Ubiquitous computing: Anytime, anyplace, anywhere?

Computers are ubiquitous, in terms that they are everywhere, but does this mean the same as ubiquitous computing? Views are divided. The convergent device (one-does-all) view posits the computer as a tool through which anything, and indeed everything, can be done (Licklider & Taylor, 1968). The divergent device (many-do-all) view, by contrast, offers a world where microprocessors are embedded in everything and communicating with one another (Weiser, 1991). This debate is implicitly present in this issue, with examples of the convergent device in Crook & Barrowcliff's paper and in Gay et al's paper, and examples of the divergent devices in Thomas & Gellersen's paper and Baber's paper. I suspect both streams of technology are likely to co-exist

Multiform Adaptive Robot Skill Learning from Humans

Object manipulation is a basic element in everyday human lives. Robotic manipulation has progressed from maneuvering single-rigid-body objects with firm grasping to maneuvering soft objects and handling contact-rich actions. Meanwhile, technologies such as robot learning from demonstration have enabled humans to intuitively train robots. This paper discusses a new level of robotic learning-based manipulation. In contrast to the single form of learning from demonstration, we propose a multiform learning approach that integrates additional forms of skill acquisition, including adaptive learning from definition and evaluation. Moreover, going beyond state-of-the-art technologies of handling purely rigid or soft objects in a pseudo-static manner, our work allows robots to learn to handle partly rigid partly soft objects with time-critical skills and sophisticated contact control. Such capability of robotic manipulation offers a variety of new possibilities in human-robot interaction.Comment: Accepted to 2017 Dynamic Systems and Control Conference (DSCC), Tysons Corner, VA, October 11-1

Using tablets for e-assessment of project-based learning

Technology is confirmed to be an effective tool for assessment and feedback, in particular for computer-assisted assessment (Irons, 2008; Challis, 2005), producing feedback (Heinrich et al., 2009) and publishing feedback (Bloxham and Boyd, 2007; Denton, 2003; Denton et al., 2008). The arrival of affordable mobile devices has introduced a new means for enhancing the above practices (Fabian and MacLean, 2014; Plimmer and Mason, 2006; Salem, 2013). Student preferences to smart phones and tablet devices steer the technological innovation towards ubiquitous mobile connectivity. Inspired by the benefits of such life and study style, educators have started exploring the use of these technologies. Tablet computers prove to become their preferred choice as they resolve some of the limitations associated with the design, readability and comprehensiveness of the feedback for mobile devices with smaller screens (Strain-Seymour, 2013, Rootman-le Grange and Lutz, 2013). This paper reports how tablets and the Form Connext mobile app have been used for engaging a sample of 300 Business Studies students in in-class online assessment and designing and providing timely comprehensive feedback. The study has followed an action research strategy that is grounded on a continuous and dynamic process of reflection (Carr and Kemmis, 2003) on the effectiveness of assessment of student projects documented electronically through wikis and electronic portfolios. It refines the use of tablets for summative and formative assessment of the project-based learning tasks through three review cycles, each of which incorporated a Reflection and Improvements stage. The experience resulted in enhancement of assessment strategies and contribution to the development of contemporary models of learning through effective assessment and feedback (Carr and Kemmis, 2003). The results of the work confirm that tablet computers are an effective tool in assessing e-materials in larger classes for two primary reasons. Firstly, design of e-forms facilitates rigorous process of reflection and understanding assessment criteria that in turn benefit students when preparing for the assessment. Hence, legible and detailed feedback is produced anytime anywhere with synchronous updates within the marking team. Secondly, students benefit from immediate comprehensive feedback allowing them to reflect on and improve their understanding of subject matters, as well as to engage in discussing specific details of the work that are captured through the form. An unexpected outcome was the enhanced reputation and respect to the tutors amongst students, the triggering of student curiosity and enthusiasm in applying similar approach to their own work. The diffusion for the practice amongst other units and identifying other purposes for which the mobile app could be used are also seen as achievements exceeding the expectations of the project team

Using Mobile Devices for Improving Learning Outcomes and Teachers’ Professionalization

Teaching in higher education is changing due to the influence of technology. More and more technological tools are replacing old teaching methods and strategies. Thus, mobile devices are being positioned as a key tool for new ways of understanding educational practices. The present paper responds to a systematic review about the benefits that mobile devices have for university students’ learning. Using inclusion and exclusion criteria in theWeb of Science and Scopus databases, 16 articles were selected to argue why Mobile learning (Mlearning) has become a modern innovative approach. The results point to an improvement in students’ learning through Mlearning, factors that encourage the use of mobile devices in universities have been identified, and e ective mobile applications in improving teaching and learning processes have been presented. The inclusion of this methodology requires a new role for teachers, whose characterization is also specified

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care

Identifying hidden contexts

In this study we investigate how to identify hidden contexts from the data in classification tasks. Contexts are artifacts in the data, which do not predict the class label directly. For instance, in speech recognition task speakers might have different accents, which do not directly discriminate between the spoken words. Identifying hidden contexts is considered as data preprocessing task, which can help to build more accurate classifiers, tailored for particular contexts and give an insight into the data structure. We present three techniques to identify hidden contexts, which hide class label information from the input data and partition it using clustering techniques. We form a collection of performance measures to ensure that the resulting contexts are valid. We evaluate the performance of the proposed techniques on thirty real datasets. We present a case study illustrating how the identified contexts can be used to build specialized more accurate classifiers