Mobile support in CSCW applications and groupware development frameworks

Computer Supported Cooperative Work (CSCW) is an established subset of the field of Human Computer Interaction that deals with the how people use computing technology to enhance group interaction and collaboration. Mobile CSCW has emerged as a result of the progression from personal desktop computing to the mobile device platforms that are ubiquitous today. CSCW aims to not only connect people and facilitate communication through using computers; it aims to provide conceptual models coupled with technology to manage, mediate, and assist collaborative processes. Mobile CSCW research looks to fulfil these aims through the adoption of mobile technology and consideration for the mobile user. Facilitating collaboration using mobile devices brings new challenges. Some of these challenges are inherent to the nature of the device hardware, while others focus on the understanding of how to engineer software to maximize effectiveness for the end-users. This paper reviews seminal and state-of-the-art cooperative software applications and development frameworks, and their support for mobile devices

A decentralized paradigm for resource-aware computing in wireless Ad hoc networks

A key factor limiting the democratisation of networked systems is the lack of trust, particularly in the wake of data-intensive applications that work on sensitive and private data, which requires providing strong network security guarantees via encryption and authentication algorithms, as well as rethinking algorithms to compute on the network peripheries without moving data. In many security and privacy-critical domains such as Home Automation IoT networks, AUV networks etc., the existence of a centralized privileged node leads to a vulnerability for leakage of sensitive information. In this paper, we have proposed a decentralized networking architecture that adopts collaborative processing techniques and operates within the tradeoff between network security and performance. We have investigated the design and sustainability of autonomous decentralized systems and evaluated the efficiency of the proposed scheme with the help of extensive simulation tools

Total order in opportunistic networks

Opportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.Peer ReviewedPostprint (author's final draft

eStorys: A visual storyboard system supporting back-channel communication for emergencies

This is the post-print version of the final paper published in Journal of Visual Languages & Computing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.In this paper we present a new web mashup system for helping people and professionals to retrieve information about emergencies and disasters. Today, the use of the web during emergencies, is confirmed by the employment of systems like Flickr, Twitter or Facebook as demonstrated in the cases of Hurricane Katrina, the July 7, 2005 London bombings, and the April 16, 2007 shootings at Virginia Polytechnic University. Many pieces of information are currently available on the web that can be useful for emergency purposes and range from messages on forums and blogs to georeferenced photos. We present here a system that, by mixing information available on the web, is able to help both people and emergency professionals in rapidly obtaining data on emergency situations by using multiple web channels. In this paper we introduce a visual system, providing a combination of tools that demonstrated to be effective in such emergency situations, such as spatio/temporal search features, recommendation and filtering tools, and storyboards. We demonstrated the efficacy of our system by means of an analytic evaluation (comparing it with others available on the web), an usability evaluation made by expert users (students adequately trained) and an experimental evaluation with 34 participants.Spanish Ministry of Science and Innovation and Universidad Carlos III de Madrid and Banco Santander

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin