Ilaria Liccardi

Harnessing Insight into Disciplinary Differences to Refine e-learning Design

Many different teaching methods are used to support learning in higher education. Research into the relationship between the knowledge traditions of fields of study and their most appropriate teaching methods identifies clear differences between the appropriate which are the most suitable in different disciplines. Increasingly, blended approaches to education are being introduced, integrating e-learning with face-to face methods. However, major influences on our understanding of the potential of e-learning have come from psychological and educational perspectives, which are not, of themselves, clearly associated with specific disciplinary needs. This paper identifies e-learning approaches which particularly suit specific disciplinary preferences. It surveys students to identify methods which they believe are particularly relevant to their studies. Their responses support the case for taking a disciplinary perspective when developing blended approaches

OntoTrix: a hybrid visualization for populated ontologies

Most Semantic Web data visualization tools structure the representation according to the concept definitions and interrelations that constitute the ontologys vocabulary. Instances are often treated as somewhat peripheral information, when considered at all. These instances, that populate ontologies, represent an essential part of any knowledge base, and are often orders of magnitude more numerous than the concept definitions that give them machine-processable meaning. We present a visualization technique designed to enable users to visualize large instance sets and the relations that connect them. This hybrid visualization uses both node-link and adjacency matrix representations of graphs to visualize different parts of the data depending on their semantic and local structural properties, exploiting ontological knowledge to drive the graph layout. The representation is embedded in an environment that features advanced interaction techniques for easy navigation, including support for smooth continuous zooming and coordinated views.

CAWS: a wiki system to improve workspace awareness to advance effectiveness of co-authoring activities

Crucial to effective collaborative writing is knowledge of what other people are doing and have done, what meaningful changes are made to a document, who is editing each section of a document and why. This is because awareness of individual and group activities is critical to successful collaboration. This paper presents the problems that surround co-authoring activities, and the advantages of using CAWS are explained and compared with other implementation and techniques for collaborative authoring. This co-authoring wiki based system (CAWS), aims to improve workspace awareness in order to improve user.s response to the document development activit.

Democratizing mobile app development for disaster management

Smartphones are being used for a wide range of activities including messaging, social networking, calendar and contact management as well as location and context-aware applications. The ubiquity of handheld computing technology has been found to be especially useful in disaster management and relief operations. Our focus is to enable developers to quickly deploy applications that take advantage of key sources that are fundamental for todays networked citizens, including Twitter feeds, Facebook posts, current news releases, and government data. These applications will also have the capability of empowering citizens involved in crisis situations to contribute via crowdsourcing, and to communicate up-to-date information to others. We will leverage several technologies to develop this application framework, namely (i) Linked Data principles for structured data, (ii) existing data sources and ontologies for disaster management, and (iii) App Inventor, which is a mobile application development framework for non-programmers. In this paper, we describe our motivating use cases, our architecture, and our prototype implementation.

Visualizing Populated Ontologies with OntoTrix

Research on visualizing Semantic Web data has yielded many tools that rely on information visualization techniques to better support the user in understanding and editing these data. Most tools structure the visualization according to the concept definitions and interrelations that constitute the ontologys vocabulary. Instances are often treated as somewhat peripheral information, when considered at all. These instances, that populate ontologies, represent an essential part of any knowledge base. Understanding instance-level data might be easier for users because of their higher concreteness, but instances will often be orders of magnitude more numerous than the concept definitions that give them machine-processable meaning. As such, the visualization of instance-level data poses different but real challenges. The authors present a visualization technique designed to enable users to visualize large instance sets and the relations that connect them. This visualization uses both node-link and adjacency matrix representations of graphs to visualize different parts of the data depending on their semantic and local structural properties. The technique was originally devised for simple social network visualization. The authors extend it to handle the richer and more complex graph structures of populated ontologies, exploiting ontological knowledge to drive the layout of, and navigation in, the representation embedded in a smooth zoomable environment.

