Classification-oriented dawid skene model for transferring intelligence from crowds to machines

When a crowdsourcing approach is used to assist the classification of a set of items,the main objective is to classify this set of items by aggregating the worker-provided labels.A secondary objective is to assess the workers’skill levels in this process.A classical model that achieves both objectives is the famous Dawid-Skene model.In this paper,we consider a third objective in this context,namely,to learn a classifier that is capable of labelling future items without further assistance of crowd workers.By extending the DawidSkene model to include the item features into consideration,we develop a Classification-Oriented Dawid Skene(CODS)model,which achieves the three objectives simultaneously.The effectiveness of CODS on this three dimensions of the problem space is demonstrated experimentally.

A model and experimental study of phosphate uptake kinetics in algae:Considering surface adsorption and P-stress

Phosphorus is an important limiting nutrient in many ecosystems. Consequently, there is increasing interest on phosphate uptake and algal growth due to the increasing frequency and magnitude of algal blooms induced by eutrophication. The co-existence of surface adsorbed and intracellular phosphorus pools indicate that phosphate uptake by phytoplankton is, to some extent, a two-stage kinetic process. However, almost all previous uptake models considered the internal uptake stage only and ignored the possible impact of surface adsorption. In this article, a two-stage kinetic uptake model considering both surface adsorption and P-stress on phosphate uptake by algae was constructed and compared to conventional one-stage models, based on experimental data on short-term uptake kinetics of a green algae S. quadricauda. Results indicated that with suitable parameters, the two-stage uptake model not only fit the experimental data better, but also gave more reasonable and realistic explanations to the phosphate uptake process. The results are meaningful as surface-adsorption of phosphate may affect the uptake process of phosphate and assist in understanding realistic phosphate uptake kinetics in phytoplankton.

Big graph search： challenges and techniques

On one hand, compared with traditional rela- tional and XML models, graphs have more expressive power and are widely used today. On the other hand, various ap- plications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the ap- plication of graph search in various scenarios. We then for- malize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques to- wards big graph search： query techniques, data techniques and distributed computing techniques.

A probabilistic framework of preference discovery from folksonomy corpus

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or re- trieval results. This paper presents a rigorous probabilis- tic framework to discover user preference from folkson- omy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applica- bility of the engaged models. The experimental results show that, with the help of the proposed framework, the user pref- erence can be effectively discovered.