Alexa,Siri, Cortana,and More: An Introduction to Voice Assistants

Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana, and Google’s Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today’s voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.     

Investigating married adults’ communal coping with genetic health risk and perceived discrimination

Increased genetic testing in personalized medicine presents unique challenges for couples, including managing disease risk and potential discrimination as a couple. This study investigated couples’ conflicts and support gaps as they coped with perceived genetic discrimination. We also explored the degree to which communal coping was beneficial in reducing support gaps and ultimately stress. Dyadic analysis of married adults (N?=?266, 133 couples), in which one person had the genetic risk for serious illness, showed that perceived discrimination predicted more frequent conflicts about alpha-1 antitrypsin deficiency-related treatment, privacy boundaries, and finances, which, in turn, predicted wider gaps in emotion and esteem support, and greater stress for both spouses. Communal coping predicted lower support gaps for both partners and marginally lower stress.     

Genetics Home Reference: A Review

Genetics Home Reference is a free, online resource created and maintained by the National Library of Medicine. It is designed to provide genetic information to a wide variety of audiences, particularly the general public. The site consists of original information and links to other curated resources.     

Preparing for Practice: Strengthening Third-Year Medical Students’ Awareness of Point-of-Care Resources

This column describes the development and implementation of a responsive library course designed to train third-year medical students as they begin their clerkships. The course design consisted of a brief face-to-face introduction during third-year orientation and an online course developed in Canvas, an online learning management system. The objective of the course was to not only introduce students to the resources but also to teach them how to effectively use those resources at the point-of-care. Students evaluated the course to assess the overall effectiveness of the instruction. Course development and content, feedback provided by students, as well as suggestions for improvement are discussed.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.     

Spaces of emergent memory: Detroit’s 8 Mile wall and public memories of civil rights injustice

This article introduces emergent memory, a conceptual extension to rhetorics of public memory, to describe memory’s genesis in sites built without commemorative commitments. Examining Detroit’s “8 Mile Wall,” a site built to reinforce segregated housing, this project argues the rhetorical tenets of emergent memory present in this space. As a relic of segregated history, the wall symbolically recalls the city’s controversial past, but has recently been the subject of a local mural project to redefine the wall’s purpose. Some consider this a step toward reclamation, as it visually repositions the disturbing remnant. For others, the murals simply cannot overwrite troubling memories of the city’s discriminatory history. This essay uses emergent memory to describe how the wall’s complicated mnemonic legacy simultaneously harkens to a difficult history and how the mural additions use that same legacy to convey an optimistic future for Detroit and those marked by this urban space.     

The Suasory Force of Sticky Messages: An Application to the Application of Sunscreen

Stickiness refers to the set of persuasive message properties: simple, unexpected, concrete, credible, emotional, and stories (SUCCES). Heath and Heath (2007) argue that a sticky message is expected to be more memorable and hence more persuasive than a non-sticky message. A 2 (sticky v. non-sticky message) x 3 (pretest v. posttest v. delayed test) longitudinal experiment is employed to examine the persuasiveness of sticky messages on applying sunscreen. Results of a mixed model analysis of variance show that the sticky message produces attitudes and behaviors more favorable to the message recommendation than the non-sticky message. Specifically, a time × message induction non-additive effect was found, which sustained only in the sticky message condition across time. Despite this interesting effect, its explanation remains elusive.     

The Role of Consistency in Detecting Deception: The Superiority of Correspondence over Coherence

Inconsistency is often considered an indication of deceit. The conceptualization of consistency used in deception research, however, has not made a clear distinction between two concepts long differentiated by philosophers: coherence and correspondence. The existing literature suggests that coherence is not generally useful for deception detection. Correspondence, however, appears to be quite useful. The present research developed a model of how correspondence is utilized to make judgments, and this article reports on four studies designed to elaborate on the model. The results suggest that judges attend strongly to correspondence and that they do so in an additive fashion. As noncorrespondent information accumulates, an increasingly smaller proportion of judges make truthful assessments of guilty suspects. This work provides a basic framework for examining how information is utilized to make deception judgments and forms the correspondence and coherence module of truth-default theory.