Accomplishing Autonomous Driving: An Unfinished Description

In his contribution on Accomplishing Autonomous Driving: An unfinished Description Göde Both stresses the multiplicity of possible answers to what his research object might be: an autonomous or self-driving car and its related practices. Drawing on ethnographic descriptions Both questions the definition of autonomy in this context in which there is a constant oscillation between manual and autonomous driving. This leads to a conceptualization of autonomous driving as a collective achievement of heterogeneous elements. Both thus argues for the multitude of spatial, temporal and personal configurations and distributions across related objects, humans and practices

Studies on Ontology Meta-Model for Isomorphic Architecture of Information Systems based on Organizational Semiotics

Interoperability is hard to tackle in both business and IT domains since semantic transaction loss exists in terms of concepts transformation from one design stage to another in information systems development. It results from different interpretations and representations of various requirements in design domains. Without an explicit structural specification of semantic linkages among design domains, the transformation cannot be efficiently identified in an appropriate way. These call for effective architectural solutions that coordinate powerful technologies with business applications to enable seamless integration. The main objective of this paper is to investigate ontology types and build ontology meta-model for IAIS (Isomorphic Architecture of Information Systems) which was built in our previous work to reach seamless and unified semantic linkages. The ontology meta-model is proposed to bridge the gap among different processes in information systems development with the same structure unit. The secondary objective of this paper is to study how to prevent semantic loss in analysis and design processes with the meta-model

Crafting a rich and personal blending learning environment: an institutional case study from a STEM perspective

Institutional pressures to make optimal use of lecture halls and classrooms can be powerful motivators to identify resources to develop technology enhanced learning approaches to traditional curricula. From the academic’s perspective, engaging students in active learning and reducing the academic workload are important and complementary drivers. This paper presents a case study of a curriculum development exercise undertaken in a STEM subject area at a research-intensive UK university. A multi-skilled team of academics and learning designers have worked collaboratively to build this module which will be realised as a mix of online and face to face activities. Since the module addresses professional issues, a strong emphasis is being placed on establishing authentic learning activities and realistic use of prominent social tools.The learning designers are working for a cross-institutional initiative to support educational innovations; therefore it is important to carefully document the development process and to identify reusable design patterns which can be easily explained to other academics.<br/

Real-Time Storytelling with Events in Virtual Worlds

We present an accessible interactive narrative tool for creating stories among a virtual populace inhabiting a fully-realized 3D virtual world. Our system supports two modalities: assisted authoring where a human storyteller designs stories using a storyboard-like interface called CANVAS, and exploratory authoring where a human author experiences a story as it happens in real-time and makes on-the-fly narrative trajectory changes using a tool called Storycraft. In both cases, our system analyzes the semantic content of the world and the narrative being composed, and provides automated assistance such as completing partially-specified stories with causally complete sequences of intermediate actions. At its core, our system revolves around events -â?? pre-authored multi-actor task sequences describing interactions between groups of actors and props. These events integrate complex animation and interaction tasks with precision control and expose them as atoms of narrative significance to the story direction systems. Events are an accessible tool and conceptual metaphor for assembling narrative arcs, providing a tightly-coupled solution to the problem of converting author intent to real-time animation synthesis. Our system allows simple and straightforward macro- and microscopic control over large numbers of virtual characters with diverse and sophisticated behavior capabilities, and reduces the complicated action space of an interactive narrative by providing analysis and user assistance in the form of semi-automation and recommendation services

Software Development in the Cloud: Exploring the Affordances of Platform-as-a-Service

Software development teams increasingly adopt platform-as-a-service (PaaS), i.e., cloud services that make software development infrastructure available over the internet. Yet, empirical evidence of whether and how software development work changes with the use of PaaS is difficult to find. We performed a grounded-theory study to explore the affordances of PaaS for software development teams. We find that PaaS enables software development teams to enforce uniformity, to exploit knowledge embedded in technology, to enhance agility, and to enrich jobs. These affordances do not arise in a vacuum. Their emergence is closely interwoven with changes in methodologies, roles, and norms that give rise to self-organizing, loosely coupled teams. Our study provides rich descriptions of PaaS-based software development and an emerging theory of affordances of PaaS for software development teams

Affordance-Based Grasping Point Detection Using Graph Convolutional Networks for Industrial Bin-Picking Applications

Grasping point detection has traditionally been a core robotic and computer vision problem. In recent years, deep learning based methods have been widely used to predict grasping points, and have shown strong generalization capabilities under uncertainty. Particularly, approaches that aim at predicting object affordances without relying on the object identity, have obtained promising results in random bin-picking applications. However, most of them rely on RGB/RGB-D images, and it is not clear up to what extent 3D spatial information is used. Graph Convolutional Networks (GCNs) have been successfully used for object classification and scene segmentation in point clouds, and also to predict grasping points in simple laboratory experimentation. In the present proposal, we adapted the Deep Graph Convolutional Network model with the intuition that learning from n-dimensional point clouds would lead to a performance boost to predict object affordances. To the best of our knowledge, this is the first time that GCNs are applied to predict affordances for suction and gripper end effectors in an industrial bin-picking environment. Additionally, we designed a bin-picking oriented data preprocessing pipeline which contributes to ease the learning process and to create a flexible solution for any bin-picking application. To train our models, we created a highly accurate RGB-D/3D dataset which is openly available on demand. Finally, we benchmarked our method against a 2D Fully Convolutional Network based method, improving the top-1 precision score by 1.8% and 1.7% for suction and gripper respectively.This Project received funding from the European Union’s Horizon 2020 research and Innovation Programme under grant agreement No. 780488