Microsoft

While email provides numerous benefits in the workplace, it is unclear how patterns of email use might affect key workplace indicators of productivity and stress. We investigate how three email use patterns: duration, interruption habit, and batching, relate to perceived workplace productivity and stress. We tracked email usage with computer logging, biosensors and daily surveys for 40 information workers in their in situ workplace environments for 12 workdays. We found that the longer daily time spent on email, the lower was perceived productivity and the higher the measured stress. People who primarily check email through self-interruptions report higher productivity with longer email duration compared to those who rely on notifications. Batching email is associated with higher rated productivity with longer email duration, but despite widespread claims, we found no evidence that batching email leads to lower stress. We discuss the implications of our results for improving organizational email practices.

With the rapid development of smartphones, spatial crowdsourcing platforms are getting popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to suitable crowd workers. Most existing studies focus on offline scenarios, where all the spatiotemporal information of micro-tasks and crowd workers is given. However, they are impractical since micro-tasks and crowd workers in real applications appear dynamically and their spatiotemporal information cannot be known in advance. In this paper, to address the shortcomings of existing offline approaches, we first identify a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem. We first extend the state-of-art algorithm for the online maximum weighted bipartite matching problem to the GOMA problem as the baseline algorithm. Although the baseline algorithm provides theoretical guarantee for the worst case, its average performance in practice is not good enough since the worst case happens with a very low probability in real world. Thus, we consider the average performance of online algorithms, a.k.a. online random order model. We propose a two-phase-based framework, based on which we present the TGOA algorithm with 1/4-competitive ratio under the online random order model. To improve its efficiency, we further design the TGOA-Greedy algorithm following the framework, which runs faster than the TGOA algorithm but has lower competitive ratio of 1/8. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Despite advances in security engineering, authentication in applications such as email and the Web still primarily relies on the X.509 public key infrastructure introduced in 1988. This PKI has many issues but is nearly impossible to replace. Leveraging recent progress in verifiable computation, we propose a novel use of existing X.509 certificates and infrastructure. Instead of receiving & validating chains of certificates, our applications receive & verify proofs of their knowledge, their validity, and their compliance with application policies. This yields smaller messages (by omitting certificates), stronger privacy (by hiding certificate contents), and stronger integrity (by embedding additional checks, e.g. for revocation). X.509 certificate validation is famously complex and errorprone, as it involves parsing ASN.1 data structures and interpreting them against diverse application policies. To manage this diversity, we propose a new format for writing application policies by composing X.509 templates, and we provide a template compiler that generates C code for validating certificates within a given policy. We then use the Geppetto cryptographic compiler to produce a zero-knowledge verifiable computation scheme for that policy. To optimize the resulting scheme, we develop new C libraries for RSA-PKCS#1 signatures and ASN.1 parsing, carefully tailored for cryptographic verifiability. We evaluate our approach by providing two real-world applications of verifiable computation: a drop-in replacement for certificates within TLS; and access control for the Helios voting protocol. For TLS, we support fine-grained validation policies, with revocation checking and selective disclosure of certificate contents, effectively turning X.509 certificates into anonymous credentials. For Helios, we obtain additional privacy and verifiability guarantees for voters equipped with X.509 certificates, such as those readily available from some national ID cards.

History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.

Recent research suggests that some students learn to program with the goal of becoming conversational programmers: they want to develop programming literacy skills not to write code in the future but mainly to develop conversational skills and communicate better with developers and to improve their marketability. To investigate the existence of such a population of conversational programmers in practice, we surveyed professionals at a large multinational technology company who were not in software development roles. Based on 3151 survey responses from professionals who never or rarely wrote code, we found that a significant number of them (42.6%) had invested in learning programming on the job. While many of these respondents wanted to perform traditional end-user programming tasks (e.g., data analysis), we discovered that two top motivations for learning programming were to improve the efficacy of technical conversations and to acquire marketable skillsets. The main contribution of this work is in empirically establishing the existence and characteristics of conversational programmers in a large software development context.

