Cognitive and innovative computation paradigms for big data and cloud computing applications

In advanced distributed computer systems, one of the most important approaches for big data analysis and Cloud information management is innovative analysis protocols and cognitive system application. Such solutions should facilitate not only to perform the big data analytics tasks but also intelligent and secure data and services management, especially using innovative machine and computational intelligence approaches. Presently, there is a great demand to efficiently store and semantically analyze a great amount of information, originated from different sources, as well as manage such information in secure manner for different application in ubiquitous and mobile computing. Possibility of creation and development of such computation technologies was connected with introduction of new computing paradigms and information systems, which allows to combine cognitive informatics with security areas, as well as big data and cloud computing technologies. These subjects, as well as others, connected with innovative computational models for security protocols and distributed data analysis were form the subject of CogInnov2018 Special Issue. The main topics of this Special Issue include the new computational approaches dedicated to Big data and Cloud security, cognitive information systems with their applications, personalized cryptography and biometrics security, ambient intelligence for Big data analysis, innovative security and privacy protocols, security of Cognitive Information systems, visual cryptography and secret data management, computational intelligence in services management, security and privacy for mobile and distributed systems, visual and cognitive CAPTCHA, advanced steganography systems, behavioral features in security solutions, as well as cognitive approaches for Big data analytics. This Special Issue selected and accepted 9 papers, which presents high quality scientific research and interesting cutting-edge topics. The paper entitled “Packer Identification Method Based on Byte Sequences” by B.H. Jung, S.I. Bae, Ch. Choi, and E.G. Im1 proposes a new packer identification method that uses two types of statistical features. These features are generated using encrypted data and feature from byte frequency distributions. To extract an encrypted data, the authors used on entropy scoring-based method. Experimental results are very optimistic and can identify packers with the accuracy approximately 91.6% on average, when the Random Forest algorithm is used. In the paper “Compression-based Steganography” by B. Carpentieri, A. Castiglione, A. De Santis, F. Palmieri, and R. Pizzolante2 new idea of steganography was proposed. The main aspects of this paper consists compression based steganography. The authors exploited the hierarchical structure of a compressed archive by using new algorithms and protocols to propose their solution. The proposed algorithm can be useful in many everyday situations, for example, alerts and data protection. The paper entitled “Personal Identification Study for Touchable devices with ECG” by H. Ko, S.B. Pan, and L. Mesicek3 describes new points of view for personal identification processes. The authors proposed analysis by ECG signal and the users' signal trend. These trends can be describe by analysis of selected values, as well as ARL, AFL, ART, ADP, calrms, calstd, and stdRR. These items can makes a pattern graph and threshold scope for a user. In the paper “Information flow control in object-based peer-to-peer publish/subscribe systems” by S. Nakamura, T. Enokido, and M. Takizawa4 the P2PPSO (P2P Publish/Subscribe with Object concept) system was presented. These systems can use also TOBS protocols, in which illegal objects are not delivered to the target peer. Evaluation processes show number of event messages carry illegal objects in the TOBS protocol. The paper entitled “A Multilevel Graph Approach for Rainfall Forecasting: A preliminary Study Case on London area” by F. Clarizia, F. Colace, M. De Santo, M. Lombardi, F. Pascale, D. Santaniello, and A. Tuker5 presents innovative approach such as the Multilevel Graph Approach to the hydrological analysis. It is possible by using the proposed methodology at the service of Early Warning Systems. The presented methods with Bayes Networks, Ontologies, and the Context Dimension Tree were successfully used in the prediction of the road accident risks within a specific borough of London. In the paper “Effect of Size of Giant Component for actor node selection in WSANs: A comparison study” by D. Elmazi, M. Cuka, M. Ikeda, and L. Barolli6 two Fuzzy Based Systems FBS1 & FBS2 for actor selection in WSANs were presented. The proposed systems decided whether the person will be selected for the required job/or not, based on information supplied by sensors and condition. The authors evaluated proposed solution by computer simulations. Comparison of FBS1 and FBS2 