SNaP! Re-using, sharing and communicating designs and design knowledge using scenarios, narratives and patterns

In order to enable a culture of critical, informed and reflective design practice we need a linguistic framework for communicating design knowledge: the knowledge of the characteristic features of a domain of practice, the challenges which inhabit it, and the established methods of resolving them. Such an infrastructure must “serve two masters”; on one hand, it should adhere to the requirements of scientific rigor, ensuring that the proposed conditions and challenges are genuine and the solutions effective. On the other hand, it should maintain pragmatic adequacy, ensuring that the insights it encapsulates are readily available for practitioners to implement in real-world situations. Several representations have been proposed to this effect: design narratives (Mor, 2011; Barab et al, 2008; Bell, Hoadley and Linn, 2004; Hoadley, 2002; Linn & Hsi, 2000), design principles (Kali, 2006, 2008; Linn, Bell, & Davis, 2004; Merrill, 2002; Quintana et al., 2004; van den Akker, 1999), and design patterns (Derntl & Motschnig-Pitrik, 2005; Goodyear, 2005; Mor & Winters, 2007; Retalis et al, 2006), to name a few. The aim of this chapter is to characterise two of these forms – design narratives and design patterns, and propose a third form – design scenarios, and suggest how these could be embedded in a cycle of reflective learning design

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201

Report of the Stanford Linked Data Workshop

The Stanford University Libraries and Academic Information Resources (SULAIR) with the Council on Library and Information Resources (CLIR) conducted at week-long workshop on the prospects for a large scale, multi-national, multi-institutional prototype of a Linked Data environment for discovery of and navigation among the rapidly, chaotically expanding array of academic information resources. As preparation for the workshop, CLIR sponsored a survey by Jerry Persons, Chief Information Architect emeritus of SULAIR that was published originally for workshop participants as background to the workshop and is now publicly available. The original intention of the workshop was to devise a plan for such a prototype. However, such was the diversity of knowledge, experience, and views of the potential of Linked Data approaches that the workshop participants turned to two more fundamental goals: building common understanding and enthusiasm on the one hand and identifying opportunities and challenges to be confronted in the preparation of the intended prototype and its operation on the other. In pursuit of those objectives, the workshop participants produced:1. a value statement addressing the question of why a Linked Data approach is worth prototyping;2. a manifesto for Linked Libraries (and Museums and Archives and …);3. an outline of the phases in a life cycle of Linked Data approaches;4. a prioritized list of known issues in generating, harvesting & using Linked Data;5. a workflow with notes for converting library bibliographic records and other academic metadata to URIs;6. examples of potential “killer apps” using Linked Data: and7. a list of next steps and potential projects.This report includes a summary of the workshop agenda, a chart showing the use of Linked Data in cultural heritage venues, and short biographies and statements from each of the participants

Privileging information is inevitable

Libraries, archives and museums have long collected physical materials and other artefacts. In so doing they have established formal or informal policies defining what they will (and will not) collect. We argue that these activities by their very nature privilege some information over others and that the appraisal that underlies this privileging is itself socially constructed. We do not cast this in a post-modernist or negative light, but regard a clear understanding of it as fact and its consequences as crucial to understanding what collections are and what the implications are for the digital world. We will argue that in the digital world it is much easier for users to construct their own collections from a combination of resources, some privileged and curated by information professionals and some privileged by criteria that include the frequency with which other people link to and access them. We conclude that developing these ideas is an important part of placing the concept of a digital or hybrid paper/digital library on a firm foundation and that information professionals need to learn from each other, adopting elements of a variety of different approaches to describing and exposing information. A failure to do this will serve to push information professional towards the margins of the information seekers perspective

This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology

The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. This effort requires the engagement of scholars and practitioners from many disciplines. And yet, these disciplines often conceptualize the operative values very differently while referring to them using the same vocabulary. The resulting conflation of ideas confuses discussions about values in technology at disciplinary boundaries. In the service of improving this situation, this paper examines the value of shared vocabularies, analytics, and other tools that facilitate conversations about values in light of these disciplinary specific conceptualizations, the role such tools play in furthering research and practice, outlines different conceptions of ``fairness''deployed in discussions about computer systems, and provides an analytic tool for interdisciplinary discussions and collaborations around the concept of fairness. We use a case study of risk assessments in criminal justice applications to both motivate our effort--describing how conflation of different concepts under the banner of ``fairness'' led to unproductive confusion--and illustrate the value of the fairness analytic by demonstrating how the rigorous analysis it enables can assist in identifying key areas of theoretical, political, and practical misunderstanding or disagreement, and where desired support alignment or collaboration in the absence of consensus

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications