Information Systems Applications (incl.Internet); Business Information Systems; Computer Appl. in Administrative Data Processing; Management of Computing and Information Systems
•PLS-PM has been subject to many improvements in last years.•Prior PLS guidelines have not covered the entire recent developments.•We explain how to perform and report an up-to-date empirical ...analysis with PLS.•We provide a fictive illustrative example on business value of social media.
Partial least squares path modeling (PLS-PM) is an estimator that has found widespread application for causal information systems (IS) research. Recently, the method has been subject to many improvements, such as consistent PLS (PLSc) for latent variable models, a bootstrap-based test for overall model fit, and the heterotrait-to-monotrait ratio of correlations for assessing discriminant validity. Scholars who would like to rigorously apply PLS-PM need updated guidelines for its use. This paper explains how to perform and report empirical analyses using PLS-PM including the latest enhancements, and illustrates its application with a fictive example on business value of social media.
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•A new “information system” is proposed for mineral exploration targeting.•The system is called “exploration information system” (EIS).•EIS uses “mineral system” concepts in ...conjunction with GIS.•Ore-forming processes are classified into pre-, syn-, and post-mineralization parts.•EIS aims to automated translation of ore-forming processes into spatial proxies.
The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.
Game testing has been long recognized as a notoriously challenging task, which mainly relies on manual playing and scripting based testing in game industry. Even until recently, automated game ...testing still remains to be largely untouched niche. A key challenge is that game testing often requires to play the game as a sequential decision process. A bug may only be triggered until completing certain difficult intermediate tasks, which requires a certain level of intelligence. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game testing, without human competitive intelligent support. However, the existing DRLs mostly focus on winning the game rather than game testing. To bridge the gap, in this paper, we first perform an in-depth analysis of 1349 real bugs from four real-world commercial game products. Based on this, we propose four oracles to support automated game testing, and further propose Wuji, an on-the-fly game testing framework, which leverages evolutionary algorithms, DRL and multi-objective optimization to perform automatic game testing. Wuji balances between winning the game and exploring the space of the game. Winning the game allows the agent to make progress in the game, while space exploration increases the possibility of discovering bugs. We conduct a large-scale evaluation on a simple game and two popular commercial games. The results demonstrate the effectiveness of Wuji in exploring space and detecting bugs. Moreover, Wuji found 3 previously unknown bugs, which have been confirmed by the developers, in the commercial games.
Enabling Things to Talk Bassi, Alessandro; Bauer, Martin; Fiedler, Martin ...
2013, 2013-10-28
eBook
This volume presents the results of a flagship European Commission project to map the conceptual reference model for the Internet of Things. It sets out an agreed IoT architecture of maximal ...interoperability, ready for use in real-world network development.
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