Reviewerly/exHarmony huggingface dataset:
What’s The Talk on VUI Guidelines? A Meta-Analysis of Guidelines for Voice User Interface Design

Over the past decade, voice user interface (VUI) design has been steadily growing, along with a growing VUI presence in consumer markets. However, there is currently a lack of widely-established guidelines for VUI design. While many sets of VUI guidelines have been proposed, they tend to be developed independently of each other, leading to a lack of consensus on appropriate guidelines for VUI design. This can hinder the wider adoption of practical VUI guidelines. To address this gap, we performed a large-scale meta-analysis of 336 VUI design guidelines that have been proposed in academic literature. Using thematic analysis, we present a unified and synthesized set of 14 guidelines, representing the most universally proposed principles of VUI design as captured by the 336 VUI guidelines identified in academic literature. We hope that this synthesized set can address several of the challenges to the adoption of VUI guidelines in design practice.
Gist and Verbatim: Understanding Speech to Inform New Interfaces for Verbal Text Composition

Recent interest in speech-to-text applications has found speech to be an efficient modality for text input. However, the spontaneity of speech makes direct transcriptions of spoken compositions effortful to edit. While previous works in Human-Computer Interaction (HCI) domain focus on improving error correction, there is a lack of theoretical ground around the understanding of speech as an input modality. This work explores literature from Cognitive Science to synthesize relevant theories and findings for the HCI audience to reference. Motivated by the literature indicating a fast memory decay of speech production and a preference towards gist abstraction in memory traces, an experiment was conducted to observe users’ immediate recall of their verbal composition. Based on the theories and findings, we introduce new interaction concepts and workflows that adapt to the characteristics of speech input.
Exploring Design Opportunities for Reflective Conversational Agents to Reduce Compulsive Smartphone Use

Conversational agents (CAs) have become ubiquitous in our daily lives. Recognizing the potential of CAs being persuasive agents, we are interested in leveraging CAs to reduce compulsive smartphone use, a widespread behavior among young adults that can lead to negative consequences. This work presents the design and development of StayFocused, a mobile app incorporating a chatbot to assist people in setting focus goals and reflecting on their phone-checking behaviors in situ. Particularly, we highlight the iterative process of curating prompts for GPT-3, and the lessons learned from our trials and errors. With StayFocused, we propose a three-week between-subjects study with college students. We envision the design of StayFocused and the proposed study will deepen our understanding of how CAs support immediate actions as well as sustained behavior change, and inform the design of persuasive technologies for reducing unintended behaviors such as compulsive smartphone use.
Secure, Comfortable or Functional: Exploring Domain-Sensitive Prompt Design for In-Car Voice Assistants

User Experience in Human-Computer Interaction is composed of a multitude of building blocks, one of which is how Voice Assistants (VAs) talk to their users. Linguistic considerations around syntax, grammar, and lexis have proven to influence users’ perception of VAs. Users have nuanced preferences regarding how they want their VAs to talk to them. Previous studies have found these preferences to differ between domains, but an exhaustive and methodical overview is still outstanding. By means of an A/B study spanning over domains as well as dialog types, this paper methodically closes this gap and explores the degree of domain-sensitivity across different types of dialogs in German. The results paint a mixed picture regarding the importance of domain-sensitivity. While some degree of domain-sensitivity was found for in-car prompts, it generally seems to play a rather minor role in users’ experience of VAs in the vehicle.
Working with Troubles and Failures in Conversation Between Humans and Robots

In order to carry out human-robot collaborative tasks efficiently, robots have to be able to communicate with their human counterparts. In many applications, speech interfaces are deployed as a way to empower robots with the ability to communicate. Despite the progress made in speech recognition and (multi-modal) dialogue systems, such interfaces continue to be brittle in a number of ways and the experience of the failure of such interfaces is commonplace amongst roboticists. Surprisingly, a rigorous and complete analysis of communicative failures is still missing, and the technical literature is positively skewed towards the success and good performance of speech interfaces. In order to address this blind spot and investigate failures in conversations between humans and robots, an interdisciplinary effort is necessary. This workshop aims to raise awareness of said blind spot and provide a platform for discussing communicative troubles and failures in human-robot interactions and potentially related failures in non-robotic speech interfaces. We aim to bring together researchers studying communication in different fields, to start a scrupulous investigation into communicative failures, to begin working on a taxonomy of such failures, and enable a preliminary discussion on possible mitigating strategies. This workshop intends to be a venue where participants can freely discuss the failures they have encountered, to positively and constructively learn from them.
Volume formula and growth rates of the balls of strings under the edit distances

