Post-quantum key exchange for the TLS protocol from the ring learning with errors problem

Lattice-based cryptographic primitives are believed to offer resilience against attacks by quantum computers. We demonstrate the practicality of post-quantum key exchange by constructing cipher suites for the Transport Layer Security (TLS) protocol that provide key exchange based on the ring learning with errors (R-LWE) problem, we accompany these cipher suites with a rigorous proof of security. Our approach ties lattice-based key exchange together with traditional authentication using RSA or elliptic curve digital signatures: the post-quantum key exchange provides forward secrecy against future quantum attackers, while authentication can be provided using RSA keys that are issued by today's commercial certificate authorities, smoothing the path to adoption. Our cryptographically secure implementation, aimed at the 128-bit security level, reveals that the performance price when switching from non-quantum-safe key exchange is not too high. With our R-LWE cipher suites integrated into the Open SSL library and using the Apache web server on a 2-core desktop computer, we could serve 506 RLWE-ECDSA-AES128-GCM-SHA256 HTTPS connections per second for a 10 KiB payload. Compared to elliptic curve Diffie-Hellman, this means an 8 KiB increased handshake size and a reduction in throughput of only 21%. This demonstrates that provably secure post-quantum key-exchange can already be considered practical.

Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

Teacher leadership : interns crossing to the domain of higher professional learning with mentors?

Over the last two decades, the notion of teacher leadership has emerged as a key concept in both the teaching and leadership literature. While researchers have not reached consensus regarding a definition, there has been some agreement that teacher leadership can operate at both a formal and informal level in schools and that it includes leadership of an instructional, organisational and professional development nature (York-Barr & Duke, 2004). Teacher leadership is a construct that tends not to be applied to pre-service teachers as interns, but is more often connected with the professional role of mentors who collaborate with them as they make the transition to being a beginning teacher. We argue that teacher leadership should be recognised as a professional and career goal during this formative learning phase and that interns should be expected to overtly demonstrate signs, albeit early ones, of leadership in instruction and other professional areas of development. The aim of this paper is to explore the extent to which teacher education interns at one university in Queensland reported on activities that may be deemed to be ‘teacher leadership.’ The research approach used in this study was an examination of 145 reflective reports written in 2008 by final Bachelor of Education (primary) pre-service teachers. These reports recorded the pre-service teachers’ perceptions of their professional learning with a school-based mentor in response to four outcomes of internship that were scaffolded by their mentor or initiated by them. These outcomes formed the bases of our research questions into the professional learning of the interns and included, ‘increased knowledge and capacity to teach within the total world of work as a teacher;’ ‘to work autonomously and interdependently’; to make ‘growth in critical reflectivity’, and the ‘ability to initiate professional development with the mentoring process’. Using the approaches of the constant comparative method of Strauss and Corbin (1998) key categories of experiences emerged. These categories were then identified as belonging to main meta-category labelled as ‘teacher leadership.’ Our research findings revealed that five dimensions of teacher leadership – effective practice in schools; school curriculum work; professional development of colleagues; parent and community involvement; and contributions to the profession – were evident in the written reports by interns. Not surprisingly, the mentor/intern relationship was the main vehicle for enabling the intern to learn about teaching and leadership. The paper concludes with some key implications for developers of preservice education programmes regarding the need for teacher leadership to be part of the discourse of these programmes.

Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

Learning in the third space : a sociocultural perspective on learning with analogies

Research on analogies in science education has focussed on student interpretation of teacher and textbook analogies, psychological aspects of learning with analogies and structured approaches for teaching with analogies. Few studies have investigated how analogies might be pivotal in students’ growing participation in chemical discourse. To study analogies in this way requires a sociocultural perspective on learning that focuses on ways in which language, signs, symbols and practices mediate participation in chemical discourse. This study reports research findings from a teacher-research study of two analogy-writing activities in a chemistry class. The study began with a theoretical model, Third Space, which informed analyses and interpretation of data. Third Space was operationalized into two sub-constructs called Dialogical Interactions and Hybrid Discourses. The aims of this study were to investigate sociocultural aspects of learning chemistry with analogies in order to identify classroom activities where students generate Dialogical Interactions and Hybrid Discourses, and to refine the operationalization of Third Space. These aims were addressed through three research questions. The research questions were studied through an instrumental case study design. The study was conducted in my Year 11 chemistry class at City State High School for the duration of one Semester. Data were generated through a range of data collection methods and analysed through discourse analysis using the Dialogical Interactions and Hybrid Discourse sub-constructs as coding categories. Results indicated that student interactions differed between analogical activities and mathematical problem-solving activities. Specifically, students drew on discourses other than school chemical discourse to construct analogies and their growing participation in chemical discourse was tracked using the Third Space model as an interpretive lens. Results of this study led to modification of the theoretical model adopted at the beginning of the study to a new model called Merged Discourse. Merged Discourse represents the mutual relationship that formed during analogical activities between the Analog Discourse and the Target Discourse. This model can be used for interpreting and analysing classroom discourse centred on analogical activities from sociocultural perspectives. That is, it can be used to code classroom discourse to reveal students’ growing participation with chemical (or scientific) discourse consistent with sociocultural perspectives on learning.

