New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering

This book provides the latest in a series of books growing out of the International Joint Conferences on Computer, Information and Systems Sciences and Engineering. It includes chapters in the most advanced areas of Computing, Informatics, Systems Sciences and Engineering. It has accessible to a wide range of readership, including professors, researchers, practitioners and students. This book includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art research projects in the areas of Computer Science, Informatics, and Systems Sciences, and Engineering. It includes selected papers form the conference proceedings of the Ninth International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering (CISSE 2013). Coverage includes topics in: Industrial Electronics, Technology & Automation, Telecommunications and Networking, Systems, Computing Sciences and Software Engineering, Engineering Education, Instructional Technology, Assessment, and E-learning.

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Engineering an Ontology for Autonomous Systems - The OASys Ontology

This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programe on autonomous systems’ engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems’ stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system’s conceptual models. The objective is to obtain and to use these models as main input for the autonomous system’s model-based control system.

Engineering Agent Systems for Decision Support

This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain.

Expert systems for quality prediction in structural engineering

This work deals with quality level prediction in concrete structures through the helpful assistance of an expert system wich is able to apply reasoning to this field of structural engineering. Evidences, hypotheses and factors related to this human knowledge field have been codified into a Knowledge Base in terms of probabilities for the presence of either hypotheses or evidences,and conditional presence of both. Human experts in structural engineering and safety of structures gave their invaluable knowledge and assistance necessary when constructing the "computer knowledge body".

Ontology driven description and engineering of autonomous systems: application to process system engineering

Autonomous systems refer to systems capable of operating in a real world environment without any form of external control for extended periods of time. Autonomy is a desired goal for every system as it improves its performance, safety and profit. Ontologies are a way to conceptualize the knowledge of a specific domain. In this paper an ontology for the description of autonomous systems as well as for its development (engineering) is presented and applied to a process. This ontology is intended to be applied and used to generate final applications following a model driven methodology.

Systems in Engineering Education: account of an experience

Systems Engineering (SE in the following) has not received much attention as a subject matter in engineering curricula. There are several dozens of universities around the world offering programs (most of them at the graduate level) on systems science and engineering. However, SE is, per se, rarely found among the courses offered by engineering schools. This observation does not strictly mean that systems concepts be left apart. For example, it is usual to find specialized courses for systems of some particular classes (e.g., courses on software systems engineering for computing curricula) or for particular phases of the system life cycle (e.g., courses on systems analysis). Even so, these kinds of courses tend to over-emphasize the importance of specific methodologies and, in consequence, to deviate the attention from the realm of systernness

Towards model-driven engineering for mixed-criticality systems: multiPARTES approach

Mixed criticality systems emerges as a suitable solution for dealing with the complexity, performance and costs of future embedded and dependable systems. However, this paradigm adds additional complexity to their development. This paper proposes an approach for dealing with this scenario that relies on hardware virtualization and Model-Driven Engineering (MDE). Hardware virtualization ensures isolation between subsystems with different criticality levels. MDE is intended to bridge the gap between design issues and partitioning concerns. MDE tooling will enhance the functional models by annotating partitioning and extra-functional properties. System partitioning and subsystems allocation will be generated with a high degree of automation. System configuration will be validated for ensuring that the resources assigned to a partition are sufficient for executing the allocated software components and that time requirements are met.

On the Analytical Approach to Present Engineering Problems: Photovoltaic Systems Behavior, Wind Speed Sensors Performance, and High-Speed Train Pressure Wave Effects in Tunnels

At present, engineering problems required quite a sophisticated calculation means. However, analytical models still can prove to be a useful tool for engineers and scientists when dealing with complex physical phenomena. The mathematical models developed to analyze three different engineering problems: photovoltaic devices analysis; cup anemometer performance; and high-speed train pressure wave effects in tunnels are described. In all cases, the results are quite accurate when compared to testing measurements.

