SeaFlows Toolset - Compliance Verification Made Easy for Process-aware Information Systems

In the light of an increasing demand on business process compliance, the verication of process models against compliance rules has become essential in enterprise computing. The SeaFlows Toolset featured in this paper extends process-aware information systems with compliance checking functionality. It provides a user-friendly environment for modeling compliance rules using a graph-based formalism and for enriching process models with these rules. To address a multitude of verification settings, we provide two complementary compliance checking approaches: The structural compliance checking approach derives structural criteria from compliance rules and applies them to detect incompliance. The data-aware behavioral compliance checking approach addresses the state explosion problem that can occur when the data dimension is explored during compliance checking. It performs context-sensitive automatic abstraction to derive an abstract process model which is more compact with regard to the data dimension enabling more efficient compliance checking. Altogether, SeaFlows Toolset constitutes a comprehensive and extensible framework for compliance checking of process models

COST 296 MIERS: conclusion

The need for more reliable and efficient communications services, especially those involving ionospheric HF communications and navigational systems, imposes increasing demand for a better knowledge of the effects imposed by the Earth’s upper atmosphere and ways to mitigate disturbing effects. Temporal and spatial changes in the upper atmosphere act to limit and degrade the performance of terrestrial and Earth-space radio systems in many different ways and this is why mitigation activities must involve several topics like ionospheric monitoring and modeling, development of new hardware for communication systems and new propagation simulator, measurements and modeling of ionospheric Total Electron Content (TEC) and ionospheric scintillations, using in particular the Global Positioning System (GPS). The European ionospheric community has long been aware that cooperation research on an international basis is essential to deal with such complex issues. In particular, international cooperation is required for the collection of data, in both the real-time and in retrospective modes, the development and verification of new methods to improve the performance of both operational and future terrestrial and Earth-space communication systems and the exchange of expertise on space plasma effects on Global Navigation Satellite Systems (GNSS). In this context the COST 296 Action MIERS on the «Mitigation of Ionospheric Effects on Radio Systems» has made a significant impact in a number of areas

Context modeling and constraints binding in web service business processes

Context awareness is a principle used in pervasive services applications to enhance their exibility and adaptability to changing conditions and dynamic environments. Ontologies provide a suitable framework for context modeling and reasoning. We develop a context model for executable business processes { captured as an ontology for the web services domain. A web service description is attached to a service context profile, which is bound to the context ontology. Context instances can be generated dynamically at services runtime and are bound to context constraint services. Constraint services facilitate both setting up constraint properties and constraint checkers, which determine the dynamic validity of context instances. Data collectors focus on capturing context instances. Runtime integration of both constraint services and data collectors permit the business process to achieve dynamic business goals

Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms

In NLP, convolutional neural networks (CNNs) have benefited less than recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i.e., it is applied to pooling) rather than as attentive convolution (i.e., it is integrated into convolution). Convolution is the differentiator of CNNs in that it can powerfully model the higher-level representation of a word by taking into account its local fixed-size context in the input text t^x. In this work, we propose an attentive convolution network, ATTCONV. It extends the context scope of the convolution operation, deriving higher-level features for a word not only from local context, but also information extracted from nonlocal context by the attention mechanism commonly used in RNNs. This nonlocal context can come (i) from parts of the input text t^x that are distant or (ii) from extra (i.e., external) contexts t^y. Experiments on sentence modeling with zero-context (sentiment analysis), single-context (textual entailment) and multiple-context (claim verification) demonstrate the effectiveness of ATTCONV in sentence representation learning with the incorporation of context. In particular, attentive convolution outperforms attentive pooling and is a strong competitor to popular attentive RNNs.Comment: Camera-ready for TACL. 16 page