Abstract More than 99% of European enterprises are SMEs (Small and Medium-sized Enterprises). While in the current market situation collaboration is evolving to an indispensable key factor of success, research is yet to produce results which can be used by SMEs. This paper introduces an interoperability approach in the context of the European Commius Project which enables SMEs collaborate easily on... More than 99% of European enterprises are SMEs (Small and Medium-sized Enterprises). While in the current market situation collaboration is evolving to an indispensable key factor of success, research is yet to produce results which can be used by SMEs. This paper introduces an interoperability approach in the context of the European Commius Project which enables SMEs collaborate easily on the level of process interoperability. The Commius solution will dock onto existing E-Mail structures and enrich incoming E-Mails with additional information dependant upon its content and provide recommendations of expedient further steps to process the E-Mail. A first picture of the Commius process interoperability layer architecture will be drawn. Furthermore proposals to adapt Commius to the specific processes every SME has, in a simple way, are being exposed. Read More Read Less

Abstract In this work we present APA Labs, a generic framework for visualizing the news article domain. APA Labs is a web-based platform enabling retrieval and analysis of news repositories provided by the Austrian Press Agency. APA Labs is designed as a rich internet application combined with a modular system of interactive visualizations. News articles are analyzed using domain specific named... In this work we present APA Labs, a generic framework for visualizing the news article domain. APA Labs is a web-based platform enabling retrieval and analysis of news repositories provided by the Austrian Press Agency. APA Labs is designed as a rich internet application combined with a modular system of interactive visualizations. News articles are analyzed using domain specific named entity extraction methods combined with language specific heuristics. The proposed methods were subject to an evaluation procedure outlined in this contribution. This article illustrates the domain, the underlying concepts and implementation details. Several visualization modules are presented and an outlook on planned modules is given. Being online for around six months the community feedback as well as the easy integration of new modules shows the success of the underlying concept and the platform itself. Read More Read Less

Abstract The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general re search question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) the feasibility of build ing ranking... The textual content of company annual reports has proven to contain predictive indicators for the company future performance. This paper addresses the general re search question of evaluating the effectiveness of applying machine learning and text mining techniques to building predictive models with annual reports. More specifically, we focus on these two questions: 1) the feasibility of build ing ranking models with annual reports to rank future firm performance and 2) the effect of integrating meta seman tic features to help improve and support our prediction. We compare models built with different ranking algorithms and document models. We evaluate our models with a simulated portfolio. Our results show significantly positive average returns over 5 years with a power law trend as we increase the ranking threshold. Adding meta features to document model has shown to improve ranking performance. The SVR & Metaaugemented model outperforms the others and pro vides potential for explaining the textual factors behind the prediction. Read More Read Less

Abstract The last detailed review of Content-based Image Retrieval Systems (CBIRS) is that from Veltkamp and Tanase [VT02], updated in 2002. Since then, many new systems emerged, other systems were improved, but many systems are no longer supported. This paper reconsiders the systems described by Veltkamp and Tanase and proposes in addition for a selection of existing CBIRS a quantitative comparison.... The last detailed review of Content-based Image Retrieval Systems (CBIRS) is that from Veltkamp and Tanase [VT02], updated in 2002. Since then, many new systems emerged, other systems were improved, but many systems are no longer supported. This paper reconsiders the systems described by Veltkamp and Tanase and proposes in addition for a selection of existing CBIRS a quantitative comparison. Read More Read Less

Abstract We analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on... We analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on a probabilistic genetic program for automatically searching the design space for good network topologies. The experimental results for synthesizing and optimizing 3- level enterprise network (65 user nodes) for an edgeserver placement has demonstrated the effectiveness of our methodology in finding good solutions with a static workload in less than five minutes. Read More Read Less

Abstract This paper describes a palette-based colored image segmentation system. In this paper we analyzed the properties of Hue, Saturation, and Luminance (HSL) color space with emphasis on the visual perception of the variation in Hue, Saturation and Luminance values of palette colors. The proposed algorithm based on the concept of re-coloring the palette colors of 256-color images using the properties... This paper describes a palette-based colored image segmentation system. In this paper we analyzed the properties of Hue, Saturation, and Luminance (HSL) color space with emphasis on the visual perception of the variation in Hue, Saturation and Luminance values of palette colors. The proposed algorithm based on the concept of re-coloring the palette colors of 256-color images using the properties of HSL color space and segmenting true type images by taking part of the image, generating a palette to it then apply the method to segment the selected region. After re-coloring of the palette colors the colors in the image will be re-colored according to its new palette, and as a result the image will be segmented to different segments. Read More Read Less

Abstract In-networks, Data Aggregation is usually warranted for distributed wireless sensor networks, owing to reliability and energy efficiency reasons. Sensor nodes are usually deployed in unattended and unsafe environments and hence are vulnerable to intentional or unintentional damages. Individual nodes are prone to different type of faults such as hardware faults, crash faults etc and other security vulnerabilities wherein one or... In-networks, Data Aggregation is usually warranted for distributed wireless sensor networks, owing to reliability and energy efficiency reasons. Sensor nodes are usually deployed in unattended and unsafe environments and hence are vulnerable to intentional or unintentional damages. Individual nodes are prone to different type of faults such as hardware faults, crash faults etc and other security vulnerabilities wherein one or more nodes are compromised to produce bogus data so as to confuse the rest of the network in collaborative sensing applications. The availability of constrained resources and the presence of faulty nodes make designing fault tolerant information aggregation mechanisms in large sensor networks particularly challenging. In our work, we consider Byzantine type of faults, which encompasses most of the common sensor node faults [9]. Faulty nodes are assumed to send inconsistent and arbitrary values to other nodes during information exchange process. These values are termed as outliers and we use a statistical test called Modified Z-score method to reliably detect and remove outliers. We show by simulation that the proposed strategy works well for 2 major classes of collaborative sensor network applications viz. (i) Target/ Event detection and (ii) Continuous data gathering. Read More Read Less