Capturing and Sharing Human Digital Memories with the Aid of Ubiquitous Peer– to–Peer Mobile Services

The explosion of mobile computing and the sharing of content ubiquitously has enabled users to create and share memories instantly. Access to different data sources, such as location, movement, and physiology, has helped to create a data rich society where new and enhanced memories will form part of everyday life. Peer–to–Peer (P2P) systems have also increased in popularity over the years, due to their ad hoc and decentralized nature. Mobile devices are “smarter” and are increasingly becoming part of P2P systems; opening up a whole new dimension for capturing, sharing and interacting with enhanced human digital memories. This will require original and novel platforms that automatically compose data sources from ubiquitous ad-hoc services that are prevalent within the environments we occupy. This is important for a number of reasons. Firstly, it will allow digital memories to be created that include richer information, such as how you felt when the memory was created and how you made others feel. Secondly, it provides a set of core services that can more easily manage and incorporate new sources as and when you are available. In this way memories created in the same location, and time are not necessarily similar – it depends on the data sources that are accessible. This paper presents DigMem, the initial prototype that is being developed to utilize distributed mobile services. DigMem captures and shares human digital memories, in a ubiquitous P2P environment. We present a case study to validate the implementation and evaluate the applicability of the approach

Creating Human Digital Memories for a Richer Recall of Life Experiences

Human digital memories focus on documenting our lifetime. This outlet allows us to capture and bring together information that is related to almost any aspect of our lives. Creating these memories allows us to recall experiences, seamlessly; and to re-live specific events, using detailed information about those experiences. The evolution of smart devices enables any object to provide us with information. With all of this data at our disposal, new opportunities are arising to incorporate this data into our digital memories. Consequently, the challenge is to develop a platform, capable of linking captured information together, to form feature rich digital memories of human experiences. This paper presents DigMem, a platform for creating human digital memories, using pervasive devices and linked data. Information is semantically structured to create temporal “memory boxes”. A working prototype has been successfully developed, which demonstrates the approach

Creating human digital memories with the aid of pervasive mobile devices

The abundance of mobile and sensing devices, within our environment, has led to a society in which any object, embedded with sensors, is capable of providing us with information. A human digital memory, created with the data from these pervasive devices, produces a more dynamic and data rich memory. Information such as how you felt, where you were and the context of the environment can be established. This paper presents the DigMem system, which utilizes distributed mobile services, linked data and machine learning to create such memories. Along with the design of the system, a prototype has also been developed, and two case studies have been undertaken, which successfully create memories. As well as demonstrating how memories are created, a key concern in human digital memory research relates to the amount of data that is generated and stored. In particular, searching this set of big data is a key challenge. In response to this, the paper evaluates the use of machine learning algorithms, as an alternative to SPARQL, and treats searching as a classification problem. In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning. Our approach produces good results with 100% sensitivity, 93% specificity, 93% positive predicted value, 100% negative predicted value, and an overall accuracy of 97%

Creating human digital memories with the aid of pervasive mobile devices

The abundance of mobile and sensing devices, within our environment, has led to a society in which any object, embedded with sensors, is capable of providing us with information. A human digital memory, created with the data from these pervasive devices, produces a more dynamic and data rich memory. Information such as how you felt, where you were and the context of the environment can be established. This paper presents the DigMem system, which utilizes distributed mobile services, linked data and machine learning to create such memories. Along with the design of the system, a prototype has also been developed, and two case studies have been undertaken, which successfully create memories. As well as demonstrating how memories are created, a key concern in human digital memory research relates to the amount of data that is generated and stored. In particular, searching this set of big data is a key challenge. In response to this, the paper evaluates the use of machine learning algorithms, as an alternative to SPARQL, and treats searching as a classification problem. In particular, supervised machine learning algorithms are used to find information in semantic annotations, based on probabilistic reasoning. Our approach produces good results with 100% sensitivity, 93% specificity, 93% positive predicted value, 100% negative predicted value, and an overall accuracy of 97%. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved

Photo Wallet : interface design for simple mobile photo albums

Tese de mestrado. Multimédia (Perfil Tecnologias). Universidade do Porto. Faculdade de Engenharia. 201

Studies of Content-Mediated Interaction: Insights into Activities, Motivations and User Experience Design

The amount of user-generated digital content in social media has exploded during recent years. Currently, it is easy to capture and produce versatile personal content, for example, activity data that is recorded with devices, such as heart rate monitors or the preference data of the music you listen to. A plethora of services exists for content sharing. Sharing digital content, such as images, audio, and video allows people to express themselves, create new contacts, strengthen ties with existing contacts, and to collaborate with other people. Social activities through content can create a sense of belonging and being part of a community. Digital content mediates social interaction through online services. For example, a shared video tells someone the story of an event that they could not be physically present at, and then shared exercise data might inform others of an interesting cycle route for a specific type of exercise. The sharing of traditional, personal digital content such as photos and videos has been widely studied, but recently it has become increasingly common to produce different types of content collaboratively and various services enable social interaction around such content – not just the sharing of it. The guidance for designers on how to build services to enable users to engage in these interactions naturally is still limited. To design better services, we need a better understanding of user activities together with the shared content and the collaborative practices that they form. Thus, this work focuses on novel types of user-generated digital content as well as the related activities, motivations, and user experiences.This compound thesis contributes to the research field of human-computer interaction; more specifically, the user experience. The thesis contains findings from six user case studies, involving a total of 328 participants. Through the case studies, we identified the elements that contribute to the user experience of content-mediated interaction with various content types. The theoretical contribution of this work is the introduction of the concept of contentmediated interaction. This work identifies the different elements that affect content-mediated interaction, and builds a content-mediated interaction model. The work extends the knowledge of user activities and the related user experience with novel types of shared content and of the user’s motivation to participate in content-mediated interaction. As a practical outcome, the thesis presents design implications. The thesis first proposes that understanding content-mediated interaction helps to design better applications and services that support online social interaction. Second, this helps to evaluate and refine the existing services as well as understand the emerging new content types in the future. Understanding the underlying activities and motivations supports the creation of new interaction features, service concepts, and finally, identifying business prospects