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Communications of the ACM


101 - 110 of 3,913 for bentley


AnalyticDB-V: a hybrid analytical engine towards query fusion for structured and unstructured data

With the explosive growth of unstructured data (such as images, videos, and audios), unstructured data analytics is widespread in a rich vein of real-world applications. Many database systems start to incorporate unstructured data analysis to meet such demands. However, queries over unstructured and structured data are often treated as disjoint tasks in most systems, where hybrid queries (i.e., involving both data types) are not yet fully supported.

In this paper, we present a hybrid analytic engine developed at Alibaba, named AnalyticDB-V (ADBV), to fulfill such emerging demands. ADBV offers an interface that enables users to express hybrid queries using SQL semantics by converting unstructured data to high dimensional vectors. ADBV adopts the lambda framework and leverages the merits of approximate nearest neighbor search (ANNS) techniques to support hybrid data analytics. Moreover, a novel ANNS algorithm is proposed to improve the accuracy on large-scale vectors representing massive unstructured data. All ANNS algorithms are implemented as physical operators in ADBV, meanwhile, accuracy-aware cost-based optimization techniques are proposed to identify effective execution plans. Experimental results on both public and in-house datasets show the superior performance achieved by ADBV and its effectiveness. ADBV has been successfully deployed on Alibaba Cloud to provide hybrid query processing services for various real-world applications.

2020-08-01
https://dl.acm.org/ft_gateway.cfm?id=3424501&dwn=1

Bear: Cyberinfrastructure for Long-Tail Researchers at the Federal Reserve Bank of Kansas City

       The Federal Reserve Bank of Kansas City has been developing environments uniquely tailored for long-tail researchers in and around the field of economics. Due to the unique requirements of researchers in the Federal Reserve System, the Bear high performance computing environment was developed to meet our researchers’ computational and data needs, which have grown dramatically over the past few decades. In addition to increased accessibility, the environment meets an increasing number of usage modalities, demanding research cycles while decreasing the barrier of entry for researchers who have little to no experience using advanced research computing environments. We took the lessons learned from our previous environment, Bull, and improved on the pain points experienced with our diverse researcher base. We have found that Bear has improved user experience and has led to better usability while finding unique solutions to delivering resources. We have also updated and facilitated better training to give researchers in the Federal Reserve System the tools necessary to take advantage of not only Bear but also other cyberinfrastructure in the larger research ecosystem.

2020-07-26
https://dl.acm.org/ft_gateway.cfm?id=3396623&dwn=1

Alexa, How Do I Build a VUI Curriculum?

As Voice User Interfaces (VUI) become more prominent in everyday life, there is an ever growing need to ensure that designers have the appropriate knowledge and tools to be able to build usable VUIs. To do this, we need to develop a curriculum for VUI design. However, there are few resources in academia and in industry to help ground the development of a new VUI-specific HCI curriculum. We discuss the limited approaches to VUI design, and particularly to preparing designers for this emerging interaction paradigm - both academia and industry training. Grounded in this, we then describe potential avenues for understanding what a VUI curriculum may entail, what would HCI educators need from it, and how such a curriculum would be validated.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406137&dwn=1

Tired of Wake Words?: Moving Towards Seamless Conversations with Intelligent Personal Assistants

In this paper, we aim to draw attention towards wake words. Wake words are an integral part of every request addressed to Intelligent Personal Assistants (IPAs). Currently, a request made to an IPA is led by wake words, making a conversation with an IPA more tiresome than a conversation with a human being. The main question we pose in this paper is, whether we can eliminate the use of wake words at least in specific contexts. Based on our experience with IPAs we propose three less burdensome alternatives that avoid the need for speaking wake words in some cases. Based on these approaches we discuss how to design seamless conversations with IPAs.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406141&dwn=1

Toward Voice-Assisted Browsers: A Preliminary Study with Firefox Voice

Web browsers allow people to find, organize, and manage information on the web. While voice interaction research has evaluated the support of web search, the broader role of voice interactions within the browser have yet to be explored at depth. We report findings from a preliminary exploration of the challenges, opportunities, and directions of voice assistants embedded in modern web browsers. We drive our inquiry with Firefox Voice, a browser extension that implements a voice assistant into the Firefox desktop browser. Through a think-aloud study (n = 5), we explore the strengths and shortcomings of Firefox Voice to better understand the role that voice interaction can play in supporting people both in the browser and beyond it.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406154&dwn=1

What Can I Say?: Effects of Discoverability in VUIs on Task Performance and User Experience

Discoverability, the ability for users to find and execute features through a user interface, is a recurrent problem with Voice User Interface (VUI) design that makes it difficult for users to understand what commands are supported by a newly encountered system. We studied the effects of two different discoverability strategies proposed in literature, one which provides informational prompts automatically and one which provides help only when the user requests it by asking 'What Can I Say?'. Our study adopted a Wizard of Oz approach that allowed users to order food delivery by voice. Through statistical analysis, we confirmed the beneficial nature of both strategies, with significantly better task performance and higher usability scores in comparison to a baseline. This suggests designers should consider the use of a discoverability strategy in the design of VUIs. While no significant differences were found between the strategies, a majority of the participants highlighted their preference for the 'What Can I Say?' strategy if they were to use the VUI more frequently. Finally, we reflect on the implications for the design of VUIs, highlighting the need to distinguish between initial use and longer-term use in the selection of a strategy.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406119&dwn=1

Don't Believe The Hype!: White Lies of Conversational User Interface Creation Tools

Following the initial hype and high expectations of conversational user interfaces (CUIs), a number of creation tools have emerged to simplify development of these complex systems. These have the potential to democratise and expand application development to those without programming skills. However, while such tools allow end-user developers to build language understanding and dialog management capability into a CUI application, actually fulfilling or executing an action still requires programmatic API integration. In this paper, we look at how CUI builders that claim to be "no code required" struggle to yield more than toy examples, with an aim to provoke the community to develop better tools for CUI creation.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406140&dwn=1

Mental Workload and Language Production in Non-Native Speaker IPA Interaction

Through smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. With linguistic coverage and varying functionality levels, many speakers engage with IPAs using a non-native language. This may impact mental workload and patterns of language production used by non-native speakers. We present a mixed-design experiment, where native (L1) and non-native (L2) English speakers completed tasks with IPAs via smartphones and smart speakers. We found significantly higher mental workload for L2 speakers in IPA interactions. Contrary to our hypotheses, we found no significant differences between L1 and L2 speakers in number of turns, lexical complexity, diversity, or lexical adaptation when encountering errors. These findings are discussed in relation to language production and processing load increases for L2 speakers in IPA interaction.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406118&dwn=1

Challenges in Supporting Exploratory Search through Voice Assistants

Voice assistants have been successfully adopted for simple, routine tasks, such as asking for the weather or setting an alarm. However, as people get more familiar with voice assistants, they may increase their expectations for more complex tasks, such as exploratory search --- e.g., "What should I do when I visit Paris with kids? Oh, and ideally not too expensive." Compared to simple search tasks such as "How tall is the Eiffel Tower?", which can be answered with a single-shot answer, the response to exploratory search is more nuanced, especially through voice-based assistants. In this paper, we outline four challenges in designing voice assistants that can better support exploratory search: addressing situationally induced impairments; working with mixed-modal interactions; designing for diverse populations; and meeting users' expectations and gaining their trust. Addressing these challenges is important for developing more "intelligent" voice-based personal assistants.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406152&dwn=1

What is Natural?: Challenges and Opportunities for Conversational Recommender Systems

This poster describes a fundamental user research to uncover the challenges and opportunities for a conversational recommender system. The study was conducted in three different environments: home, work and street. During the sessions, the participants (i) recounted their previous experiences of interacting with conversational agents in a critical incident interview, (ii) performed a task to explore restaurant recommendations with Google Assistant, and (iii) discussed their views on conversational recommendations. We organize the challenges uncovered into three levels: environment, conversation, and system. Further, we illustrate the identified opportunities of whispering interaction, collaborative recommendations, driving scenario, and local-like conversations for delivering recommendations.

2020-07-22
https://dl.acm.org/ft_gateway.cfm?id=3406174&dwn=1