The chance to influence the lives of a million children does not come often. Through a partnership between the BBC and several technology companies, a small instructional computing device called the BBC micro:bit will be given to a million children in the UK in 2016. Moreover, using the micro:bit will be part of the CS curriculum. We describe how Microsoft's Touch Develop programming platform works with the BBC micro:bit. We describe the design and architecture of the micro:bit and the software engineering hurdles that had to be overcome to ensure it was as accessible as possible to children and teachers. The combined hardware/software platform is evaluated and early anecdotal evidence is presented. A video about the micro:bit is available at http://aka.ms/bbcmicrobit.

We present an interactivity demonstration of Expressy. A system that augments existing touchscreen devices with a variety of continuous expressive interaction capabilities, using movement data from a wrist-worn IMU. Our demonstration comprises a set of applications that show how the expressive touch interaction capabilities, offered by Expressy, can enable intuitive and meaningful interactions, in contexts including productivity, entertainment and lifestyle apps. This demo submission accompanies a full paper, describing a conceptual model of expressive touch interaction and the implementation and evaluation of Expressy.

We explored the social acceptability and user experience of wearable form factors as a portable option for Bright Light Therapy (BLT). BLT remains the predominant therapy for Seasonal Affective Disorder despite a non-compliance rate of ~70% commonly attributed to the inconvenience of prolonged daily sitting in front of light boxes. To date, attempts to address convenience using wearable/portable light treatment options have been met with limited success for nuanced reasons (i.e., stigma, efficacy, etc.). In an effort to more substantively explore factors related to the wearability, convenience, contextual appropriateness, and social acceptability of on-body light therapy usage, we developed and evaluated six fashion-aligned wearable therapy prototypes leveraging light-emitting materials and lowprofile hardware. Our results showed that participants preferred more mainstream and convenient form factors (e.g., glasses, gol fer’s hat, scarf) , were open to wearing their BLT in certain public and private locations, and appreciated device duality and the fashionable potential of treatment (to counter stigma).

Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.

Email is far from dead; in fact the volume of messages exchanged daily, the number of accounts per user, and the number of devices on which email is accessed have been constantly “Email growing. Most previous studies on email have focused on management and retrieval behaviour within a single account and on a single device. In this paper, we examine how people retrieve email in today’s ecosystem through an in-depth qualitative diary study with 16 participants. We found that personal and work accounts are managed differently, resulting in diverse retrieval strategies: while work accounts are more structured and thus email is retrieved through folders, personal accounts have fewer folders and users rely primarily on the built-in search option. Moreover, retrieval occurs primarily on laptops and PCs compared to smartphones. We explore the reasons, and uncover barriers and workarounds related to managing multiple accounts and devices. Finally, we consider new design possibilities for email clients to better support how email is used today.

Timing online auctions to attract a large number of prospective buyers is important for sellers. This study examines whether online auction sellers need to account for exogenous effects like TV viewing when timing and predicting their auction results. An ongoing debate questions whether TV viewers can spread their attention across multiple devices while watching TV, for example, by concurrently shopping online or posting on social media. Recent research has focused on understanding cross-media effects; however, little attention has been given to TV viewership’s relationship with a very important economic activity, namely participation in online auctions. We examine this potential cross-media effect by analyzing the four-year sales history of a German online auction platform and addressing potential endogeneity problems with an instrumental variable approach. We use three different instrumental variables that have different advantages and disadvantages but can, in sum, be used for triangulation as they lead to the same result. The analyses reveal a significant negative cross media effect between TV consumption and online auction sales, indicating that TV consumption and online auction sales might compete for the scarce attention of consumers and are thus substitutes for each other rather than complements.

An elliptic curve addition law is said to be complete if it correctly computes the sum of any two points in the elliptic curve group. One of the main reasons for the increased popularity of Edwards curves in the ECG community is that they can allow a complete group law that is also relatively efficient (e.g., when compared to all known addition laws on Edwards curves). Such complete addition formulas can simplify the task of an ECG implementer and, at the same time, can greatly reduce the potential vulnerabilities of a cryptosystem. Unfortunately, until now, complete addition laws that are relatively efficient have only been proposed on curves of composite order and have thus been incompatible with all of the currently standardized prime order curves. In this paper we present optimized addition formulas that are complete on every prime order short Weierstrass curve defined over a field k with char(k) not 2 or 3. Compared to their incomplete counterparts, these formulas require a larger number of field additions, but interestingly require fewer field multiplications. We discuss how these formulas can be used to achieve secure, exception-free implementations on all of the prime order curves in the NIST (and many other) standards.

