Sign In

Communications of the ACM

101 - 110 of 2,714 for bentley

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.


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.


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.


Deterministic Sparse Suffix Sorting in the Restore Model

Given a text T of length n, we propose a deterministic online algorithm computing the sparse suffix array and the sparse longest common prefix array of T in O(c √ lg n + m lg m lg n lg* n) time with O(m) words of space under the premise that the space of T is rewritable, where mn is the number of suffixes to be sorted (provided online and arbitrarily), and c is the number of characters with mcn that must be compared for distinguishing the designated suffixes.


Constraint handling in genotype to phenotype mapping and genetic operators for project staffing

Project staffing in many organisations involves the assignment of people to multiple projects while satisfying multiple constraints. The use of a genetic algorithm with constraint handling performed during a genotype to phenotype mapping process provides a new approach. Experiments show promise for this technique.


GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

GECCO is the largest peer-reviewed conference in the field of Evolutionary Computation, and the main conference of the Special Interest Group on Genetic and Evolutionary Computation (SIGEVO) of the Association for Computing Machinery (ACM).


Communication vs Synchronisation in Parallel String Comparison

The longest common subsequence (LCS) problem is fundamental in computer science and its many applications. Parallel algorithms for this problems have been studied previously, in particular in the bulk-synchronous parallelism (BSP) model, which treats local computation, communication and synchronisation as independent scarce resources of a computing system. We consider primarily BSP algorithms running in either constant or polylogarithmic synchronisation. Based on our previous results on the algebraic structure and efficient algorithms for semi-local LCS, we present BSP algorithms for parallel semi-local LCS, improving on the existing upper bounds on communication and synchronisation; in particular we present the first constant-sync work-optimal LCS algorithm.


Project Us: A Wearable for Enhancing Empathy

Enhancing the empathy of our human interactions has been the object of intensive psychological studies for decades. The emergence of affective computing has opened the door towards technologically-enabled solutions. Yet, existing techniques struggle to attain their desired impact, often being difficult and expensive to deliver, and disconnected from daily life. Project Us' goal is to help overcome these challenges through a pair of wearable devices (in this case wristbands) that aim to trigger an empathy-enhancing effect, when being worn by two people during day-to-day conversations. The small-sized, wireless devices sense each person's electrodermal activity, associated with their level of emotional arousal, and share it to the other partner (when a threshold is exceeded) through a discreet, haptic nudge, creating a real-time feedback loop. The user study performed with 18 participants (nine romantically engaged couples) revealed that most of them found the wristbands to increase their level of awareness of the partner's emotional experience. Their interaction was analyzed based on interviews (qualitatively), and natural language processing techniques (quantitatively).