Measuring the publishing productivity of economics departments in Europe

Industrial technology grouping is a common phenomenon that occurs as an industry develops and evolves. However, the research on innovation diffusion has given little attention to the role of industrial technology grouping. This paper extends the prior research to analyze the impact of industrial technology grouping on innovation diffusion within the framework of structural embeddedness. In our empirical study, we selected a sample of patents in the smart phone industry during the 2004–2014 period. We used both hierarchical regression analysis and patent citation analysis to explore the impact of industrial technology grouping on innovation diffusion in the two dimensions of clustering and bridging ties, which yielded several valuable results. First, industrial technology grouping is a common phenomenon in the development of industrial technology. Moreover, the dynamic changes of technology clusters are an important driving force shaping the trends and diversity of industrial technology. Second, industrial technology grouping does not have a significant effect on firm innovation diffusion, whereas structural embeddedness directly affects innovation diffusion. Third, industrial technology grouping positively moderates the impact of structural embeddedness on firm innovation diffusion in both dimensions of clustering and bridging ties.     

Discovering user communities on the Internet using unsupervised machine learning techniques

Interest in the analysis of user behaviour on the Internet has been increasing rapidly, especially since the advent of electronic commerce. In this context, we argue here for the usefulness of constructing communities of users with common behaviour, making use of machine learning techniques. In particular, we assume that the users of any service on the Internet constitute a large community and we aim to construct smaller communities of users with common characteristics. The paper presents the results of three case studies for three different types of Internet service: a digital library, an information broker and a Web site. Particular attention is paid on the different types of information access involved in the three case studies: query-based information retrieval, profile-based information filtering and Web-site navigation. Each type of access imposes different constraints on the representation of the learning task. Two different unsupervised learning methods are evaluated: conceptual clustering and cluster mining. One of our main concerns is the construction of meaningful communities that can be used for improving information access on the Internet. Analysis of the results in the three case studies brings to surface some of the important properties of the task, suggesting the feasibility of a common methodology for the three different types of information access on the Internet.     

Improved Approximations for Weighted and Unweighted Graph Problems

We obtain improved approximation ratios for problems of a broad class called weighted hereditary induced-subgraph maximization problems, in particular for the maximum independent set, maximum clique and maximum ℓ-colorable induced subgraph, as well as for the minimum coloring problem. We also study the minimum chromatic sum and show that its weighted version polynomially reduces to the weighted independent set problem in such a way that approximation ratios are preserved (up to a multiplicative constant).     

The Probabilistic Minimum Vertex-covering Problem

An instance of the probabilistic vertex-covering problem is a pair ( G =( V , E ),Pr) obtained by associating with each vertex υ _i ∈ V an 'occurrence' probability p _i . We consider a modification strategy Μ transforming a vertex cover C for G into a vertex cover C _I for the subgraph of G induced by a vertex-set I ⊆ V . The objective for the probabilistic vertex-covering is to determine a vertex cover of G minimizing the sum, over all subsets I ⊆ V , of the products: probability of I times C _I . In this paper, we study the complexity of optimally solving probabilistic vertex-covering.     

ER designer toolkit: a graphical event definition authoring tool

Modern assistive environments have the ability to collect data from various distributed sources and need to react swiftly to changes. As information flows, in the form of simple, source events, it becomes more and more difficult to quickly analyze the collected data in an automated way and transform them into operational knowledge. Event recognition (ER) addresses this problem. Several tools exist for defining ER rules, but only a few of them offer graphical design environments. Each such tool supports a single ER language, either query-based or rule-based. Also, many of these systems do not support the addition of user-defined operators, thus limiting the flexibility in rule design. This paper presents the Event Recognition Designer Toolkit (ERDT), a graphical authoring tool, with which a domain expert can design event recognition rules and produce standalone ER. The goal was to develop a user-friendly graphical tool with a basic set of operators, so that a user could easily produce recognizers for different domains and, when needed, easily extend the tool in order to satisfy domain-specific requirements. The ERDT uses an extendable pool of ER language libraries (at the moment SQL and Event Calculus are supported) and transforms the designed rules into Event Recognizers that use the preferred ER language. The same rule can be expressed in different languages without any changes to the design. Furthermore, the authoring tool is cross platform, free, and open source, so that it can be shared with the community, maximizing its potential impact and possible extension.     

Approximation of min coloring by moderately exponential algorithms

We study in this note approximation algorithms for the problem of coloring the vertices of a graph with as few colors as possible, with moderately exponential running times (and using either polynomial or exponential space), better than those of exact computation. Study of approximation is performed with respect to optimality measures, the minimum number of used colors and the maximum number of unused colors.     

Magnetic force microscopy on nanocrystalline Co films

Pioneer works in ultrathin magnetic films have shown perpendicular magnetic domains in the demagnetized state. The source of this perpendicular anisotropy is the interface anisotropy developed at the interface. Similar domains could be observed in tetragonally distorted ultrathin films due to the magnetoelastic anisotropy. On the other hand, single-crystalline hexagonal close packed (hcp) Co films when grown epitaxially with the c-axis oriented perpendicular to the film plane may show perpendicular stripe magnetic domains even up to a thickness of about 500 nm. In that case the source of perpendicular anisotropy was the magnetocrystalline anisotropy of bulk Co, which favors the c-axis. In this work, we have grown by radio frequency magnetron sputtering Co films in the thickness range 15-4500 nm. We have used various substrates, such as Corning glass, silicon and Al-foil. The substrate temperature was about 350 K. The films have been found by X-ray diffraction experiments to present various structures and textures depending on the preparation conditions, mainly the Ar-pressure and deposition rate. Stripe- and labyrinth-like domain configurations are observed in films textured along the c-axis, and in films with a mixture of hcp and fcc grains, repectively. Films which show mainly fcc or amorphous structure do not form perpendicular domains. The results are discussed with respect to magnetization loops.     

Average-case complexity for the execution of recursive definitions on relational databases

The execution costs of various types of database queries, expressed in terms of linear recusive definitions, are evaluated for two common query evaluation algorithms in the case where the database relations are represented by forests of labelled oriented trees. In a first stage, the execution costs are computed for a given forest. A key issue in this computation is the partition of the set of nodes in the forest into equivalence classes, the properties of which are explored. Moreover, the representation adopted is conceptually simple and provides additional results which are of interest by themselves. In a second stage, the averages of these costs, computed over all databases representable by forests with a given number of nodes, are also evaluated. Finally, the execution cost of the considered database queries is computed for the case where the underlined database relations are modelled as Hamiltonian digraphs. Received: 4 October 1995 / 19 February 1997     

Building intelligent collaborative interface agents with the ICAGENT development framework

This paper describes the implementation of intelligent collaborative interface agents using the intelligent collaborative agent (ICagent) development framework. In particular, the paper presents the implementation of a collaborative interface agent that acts as a tutor in the context of an educational software application. The agent deliberates socially with users following the SharedPlans model of collaborative activity. Social deliberation requires interface agents to make their desires and intentions clear to the application users, being in constant communication with them, to understand the context of their activity and to reconcile their own and users’ desires in the overall context of action. Reconciliation of users’ desires allows agents to recognize the situations where users need help. The paper briefly presents the ICagent development framework, describes the implementation of the interface agent, and discusses an example of the behavior of the agent during a collaboration session.