This paper presents the design and optimization of a self-adaptive, a.k.a. underactuated, finger targeted to be used with collaborative robots. Typical robots, whether collaborative or not, mostly rely on standard translational grippers for pickand-place operations. These grippers are constituted from an actuated motion platform on which a set of jaws is rigidly attached. These jaws are often d...

In this work, we initiate the investigation of optimization opportunities in collaborative crowdsourcing. Many popular applications, such as collaborative document editing, sentence translation, or citizen science resort to this special form of human-based computing, where, crowd workers with appropriate skills and expertise are required to form groups to solve complex tasks. Central to any col...

A hierarchical approach to robust design optimization considering the needs of robust performance from multiple disciplines is proposed in this paper. The approach extends the framework of Collaborative Optimization to perform robust design with multiple objectives from different disciplines under uncertainty. The proposed approach is demonstrated by examples and compared with the all-in-one ap...

research on pre-task planning has revealed that planned conditions have produced more fluent and complex language than unplanned conditions. to date, most of these studies have investigated the effects of individual planning on language production while collaborative planning has received scant attention. to determine the effects of pre-task planning on second language written production, the p...

Collaborative recommender system has been an important and popular approach in making recommendations. However, it suffers with the cold start and sparsity problems. Therefore, to alleviate the problems, we have combined a set similarity and user evaluation method in collaborative filtering by introducing some additional weight parameters. Further, we optimize these parameters by Particle Swarm...

We present DiNNO, a distributed algorithm that enables group of robots to collaboratively optimize deep neural network model while communicating over mesh network. Each robot only has access its own data and maintains version the network, but eventually learns is as good if it had been trained on all centrally. No sends raw wireless preserving privacy ensuring efficient use bandwidth. At each i...

Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware sta...