shows that Size of Giant Component parameter that was used in FBS2 gives better results due to the growth of SGC parameter. The paper entitled “A MapReduce based Modified Grey Wolf Optimizer for QoS-aware Big Service Composition” by B. Bhaskar, Ch. Jatoth, G.R. Gangadharan, and U. Fiore7 presents new efficient QoS-aware Big service composition by applying a MapReduce based on Modified GreyWolf Optimizer (MR-MGWO). It explores more search space dedicated to multidimensional environment. The authors present optimal balance of exploration and exploitation in their solution that enhances the convergence rate and minimizes the computational time. The authors show that the performance of MR-MGWO is superior to other similar approaches for solving Big service composition. In the paper “Cognitive security paradigm for cloud computing applications” by L. Ogiela and M.R. Ogiela8 new paradigms of data security were described. The presented algorithms are dedicated to enhancing the cryptographic data sharing schemes. New proposition was enriched by linguistic and biometric protocols to guarantee protection of the shared data by means of biometric labeling or by means of user verification with the application of meaning interpretation of individual secret parts. The novelty in this schemes is the application of cognitive algorithms to describe correctly the shared data. The paper entitled “JPEG Steganography with Particle Swarm Optimization Accelerated by AVX” by V. Snasel, P. Kromer, J. Safarik, and J. Platos9 presents new digital steganography aspects. These types of steganography aim at hiding secret data in digital form transmitted over insecure channels. The authors presented the JPEG format in digital protocols often used as cover objects in digital steganography. Optimization methods improved the properties of data with embedded secret. Moreover, it is necessary to introduce additional computational complexity in the processing stage. AVX instructions available in modern CPUs were used to accelerate secret parallel operations of image steganography. This Special Issue has described new aspects of cognitive and innovative computation paradigms dedicated especially for novel big data and cloud computing applications. Cognitive paradigms were oriented by semantic aspects of data description, analysis, interpretation, and security. Innovative paradigms were presented with reference to new possibilities and areas of application of the discussed solutions. A wide range of the presented topics indicates an extraordinary variety of analysis processes and data protection. The issues discussed were mostly open topics, which indicates the possibility of further development of the discussed topics. LIDIA OGIELA Professor Lidia Ogiela, Ph.D. – computer scientist, mathematician, economist. She received Master of Science in mathematics from the Pedagogical University in Krakow, Poland, and Master of Business Administration in management and marketing from AGH University of Science and Technology in Krakow, Poland, both in 2000. In 2005, she was awarded the title of Doctor of Computer Science and Engineering at the Faculty of Electrical, Automatic Control, Computer Science and Electronic Engineering of the AGH University of Science and Technology for her thesis and research on cognitive analysis techniques and its application in intelligent information systems. In 2016, she received habilitation (docent title) at VSB Technical University of Ostrava in Czech Republic. In 2018, she was awarded the second title of Doctor of Computer Science and Engineering at the Hosei University of Tokyo, Japan, for her thesis and research oriented on human centered computing for future generation computer systems. She is an author a more than 190 scientific international publications on information systems, cognitive analysis techniques, biomedical engineering, security techniques, and computational intelligence methods. She is a member of few prestigious international scientific societies such as SIAM – Society for Industrial and Applied Mathematics, IEEE Computer Society, CSS Cognitive Science Society, and SPIE – The International Society for Optical Engineering. Currently, she is at the professor position and works at Institute of Computer Science at Pedagogical University of Krakow, Poland. I would like to specially thank the Editor in Chief Professor Geoffrey Fox for the opportunity to run this Special Issue and for his great kindness and help, as well as unique opportunity to present new and interesting scientific works in Concurrency and Computation: Practice and Experience Journal. I would like to thank all the authors who have submitted their papers to this Special Issue. I congratulate the authors whose works have been accepted and positively evaluated. These works bring a great contribution to the development of the computer science and show new directions of research works as well as an innovative view of the previously developed experience and science.