Analogs of balls in n-dimensional Euclidean space are defined in the monoid of strings composed of letters of an alphabet if a metric is defined on the monoid. These analogs are not only balls in the monoid of strings but also regular languages. Since the Levenshtein distance, a reasonable metric between strings, was introduced in 1965, researchers have aimed to determine a formula for computing the volumes of balls of strings under this distance and their growth rate. However, this problem remains open. In this paper, we first derive a formula for the volumes of balls of strings under a metric obtained by extending the Hamming distance as a metric on the whole monoid and subsequently reveal the growth rate of balls of strings under the Levenshtein distance using this formula. Furthermore, we describe a conjecture on the formula for the volumes of spheres of strings under the Levenshtein distance. Next, we construct an efficient randomized algorithm that computes estimates of the volumes of the balls whose estimation errors are less than a given value with a probability greater than a specified value. Finally, we examine the validity of the proposed conjecture, using the (true) volumes obtained by exhaustive searches for small spheres and estimates of the volumes obtained by this algorithm for large ones.
Exploring Usability Issues in Instruction-Based and Schema-Based Authoring of Task-Oriented Dialogue Agents

Platforms such as Google DialogFlow and Amazon Lex have enabled easier development of conversational agents. The standard approach to training these agents involve collecting and annotating in-domain data in the form of labelled utterances. However, obtaining in-domain data for training machine learning models remains a bottleneck. Schema-based dialogue, which involves laying out a structured representation of the flow of a "typical" dialogue, and prompt-based methods, which involve writing instructions in natural language to large language models such as GPT-3, are promising ways to tackle this problem. However, usability issues when translating these methods into practice are less explored. Our study takes a first step towards addressing this gap by having 23 students who had finished a graduate-level course on spoken dialogue systems report their experiences as they defined structured schemas and composed instruction-based prompts for two task-oriented dialogue scenarios. Through inductive coding and subsequent thematic analysis of the survey data, we explored users' authoring experiences with schema and prompt-based methods. The findings provide insights for future data collection and authoring tool design for dialogue systems.
You Don’t Need to Speak, You Need to Listen: Robot Interaction and Human-Like Turn-Taking

The focus on one-to-one speak/wait conversational interaction with artificial system is partly misguided and partly cynical. Misguided because it pre-supposes that our relationship with such a system should be one-to-one and that human-like turn taking is never required. Cynical because we avoid the difficult challenge of building complex systems with a problematic route for publication. Whereas vision systems are regularly used in social robots and virtual agents to detect multiple dialogue partners and aid diarization, speech analysis and human-like turn-taking has lagged far behind. In this positional paper we make the case for focusing on human-like turn taking and multi-party interaction, discuss why realtime speech analysis and conversational management has been neglected, and put forward a program to correct this.
Investigating the Usability of a Speech Agent-based Role-playing Game

A small number of role-playing games can be played on speech agents like Amazon Alexa and Google Assistant. These offer a new, accessible way for people to access a game genre that is most commonly played on video game consoles or PCs. A sample of 8 participants were asked to play 15-20 minutes of The Bard’s Tale: Warlocks of Largefearn using Amazon Alexa after which they took part in a semi-structured interview. The interviews and play sessions were transcribed and analysed using inductive thematic analysis. Six themes were generated: Technical Issues, Frustration, Immersion, Memory and Cognitive Load, Simplicity vs. Detail, and Speech Agents as a Medium for Games. Participants found the experience interesting and novel but were frustrated by a number of user experience issues, and had notable strain placed on their memory and cognitive load among other problems. A list of 10 design guidelines for speech agent-based role-playing games was created.
Is CUI Design Ready Yet? A Workshop on Community Practices and Gaps in CUI Design &amp; Resource Development

As CUIs become more prevalent in both academic research and the commercial market, it becomes more essential to design usable and adoptable CUIs. While research has been growing on the methods for designing CUIs for commercial use, there has been little discussion on overall community practice of developing design resources to aid in practical CUI design. The aim of this workshop therefore is to bring the CUI community together to discuss the current practices for developing tools and resources for practical CUI design, the adoption (or non-adoption) of these tools and resources, and how these resources are utilized in the training and education of new CUI designers entering the field. This workshop will bring together all parts of the CUI community to have meaningful discussions on how CUI design resources are currently developed, and how we can improve these resources and tools to aid in their adoption in practical CUI design, and CUI academic & industry design training.
Human Speakers Help Machine Listeners To account For Visual Asymmetries in Dialogue

