Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Using technology in language arts to motivate students

This project will serve as a media rich tool to aid students and teachers in the classroom. It is designed to examine whether or not a computer based learning environment motivates and helps fourth grade at-risk students in vocabulary development. The project included creating a guide in Macromedia Authorware to help educators develop a multimedia environment in the classroom

Analysis of interaction patterns - attention

Dissertação de mestrado em Engenharia InformáticaThe attention, or focus, with which people perform their tasks has an important role on their success. Such tasks can be like study or work. Nowadays, our lifestyle can lead to a scatter of focus, especially when attending to several tasks or filtering loads of information, which in a laptop or desktop, can mean interacting with several applications simultaneously. This project will analyze and monitor the behavior of the users of desktop applications with the main aim to measure changes in user attention and focus throughout the day, and so, to know when a user is more focused and on better conditions to work. Leisure and work applications will also have an important play in this project, as the interaction with the laptop will be analyzed on both applications categories.A atenção, ou concentração, com que realizámos tarefas tem um papel importante na realização com sucesso das mesmas. Tais tarefas podem ser de estudo ou de trabalho. Hoje em dia, o nosso estilo de vida pode levar a uma dispersão da atenção, especialmente quando lidámos com várias tarefas ou informação, que num ambiente de trabalho pode significar interagir com várias aplicações simultaneamente. Este projecto irá analisar e monitorizar o comportamento dos utilizadores em aplicações com o objectivo principal de medir as mudanças na atenção do utilizador durante todo o dia, e assim saber quando um utilizador está mais focado e em melhores condições de realizar trabalho. Aplicações afectas ao lazer e ao trabalho também serão importantes neste projecto, dado que a interacção do utilizador com o portátil serão analisados em ambas as categorias.In order to be more productive at work, being focused is very important, as well as when studying or attending any other task. Nowadays, our lifestyle it can lead to a scatter of focus, especially when attending to several tasks, which in a laptop, or desktop, can mean interacting with several applications simultaneously. This project will analyze the behavior of the users of desktop applications with the main aim to measure changes in user attention and focus throughout the day. For this study data from a group of 20 people will be collected

Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace

The symposium Vision-21: Interdisciplinary Science and Engineering in the Era of Cyberspace was held at the NASA Lewis Research Center on March 30-31, 1993. The purpose of the symposium was to simulate interdisciplinary thinking in the sciences and technologies which will be required for exploration and development of space over the next thousand years. The keynote speakers were Hans Moravec, Vernor Vinge, Carol Stoker, and Myron Krueger. The proceedings consist of transcripts of the invited talks and the panel discussion by the invited speakers, summaries of workshop sessions, and contributed papers by the attendees

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

Ubiquitous Computing

The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

Affect-based Modeling and its Application in Multimedia Analysis Problems

The multimedia domain is undergoing a rapid development phase with transition in audio, image, and video systems such as VoIP, Telepresence, Live/On-Demand Internet Streaming, SecondLife, and many more. In such a situation, the analysis of multimedia systems, like retrieval, quality evaluation, enhancement, summarization, and re-targeting applications, from various context is becoming critical. Current methods for solving the above-mentioned analysis problems do not consider the existence of humans and their affective characteristics in the design methodology. This contradicts the fact that most of the digital media is consumed only by the human end-users. We believe incorporating human feedback during the design and adaptation stage is key to the building process of multimedia systems. In this regard, we observe that affect is an important indicator of human perception and experience. This can be exploited in various ways for designing effective systems that will adapt more closely to the human response. We advocate an affect-based modeling approach for solving multimedia analysis problems by exploring new directions. In this dissertation, we select two representative multimedia analysis problems, e.g. Quality-of-Experience (QoE) evaluation and Image Enhancement in order to derive solutions based on affect-based modeling techniques. We formulate specific hypothesis for them by correlating system parameters to user\u27s affective response, and investigate their roles under varying conditions for each respective scenario. We conducted extensive user studies based on human-to-human interaction through an audio conferencing system.We also conducted user studies based on affective enhancement of images and evaluated the effectiveness of our proposed approaches. Moving forward, multimedia systems will become more media-rich, interactive, and sophisticated and therefore effective solutions for quality, retrieval, and enhancement will be more challenging. Our work thus represents an important step towards the application of affect-based modeling techniques for the future generation of multimedia systems

Enabling Artificial Intelligence Analytics on The Edge

This thesis introduces a novel distributed model for handling in real-time, edge-based video analytics. The novelty of the model relies on decoupling and distributing the services into several decomposed functions, creating virtual function chains (V F C model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the V F C model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the V F C model have shown that it can reduce the total edge cost, compared with a monolithic and a simple frame distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Apache Spark ©), have been deployed. A cloud service has also been considered. Experiments have shown that V F C can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a migration model, a caching model and a QoS monitoring service based on Long-Term-Short-Term models are introduced

Deep Learning for Head Pose Estimation: A Survey

Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic