Users' experiences are critical to understanding people's reactions to smart home technology (SHT). Moreover, comparing users' experiences with differing SHT usefully situates those technologies in the context of their competitors. To that end, we compare the brand "titans" of SHT in a controlled, laboratory-based comparative usability study. Forty-nine participants engaged in 21 identical tasks in three "living room" labs—an Amazon room, Apple room, and Google room—each set up with a system of connected SHT devices. Analysis of qualitative and quantitative data shows that, generally, Apple was preferred most overall, followed by Google, then Amazon; however, voice interaction with Apple's Siri was rated lower than the other two. Hubs were generally dispreferred, compared to using voice commands or phone apps, though participants also criticized voice command functionality and app interfaces. The primary themes that emerged as most important for users' evaluations included their prior experience with devices' brand, user friendliness, the layout of device interfaces, and the quality of virtual assistants' functionality.

How do robot designers anthropomorphize their own creations? Because robot designers have the ability to alter the robot, identify as its creator, and understand their robot’s internal makeup, their process of anthropomorphism and its outcomes may be different from that of the typical robot user. We investigate this research question in the domain of combat robots, where anthropomorphism is critical to decision-making, communication, and trust in high-stakes, high-emotion combat situations faced by robot-soldier teams. We conducted an in-depth case study of a university’s student-led combat robotics design team over the design, construction, testing, and competition phases for their competitive combat robot. Based on inductive computational and human coding of extensive field notes, supplemented with interviews and surveys, we found that these robot designers anthropomorphize for three purposes. First, they anthropomorphize the bot to manage impressions of it within their team and to outsiders like competitors, spectators, and sponsors, specifically presenting it as a warrior. Second, they anthropomorphize it like a child, a pet, or simply treat it as a non-anthropomorphic mechanical set of parts as a way to calibrate their relationship and attach with or detach from their own creation. Third, they anthropomorphize the bots to assign blame either blaming it, its parts, or others based on their expectations of whether it is performing based on how they designed it. We conclude with implications for anthropomorphism by robot designers and application to military robot design.

This article offers perspectives on adopting smart home technology into usability testing for technical and professional communication (TPC) courses. Usability is a valued skill for technical communicators. However, usability testing methods have their problems as pedagogical tools. Internet-of-Things (IoT) devices and Smart Home Technology (SHT) may offer instructors tools to overcome some of those problems. This article details advantages and concerns associated with using SHT for curricular usability testing.

Language bias, both positive and negative, is a well-documented phenomenon exhibited among human interlocutors. We examine whether this bias is exhibited toward virtual assistants, specifically, Apple's Siri and Google Assistant, with various accents. We conducted three studies with different stimuli and designs to investigate U.S. English speakers’ attitudes toward Google's British, Indian, and American voices and Apple's Irish, Indian, South African, British, Australian, and American voices. Analysis reveals consistently lower fluency ratings for Irish, Indian, and South African voices (compared with American) but no consistent results of bias related to competence, warmth, or willingness to interact. Moreover, participants often misidentified voices’ countries of origin but correctly identified them as artificial. We conclude that this overall lack of bias may be due to two possibilities: lack of humanlikeness of the voices and lack of availability of nonstandardized voices and voices from countries toward which those in the United States typically show bias.

Interest in and ownership of smart home voice assistants like Amazon Alexa and Google Home devices have exponentially increased in recent years. Many people may purchase or be gifted such devices without knowing their potential for connecting with other home technology, listening to private conversations, sharing information with companies, and creating problems due to misunderstanding vocal commands or technological capabilities. Concerns and worries about these devices may be exacerbated over time or by a specific incident. To understand reactions to such situations, we conducted semi-structured in-depth interviews with 10 people who reported different types of worrying incidents with a range of smart home devices and their reactions to reduce that worry. Conducting a thematic coding analysis, we detail how each case study shows a person's worries about their smart home technology developed vis-a-vis the incident or over time, and their strategies to alleviate their worry. The two dominant reactions were restricted acceptance or discontinuance of the smart home technology, while three other interviews revealed nuanced reactions on the acceptance-rejection continuum. For each interviewee, we highlight their technology use, any major incidents, and their psychological processes leading up to their actions to reduce worry. This provides an in-depth look at worry around smart home technology products themselves, not their ability to perform, and how discontinuance, restricted acceptance, and other reactions reduce those worries.

