Process-oriented Enterprise Mashups

Mashups, a new Web 2.0 technology provide the ability for easy creation of Web-Based applications by end-users. The uses of the mashups are often consumer related. In this paper we explore how mashups can be used in the enterprise area and hat the criteria for enterprise mashups are. We provide categories for the classification of enterprise mashups, and based upon a motivating example we go further in depth on business process enterprise mashup

A new species of Archaeoryctes from the Middle Paleocene of China and the phylogenetic diversification of Didymoconidae

Didymoconidae are an enigmatic group of Asian endemic insectivorous mammals. We describe the new didymoconid species Archaeoryctes wangi sp. nov. from the Upper Member of the Wanghudun Formation (Middle Paleocene). This new species from the Qianshan Basin (Anhui Province, China) forms an interesting geographical intermediate between A. notialis from South China and A. borealis and A. euryalis from the Mongolian Plateau. To better understand the origin and evolutionary diversification of Didymoconidae, we performed a cladistic and stratocladistic study of the Didymoconidae and various outgroups. This study of dental material did not resolve the higher level affinities of Didymoconidae, but confirms the validity of the family and its distinctiveness from the morphologically similar Sarcodontidae. Moreover, our results corroborate the current didymoconid classification with the distinction of three subfamilies: “Ardynictinae”, Kennatheriinae and Didymoconinae; “Ardynictinae” are a paraphyletic stemgroup for the two other subfamilies. Our results suggest three distinct didymoconid radiations: (1) primitive ardynictines appeared in South China from the start of the Nongshanian; their evolution continues on the Mongolian Plateau with (2) the radiation of more evolved ardynictines and kennatheriines at the start of the Middle Eocene Arshantan and (3) the origin of didymoconines at the start of the Late Eocene Ergilian

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering

Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets.Comment: Accepted by IEEE TPAMI, extended version of conference paper arXiv:2110.0420