Human-machine dialogue (HMD) research debates the degree to which language production in this context is egocentric or allocentric. That is, the degree to which a person might take a machine’s perspective into account. Our study aims to identify whether users produce allocentric or egocentric language within speech-based HMD when there is asymmetry in the information available to both partners. Through an adapted referential communication task, we manipulated the presence or absence of visual distractors and occlusions, similarly to previous referential tasks used in psycholinguistic research. Results show that people are sensitive to the presence of distractors and occlusions and tend to produce more informative expressions to help machine partners account for the visual asymmetries. We discuss the findings on how allocentric production in HMD is explained by how the division of labour manifests in spoken HMD. The findings further our understanding of the language production mechanisms in HMD.
Conversations with the News: Co-speculation into Conversational Interactions with News Content

Conversational agents have limited conversational capabilities and there is a debate as to whether interactions with conversational user interfaces (CUIs) are truly conversational. Currently, most news and journalistic content is presented in a monologic form. Simultaneously, there is an expectation that CUIs can change how we interact with news content. To explore what conversational interactions with the news could look like, two co-speculation workshops were arranged. The design-led inquiries focus on how conversations can be used as a resource for designing interactions with CUIs for news. Three different prototyping techniques were used in the design explorations: storyboarding, scripting and role-playing. Our work offers two main contributions: 1) We identify three dimensions relevant to the design space of CUI for news: the CUIs’ role, conversational capabilities, and locus of control, and 2) a critical reflection on the potential of different techniques for prototyping CUIs.
Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues

Spoken dialogue systems (SDSs) have been separately developed under two different categories, task-oriented and chit-chat. The former focuses on achieving functional goals and the latter aims at creating engaging social conversations without special goals. Creating a unified conversational model that can engage in both chit-chat and task-oriented dialogue is a promising research topic in recent years. However, the potential “initiative” that occurs when there is a change between dialogue modes in one dialogue has rarely been explored. In this work, we investigate two kinds of dialogue scenarios, one starts from chit-chat implicitly involving task-related topics and finally switching to task-oriented requests; the other starts from task-oriented interaction and eventually changes to casual chat after all requested information is provided. We contribute two efficient prompt models which can proactively generate a transition sentence to trigger system-initiated transitions in a unified dialogue model. One is a discrete prompt model trained with two discrete tokens, the other one is a continuous prompt model using continuous prompt embeddings automatically generated by a classifier. We furthermore show that the continuous prompt model can also be used to guide the proactive transitions between particular domains in a multi-domain task-oriented setting.
Machine learning for rectal cancer prediction based on metabolic changes on amino acids

Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. Currently the amount of information available in different areas, especially in health care where data collection and analysis seek to define personalized medicine strategies. This is a field where using Machine Learning-based tools can assume a relevant role. This work presents a study of diverse classification algorithms in the area of machine learning applied to identification of amino acid profiles. The authors defined as a major objective to develop a new biomarker profile for prediction and prognosis of rectal cancer. The data involved in the training of classification algorithms refer to patients with metabolic diseases and rectal cancer. The best machine learning classification models will be tested to achieve the most effective decision support system for a most adequate treatment option selection in order to reduce the morbidity and mortality rate.
Responsive List Width for Portable Devices With Different Widths of Screen

With the increasing use of large-screen portable devices and the prevalence of list-based user interfaces, it has become critically important to design list interfaces that are visually appealing and user-friendly across various devices and screen sizes. The rules for adapting list-based user interfaces on large screens warrant investigation. Thus, the present study aimed to determine the responsive list width that can enhance visual search efficiency and improve user experience on portable devices with different widths of screen. Two experiments were conducted, in which we manipulated the width of single-column and parent-child lists on portable devices with medium- (Experiment 1; N = 80) and large-width screens (Experiment 2; N = 41), varying the range of list width from very narrow to very wide. Results show that for the single-column lists on a medium-width screen, users demonstrated the highest level of preference and gave the highest ratings for satisfaction and visual aesthetics when the lists were moderately wide. For the single-column lists on a large-width screen, users preferred both the moderately-narrow and moderately-wide lists. However, for parent-child lists, the results show that both the moderately-wide and very-wide lists were favored on both the medium-width and large-width screens. These findings may be attributed to users’ preference for the appropriate white space on different screens, thereby providing useful guidelines for the responsive design of lists on portable devices.
From C-3PO to HAL: Opening The Discourse About The Dark Side of Multi-Modal Social Agents

