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Communications of the ACM

121 - 130 of 371 for bentley

Exploring the Quality, Efficiency, and Representative Nature of Responses Across Multiple Survey Panels

A common practice in HCI research is to conduct a survey to understand the generalizability of findings from smaller-scale qualitative research. These surveys are typically deployed to convenience samples, on low-cost platforms such as Amazon's Mechanical Turk or Survey Monkey, or to more expensive market research panels offered by a variety of premium firms. Costs can vary widely, from hundreds of dollars to tens of thousands of dollars depending on the platform used. We set out to understand the accuracy of ten different survey platforms/panels compared to ground truth data for a total of 6,007 respondents on 80 different aspects of demographic and behavioral questions. We found several panels that performed significantly better than others on certain topics, while different panels provided longer and more relevant open-ended responses. Based on this data, we highlight the benefits and pitfalls of using a variety of survey distribution options in terms of the quality, efficiency, and representative nature of the respondents and the types of responses that can be obtained.

Exploring best practices for card interactions through a three-method triangulation

We describe a series of studies using three methods to understand usage of a cards-based mobile queryless search experience. An existing product was chosen as stimuli to allow for quick execution to learn from participants who have had experience over an extended period of time, as opposed to only studying first time use. Findings from these studies provide implications that can be used to inform products aimed at satisfying user needs for personal information while on the go. Focus is placed on the ability for queryless search results to display actionable information as well as provide direct actions without additional clicks.

Video interaction - making broadcasting a successful social media

Video has slowly been gaining popularity as a social media. We are now witnessing a step where capture and live broadcasts is released from the constraints of the desktop computer, which further accentuate issues such as video literacy, collaboration, hybridity, utility and privacy, that needs to be addressed in order to make video useful for large user groups.

Music, Search, and IoT: How People (Really) Use Voice Assistants

Voice has become a widespread and commercially viable interaction mechanism with the introduction of voice assistants (VAs), such as Amazon’s Alexa, Apple’s Siri, Google Assistant, and Microsoft’s Cortana. Despite their prevalence, we do not have a detailed understanding of how these technologies are used in domestic spaces. To understand how people use VAs, we conducted interviews with 19 users, and analyzed the log files of 82 Amazon Alexa devices, totaling 193,665 commands, and 88 Google Home Devices, totaling 65,499 commands. In our analysis, we identified music, search, and IoT usage as the command categories most used by VA users. We explored how VAs are used in the home, investigated the role of VAs as scaffolding for Internet of Things device control, and characterized emergent issues of privacy for VA users. We conclude with implications for the design of VAs and for future research studies of VAs.

"I thought she would like to read it": Exploring Sharing Behaviors in the Context of Declining Mobile Web Use

The use of applications on mobile devices has changed dramatically over the past few years. While web browsing was once a common activity, it's now reported that 86% of time on mobile phones is in apps other than the browser. We set out to understand how the mobile web was currently fitting into people's lives and what web sessions looked like. Finding a dramatic reduction in mobile web revisi-tation rates compared to previous work and that a large number of sessions comprised single page views, we then studied how web content was shared with others in mobile messaging, the source of many single page sessions. The HCI community has not heavily studied this sharing activity that many people perform daily. We conclude with design implications for new mobile applications from our two studies with a combined 287 participants where we studied actual logs of mobile web use and link sharing behavior.

Verification: what works and what doesn't

Today's leading chip and system companies are faced with ever increasing design verification challenges; industry studies reveal that as much as 50% of the total schedule is being spent in verification. Large companies, with almost infinite resources, have shown that throwing CPU cycles and people at the simulation problem still doesn't guarantee a level of coverage desired by the design team.

Algorithmic and visual analysis of spatiotemporal stops in movement data

Analyzing the occurrence of stops in transportation systems is an important challenge to better understand traffic congestion problems and find corresponding solutions. We propose an efficient system to analyze stop occurrences. It consists of two major parts: (1) an efficient clustering algorithm to partition the stops into groups based on strongly connected components (2) an interactive visual representation of the results to provide insights to domain experts.

Research in the large 3.0: app stores, wide distribution, and big data in MobileHCI research

Mobile HCI studies are often conducted in a highly controlled environment and with a small convenient sample. The findings cannot always be generalized to the behaviour of real users in real contexts. In contrast, researchers recently started to use apps and other wide distribution channels as an apparatus for mobile HCI research. Publishing apps in mobile application stores and public APIs for mobile services enable researchers to study large samples in their 'natural habitat'. This workshop continues the successful Research in the Large workshop series held at UbiComp 2010 and 2011. Relevant topics include the design of large-scale studies, reaching target users, dealing with new types of evaluation data, and heterogeneous usage contexts. We seek ways to systematically collect, analyse and make sense of large datasets, potentially in real-time. The goal of this workshop is to provide a forum for researchers and developers from academia and industry to exchange experiences, insights and strategies for wide distribution of user studies towards large-scale mobile HCI research.

Understanding the Long-Term Use of Smart Speaker Assistants

Over the past two years the Ubicomp vision of ambient voice assistants, in the form of smart speakers such as the Amazon Echo and Google Home, has been integrated into tens of millions of homes. However, the use of these systems over time in the home has not been studied in depth. We set out to understand exactly what users are doing with these devices over time through analyzing voice history logs of 65,499 interactions with existing Google Home devices from 88 diverse homes over an average of 110 days. We found that specific types of commands were made more often at particular times of day and that commands in some domains increased in length over time as participants tried out new ways to interact with their devices, yet exploration of new topics was low. Four distinct user groups also emerged based on using the device more or less during the day vs. in the evening or using particular categories. We conclude by comparing smart speaker use to a similar study of smartphone use and offer implications for the design of new smart speaker assistants and skills, highlighting specific areas where both manufacturers and skill providers can focus in this domain.

The 32 Days of Christmas: Understanding Temporal Intent in Image Search Queries

Temporal terms, such as 'winter', 'Christmas', or 'January' are often used in search queries for personal images. But how do people's memories and perceptions of time match with the actual dates when their images were captured? We compared the temporal terms that 74 Flickr users used to search their own photo collections, and compared them to the date captured data in the target image. We also conducted a larger study across several billion images, comparing user-applied tags for holidays and seasons to the dates the images were captured. We demonstrate that various query terms and tags can be in conflict with the actual dates photos were taken for specific types of temporal terms up to 40% of the time. We will conclude by highlighting implications for search systems where users are querying for personal content by date.