Usability evaluation of digital libraries: a tutorial

This one-day tutorial is an introduction to usability evaluation for Digital Libraries. In particular, we will introduce Claims Analysis. This approach focuses on the designers’ motivations and reasons for making particular design decisions and examines the effect on the user’s interaction with the system. The general approach, as presented by Carroll and Rosson(1992), has been tailored specifically to the design of digital libraries. Digital libraries are notoriously difficult to design well in terms of their eventual usability. In this tutorial, we will present an overview of usability issues and techniques for digital libraries, and a more detailed account of claims analysis, including two supporting techniques – simple cognitive analysis based on Norman’s ‘action cycle’ and Scenarios and personas. Through a graduated series of worked examples, participants will get hands-on experience of applying this approach to developing more usable digital libraries. This tutorial assumes no prior knowledge of usability evaluation, and is aimed at all those involved in the development and deployment of digital libraries

Proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET 2013)

"This book contains the proceedings of the International Workshop on EuroPLOT Persuasive Technology for Learning, Education and Teaching (IWEPLET) 2013 which was held on 16.-17.September 2013 in Paphos (Cyprus) in conjunction with the EC-TEL conference. The workshop and hence the proceedings are divided in two parts: on Day 1 the EuroPLOT project and its results are introduced, with papers about the specific case studies and their evaluation. On Day 2, peer-reviewed papers are presented which address specific topics and issues going beyond the EuroPLOT scope. This workshop is one of the deliverables (D 2.6) of the EuroPLOT project, which has been funded from November 2010 – October 2013 by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission through the Lifelong Learning Programme (LLL) by grant #511633. The purpose of this project was to develop and evaluate Persuasive Learning Objects and Technologies (PLOTS), based on ideas of BJ Fogg. The purpose of this workshop is to summarize the findings obtained during this project and disseminate them to an interested audience. Furthermore, it shall foster discussions about the future of persuasive technology and design in the context of learning, education and teaching. The international community working in this area of research is relatively small. Nevertheless, we have received a number of high-quality submissions which went through a peer-review process before being selected for presentation and publication. We hope that the information found in this book is useful to the reader and that more interest in this novel approach of persuasive design for teaching/education/learning is stimulated. We are very grateful to the organisers of EC-TEL 2013 for allowing to host IWEPLET 2013 within their organisational facilities which helped us a lot in preparing this event. I am also very grateful to everyone in the EuroPLOT team for collaborating so effectively in these three years towards creating excellent outputs, and for being such a nice group with a very positive spirit also beyond work. And finally I would like to thank the EACEA for providing the financial resources for the EuroPLOT project and for being very helpful when needed. This funding made it possible to organise the IWEPLET workshop without charging a fee from the participants.

Short Text Categorization using World Knowledge

The content of the World Wide Web is drastically multiplying, and thus the amount of available online text data is increasing every day. Today, many users contribute to this massive global network via online platforms by sharing information in the form of a short text. Such an immense amount of data covers subjects from all the existing domains (e.g., Sports, Economy, Biology, etc.). Further, manually processing such data is beyond human capabilities. As a result, Natural Language Processing (NLP) tasks, which aim to automatically analyze and process natural language documents have gained significant attention. Among these tasks, due to its application in various domains, text categorization has become one of the most fundamental and crucial tasks. However, the standard text categorization models face major challenges while performing short text categorization, due to the unique characteristics of short texts, i.e., insufficient text length, sparsity, ambiguity, etc. In other words, the conventional approaches provide substandard performance, when they are directly applied to the short text categorization task. Furthermore, in the case of short text, the standard feature extraction techniques such as bag-of-words suffer from limited contextual information. Hence, it is essential to enhance the text representations with an external knowledge source. Moreover, the traditional models require a significant amount of manually labeled data and obtaining labeled data is a costly and time-consuming task. Therefore, although recently proposed supervised methods, especially, deep neural network approaches have demonstrated notable performance, the requirement of the labeled data remains the main bottleneck of these approaches. In this thesis, we investigate the main research question of how to perform \textit{short text categorization} effectively \textit{without requiring any labeled data} using knowledge bases as an external source. In this regard, novel short text categorization models, namely, Knowledge-Based Short Text Categorization (KBSTC) and Weakly Supervised Short Text Categorization using World Knowledge (WESSTEC) have been introduced and evaluated in this thesis. The models do not require any hand-labeled data to perform short text categorization, instead, they leverage the semantic similarity between the short texts and the predefined categories. To quantify such semantic similarity, the low dimensional representation of entities and categories have been learned by exploiting a large knowledge base. To achieve that a novel entity and category embedding model has also been proposed in this thesis. The extensive experiments have been conducted to assess the performance of the proposed short text categorization models and the embedding model on several standard benchmark datasets