Modelling spatial decisión support systems focused on the development of a data warehouse: applying software engineering

Nowadays, organizations have plenty of data stored in DB databases, which contain invaluable information. Decision Support Systems DSS provide the support needed to manage this information and planning médium and long-term ?the modus operandi? of these organizations. Despite the growing importance of these systems, most proposals do not include its total evelopment, mostly limiting itself on the development of isolated parts, which often have serious integration problems. Hence, methodologies that include models and processes that consider every factor are necessary. This paper will try to fill this void as it proposes an approach for developing spatial DSS driven by the development of their associated Data Warehouse DW, without forgetting its other components. To the end of framing the proposal different Engineering Software focus (The Software Engineering Process and Model Driven Architecture) are used, and coupling with the DB development methodology, (and both of them adapted to DW peculiarities). Finally, an example illustrates the proposal.

Emotions and the Engineering of Adaptiveness in Complex Systems

A major challenge in the engineering of complex and critical systems is the management of change, both in the system and in its operational environment. Due to the growing of complexity in systems, new approaches on autonomy must be able to detect critical changes and avoid their progress towards undesirable states. We are searching for methods to build systems that can tune the adaptability protocols. New mechanisms that use system-wellness requirements to reduce the influence of the outer domain and transfer the control of uncertainly to the inner one. Under the view of cognitive systems, biological emotions suggests a strategy to configure value-based systems to use semantic self-representations of the state. A method inspired by emotion theories to causally connect to the inner domain of the system and its objectives of wellness, focusing on dynamically adapting the system to avoid the progress of critical states. This method shall endow the system with a transversal mechanism to monitor its inner processes, detecting critical states and managing its adaptivity in order to maintain the wellness goals. The paper describes the current vision produced by this work-in-progress.

Enhancing regression models for complex systems using evolutionary techniques for feature engineering

This work proposes an automatic methodology for modeling complex systems. Our methodology is based on the combination of Grammatical Evolution and classical regression to obtain an optimal set of features that take part of a linear and convex model. This technique provides both Feature Engineering and Symbolic Regression in order to infer accurate models with no effort or designer's expertise requirements. As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. These facilities consume from 10 to 100 times more power per square foot than typical office buildings. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. For this case study, our methodology minimizes error in power prediction. This work has been tested using real Cloud applications resulting on an average error in power estimation of 3.98%. Our work improves the possibilities of deriving Cloud energy efficient policies in Cloud data centers being applicable to other computing environments with similar characteristics.

A system of systems engineering approach to design new smarter and energy efficient products

The concept of smartness of energy efficient products and systems from a business perspective has been investigated by several authors. The problem of understanding, designing, engineering and governing these technologies requires new concepts. The emergence of these modern technologies causes a myriad of interconnected systems, which are working together to satisfy the necessities of modern life. The problem of understanding, designing, engineering, and governing these technologies requires new concepts. Development of System of System Engineering (SoSE) is an attempt by the systems engineering and science community to fulfill this requirement.

Emotions and the Engineering of Adaptiveness in Complex Systems

A major challenge in the engineering of complex and critical systems is the management of change, both in the system and in its operational environment. Due to the growing of complexity in systems, new approaches on autonomy must be able to detect critical changes and avoid their progress towards undesirable states. We are searching for methods to build systems that can tune the adaptability protocols. New mechanisms that use system-wellness requirements to reduce the influence of the outer domain and transfer the control of uncertainly to the inner one. Under the view of cognitive systems, biological emotions suggests a strategy to configure value-based systems to use semantic self-representations of the state. A method inspired by emotion theories to causally connect to the inner domain of the system and its objectives of wellness, focusing on dynamically adapting the system to avoid the progress of critical states. This method shall endow the system with a transversal mechanism to monitor its inner processes, detecting critical states and managing its adaptivity in order to maintain the wellness goals. The paper describes the current vision produced by this work-in-progress.

Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems

Emotion is generally argued to be an influence on the behavior of life systems, largely concerning flexibility and adaptivity. The way in which life systems acts in response to a particular situations of the environment, has revealed the decisive and crucial importance of this feature in the success of behaviors. And this source of inspiration has influenced the way of thinking artificial systems. During the last decades, artificial systems have undergone such an evolution that each day more are integrated in our daily life. They have become greater in complexity, and the subsequent effects are related to an increased demand of systems that ensure resilience, robustness, availability, security or safety among others. All of them questions that raise quite a fundamental challenges in control design. This thesis has been developed under the framework of the Autonomous System project, a.k.a the ASys-Project. Short-term objectives of immediate application are focused on to design improved systems, and the approaching of intelligence in control strategies. Besides this, long-term objectives underlying ASys-Project concentrate on high order capabilities such as cognition, awareness and autonomy. This thesis is placed within the general fields of Engineery and Emotion science, and provides a theoretical foundation for engineering and designing computational emotion for artificial systems. The starting question that has grounded this thesis aims the problem of emotion--based autonomy. And how to feedback systems with valuable meaning has conformed the general objective. Both the starting question and the general objective, have underlaid the study of emotion, the influence on systems behavior, the key foundations that justify this feature in life systems, how emotion is integrated within the normal operation, and how this entire problem of emotion can be explained in artificial systems. By assuming essential differences concerning structure, purpose and operation between life and artificial systems, the essential motivation has been the exploration of what emotion solves in nature to afterwards analyze analogies for man--made systems. This work provides a reference model in which a collection of entities, relationships, models, functions and informational artifacts, are all interacting to provide the system with non-explicit knowledge under the form of emotion-like relevances. This solution aims to provide a reference model under which to design solutions for emotional operation, but related to the real needs of artificial systems. The proposal consists of a multi-purpose architecture that implement two broad modules in order to attend: (a) the range of processes related to the environment affectation, and (b) the range or processes related to the emotion perception-like and the higher levels of reasoning. This has required an intense and critical analysis beyond the state of the art around the most relevant theories of emotion and technical systems, in order to obtain the required support for those foundations that sustain each model. The problem has been interpreted and is described on the basis of AGSys, an agent assumed with the minimum rationality as to provide the capability to perform emotional assessment. AGSys is a conceptualization of a Model-based Cognitive agent that embodies an inner agent ESys, the responsible of performing the emotional operation inside of AGSys. The solution consists of multiple computational modules working federated, and aimed at conforming a mutual feedback loop between AGSys and ESys. Throughout this solution, the environment and the effects that might influence over the system are described as different problems. While AGSys operates as a common system within the external environment, ESys is designed to operate within a conceptualized inner environment. And this inner environment is built on the basis of those relevances that might occur inside of AGSys in the interaction with the external environment. This allows for a high-quality separate reasoning concerning mission goals defined in AGSys, and emotional goals defined in ESys. This way, it is provided a possible path for high-level reasoning under the influence of goals congruence. High-level reasoning model uses knowledge about emotional goals stability, letting this way new directions in which mission goals might be assessed under the situational state of this stability. This high-level reasoning is grounded by the work of MEP, a model of emotion perception that is thought as an analogy of a well-known theory in emotion science. The work of this model is described under the operation of a recursive-like process labeled as R-Loop, together with a system of emotional goals that are assumed as individual agents. This way, AGSys integrates knowledge that concerns the relation between a perceived object, and the effect which this perception induces on the situational state of the emotional goals. This knowledge enables a high-order system of information that provides the sustain for a high-level reasoning. The extent to which this reasoning might be approached is just delineated and assumed as future work. This thesis has been studied beyond a long range of fields of knowledge. This knowledge can be structured into two main objectives: (a) the fields of psychology, cognitive science, neurology and biological sciences in order to obtain understanding concerning the problem of the emotional phenomena, and (b) a large amount of computer science branches such as Autonomic Computing (AC), Self-adaptive software, Self-X systems, Model Integrated Computing (MIC) or the paradigm of models@runtime among others, in order to obtain knowledge about tools for designing each part of the solution. The final approach has been mainly performed on the basis of the entire acquired knowledge, and described under the fields of Artificial Intelligence, Model-Based Systems (MBS), and additional mathematical formalizations to provide punctual understanding in those cases that it has been required. This approach describes a reference model to feedback systems with valuable meaning, allowing for reasoning with regard to (a) the relationship between the environment and the relevance of the effects on the system, and (b) dynamical evaluations concerning the inner situational state of the system as a result of those effects. And this reasoning provides a framework of distinguishable states of AGSys derived from its own circumstances, that can be assumed as artificial emotion.