Murphy's surfers or: Where is the green? lure and lore on the internet

In this paper, we explore some characteristics of the Information Superhighway and the World Wide Web metaphors in the light of the current developments in information technology. We propose that these characteristics constitute a form of conceptual slippage (often in the form of lexical leakage), which helps us detect and predict the tacit impact that the currently available information delivery systems are having on human cognition. We argue that the particular language associated with these systems evolves as a direct result of human cognitive adaptation to the demands, resources, and constraints of highly technological environments. It reflects a growing alienation of the users of computerized systems from the physical aspects of the natural environment in which the disseminated information was originally grounded. We believe that a careful investigation of the pragmatic phenomena which are operative when the information media jargon is used is appropriate to the aims of Cognitive Technology. The role of metaphor as a vehicle for self-expression, as mediated by criteria of relevance, is discussed from this perspective.     

Experimental evaluation of dynamic resource orchestration in multi-layer (packet over flexi-grid optical) networks

Network services in 5G will be rolled out as pools of virtual network functions (VNFs) exploiting the advantages of both software-defined networking and network function virtualization. In this context, 5G network services are envisaged as ordered sequences of VNFs resulting in the so-called VNF Forwarding Graphs (VNFFGs). Such VNFs can be allocated over a number of distributed but interconnected data centers (DCs). In this work, a cloud/network orchestrator is discussed to dynamically process and accommodate VNFFG requests over a pool of DCs interconnected by a multi-layer (packet/flexi-grid optical) transport network infrastructure. Two different cloud and network resource allocation algorithms are proposed aiming at: (1) minimizing the distance between the selected DCs and (2) minimizing the load (i.e., consumed cloud resources) of the chosen DCs. In the performance evaluation, the proposed algorithms are experimentally validated and compared on the CTTC ADRENALINE testbed.

     

Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces

Graphs are a flexible and general formalism providing rich models in various important domains, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in the graph structure into account, that is, changes in the number of nodes or in the graph connectivity. Predicting such changes within graphs can be expected to yield important insight with respect to the underlying dynamics, e.g. with respect to user behaviour. However, predictive techniques in the past have almost exclusively focused on single edges or nodes. In this contribution, we attempt to predict the future state of a graph as a whole. We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels. The output of the regression is a point embedded in a pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity- or kernel-based processing methods. We discuss strategies to speed up Gaussian processes regression from cubic to linear time and evaluate our approach on two well-established theoretical models of graph evolution as well as two real data sets from the domain of intelligent tutoring systems. We find that simple regression methods, such as kernel regression, are sufficient to capture the dynamics in the theoretical models, but that Gaussian process regression significantly improves the prediction error for real-world data.     

A multivariate process monitoring strategy and control concept for a small-scale fermenter in a PAT environment

This work describes a multivariate monitoring and control concept for bioprocesses based on historical process data. The concept is demonstrated for a Saccharomyces Cerevisiae (baker’s yeast) fermentation process executed in a small-scale bioreactor, which is equipped with common probes to analyze the broth and off-gases. The data of “in-control” fermentation processes were evaluated by means of a principal component analysis to define confidence limits for subsequent fermentations. A violation of these limits indicated that a process had to be classified as “out-of-control”. Fault diagnosis was provided by the components of the squared prediction error, which can also be used to determine the appropriate counteractions, e.g. via an expert system control strategy as described in this study. The sensitivity of fault diagnosis was demonstrated via various erroneous runs. The duration of bioprocesses can vary distinctly, which complicates the definition of time dependent control limits. Therefore, this study utilizes a three-component partial least squares regression model to quantify the current batch maturity during the process. This maturity is then used to reference current data to the appropriate historical data and the assigned control limits.     

Time prediction on multi-perspective declarative business processes

Process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.     

How do humans inspect BPMN models: an exploratory study

Even though considerable progress regarding the technical perspective on modeling and supporting business processes has been achieved, it appears that the human perspective is still often left aside. In particular, we do not have an in-depth understanding of how process models are inspected by humans, what strategies are taken, what challenges arise, and what cognitive processes are involved. This paper contributes toward such an understanding and reports an exploratory study investigating how humans identify and classify quality issues in BPMN process models. Providing preliminary answers to initial research questions, we also indicate other research questions that can be investigated using this approach. Our qualitative analysis shows that humans adapt different strategies on how to identify quality issues. In addition, we observed several challenges appearing when humans inspect process models. Finally, we present different manners in which classification of quality issues was addressed.     

Transfer of fibroblast sheets cultured on thermoresponsive dishes with membranes

In cell or tissue engineering, it is essential to develop a support for cell-to-cell adhesion, which leads to the generation of cell sheets connected by extracellular matrix. Such supports must be hydrophobic and should result in a detachable cell sheet. A thermoresponsive support that enables the cultured cell sheet to detach using only a change in temperature could be an interesting alternative in regenerative medicine. The aim of this study was to evaluate plates covered with thermoresponsive polymers as supports for the formation of fibroblast sheets and to develop a damage-free procedure for cell sheet transfer with the use of membranes as transfer tools. Human skin fibroblasts were seeded on supports coated with a thermoresponsive polymer: commercial UpCell? dishes (NUNC?) coated with thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) and dishes coated with thermoresponsive poly(tri(ethylene glycol) monoethyl ether methacrylate) (P(TEGMA-EE)). Confluent fibroblast sheets were effectively cultured and harvested from both commercial PNIPAM-coated dishes and laboratory P(TEGMA-EE)-coated dishes. To transfer a detached cell sheet, two membranes, Immobilon-P^® and SUPRATHEL^®, were examined. The use of SUPRATHEL for relocating the cell sheets opens a new possibility for the clinical treatment of wounds. This study established the background for implementing thermoresponsive supports for transplanting in vitro cultured fibroblasts.