Dance of the bulrushes: building conversations between social creatures

The interactive installation is in vogue. Interaction design and physical installations are accepted fixtures of modern life, and with these technology-driven installations beginning to exert influence on modes of mass communication and general expectations for user experiences, it seems appropriate to explore the variety of interactions that exist. This paper surveys a number of successful projects with a critical eye toward assessing the type of communication and/or conversation generated between interactive installations and human participants. Moreover, this exploration seeks to identify whether specific tactics and/or technologies are particularly suited to engendering layers of dialogue or ‘conversations’ within interactive physical computing installations. It is asserted that thoughtful designs incorporating self-organizational abilities can foster rich dialogues in which participants and the installation collaboratively generate value in the interaction. To test this hypothesis an interactive installation was designed and deployed in locations in and around London. Details of the physical objects and employed technologies are discussed, and results of the installation sessions are shown to corroborate the key tenets of this argument in addition to highlighting other concerns that are specifically relevant to the broad topic of interactive design

Computers in writing instruction

For computers to be useful in writing instruction, innovations should be valuable for students and feasible for teachers to implement. Research findings yield contradictory results in measuring the effects of different uses of computers in writing, in part because of the methodological complexity of such measurements. Yet the computer seems to be a promising tool in several new, theoretically based approaches to writing instruction. Research of these kinds of computer applications should continue, paying attention to context variables that influence the implementation process importantly

Exploring the Integration of Disability Awareness into Tertiary Teaching and Learning Activities

A desire to have every student attending our University be aware of, and reflect on, disability in their studies and future careers, initiated our project to explore how to enhance disability awareness within all our University’s papers. In this project we systematically reviewed pertinent literature and ran an action research workshop for staff. Strategies to enhance disability awareness identified in the literature and workshop were presented and verified at an interactive conference presentation. Embedding disability awareness into curricula is challenging; staff considered themselves powerless to bring about change in their departments, but thought that one way to do so would be by modelling inclusive behaviour and by introducing subtle inclusive practices into papers taught. The identified strategies may be of use to others contemplating similar curricular modifications

TRECVID 2008 - goals, tasks, data, evaluation mechanisms and metrics

The TREC Video Retrieval Evaluation (TRECVID) 2008 is a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last 7 years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. In 2008, 77 teams (see Table 1) from various research organizations --- 24 from Asia, 39 from Europe, 13 from North America, and 1 from Australia --- participated in one or more of five tasks: high-level feature extraction, search (fully automatic, manually assisted, or interactive), pre-production video (rushes) summarization, copy detection, or surveillance event detection. The copy detection and surveillance event detection tasks are being run for the first time in TRECVID. This paper presents an overview of TRECVid in 2008

Language-Based Image Editing with Recurrent Attentive Models

We investigate the problem of Language-Based Image Editing (LBIE). Given a source image and a natural language description, we want to generate a target image by editing the source image based on the description. We propose a generic modeling framework for two sub-tasks of LBIE: language-based image segmentation and image colorization. The framework uses recurrent attentive models to fuse image and language features. Instead of using a fixed step size, we introduce for each region of the image a termination gate to dynamically determine after each inference step whether to continue extrapolating additional information from the textual description. The effectiveness of the framework is validated on three datasets. First, we introduce a synthetic dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE system. Second, we show that the framework leads to state-of-the-art performance on image segmentation on the ReferIt dataset. Third, we present the first language-based colorization result on the Oxford-102 Flowers dataset.Comment: Accepted to CVPR 2018 as a Spotligh

Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method