I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data

This research measures human performance in inferring the functional types (i.e., home, work, leisure and transport) of locations in geo-location data using different visual representations of the data (textual, static and animated visualizations) along with different amounts of data (1, 3 or 5 day(s)). We first collected real life geo-location data from tweets. We then asked the data owners to tag their location points, resulting in ground truth data. Using this dataset we conducted an empirical study involving 45 participants to analyze how accurately they could infer the functional location of the original data owners under different conditions, i.e., three data representations, three data densities and four location types. The study results indicate that while visual techniques perform better than textual ones, the functional locations of human activities can be inferred with a relatively high accuracy even using only textual representations and a low density of location points. Workplace was more easily inferred than home while transport was the functional location with the highest accuracy. Our results also showed that it was easier to infer functional locations from data exhibiting more stable and consistent mobility patterns, which are thus more vulnerable to privacy disclosures. We discuss the implications of our findings in the context of privacy preservation and provide guidelines to users and companies to help preserve and safeguard peoples privacy.

Negotiation as an Interaction Mechanism for Deciding App Permissions

On the Android platform, apps make use of personal data as part of their business model, trading location, contacts, photos and more for app use. Few people are particularly aware of the permission settings or make changes to them. We hypothesize that both the difficulty in checking permission settings for all apps on a device, along with the lack of flexibility in deciding what happens to ones data, makes the perceived cost to protect ones privacy too high. In this paper, we present the preliminary results of a study that explores what happens when permission settings are more discretional at install time. We present the results of a pilot experiment, in which we ask users to negotiate which data they are happy to share, and we show that this results in higher user satisfaction than the typical take-it-or-leave-it setting. Our preliminary findings suggest negotiating consent is a powerful interaction mechanism that engages users and can enable them to strike a balance between privacy and pricing concerns.

Can apps play by the COPPA Rules

We review current technical and social barriers to COPPA compliance for popular online services aimed at children. We show that complying with COPPA has proven difficult for developers, even when a genuine attempt was made. We investigate reasons for this lack of compliance and identify common causes: specifically, difficulties obtaining verifiable parental control as well as supply mechanisms for parents to understand, review, grant access and monitor collection of their childrens personal data. Unless part of online services, mobile apps do not need to comply with COPPA. We identify 38,842 (out of 635,264) apps which are self-described (by their developers) as suitable for young users. Half of these apps have the ability to collect personal data and only 6% present a privacy policy. Parents often have little to no knowledge or understanding of what data is accessed. Due to Androids design they must grant all access regardless of permission type or need. Among the self-described apps we find different levels of content rating; these are not a reflection of the content of the app itself but rather the required access to personal data. We present a design for a new framework aimed at helping mobile apps to comply with COPPA. This framework aims to simplify the process for developers by providing appropriate tools and mechanisms to help comply with the COPPA rules while presenting an easily understandable interface for parents to review, navigate, understand and then grant access to their childrens personal data.

Building privacy-preserving location-based apps

Social apps usually require a lot of personal information in order to be tailored to the needs of individual users. However, the inherent social exchange of data exposes a users personal data to other app users or publicly for anyone to see. In this paper, we present an app that enables users to determine the optimal location and time to meet without exposing their information to other users. We compare this app to other research-based and commercial social apps and show that ours is the only one where the risk of exposure is not present. In order to provide such improved privacy protections, we use openPDS, a decentralized and open-source framework. openPDS enables users to store their data on their own servers and participate in group computations without exposing their raw data.

A Semantic Framework for Content-Based Access Controls

Social networking sites provide role-or group-based access controls to help users specify their privacy settings. However, information posted on these sites is often intentionally or unintentionally leaked and has caused harm or distress to users. In this paper, we investigate possible improvements to existing implementations by introducing content-based access control policies using Linked Data. Users are able to specify the type of content in the form of tags or keywords in order to indicate which information they wish to protect from certain roles (for example employment), groups or individuals. Providing all possible keywords matching a specific topic may be too time consuming and prone to error for users. Hence using Linked Data we enrich the provided keywords by identifying other meaningful and related concepts. This paper presents the implementation and challenges of developing such a semantic framework. We have qualitatively evaluated this framework using 23 participants. Feedback from participants suggests that such a framework will help ease privacy concerns while posting and sharing social network content.