Critical literacy in an Indonesian EFL setting: sustaining professional learning

 This study investigates factors leading to the successful design and implementation of critical literacy pedagogy in an English as a Foreign Language (EFL) class at a secondary school in the city of Bandung, West Java, Indonesia. Critical literacy pedagogy appears to have helped students to become more critical and socially aware.

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

Context detection and abstraction in smart environments

Context-aware computing is currently considered the most promising approach to overcome information overload and to speed up access to relevant information and services. Context-awareness may be derived from many sources, including user profile and preferences, network information, sensor analysis; usually context-awareness relies on the ability of computing devices to interact with the physical world, i.e. with the natural and artificial objects hosted within the "environment”. Ideally, context-aware applications should not be intrusive and should be able to react according to user’s context, with minimum user effort. Context is an application dependent multidimensional space and the location is an important part of it since the very beginning. Location can be used to guide applications, in providing information or functions that are most appropriate for a specific position. Hence location systems play a crucial role. There are several technologies and systems for computing location to a vary degree of accuracy and tailored for specific space model, i.e. indoors or outdoors, structured spaces or unstructured spaces. The research challenge faced by this thesis is related to pedestrian positioning in heterogeneous environments. Particularly, the focus will be on pedestrian identification, localization, orientation and activity recognition. This research was mainly carried out within the “mobile and ambient systems” workgroup of EPOCH, a 6FP NoE on the application of ICT to Cultural Heritage. Therefore applications in Cultural Heritage sites were the main target of the context-aware services discussed. Cultural Heritage sites are considered significant test-beds in Context-aware computing for many reasons. For example building a smart environment in museums or in protected sites is a challenging task, because localization and tracking are usually based on technologies that are difficult to hide or harmonize within the environment. Therefore it is expected that the experience made with this research may be useful also in domains other than Cultural Heritage. This work presents three different approaches to the pedestrian identification, positioning and tracking: Pedestrian navigation by means of a wearable inertial sensing platform assisted by the vision based tracking system for initial settings an real-time calibration; Pedestrian navigation by means of a wearable inertial sensing platform augmented with GPS measurements; Pedestrian identification and tracking, combining the vision based tracking system with WiFi localization. The proposed localization systems have been mainly used to enhance Cultural Heritage applications in providing information and services depending on the user’s actual context, in particular depending on the user’s location.

Modelling context information with ORM

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must address a unique combination of requirements, including the ability to model information supplied by both sensors and people, to represent imperfect information, and to capture context histories. As the field of context-aware computing is relatively new, mature solutions for context modelling do not exist, and researchers rely on information modelling solutions developed for other purposes. In our research, we have been using a variant of Object-Role Modeling (ORM) to model context. In this paper, we reflect on our experiences and outline some research challenges in this area.

Balancing Energy and Performance in Dense Linear System Solvers for Hybrid ARM+GPU platforms

The high performance computing community has traditionally focused uniquely on the reduction of execution time, though in the last years, the optimization of energy consumption has become a main issue. A reduction of energy usage without a degradation of performance requires the adoption of energy-efficient hardware platforms accompanied by the development of energy-aware algorithms and computational kernels. The solution of linear systems is a key operation for many scientific and engineering problems. Its relevance has motivated an important amount of work, and consequently, it is possible to find high performance solvers for a wide variety of hardware platforms. In this work, we aim to develop a high performance and energy-efficient linear system solver. In particular, we develop two solvers for a low-power CPU-GPU platform, the NVIDIA Jetson TK1. These solvers implement the Gauss-Huard algorithm yielding an efficient usage of the target hardware as well as an efficient memory access. The experimental evaluation shows that the novel proposal reports important savings in both time and energy-consumption when compared with the state-of-the-art solvers of the platform.

Crowdfunding for non-commercial initiatives

Organizations and individuals dealing with non-commercial initiatives are in permanent search for funding. Crowdfunding is an alternative way of collecting funds from general public through Internet-based platforms, which is currently gaining popularity all over the world. There are several research initiatives in that field that show the influence of different factors on the success of campaigns, both with commercial and non-commercial objectives. Non-profit nature of the project is named among key predictors of positive outcome. In this context, the purpose of this work is to check whether the tendencies detected by scholars are valid for non-commercial initiatives, especially those having socially aware objectives, posted on the Belarusian crowdfunding platform Ulej. The method used for validation of the research hypotheses is binary logistic regression and statistical test. The results showed that the dependent variable success is influenced by such independent variables as the funding goal, the sum collected, the number of sponsors and the average pledge. On the other hand, the effect of the duration period is not significant. Inferential analysis shows that there is no difference in the level of success between commercial and non-commercial projects and that social orientation does not increase the likelihood of meeting financial goals. The findings are opposite to those provided in literature. However that could be explained by the short period of functioning of platform and the small number of projects.

