Grid Service Discovery in the Financial Markets Sector

Investment Banking requires a diverse system set in supporting a range of markets from bonds to trading options on weather. The challenge to this community is the ability to adapt to new business requirements in an effective manner, utilizing their network of capabilities in a flexible, dynamic way. A semantic approach to discovery can be used in a pragmatic, practical manner. The use of richer explicit knowledge, that is system readable, provides the basis for discovering capabilities on this exemplar Business Grid—“the grid of services”. This design research project focuses on the utilization of disparate knowledge during discovery

MicroRNAs as Clinical Biomarkers and Therapeutic Tools in Perioperative Medicine

Over the past decade, evolutionarily conserved, noncoding small RNAs-so-called microRNAs (miRNAs)-have emerged as important regulators of virtually all cellular processes. miRNAs influence gene expression by binding to the 3'-untranslated region of protein-coding RNA, leading to its degradation and translational repression. In medicine, miRNAs have been revealed as novel, highly promising biomarkers and as attractive tools and targets for novel therapeutic approaches. miRNAs are currently entering the field of perioperative medicine, and they may open up new perspectives in anesthesia, critical care, and pain medicine. In this review, we provide an overview of the biology of miRNAs and their potential role in human disease. We highlight current paradigms of miRNA-mediated effects in perioperative medicine and provide a survey of miRNA biomarkers in the field known so far. Finally, we provide a perspective on miRNA-based therapeutic opportunities and perspectives. (Anesth Analg 2018;126: 670-81

Clonal Selection based Fuzzy C-Means Algorithm for Clustering

ABSTRACT In recent years, fuzzy based clustering approaches have shown to outperform state-of-the-art hard clustering algorithms in terms of accuracy. The difference between hard clustering and fuzzy clustering is that in hard clustering each data point of the data set belongs to exactly one cluster, and in fuzzy clustering each data point belongs to several clusters that are associated with a certain membership degree. Fuzzy c-means clustering is a well-known and effective algorithm, however, the random initialization of the centroids directs the iterative process to converge to local optimal solutions easily. In order to address this issue a clonal selection based fuzzy c-means algorithm (CSFCM) is introduced. CSFCM is compared with the basic Fuzzy C-Means (FCM) algorithm, a genetic algorithm based FCM (GAFCM) algorithm, and a particle swarm optimization based FCM (PSOFCM) algorithm

Weight Assignment of Semantic Match using User Values and a Fuzzy Approach

Abstract. Automatic discovery of services is a crucial task for the eScience and e-Business communities. Finding a suitable way to address this issue has become one of the key points to convert the Web into a distributed source of computation, as it enables the location of distributed services to perform a required functionality. To provide such an automatic location, the discovery process should be based on the semantic match between a declarative description of the service being sought and a description being offered. This problem requires not only an algorithm to match these descriptions, but also a language to declaratively express the capabilities of services. The proposed matchmaking approach is based on semantic descriptions for service attributes, descriptions and metadata. For the ranking of service matches a match score is calculated whereby the weight values are either given by the user or estimated using a fuzzy approach

Experimental research on bilateral negotiations

Research on bilateral negotiation

Doing Math Modelling Outdoors- A Special Math Class Activity designed with MathCityMap

[EN] The use of smartphones in classrooms is unfortunately due to the restriction legislated by administrations not very popular. With the MathCityMap project (MCM) we show one possibility to use the own mobile device in a substancial and authentic learning environment. MathCityMap combine the well known math trail idea with the current technological possibilities of mobile devices. The MathCityMap is a two component system. The first component is a webportal (www.mathcitymap.eu) which served as a open access database for authentic math problems in the environment. The other component, the MCM-App, shows on a map where in the environment the problems are hidden. Additional it provides hints, feedback and a sample solution. To solve such an authentic MCM problem you need mathematical modelling competencies. We show with the help of one example in which way the students work with the tasks and how the system deal with different solution which occurs by solving a modelling task.Ludwig, M.; Jablonski, S. (2019). Doing Math Modelling Outdoors- A Special Math Class Activity designed with MathCityMap. En HEAD'19. 5th International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 901-909. https://doi.org/10.4995/HEAD19.2019.9583OCS90190

Immune Network Algorithm applied to the Optimization of Composite SaaS in Cloud Computing

Abstract-In order to serve the different application needs of the different Cloud users efficiently and effectively, a possible solution is the decomposition of the software or so-called composite SaaS (Software as a Service). A composite SaaS constitutes a group of loosely-coupled applications that communicate with each other to form higher-level functionality. The benefits to the SaaS providers are reduced delivery cost and flexible SaaS functions, and the benefit for the users is the decreased cost of subscription. For this to be achieved effectively, the optimization of the process is required in order to manage the SaaS resources in the data center efficiently. In this paper, the optimization task of composite SaaS is investigated using an Immune network optimization approach. The approach makes use of activation and suppression that are mimicked by the natural immune system triggering an immune response not only when antibodies interact with antigens but also when they interact with other antibodies. Experiments are conducted with a series of SaaS configurations and the proposed immune network algorithm is compared with a formerly proposed grouping genetic algorithm. The results show that the immune network algorithm outperforms the grouping genetic algorithm

Color Image Segmentation Using Fuzzy C-Regression Model

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms