Unsupervised Domain Adaptation using Graph Transduction Games

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain. In this paper, we propose to cast this problem in a game-theoretic setting as a non-cooperative game and introduce a fully automatized iterative algorithm for UDA based on graph transduction games (GTG). The main advantages of this approach are its principled foundation, guaranteed termination of the iterative algorithms to a Nash equilibrium (which corresponds to a consistent labeling condition) and soft labels quantifying the uncertainty of the label assignment process. We also investigate the beneficial effect of using pseudo-labels from linear classifiers to initialize the iterative process. The performance of the resulting methods is assessed on publicly available object recognition benchmark datasets involving both shallow and deep features. Results of experiments demonstrate the suitability of the proposed game-theoretic approach for solving UDA tasks.Comment: Oral IJCNN 201

Clustering tales from the Greek construction sector: lessons from experience

The idea of increasing regional and national economic competitiveness through the implementation of cluster strategies is not something new. In each business sector, in each country, the creation of clusters has been used to capitalise on sector characteristics and address country specific productivity needs. While clusters have met with significant success in many context, the Greek context and in particularly the Greek Construction sector has not been so fruitful. This paper, through the development of a conceptual framework, questionnaires with 92 firms and interviews with 10 key firms, sought to investigate the critical success factors for the creation of a cluster within the challenging context of the Greek construction sector. Using evidence of good practicefrom other European countries facing similar challenges and the empirical data, the findings indicated a series of factors which firms could adopt, mitigate against or manage to help improve the potential success of the cluster. The findingstherefore have important implications for interventions not only by the state and local authorities that will encourage construction firms to participate in a cluster, but also by the managers/owners/practitioners for the creation of the required foundations for their participation in an environment where competitors cooperate

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

The ‘de-territorialisation of closeness’ - a typology of international successful R&D projects involving cultural and geographic proximity

Although there is a considerable amount of empirical evidence on inter-firm collaborations within technology-based industries, there are only a few works concerned with R&D cooperation by low-tech firms, especially SMEs. Providing further and new evidence based on a recently built database of CRAFT projects, this study analyzes the relationship between technology and proximity in international R&D networks using Homogeneity Analysis by Means of Alternating Least Squares (HOMALS) and statistical cluster techniques. The resulting typology of international cooperative R&D projects highlights that successful international cooperative R&D projects are both culturally/geographically closer and distant. Moreover, and quite interestingly, geographically distant projects are technologically more advanced whereas those located near each other are essentially low tech. Such evidence is likely to reflect the tacit-codified knowledge debate boosted recently by the ICT “revolution” emphasized by the prophets of the “Death of Distance” and the “End of Geography”.Research and Development (R&D); proximity; SMEs

An Attention-based Collaboration Framework for Multi-View Network Representation Learning

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.Comment: CIKM 201