Shallow Linear Action Graphs and their Embeddings

Action calculi, which generalise process calculi such as Petri nets, π-calculusand ambient calculus, have been presented in terms of action graphs. We here offer linear action graphs as a primitive basis for action calculi. This paper presents the category of embeddings of undirected linear action graphs without nesting, using a novel form of graphical reasoning which simplifies some otherwise complex manipulations in regular algebra. The results are adapted in a few lines to directed graphs. This work is part of a long-term search for a uniform behavioural theory for process calculi. Received October 2000 / Accepted in revised form April 2001     

The new organisation of work: Building coalitions

This article introduces the theme of the special issue, linking current concerns in European social and industrial relations policy with the research traditions covered byAI & Society. Human centredness, skill and technology, and the central importance of education and learning are emphasised as we build new development coalitions.     

The philosophical technologies

The thesis of this paper is that ethics and action theory are the two philosophical branches of technology. The reason is that they, just like mechanical engineering and management science, are concerned with human action. But of course there are differences between the philosophical technologies and the others, among them their degree of generality. An example from ethics and another from praxiology are discussed, to show that these disciplines can be just as exact and relevant to real contemporary life as electrical engineering and law. Finally, some consequences for the classification of technologies and for the design of academic curricula are drawn.     

三维动画表演教学研究——浅析动画表演在三维动画教学以及动画创作中的重要性

影视表演艺术学被广泛应用在电影电视以及舞台戏剧中,动画艺术作为电影艺术的一个分支,表演艺术也随之融入到三维动画创作中。动画表演为动画师创作鲜活的角色提供丰富的创作灵感。由于计算机图形学飞速发展,极大简化了动画艺术的制作流程,在三维动画中应用表演理论及艺术方法会极大完善角色的肢体行为,使动画师深入理解角色动作行为及把握角色性格,表现出鲜活的角色形象。     

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.     

Laplacian group sparse modeling of human actions

Recently, many local-feature based methods have been proposed for feature learning to obtain a better high-level representation of human behavior. Most of the previous research ignores the structural information existing among local features in the same video sequences, while it is an important clue to distinguish ambiguous actions. To address this issue, we propose a Laplacian group sparse coding for human behavior representation. Unlike traditional methods such as sparse coding, our approach prefers to encode a group of relevant features simultaneously and meanwhile allow as less atoms as possible to participate in the approximation so that video-level sparsity is guaranteed. By incorporating Laplacian regularization the method is capable to ensure the similar approximation of closely related local features and the structural information is successfully preserved. Thus, a compact but discriminative human behavior representation is achieved. Besides, the objective of our model is solved with a closed-form solution, which reduces the computational cost significantly. Promising results on several popular benchmark datasets prove the efficiency and effectiveness of our approach.     

An economic–ecological model to evaluate impacts of nutrient abatement in the Baltic Sea

This paper presents a coupled economic–ecological model that integrates a catchment model with a marine model and incorporates economic data to analyse the long-term economic and ecological consequences of nutrient abatement in the Baltic Sea. The spatially explicit model describes dynamics of soil phosphorus in arable land, developments of nutrient concentrations and phytoplankton biomass in the sea basins, and inter-annual variation in nutrient loads and biophysical processes. The performance of the model is demonstrated by computing the least-cost solution to reach the good environmental state of the sea – as implied by the Baltic Sea Action Plan – within a time span of 40 years. The total cost of achieving this target is 1487 M€ annually. Spatially optimal allocation of load reductions differs from the load reduction targets of the Baltic Sea Action Plan, and focuses more on the control of phosphorus loads.