Dynamic Composite Data Physicalization Using Wheeled Micro-Robots

This paper introduces dynamic composite physicalizations, a new class of physical visualizations that use collections of self-propelled objects to represent data. Dynamic composite physicalizations can be used both to give physical form to well-known interactive visualization techniques, and to explore new visualizations and interaction paradigms. We first propose a design space characterizing composite physicalizations based on previous work in the fields of Information Visualization and Human Computer Interaction. We illustrate dynamic composite physicalizations in two scenarios demonstrating potential benefits for collaboration and decision making, as well as new opportunities for physical interaction. We then describe our implementation using wheeled micro-robots capable of locating themselves and sensing user input, before discussing limitations and opportunities for future work

The role of re-appropriation in open design : a case study on how openness in higher education for industrial design engineering can trigger global discussions on the theme of urban gardening

This case study explores the opportunities for students of Industrial Design Engineering to engage with direct and indirect stakeholders by making their design process and results into open-ended Designed Solutions. The reported case study involved 47 students during a two-weeks intensive course on the topic of urban gardening. Observations were collected during three distinctive phases: the co-design phase, the creation of an Open Design and the sharing of these design solutions on the online platform Instructables.com. The open sharing of local solutions triggered more global discussions, based on several types of feedbacks: from simple questions to reference to existing works and from suggestions to critiques. Also some examples of re-appropriation of the designed solutions were reported. These feedbacks show the possibilities for students to have a global vision on their local solutions, confronting them with a wider and more diverse audience. The case study shows on the other hand the difficulty in keeping students engaged in this global discussion, considering how after a few weeks the online discussions dropped to an almost complete silence. It is also impossible with such online platforms to follow the re-appropriation cycles, losing the possibility of exploring the new local context were the replication / modification of the designed product occurred. The course’s focus on Open Design is interesting both under the design and educational points of view. It implies a deep change in the teaching approach and learning attitude of students, allowing unknown peers to take part of the design process and fostering a global discussion starting from unique and local solutions

Towards an aspect weaving BPEL engine

This position paper proposes the use of dynamic aspects and the visitor design pattern to obtain a highly configurable and extensible BPEL engine. Using these two techniques, the core of this infrastructural software can be customised to meet new requirements and add features such as debugging, execution monitoring, or changing to another Web Service selection policy. Additionally, it can easily be extended to cope with customer-specific BPEL extensions. We propose the use of dynamic aspects not only on the engine itself but also on the workflow in order to tackle the problems of Web Service hot deployment and hot fixes to long running processes. In this way, composing aWeb Service "on-the-fly" means weaving its choreography interface into the workflow

CoCo: A tool for automatically assessing conceptual complexity of texts

Traditional text complexity assessment usually takes into account only syntactic and lexical text complexity. The task of automatic assessment of conceptual text complexity, important for maintaining reader's interest and text adaptation for struggling readers, has only been proposed recently. In this paper, we present CoCo - a tool for automatic assessment of conceptual text complexity, based on using the current state-of-the-art unsupervised approach. We make the code and API freely available for research purposes, and describe the code and the possibility for its personalization and adaptation in details. We compare the current implementation with the state of the art, discussing the influence of the choice of entity linker on the performances of the tool. Finally, we present results obtained on two widely used text simplification corpora, discussing the full potential of the tool

Integrating serious games in adaptive hypermedia applications for personalised learning experiences

Game-based approaches to learning are increasingly recognized for their potential to stimulate intrinsic motivation amongst learners. While a range of examples of effective serious games exist, creating high-fidelity content with which to populate games is resource-intensive task. To reduce this resource requirement, research is increasingly exploring means to reuse and repurpose existing games. Education has proven a popular application area for Adaptive Hypermedia (AH), as adaptation can offer enriched learning experiences. Whilst content has mainly been in the form of rich text, various efforts have been made to integrate serious games into AH. However, there is little in the way of effective integrated authoring and user modeling support. This paper explores avenues for effectively integrating serious games into AH. In particular, we consider authoring and user modeling aspects in addition to integration into run-time adaptation engines, thereby enabling authors to create AH that includes an adaptive game, thus going beyond mere selection of a suitable game and towards an approach with the capability to adapt and respond to the needs of learners and educators

PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems

Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 201

The Design and Implementation of Low-Latency Prediction Serving Systems

Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints

Context-Aware and Adaptable eLearning Systems

The full text file attached to this record contains a copy of the thesis without the authors publications attached. The list of publications that are attached to the complete thesis can be found on pages 6-7 in the thesis.This thesis proposed solutions to some shortcomings to current eLearning architectures. The proposed DeLC architecture supports context-aware and adaptable provision of eLearning services and electronic content. The architecture is fully distributed and integrates service-oriented development with agent technology. Central to this architecture is that a node is our unit of computation (known as eLearning node) which can have purely service-oriented architecture, agent-oriented architecture or mixed architecture. Three eLeaerning Nodes have been implemented in order to demonstrate the vitality of the DeLC concept. The Mobile eLearning Node uses a three-level communication network, called InfoStations network, supporting mobile service provision. The services, displayed on this node, are to be aware of its context, gather required learning material and adapted to the learner request. This is supported trough a multi-layered hybrid (service- and agent-oriented) architecture whose kernel is implemented as middleware. For testing of the middleware a simulation environment has been developed. In addition, the DeLC development approach is proposed. The second eLearning node has been implemented as Education Portal. The architecture of this node is poorly service-oriented and it adopts a client-server architecture. In the education portal, there are incorporated education services and system services, called engines. The electronic content is kept in Digital Libraries. Furthermore, in order to facilitate content creators in DeLC, the environment Selbo2 was developed. The environment allows for creating new content, editing available content, as well as generating educational units out of preexisting standardized elements. In the last two years, the portal is used in actual education at the Faculty of Mathematics and Informatics, University of Plovdiv. The third eLearning node, known as Agent Village, exhibits a purely agent-oriented architecture. The purpose of this node is to provide intelligent assistance to the services deployed on the Education Pportal. Currently, two kinds of assistants are implemented in the node - eTesting Assistants and Refactoring eLearning Environment (ReLE). A more complex architecture, known as Education Cluster, is presented in this thesis as well. The Education Cluster incorporates two eLearning nodes, namely the Education Portal and the Agent Village. eLearning services and intelligent agents interact in the cluster