The increasing prevalence of communicative agents raises questions about human-agent communication and the impact of such interaction on people’s behavior in society and human-human communication. This workshop aims to address three of those questions: (i) How can we identify malicious design strategies – known as dark patterns – in social agents?; (ii) What is the necessity for and the effects of present and future design features, across different modalities and social contexts, in social agents?; (iii) How can we incorporate the findings of the first two questions into the design of social agents? This workshop seeks to conjoin ongoing discourses of the CUI and wider HCI communities, including recent trends focusing on ethical designs. Out of the collaborative discussion, the workshop will produce a document distilling possible research lines and topics encouraging future collaborations.
Increasing Transparency to Design Inclusive Conversational Agents (CAs): Perspectives and Open Issues

Artificial Intelligence (AI)-based Conversational Agents (CAs) have a great potential to include marginalized and vulnerable populations. However, some issues still make these interfaces exclusive for some users. This paper proposes to discuss how increasing CAs’ transparency can contribute to these systems’ inclusiveness and indicates open issues that must be addressed to make AI-based CAs more transparent and inclusive. We argue that adding more guidance to users on how CAs work, what they can do, and how they may be operated might alleviate older adults’ misperceptions about functioning and privacy that hamper CAs’ adoption, facilitate its usage for people with impairments, and help identify possible prejudicial biases. As challenges, researchers and practitioners should investigate how to determine appropriate levels of transparency through personalization, produce human-centered knowledge on transparency, and study new methods, tools, and processes to support CA development that considers inclusiveness.
A Pilot Evaluation of a Conversational Listener for Conversational User Interfaces

Current spoken conversational user interfaces (CUIs) are predominantly implemented using a sequential, utterance based, two-party, speak-wait/speak-wait approach. Human-human conversation 1) is not sequential, with overlap, interruption and back channels; 2) processes utterances before they are complete and 3) are often multi-party. As part of Honda Research Institute’s Haru project a light weight word spotting speech recognition system - A conversational listener - was implemented to allow very fast turn-taking in simple voice interaction conditions. In this paper, we present a pilot evaluation of the conversational listener in a script follower context (which allows a robot to act out a dialog with a user). We compare a disembodied version of the system with expressive synthesis to Alexa with and without fast turn-taking. Qualitative results indicate that users were sensitive to turn-taking delay and characterful speech synthesis.
SACA-UNet:Medical Image Segmentation Network Based on Self-Attention and ASPP

In recent years, deep learning based techniques have been successfully applied to medical image segmentation, which plays an important role in intelligent lesion analysis and disease diagnosis. At present, the mainstream segmentation models are primarily based on the U-Net model for extracting local features through multi-layer convolution, which lacks global information and the multi-scale semantic information interaction between the Encoder and Decoder process, leading to sub-optimal segmentation performance. To address such issues, in this work we propose a new medical image segmentation network, namely SACA-UNet, which improves the U-Net model via the self-attention and cross atrous spatial pyramid pooling (Cross-ASPP) mechanisms. In specific, SACA-UNet first utilizes the self- attention mechanism to capture the global feature, it next devises a Cross-ASPP module to extract and fuse features of varying reception fields to prompt multi-scale semantic interaction. We evaluate the segmentation performance of our proposed model on four benchmark datasets including the ISIC2018, BUSI, CVC- ClinicDB, and COVID-19 datasets, in terms of both the Dice coefficient and IoU metrics. Experimental results demonstrate that SACA-UNet remarkably outperforms the baseline methods.
CoqCryptoLine: A Verified Model Checker with Certified Results

Abstract We present the verified model checker CoqCryptoLine for cryptographic programs with certified verification results. The CoqCryptoLine verification algorithm consists of two reductions. The algebraic reduction transforms into a root entailment problem; and the bit-vector reduction transforms into an SMT QF_BV problem. We specify and verify both reductions formally using Coq with MathComp . The CoqCryptoLine tool is built on the OCaml programs extracted from verified reductions. CoqCryptoLine moreover employs certified techniques for solving the algebraic and logic problems. We evaluate CoqCryptoLine on cryptographic programs from industrial security libraries.
Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification

The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advantages of quantum algorithms on artificially generated datasets, and initial experiments with small binary classification problems have yielded comparable outcomes to classical algorithms. However, it is essential to investigate the potential quantum advantage using real-world data. To the best of our knowledge, this study is the first to propose the utilization of quantum systems to classify neuron morphologies, thereby enhancing our understanding of the performance of automatic multiclass neuron classification using quantum kernel methods. We examined the influence of feature engineering on classification accuracy and found that quantum kernel methods achieved similar performance to classical methods, with certain advantages observed in various configurations.