Towards a debugging tutor for object-oriented environments

Programming has provided a rich domain for Artificial Intelligence in Education and many systems have been developed to advise students about the bugs in their programs, either during program development or post-hoc. Surprisingly few systems have been developed specifically to teach debugging. Learning environment builders have assumed that either the student will be taught these elsewhere or thatthey will be learnt piecemeal without explicit advice.This paper reports on two experiments on Java debugging strategy by novice programmers and discusses their implications for the design of a debugging tutor for Java that pays particular attention to how students use the variety of program representations available. The experimental results are in agreement with research in the area that suggests that good debugging performance is associated with a balanced use ofthe available representations and a sophisticated use of the debugging step facility which enables programmers to detect and obtain information from critical momentsin the execution of the program. A balanced use of the available representations seemsto be fostered by providing representations with a higher degree of dynamic linkingas well as by explicit instruction about the representation formalism employed in the program visualisations

Electrically induced tunable cohesion in granular systems

Experimental observations of confined granular materials in the presence of an electric field that induces cohesive forces are reported. The angle of repose is found to increase with the cohesive force. A theoretical model for the stability of a granular heap, including both the effect of the sidewalls and cohesion is proposed. A good agreement between this model and the experimental results is found. The steady-state flow angle is practically unaffected by the electric field except for high field strengths and low flow rates.Comment: accepted for publication in "Journal of Statistical Mechanics: Theory and Experiment

DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Situational reasoning for road driving in an urban environment

Robot navigation in urban environments requires situational reasoning. Given the complexity of the environment and the behavior specified by traffic rules, it is necessary to recognize the current situation to impose the correct traffic rules. In an attempt to manage the complexity of the situational reasoning subsystem, this paper describes a finite state machine model to govern the situational reasoning process. The logic state machine and its interaction with the planning system are discussed. The approach was implemented on Alice, Team Caltech’s entry into the 2007 DARPA Urban Challenge. Results from the qualifying rounds are discussed. The approach is validated and the shortcomings of the implementation are identified

SMILE: the creation of space for interaction through blended digital technology

Interactive Learning Environments at Sussex University is a course in which students are given mobile devices (XDAs) with PDA functionality and full Internet access for the duration of the term. They are challenged to design and evaluate learning experiences, both running and evaluating learning sessions that involve a blend of technologies. Data on technology usage was collected via backups, email and web-site logging as well as video and still photography of student-led sessions. Initial analysis indicates that large amounts of technical support, solid pedagogical underpinning and a flexible approach to both delivery context and medium are essential. The project operated under the acronym SMILE – Sussex Mobile Interactive Learning Environment

Whether it's M-learing or E-learning, it must be ME learning: a case study of mobile learning in Higher Education

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Separation probabilities for products of permutations

We study the mixing properties of permutations obtained as a product of two uniformly random permutations of fixed cycle types. For instance, we give an exact formula for the probability that elements $1,2,...,k$ are in distinct cycles of the random permutation of $\{1,2,...,n\}$ obtained as product of two uniformly random $n$-cycles