Radio-aware service-level scheduling to minimize downlink traffic delay through Mobile Edge Computing

One of the most challenging problems in mobile broadband networks is how to assign the available radio resources among the different mobile users. Traditionally, research proposals are either speci c to some type of traffic or deal with computationally intensive algorithms aimed at optimizing the delivery of general purpose traffic. Consequently, commercial networks do not incorporate these mechanisms due to the limited hardware resources at the mobile edge. Emerging 5G architectures introduce cloud computing principles to add flexible computational resources to Radio Access Networks. This paper makes use of the Mobile Edge Computing concepts to introduce a new element, denoted as Mobile Edge Scheduler, aimed at minimizing the mean delay of general traffic flows in the LTE downlink. This element runs close to the eNodeB element and implements a novel flow-aware and channel-aware scheduling policy in order to accommodate the transmissions to the available channel quality of end users.

A Capabilities-Aware Framework for Using Computational Accelerators in Data-Intensive Computing

Multicore computational accelerators such as GPUs are now commodity components for highperformance computing at scale. While such accelerators have been studied in some detail as stand-alone computational engines, their integration in large-scale distributed systems raises new challenges and trade-offs. In this paper, we present an exploration of resource management alternatives for building asymmetric accelerator-based distributed systems. We present these alternatives in the context of a capabilities-aware framework for data-intensive computing, which uses an enhanced implementation of the MapReduce programming model for accelerator-based clusters, compared to the state of the art. The framework can transparently utilize heterogeneous accelerators for deriving high performance with low programming effort. Our work is the first to compare heterogeneous types of accelerators, GPUs and a Cell processors, in the same environment and the first to explore the trade-offs between compute-efficient and control-efficient accelerators on data-intensive systems. Our investigation shows that our framework scales well with the number of different compute nodes. Furthermore, it runs simultaneously on two different types of accelerators, successfully adapts to the resource capabilities, and performs 26.9% better on average than a static execution approach.

A Programming Model and Runtime System for Significance-Aware Energy-Efficient Computing.

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. This is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. The runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately.

The experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.

Modeling dual role preferences for trust-aware recommendation

Unlike in general recommendation scenarios where a user has only a single role, users in trust rating network, e.g. Epinions, are associated with two different roles simultaneously: as a truster and as a trustee. With different roles, users can show distinct preferences for rating items, which the previous approaches do not involve. Moreover, based on explicit single links between two users, existing methods can not capture the implicit correlation between two users who are similar but not socially connected. In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions. In particular, local links structure of trust network are exploited as two regularization terms to capture the implicit user correlation, in terms of truster/trustee-specific preferences. Using a real-world and open dataset, we conduct a comprehensive experimental study to investigate the performance of the proposed model, RoRec. The results show that RoRec outperforms other trust-aware recommendation approaches, in terms of prediction accuracy. Copyright 2014 ACM.

Some techniques for automated, resource-aware distributed and mobile computing in a multi-paradigm programming system

Distributed parallel execution systems speed up applications by splitting tasks into processes whose execution is assigned to different receiving nodes in a high-bandwidth network. On the distributing side, a fundamental problem is grouping and scheduling such tasks such that each one involves sufñcient computational cost when compared to the task creation and communication costs and other such practical overheads. On the receiving side, an important issue is to have some assurance of the correctness and characteristics of the code received and also of the kind of load the particular task is going to pose, which can be specified by means of certificates. In this paper we present in a tutorial way a number of general solutions to these problems, and illustrate them through their implementation in the Ciao multi-paradigm language and program development environment. This system includes facilities for parallel and distributed execution, an assertion language for specifying complex programs properties (including safety and resource-related properties), and compile-time and run-time tools for performing automated parallelization and resource control, as well as certification of programs with resource consumption assurances and efñcient checking of such certificates.

Developing context-aware pervasive computing applications: Models and approach

There is growing interest in the use of context-awareness as a technique for developing pervasive computing applications that are flexible, adaptable, and capable of acting autonomously on behalf of users. However, context-awareness introduces a variety of software engineering challenges. In this paper, we address these challenges by proposing a set of conceptual models designed to support the software engineering process, including context modelling techniques, a preference model for representing context-dependent requirements, and two programming models. We also present a software infrastructure and software engineering process that can be used in conjunction with our models. Finally, we discuss a case study that demonstrates the strengths of our models and software engineering approach with respect to a set of software quality metrics.