Perugini et al., (2022). An Active Learning Laboratory Manual for Teaching a Contemporary Undergraduate Operating System Course. Journal of Open Source Education, 5(58), 162, https://doi.org/10.21105/jose.00162

Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes. To address the promiscuity challenge and the documented runtime-performance challenge in real-life KG embedding tools, we propose to use domain- and task-specific information to specify regular-expression pathways that define neighborhoods of KG nodes of interest. Our proposed CompactWalks framework uses these semantic subgraphs to enable meaningful compact walks in random-walk based KG embedding methods. We report the results of case studies for the task of determining which pharmaceutical drugs could treat the same diseases. The findings suggest that our CompactWalks approach has the potential to address the promiscuity and runtime-performance challenges in applying embedding tools to large-scale KGs in real life, in the biomedical domain and possibly beyond.

Interest in and ownership of smart home voice assistants like Amazon Alexa and Google Home devices have exponentially increased in recent years. Many people may purchase or be gifted such devices without knowing their potential for connecting with other home technology, listening to private conversations, sharing information with companies, and creating problems due to misunderstanding vocal commands or technological capabilities. Concerns and worries about these devices may be exacerbated over time or by a specific incident. To understand reactions to such situations, we conducted semi-structured in-depth interviews with ten people who reported different types of worrying incidents with a range of smart home devices and their reactions to reduce that worry. The two dominate reactions were restricted acceptance or discontinuance of the smart home technology, while three other interviews revealed nuanced reactions on the acceptance-rejection continuum.

Smart home products continue to rise in popularity but have yet to achieve widespread adoption. There is little research on how the general population perceives benefits of different smart home devices beyond general surveys. Using a living laboratory of five solar houses that we equipped with a range of smart home devices, we assessed how university student residents learn about, use, and gain interest in adopting this smart home technology. Analysis of data confirms that users find lifestyle benefits to be the most important motivators for adopting smart home technology. Yet without training in using that technology, these benefits do not outweigh the risks associated with learning to operate that technology.

Obtaining answers to domain-specific questions over large-scale unstructured (text) data is an important component of data analytics in many application domains. As manual question answering does not scale to large text corpora, it is common to use information extraction (IE) to preprocess the texts of interest prior to posing the questions. This is often done by transforming text corpora into the knowledge-graph (KG) triple format that is suitable for efficient processing of the user questions in graph-oriented data-intensive systems.In a number of real-life scenarios, trustworthiness of the answers obtained from domain-specific texts is vital for downstream decision making. In this paper we focus on one critical aspect of trustworthiness, which concerns aligning with the given domain vocabularies (ontologies) those KG triples that are obtained from the source texts via IE solutions. To address this problem, we introduce a scalable domain-independent text-to-KG approach that adapts to specific domains by using domain ontologies, without having to consult external triple repositories. Our IE solution builds on the power of neural-based learning models and leverages feature engineering to distinguish ontology-aligned data from generic data in the source texts. Our experimental results indicate that the proposed approach could be more dependable than a state-of-the-art IE baseline in constructing KGs that are suitable for trustworthy domain question answering on text data.

While various forms of smart home technology have been available for decades, they have yet to achieve widespread adoption. Although they have risen in popularity during recent years, the general public continue to rate smart home devices as overly complex compared to their benefits. This article reports the results of an eight-month study into the effects of training on smart home technology adoption. Building upon the results of a previous study, and using the same living laboratory approach, we studied the effects of training on the attitudes of a group of residents toward use of smart home technology. Results show that training influences those attitudes toward smart home technology, including increased confidence in future use, and increased actual use of more complex smart home features. Results also indicate that users tended to seek out other users rather than training materials for advice, and that privacy concerns were not a deterrent to using smart home devices.

The Rare Disease InfoHub (​https://rarediseases.oscar.ncsu.edu/​) is a web-based platform that provides a one-stop knowledge resource, delivering the latest available information appropriate for patients, clinicians, and researchers alike while providing structural organization and curation to the Rare Disease knowledge continuum. The InfoHub is designed so that as knowledge and computational algorithms advance and new data science tools become available, they can be easily integrated into the platform to take advantage of sources such as:

Advances in technology over recent years made it possible to use machines and artificial intelligence to develop commercially viable solutions for companies to listen to consumers, decode the meaning, and respond accordingly. In parallel, solutions have been developed that are able to automatically track facial expressions of consumers when reacting to a given marketing stimulus.The authors look at how marketing executives can apply these technologies to generate enhanced customer insights, providing a realistic context for future applications. The focus is on bringing researchers and managers closer to those moments of truth and our ability to understand customer emotions, emotional reaction, everyday language, and ultimately brand engagement.The chapter covers the application of commercially viable use cases for (1) the automated measurement of emotions through facial coding to optimize advertizing and content, and (2) the use of voice coding technology to design interactive chatbots as an alternative to traditional surveys. In the outlook, the authors describe the potential that these technologies provide for future research and further use cases.

