Search

Bigdata handles the datasets which exceeds the ability of commonly used software tools for storing, sharing and processing the data. Classification of workload is a major issue to the Big Data community namely job type evolution and job size evolution. On the basis of job type, job size and disk performance, clusters are been formed with data node, name node and secondary name node. To classify the workload and to perform the job scheduling, mapreduce algorithm is going to be applied. Based on the performance of individual machine, workload has been allocated. Mapreduce has two phases for processing the data: map and reduce phases. In map phase, the input dataset taken is splitted into keyvalue pairs and an intermediate output is obtained and in reduce phase that key value pair undergoes shuffle and sort operation. Intermediate files are created from map tasks are written to local disk and output files are written to distributed file system of Hadoop. Scheduling of different jobs to different disks are identified after completing mapreduce tasks. Johnson algorithm is used to schedule the jobs and used to find out the optimal solution of different jobs. It schedules the jobs into different pools and performs the scheduling. The main task to be carried out is to minimize the computation time for entire jobs and analyze the performance using response time factors in hadoop distributed file system. Based on the dataset size and number of nodes which is formed in hadoop cluster, the performance of individual jobs are identified Keywords — hadoop; mapreduce; johnson algorithm

Without a proper restriction on mappings, virtually any system could be seen as implementing any computation. That would not allow characterization of systems in terms of implemented computations and is not compatible with a computationalist philosophy of mind. Information-based criteria for independence of substates within structured states are proposed as a solution. Objections to the use of requirements for transitions in counterfactual states are addressed, in part using the partial-brain argument as a general counterargument to neural replacement arguments.

Without a proper restriction on mappings, virtually any system could be seen as implementing any computation. That would not allow characterization of systems in terms of implemented computations and is not compatible with a computationalist philosophy of mind. Information-based criteria for independence of substates within structured states are proposed as a solution. Objections to the use of requirements for transitions in counterfactual states are addressed, in part using the partial-brain argument as a general counterargument to neural replacement arguments.

Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs) in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life’s origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important question of the minimal living organism by defining the functional relations that would have made life possible in such a minimal system- a grandad and grandma of all living organisms on Earth.

A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Łukasiewicz Topos with an N-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

he International Conference on Information and Communication Systems (ICICS 2011) is a forum for scientists, engineers, and practitioners to present their latest research results, ideas, developments, and applications in all areas of Computer and Information Sciences.

Cognitive functions such as a perception, thinking and acting are based on the working of the brain, one of the most complex systems we know. The traditional scientific methodology, however, has proved to be not sufficient to understand the relation between brain and cognition. The aim of this paper is to review an alternative methodology – nonlinear dynamical analysis – and to demonstrate its benefit for cognitive neuroscience in cases when the usual reductionist method fails.

In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the k-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.

In this paper we present a novel strategy to discover the community structure of (possibly, large) networks. This approach is based on the well-know concept of network modularity optimization. To do so, our algorithm exploits a novel measure of edge centrality, based on the k-paths. This technique allows to efficiently compute a edge ranking in large networks in near linear time. Once the centrality ranking is calculated, the algorithm computes the pairwise proximity between nodes of the network. Finally, it discovers the community structure adopting a strategy inspired by the well-known state-of-the-art Louvain method (henceforth, LM), efficiently maximizing the network modularity. The experiments we carried out show that our algorithm outperforms other techniques and slightly improves results of the original LM, providing reliable results. Another advantage is that its adoption is naturally extended even to unweighted networks, differently with respect to the LM.

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.

Online Social Networks (OSN) during last years acquired a huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a signi�cant sample of data re ecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities between the developing of OSN and real-life social networks.

Making decisions about the structure of a future military fleet is challenging. Several issues need to be considered, including multiple competing objectives and the complexity of the operating environment. A particular challenge is posed by the various types of uncertainty that the future holds. It is uncertain what future events might be encountered and how fleet design decisions will influence these events. In order to assist strategic decision-making, an analysis of future fleet options needs to account for conditions in which these different uncertainties are exposed. It is important to understand what assumptions a particular fleet is robust to, what the fleet can readily adapt to, and what conditions present risks to the fleet. We call this the analysis of a fleet’s strategic positioning. Our main aim is to introduce a framework that captures information useful to a decision maker and defines the concepts of robustness and adaptability in the context of future fleet design. We demonstrate our conceptual framework by simulating an air transportation fleet problem. We account for uncertainty by employing an explorative scenario-based approach. Each scenario represents a sampling of different future conditions and different model assumptions. Proposed changes to a fleet are then analysed based on their influence on the fleet’s robustness, adaptability, and risk to different scenarios.