B2C quality evaluation factors from Jordanian consumer perspective

The consumer of B2C business plays a significant role in sustaining B2C business companies. However, many companies neglect to incorporate consumers need in their websites developments, resulting unachieved business objectives.Companies must identify consumers’ factors in their websites developments so that the B2C websites receive higher hits. This study aims to investigate and identify the B2C quality factors from the consumers’ perspective, to rank these factors according to their importance, and to categorize these factors into meaningful groups.Methodology from three phases has been conducted to achieve the objectives.These phases include identification, ranking, and categorization of factors. Data was gathered from the literature and analyzed using SPSS. Simple descriptive statistics such as mean and frequency were used to rank the quality factors. In addition, factor analysis was used to categorize the quality factors. Seventeen quality factors were found to be important from the consumers’ perspective. The seventeen quality factors were further categorized into three groups: E-usage, E-information, and E- services. These categories will be used to construct quality evaluation framework in the next stage of the study

Workplace Violence and Security: Are there Lessons for Peacemaking?

Workplace violence has captured the attention of commentators, employers, and the public at large. Although statistically the incidents of workplace homicide and assault are decreasing, public awareness of the problem has heightened, largely through media reports of violent incidents. Employers are exhorted to address the problem of workplace violence and are offered a variety of programs and processes to prevent its occurrence. Many techniques, however, conflict with values that are critical to achieving sustainable peace. We focus on types of workplace violence that are triggered by organizational factors. From among the plethora of recommendations, we identify those responses that are most and least consistent with positive peace. We find that processes that promote privacy, transparency, and employee rights hold the most promise for peacemaking. We submit that such structures and processes can be transportable beyond the workplace to promote peace locally, nationally, and globally.http://deepblue.lib.umich.edu/bitstream/2027.42/39920/3/wp535.pd

FashionCLIP: Connecting Language and Images for Product Representations

The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from more transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model for the fashion industry. We showcase its capabilities for retrieval, classification and grounding, and release our model and code to the community.Comment: Code will soon be available at https://github.com/patrickjohncyh, dataset at https://github.com/Farfetc

The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps

Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user's data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google's Chrome store, we discover that the existing permission model is quite often misused: around two thirds of analyzed apps are over-privileged, i.e., they access more data than is needed for them to function. In this work, we analyze three different permission models that aim to discourage users from installing over-privileged apps. In experiments with 210 real users, we discover that the most successful permission model is our novel ensemble method that we call Far-reaching Insights. Far-reaching Insights inform the users about the data-driven insights that apps can make about them (e.g., their topics of interest, collaboration and activity patterns etc.) Thus, they seek to bridge the gap between what third parties can actually know about users and users perception of their privacy leakage. The efficacy of Far-reaching Insights in bridging this gap is demonstrated by our results, as Far-reaching Insights prove to be, on average, twice as effective as the current model in discouraging users from installing over-privileged apps. In an effort for promoting general privacy awareness, we deploy a publicly available privacy oriented app store that uses Far-reaching Insights. Based on the knowledge extracted from data of the store's users (over 115 gigabytes of Google Drive data from 1440 users with 662 installed apps), we also delineate the ecosystem for third-party cloud apps from the standpoint of developers and cloud providers. Finally, we present several general recommendations that can guide other future works in the area of privacy for the cloud