Promoting student learning with online videos : a research agenda

Gen Y students are digital natives (Prensky 2001) who learn in complex and diverse ways, with a variety of learning styles apparent in any given course. This paper proposes a web 2.0 conceptual learning solution–online student videos–to respond to different learning styles that exist in the classroom.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the “ideal” algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.

Correction to “The Importance of Convexity in Learning With Squared Loss” [Sep 98 1974-1980]

The paper "the importance of convexity in learning with squared loss" gave a lower bound on the sample complexity of learning with quadratic loss using a nonconvex function class. The proof contains an error. We show that the lower bound is true under a stronger condition that holds for many cases of interest.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the "ideal" algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.

Adaptive teachers embracing new ways of learning with robotics in Chinese schools

In July 2010, China announced the “National Plan for Medium and Long-term Education Reform and Development(2010-2020)” (PRC 2010). The Plan calls for an education system that: • promotes an integrated development which harnesses everyone’s talent; • combines learning and thinking; unifies knowledge and practice; • allows teachers to teach according to individuals’ needs; and • reforms education quality evaluation and personnel evaluation systems focusing on performance including character, knowledge, ability and other factors. This paper discusses the design and implementation of a Professional Learning Program (PLP) undertaken by 432 primary, middle and high school teachers in China. The aim of this initiative was to develop adaptive expertise in using technology that facilitated innovative science and technology teaching and learning as envisaged by the Chinese Ministry of Education’s (2010-2020) education reforms. Key principles derived from literature about professional learning and scaffolding of learning informed the design of the PLP. The analysis of data revealed that the participants had made substantial progress towards the development of adaptive expertise. This was manifested not only by advances in the participants’ repertoires of Subject Matter Knowledge and Pedagogical Content Knowledge but also in changes to their levels of confidence and identities as teachers. It was found that through time the participants had coalesced into a professional learning community that readily engaged in the sharing, peer review, reuse and adaption, and collaborative design of innovative science and technology learning and assessment activities.

Learning with FIRST LEGO League

Robotics is a valuable tool for engaging students in the hands-on application of science, technology, engineering, and mathematics (STEM) concepts. Robotics competitions such as FIRST LEGO League (FLL) can increase students’ interest in the STEM subjects and can foster their problem solving and teamwork skills. This paper reports on a study investigating students’ perceptions on the influence of participating in a FLL competition on their learning. The students completed questionnaires regarding their perceptions of their learning during the FLL challenge and were also interviewed to gain a deeper understanding of their questionnaire responses. The results show that the students were engaged with the FLL challenge and held positive views regarding their experience. The results also suggest that students involved with the FLL challenge improved their learning about real-world applications, problem solving, engagement, communication, and the application of the technology/engineering cycle.

Learning opportunities : a partnership, connecting learning with projects

Major construction projects undertaken on university campuses are an ideal opportunity to connect learners in related disciplines to the real thing. How often do universities take that opportunity, make the connection and value add to projects being carried out? Discussion with students and academic staff will consistently generate enthusiasm for creating learning activities and resources related to projects. Some typical disciplines are project management, all fields of engineering, architecture, interior design and information technology. Some other areas that may not at first seem obvious are business, marketing, communication and public relations. The authors will provide a case study based on the new Queensland University of Technology (QUT) Science and Engineering Centre project of how the partnership between QUT and Leighton Contractors, the managing contractor, has delivered excellent learning opportunities through the design and construction phases of the Science and Engineering Centre project.

Mixing higher education teaching and learning with mobile devices and social media

The use of mobile devices and social media technologies are becoming all-pervasive in society: they are both transformative and constant. The high levels of mobile device ownership and increased access to social media technologies enables the potential for ‘anytime, anywhere’ cooperation and collaboration in education. While recent reports into emerging technologies in higher education predict an increase in the use of mobile devices and social media technologies (Horizon Report, 2013), there is a lack of theory-based research to indicate how these technologies can be most effectively harnessed to support and enhance student learning and what the impacts of these technologies are on both students and educators. In response to the need to understand how these technologies can be better embraced within higher education, this study investigated how first year education students used mobile devices and social media technologies. More specifically, the study identified how students spent most of their time when connected online with mobile devices and social media technologies and whether the online connected time engaged them in their learning or whether it was a distraction.