In the field of artificial life there is no agreement on what defines ‘autonomy’. This makes it difficult to measure progress made towards understanding as well as engineering autonomous systems. Here, we review the diversity of approaches and categorize them by introducing a conceptual distinction between behavioral and constitutive autonomy. Differences in the autonomy of artificial and biological agents tend to be marginalized for the former and treated as absolute for the latter. We argue that with this distinction the apparent opposition can be resolved.

In this study we investigated the effects of different morphological configurations on a robot swinging task using van der Pol oscillators. The task was examined using two separate degrees of freedom (DoF), both in the presence and absence of neural entrainment. Neural entrainment stabilises the system, reduces time-to-steady state and relaxes the requirement for a strong coupling with the environment in order to achieve mechanical entrainment. It was found that staged release of the distal DoF does not have any benefits over using both DoF from the onset of the experimentation. On the contrary, it is less efficient, both with respect to the time needed to reach a stable oscillatory regime and the maximum amplitude it can achieve. The same neural architecture is successful in achieving neuromechanical entrainment for a robotic walking task.

Operation of autonomic communication networks with complicated user-oriented functions should be described as unreduced many-body interaction process. The latter gives rise to complex-dynamic behaviour including fractally structured hierarchy of chaotically changing realisations. We recall the main results of the universal science of complexity (http://cogprints.org/4471/) based on the unreduced interaction problem solution and its application to various real systems, from nanobiosystems (http://cogprints.org/4527/) and quantum devices to intelligent networks (http://cogprints.org/4114/) and emerging consciousness (http://cogprints.org/3857/). We concentrate then on applications to autonomic communication leading to fundamentally substantiated, exact science of intelligent communication and software. It aims at unification of the whole diversity of complex information system behaviour, similar to the conventional, "Newtonian" science order for sequential, regular models of system dynamics. Basic principles and first applications of the unified science of complex-dynamic communication networks and software are outlined to demonstrate its advantages and emerging practical perspectives.

Universal and rigorously derived concept of dynamic complexity shows that any system of interacting components, including society and civilisation, exists only as a process of highly inhomogeneous, qualitative development of its complexity. Modern state of civilisation corresponds to the end of unfolding of a big enough level of complexity. Such exhausted, totally "replete" structure cannot be sustainable in principle and shows instead increased instability, realising its inevitable replacement by a new kind of structure with either low or much higher level of complexity (degrading or progressive development branch, respectively). Unrestricted sustainability can emerge only after transition to the next, superior level of civilisation complexity, which implies qualitative and unified changes in all aspects of life, including knowledge, production, social organisation, and infrastructure (cogprints.org/4113). These changes are specified by the rigorous analysis of underlying interaction processes. The unitary, effectively one-dimensional and rigidly fixed kind of thinking, knowledge, and social structure at the current level of complexity will be replaced by "dynamically multivalued", intrinsically creative kind of structure at the forthcoming superior level of development and consciousness. We propose mathematically rigorous description of unreduced civilisation complexity development, including universal criterion of progress. One obtains thus a working basis for the causally complete, objectively exact and reliable development science and futurology.

Dynamics of arbitrary communication system is analysed as unreduced interaction process. The applied generalised, universally nonperturbative method of effective potential reveals the phenomenon of dynamic multivaluedness of competing system configurations forced to permanently replace each other in a causally random order, which leads to universally defined dynamical chaos, complexity, fractality, self-organisation, and adaptability. We demonstrate the origin of huge, exponentially high efficiency of the unreduced, complex network dynamics and specify the universal symmetry of complexity as the fundamental guiding principle for creation and control of such qualitatively new kind of networks and devices. The emerging intelligent communication paradigm and its practical realisation in the form of knowledge-based networks involve the properties of true, unreduced intelligence and consciousness (http://cogprints.ecs.soton.ac.uk/archive/00003857/) appearing in the complex (multivalued) network dynamics and results.