With more and more knowledge graphs appear in the real-world data presentation, the complexity of the knowledge graph is increasing, such as heterogeneous graphs in the biomedical area [5, 8]. Analytics on heterogeneous knowledge graphs is challenging because the types of nodes and edges are different. To be efficient, methods of extracting linear paths from knowledge graphs might fail to capture the rich semantics and topology of knowledge graphs. To be effective, methods of using the entire knowledge graph might introduce too much noise irrelevant to the topics. One of the downstream analytical tasks connecting graphs and data analysis is graph embedding, which converts the graph presentation into the numerical presentation. Among various embedding methods, randomwalk based methods are known to be an efficient way to execute such transformation [1, 10]. However, they are originally developed for homogeneous graphs [4, 9]. Without considering any limitations, the random walk will walk through any nodes including those that are irrelevant. It causes inaccurate information extraction and therefore incorrect inference from downstream data analysis. A solution to limit the node types (meta-path) was proposed to avoid such incorrect walks in citation networks [2]. Nevertheless, they assume edge type is unique between two types of nodes. Knowledge graphs in the biomedical area contain more complicated relations between entities. Toy Example. To illustrate, Figure 1 showcases a toy example involving a biomedical knowledge graph. Suppose now we need to execute a graph embedding task for the downstream data analysis. The graph embedding pipeline takes the graph as input shown in Figure 1.

What are the relationships between knowledge of, perceived benefits, adoption of, and use of smart home products? To explore this question, in our first two studies we focus on the general population's perceptions of benefits across many types of smart home products by creating a corpus of smart home product descriptions. Study 1 (n = 399) shows that previous product knowledge influences a range of perceived benefits. Study 2 (n = 242) demonstrates which benefits increase non-owners' likelihood of adopting these products. In study 3, we longitudinally survey eight residents in living laboratory houses equipped with 10 integrated smart home products. We find over a year access to the products increases perceptions of their benefits, but does not increase their actual use. Collectively, these studies contribute to an increased understanding of the relationship among benefits, use, and adoption of this emerging technology.

Purpose: As digital media continue to proliferate into every aspect of our lives, sound is playing an ever-increasing role in the conveyance of information. Because digital messages via the Internet, cell phones, smart devices, and personal devices are typically shorter and more direct than traditional text-based communication, digital communication will necessarily improve via other, less textual cues. Visually, we already see this through the use of digital cues, such as emojis and graphical displays (e.g., GPS charts). But digital communication, in the future, will gain much from the expanded use of sound. As yet, no analytical framework for classifying sound in technical communication has been established. Method: The author revisits some historical uses of sound in technical communication before proposing a model for analyzing current and future sounds. Results: Tools that encompass signaling, linguistics, paralinguistics, extralinguistics, and rhetoric can be used to analyze complex sonic combinations and to generate new sounds for technical communication. Conclusions: A first model is proposed along with recommendations for the future.

Smart home products continue to rise in popularity but have yet to achieve widespread adoption. There is little research on how the general population perceives benefits of different smart home devices beyond general surveys. Using a living laboratory of five solar houses that we equipped with a range of smart home devices, we assessed how university student residents learn about, use, and gain interest in adopting this smart home technology. Analysis of data confirms that users find lifestyle benefits to be the most important motivators for adopting smart home technology. Yet without training in using that technology these benefits do not outweigh the risks associated with learning to operate that technology.

Although various types of documents are called white papers, in technical marketing communication the white paper is usually a document that describes a new or improved technology in order to generate interest inand promote sales ofthat technology. Most sources discussing the history of the white paper assume that marketing white papers evolved from government white papers. They conflate genre history with etymology. At some point in the mid-20th century, the term white paperdenoting a type of government policy documentbegan being applied to other types of documents, including eventually a particular form of technical marketing communication. This article proposes a revised history of the marketing white paper as a genre. By examining the formal features and characteristic substance of white papers through the lens of their pragmatic value as social action, we show that the marketing white paper of today has much in common with documents from the 16th, 17th, and 18th centuries.

The controversy surrounding the ending of Mass Effect 3 serves as a case study of a company's rejection of cocreation with customers. The game designers and players battled for control of the aesthetic space of the game. The company failed to resolve their conflict effectively, allowing players to use social media to transform tactical action into strategic action. This case study has implications for technical communicators who increasingly are collaborating with users in cocreative relationships.