 |
The Ninth International Conference on Future Computational Technologies and Applications FUTURE COMPUTING 2017 |
| Tutorials |
T1. Secure Software Engineering in the Cloud
Prof. Dr. Aspen Olmsted, College of Charleston, USA
T2. The Dynamical Theory of Information and the Basis for Natural-Constructive Approach for Modeling a Cognitive Process
Dr. Olga Dmitrievna Chernavskaya, Lebeved Physical Institute – Moscow, Russia
T3. Theory and Practice – A Discipline-Independent Pattern Approach for Capturing and Communicating Problem Solutions.
From Mining, to Iteration, to Finalization
Alexander Mirnig, University of Salzburg, Austria
Detailed Information
T1. Secure Software Engineering in the Cloud
Prof. Dr. Aspen Olmsted, College of Charleston, USA
In his talk, Olmsted will investigate the problem of developing secure cloud-based enterprise applications. Consistency, availability, and durability are investigated for Web Service (WS) transactions. He proposes an approach that matches the availability of the popular lazy replica update propagation method while increasing durability and consistency. His replica update propagation method is called the “Buddy System”, which requires that updates are preserved synchronously in two replicas. The first implementation schedules fine-grained WS transactions. In these transactions, each activity is a low-level database operation. Later, he considers each transaction as a black box, with only the corresponding Metadata, expressed as UML specifications, as transaction semantics. He refers to these WS transactions as coarse-grained WS transactions. The “Buddy System” can handle these coarse-grained WS transactions, using UML stereotypes that allow scheduling semantics to be embedded into the design model.
Dr. Olmsted shows that his approach guarantees one-copy serializability, matches the performance of the lazy update propagation methods, and increases durability in the presence of hardware failures.
The talk will conclude with current work investigating consistency guarantees for integration of external systems, cloud-based data models, and payment security.
T2. The Dynamical Theory of Information and the Basis for Natural-Constructive Approach for Modeling a Cognitive Process
Dr. Olga Dmitrievna Chernavskaya, Lebeved Physical Institute – Moscow, Russia
The Dynamical Theory of information (DTI) has been proposed and elaborated at the end of XX century by I. Prigogine (“The End of Certainty”, 1997), H. Haken (“Information and Self-Organization: A macroscopic approach to complex systems”, 2000), and D. S. Chernavskii (“Synergetic and Information”, 2004). This theory has greatly influenced the development of modeling the human cognitive process and to understanding of mental mechanisms. Indeed, information represents specific object of dual nature: it has as material, as well as virtual components, just as human thinking process. It is realized by material brain neurons, but results in quite ‘spiritual’ phenomena like consciousness and self-appraisal (Mind). The relation between the “Brain” and the “Mind” represents one of the most intriguing challenges of XXI century (so called “problem of Explanatory Gap”).
DTI is based on the Quastler’s definition of information as “memorized choice of one option among several similar ones”. This definition does not contradict to other popular ones, but provides constructive answers to many enigmas associated with origin and evolution of information. In particular, it gives possibility of division into Objective and Subjective information in dependence on who makes the choice. The choice made by Nature corresponds to Objective information, which corresponds to the laws of physics, biology, etc.; this choice obeys the energy conservation low and should be the most efficient (better than others) one. On the contrary, Subjective Information (or “Conventional Information”) represents the choice made by the collective of living subjects as a result of their interaction (struggle, competition, cooperation, convention, etc.). The most pronounced example of such type of information is a language. This choice should not (and often could not) be the best one (it is quite senselessly to debate, what language is better – English or Russian); it should be done and memorized and then, it becomes an element of social organization and culture. This is the choice made by the Brain neurons that provides the possibility to generate personal Subjective information, i.e., the individuality (the “Mind”).
Another very important inference of DTI is the analysis of the role of random (chaotic) element (the noise). It was shown that generation of new information in the given system requires mandatory the presence of noise. Under DTI, the noise is treated not as annoying disturbance (as it is in many physical and technical areas), but as important and necessary member of all the processes associated with creativity.
The Natural-Constructive Approach (NCA) to modeling the cognitive process is based, in particular, on DTI. It provides the possibility to simulate and interpret such inherent human features as individuality, unpredictable decisions, intuition, emotionality, etc. In contrast to Artificial Intelligence (AI) field that is aimed to creating artificial systems, which could solve certain set of problems better than humans (more efficiently, faster, more reliable, etc. ), the subject of NCA is modeling and interpreting just the human way of thinking.
T3. Theory and Practice – A Discipline-Independent Pattern Approach for Capturing and Communicating Problem Solutions.
From Mining, to Iteration, to Finalization
Alexander Mirnig, University of Salzburg, Austria
Patterns, as introduced by Christopher Alexander (1979, 1997) to document problem solution in the field of architecture, have been recognised by several other disciplines as powerful tools for documentation of individual solutions to reoccurring problems. One of these disciplines is software engineering (Gamma et al. 1994), another is Human-Computer Interaction (HCI). In this tutorial, a pattern approach from the field of HCI, which was developed to be used no only in HCI, but any discipline where documented problem solutions can be useful, is presented.
In the first part of the tutorial, the theoretical foundations of Patterns are presented. Unlike guidelines, manuals, and other general means of guidance, Patterns focus on very individual problems and their surrounding context. This makes their scope more limited, but allows them to cover specific issues that more general guidance can not reach. In addition, this higher level of detail makes Pattern solutions more suitable for individuals with medium to low expertise in their respective field. In this part, Patterns are categorized into three types and minimum requirements for complete Patterns are defined.
The second part of the tutorial focuses on practical application of the Pattern approach. Initial problem definition or “pattern mining” is presented, together with further iteration steps in the pattern writing process (as workshops with different foci) and a standardized rating scale to facilitate iterations and finalization. These will be illustrated with examples (both positive and negative) from the speaker’s own work on Patterns in HCI.
The tutorial concludes with an outlook on formalizing the Pattern approach and additional challenges for Pattern database integration. The tutorial is intended for interested individuals from all disciplines and prior knowledge about Patterns is not required.
