more people! Researchers can intricately study how users interact with the platform, and users can directly contribute to help make it better. Of course, making it open source extends the tools to the greatest possible number of spatial crowdsourcing researchers. Most importantly, we believe opening the source code helps us innovate faster and provide more ways to collaborate with other developers or just about anyone else who’s interested in the project. You can find more details about project at HKUST’s website.

Improving datacenter efficiency with Vortex

In the same spirit of openness, we’ve worked with Professor Byung-Gon Chun from Seoul National University (SNU) to develop Vortex in an effort to address the problem of wasted resources at datacenters. Tapping these sometimes vast computing resources — that remain largely unused outside of peak usage — represents a huge opportunity to improve datacenter efficiency and save energy.

Although current resource managers like Google’s Borg system and Apache Mesos attempt to reclaim idle resources for other tasks, they largely fall short when reclaimed resources are inevitably preempted by latency critical tasks. The more aggressively the resources are reclaimed, the more frequently they’re preempted due to conflict, resulting in transient resources.  The upshot of all this is that current data processing systems that rely on transient resources cannot efficiently complete jobs.

Vortex, on the other hand, maintains high performance despite frequent preemptions. Developed by SNU grad students, Yunseong Lee and Youngseok Yang during their internship at MSRA, the pair are continuing to work on Vortex after returning to school. Joining the project is SNU undergraduate student Geon-Woo Kim along with contributors from other institutions and Microsoft.

Vortex team in SNU (from left to right); Geon-Woo Kim, Youngseok Yang, Byung-Gon Chun, and Yunseong Lee

Vortex team in SNU (from left to right); Geon-Woo Kim, Youngseok Yang, Byung-Gon Chun, and Yunseong Lee

Experimental evaluations have been conducted on Microsoft Azure to measure the Vortex system’s effectiveness. The results show that Vortex can scale out much better with frequently preempted transient resources than Apache Spark. In certain cases, Apache Spark failed to complete jobs.

Hosted on GitHub, Vortex has been developed as an application of Apache REEF — an open source library for big data applications — in what has since proved to be a mutually beneficial project.  Vortex is succeeding in leveraging the Apache methods of growing open source projects: Development issues were openly discussed and pull requests were thoroughly reviewed. Meanwhile, the Apache REEF community was able to closely observe how Vortex uses Apache REEF as well as learn about the overall Vortex requirements.

Vortex

Vortex and GeoMission — as well as other projects like them — clearly have the potential to succeed in the marketplace. However, we believe that releasing them as open source opens the way to greater long term value for the global community of researchers and developers whose collaborative efforts can sometimes trigger unimaginable breakthroughs. At Microsoft Research Asia, we see a future that includes many more opportunities to collaborate with the open source community — to the benefit of all.

Learn more

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Researchers team up with Chinese botanists on machine learning, flower-recognition project https://www.microsoft.com/en-us/research/researchers-team-up-with-chinese-botanists-on-machine-learning-flower-recognition-project/ https://www.microsoft.com/en-us/research/researchers-team-up-with-chinese-botanists-on-machine-learning-flower-recognition-project/#respond Mon, 25 Jul 2016 14:46:11 +0000 https://www.microsoft.com/en-us/research/?p=264486 By Guobin Wu, Senior Research Program Manager, Microsoft Research Asia Has this ever happened to you? You’re out walking with your daughter. She finds a beautiful flower, quizzes you on it, but you’re stumped — you have no idea what it is. Instead of having to admit you don’t know, what if you could quickly […]

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By Guobin Wu, Senior Research Program Manager, Microsoft Research Asia

Has this ever happened to you? You’re out walking with your daughter. She finds a beautiful flower, quizzes you on it, but you’re stumped — you have no idea what it is. Instead of having to admit you don’t know, what if you could quickly identify the flower or any other plant wherever you happen to be? But how? At least 250,000 species of flowers exist and even experienced botanists have trouble identifying them all. Now there’s a way thanks to the rising power and sophistication of image recognition and the ease of taking pictures with your smartphone.

Smart Flower Recognition System

It’s called the Smart Flower Recognition System but it might never have happened were it not for a chance encounter last year between Microsoft researchers and botanists at the Institute of Botany, Chinese Academy of Sciences (IBCAS). Yong Rui, assistant managing director of Microsoft Research Asia (MSRA), was explaining image-recognition technology at a seminar — much to the delight of IBCAS botanists whose own arduous efforts to collect data on regional flower distribution were experiencing poor results. The IBCAS botanists soon realized the potential of MSRA’s image-recognition technology. At the same time, Yong Rui knew he had found the perfect vehicle to improve image recognition while addressing a reality-based problem that benefits society. It also helped that IBCAS had accumulated a massive public store of 2.6 million images. Since anyone in the world could upload pictures to this flower photo dataset — and no human could possibly supervise the uploads — the MSRA team had to create algorithms to filter out the “bad” pictures. That was the first of many difficult problems facing researcher Jianlong Fu and his team in building a tool capable of discerning tiny anomalies among the many species of flowers.

To do so they trained a deep neural network to recognize images using a set of learnable filters. In a nutshell, it works like this:

During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the entries of the filter and the input. This produces a 2-dimensional activation map of that filter. As a result, the network learns filters that activate per specific types of features at a given spatial position in the input.

Inputting millions of pictures into the deep-learning framework, MSRA researchers eventually enabled the engine to accurately identify images more than 90 percent of the time, an astonishing rate just shy of human capabilities.

Caffe deep-learning framework

And the project greatly helped the Chinese botanists in meeting their goals. “The flower-recognition engine enables domain experts to acquire plant distribution in China in an efficient way,” said Zheping Xu, assistant director of IBCAS. “Not only that, this engine can help ordinary people who have a strong interest in flowers to gain more knowledge.”

flower project3.png

In the future, MSRA and IBCAS will continue the collaboration, hoping to create applications based on the flower-recognition engine, so that botanists can conduct their research, parents can appear infallible to their kids, and everyone can appreciate flowers on an even deeper level.

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