We present Tattio, a fabrication process that draws from current body decoration processes (i.e., jewelry like metallic temporary tattoos) for the creation of on skin technology. The fabrication process generates functional components such as NFC tags, circuitry, to Thermochromic tattoos, while maintaining the aesthetics and user experience of existing metallic temporary tattoos. The fabrication process is low cost, accessible and customizable; we seek to enable individuals to design, make, and wear their own skin technology creations. We present the fabrication flow, fabricated components, and also an initial user study probing perceptions towards wearing Tattio circuitry.

We explored the social comfort and user experience of wearable form factors as a portable option for Bright Light Therapy (BLT). BLT remains the predominant therapy for Seasonal Affective Disorder despite a non-compliance rate of ~70% commonly attributed to the inconvenience of prolonged daily sitting in front of light boxes. To date, attempts to address convenience using wearable/portable light treatment options have been met with limited success for nuanced reasons (i.e., stigma, efficacy, etc.). In an effort to more substantively explore factors related to wearability, convenience, and contextual appropriateness/acceptability of on-body light therapy usage, we developed and evaluated six fashion-aligned wearable therapy prototypes leveraging light-emitting materials and low-profile hardware. Our results showed that participants preferred more mainstream and convenient form factors (e.g., glasses, golfer’s hat, scarf), were open to wearing their BLT in certain public and private locations, and appreciated device duality and the fashionable potential of treatment (to counter stigma).

In game development, software teams often conduct postmortems to reflect on what went well and what went wrong in a project. The postmortems are shared publicly on gaming sites or at developer conferences. In this paper, we present an analysis of 155 postmortems published on the gaming site Gamasutra.com. We identify characteristics of game development, link the characteristics to positive and negative experiences in the postmortems and distill a set of best practices and pitfalls for game development.

In this paper we present the results from a survey about the beliefs, practices, and personalities of software engineers in a large software company. The survey received 797 responses. We report statistics about beliefs of software engineers, their work practices, as well as differences in those with respect to personality traits. For example, we observed no personality differences between developers and testers; managers were conscientious and more extraverted. We observed several differences for engineers who are listening to music and for engineers who have built a tool. We also observed that engineers who agree with the statement “Agile development is awesome” were more extroverted and less neurotic.

Empirical software engineering has produced a steady stream of evidence-based results concerning the factors that affect important outcomes such as cost, quality, and interval. However, programmers often also have strongly-held a priori opinions about these issues. These opinions are important, since developers are highlytrained professionals whose beliefs would doubtless affect their practice. As in evidence-based medicine, disseminating empirical findings to developers is a key step in ensuring that the findings impact practice. In this paper, we describe a case study, on the prior beliefs of developers at Microsoft, and the relationship of these beliefs to actual empirical data on the projects in which these developers work. Our findings are that a) programmers do indeed have very strong beliefs on certain topics b) their beliefs are primarily formed based on personal experience, rather than on findings in empirical research and c) beliefs can vary with each project, but do not necessarily correspond with actual evidence in that project. Our findings suggest that more effort should be taken to disseminate empirical findings to developers and that more in-depth study the interplay of belief and evidence in software practice is needed.

Creating and running software produces large amounts of raw data about the development process and the customer usage, which can be turned into actionable insight with the help of skilled data scientists. Unfortunately, data scientists with the analytical and software engineering skills to analyze these large data sets have been hard to come by; only recently have software companies started to develop competencies in software-oriented data analytics. To understand this emerging role, we interviewed data scientists across several product groups at Microsoft. In this paper, we describe their education and training background, their missions in software engineering contexts, and the type of problems on which they work. We identify five distinct working styles of data scientists: (1) Insight Providers, who work with engineers to collect the data needed to inform decisions that managers make; (2) Modeling Specialists, who use their machine learning expertise to build predictive models; (3) Platform Builders, who create data platforms, balancing both engineering and data analysis concerns; (4) Polymaths, who do all data science activities themselves; and (5) Team Leaders, who run teams of data scientists and spread best practices. We further describe a set of strategies that they employ to increase the impact and actionability of their work.