Dynamics of arbitrary communication system is analysed as unreduced interaction process. The applied generalised, universally nonperturbative method of effective potential reveals the phenomenon of dynamic multivaluedness of competing system configurations forced to permanently replace each other in a causally random order, which leads to universally defined dynamical chaos, complexity, fractality, self-organisation, and adaptability. We demonstrate the origin of huge, exponentially high efficiency of the unreduced, complex network dynamics and specify the universal symmetry of complexity as the fundamental guiding principle for creation and control of such qualitatively new kind of networks and devices. The emerging intelligent communication paradigm and its practical realisation in the form of knowledge-based networks involve the properties of true, unreduced intelligence and consciousness (http://cogprints.ecs.soton.ac.uk/archive/00003857/) appearing in the complex (multivalued) network dynamics and results.

In this paper we present a deeper analysis than has previously been carried out of a selective attention problem, and the evolution of continuous-time recurrent neural networks to solve it. We show that the task has a rich structure, and agents must solve a variety of subproblems to perform well. We consider the relationship between the complexity of an agent and the ease with which it can evolve behavior that generalizes well across subproblems, and demonstrate a shaping protocol that improves generalization.

In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We compared the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN). Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Results of a questionnaire given to the subjects confirmed that the FFNN gives better mental impressions, especially from the aspect of operability. When the robot used a consolidation-learning algorithm using the rehearsal outputs of the RNN, the performance improved even when interactive learning continued for a long time. The questionnaire results then also confirmed that the subject's mental impressions of the RNN improved significantly. The dynamical systems analysis of RNNs support these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions.

Physical skills such as playing the musical instrument are hard to learn and take long time to master. To investigate what makes physical skills so dificult to learn and how we can evaluate the level of skills, we examined the kneading in ceramic art, an action to prepare the clay for shaping and studied the physical movements of both the learners and experts. Kneading is an appropriate sample of physical skill for studying the body movement because all the parts of body need to be coordinated to accomplish the task. The task is not hopelessly difficult for the complete novices to follow the instruction although the end result is not satisfactory. It normally takes about three years to master the kneading skill. It is also relatively easy to judge how well the subjects accomplished the task by observing the shape of the clay. After careful examination of the movement using video tapes, we employed a motion capture device to collect the data of movement from an expert, an experienced person, and three novices. We discovered that the expert elegantly splits his body into two parts, torso and arms, and effectively coordinates these two parts while kneading the clay.

A compact integrated CMOS circuit for temporal differentiation is presented. It consists of a high-gain inverting amplifier, an active non-linear transconductance and a capacitor and requires only 4 transistors in its minimal configuration.The circuit provides two rectified current outputs that are proportional to the temporal derivative of the input voltage signal. Besides the compactness of its design, the presented circuit is not dependent on the DC-value of the input signal, as compared with known integrated differentiator circuits. Measured chip results show that the circuit operates on a large input frequency range for which it provides nearideal temporal differentiation. The circuit is particularly suited for focal-plane implementations of gradient-based visual motion systems.

The main objective of the present work is to develop a framework for modelling and controlling of a real world multi-input and multi-output (MIMO) continuously drifting metallurgical process, which is shown to be a complex system. A small change in the properties of the charge composition may lead to entirely different outcome of the process. The newly emerging paradigm of soft-computing or Hybrid Computational Intelligence Systems approach which is based on neural networks, fuzzy sets, genetic algorithms and chaos theory has been applied to tackle this problem In this framework first a feed-forward neuro-model has been developed based on the data collected from a working Submerged Arc Furnace (SAF). Then the process is analysed for the existence of the chaos with the chaos theory (calculating indices like embedding dimension, Lyapunov exponent etc). After that an effort is made to evolve a fuzzy logic controller for the dynamical process using combination of genetic algorithms and the neural networks based forward model to predict the system’s behaviour or conditions in advance and to further suggest modifications to be made to achieve the desired results.

Starting with fewer degrees of freedom has been shown to enable a more efficient exploration of the sensorimotor space. While not necessarily leading to optimal task performance, it results in a smaller number of directions of stability, which guide the coordination of additional degrees of freedom. The developmental release of additional degrees of freedom is then expected to allow for optimal task performance and more tolerance and adaptation to environmental interaction. In this paper, we test this assumption with a small-sized humanoid robot that learns to swing under environmental perturbations. Our experiments show that a progressive release of degrees of freedom alone is not sufficient to cope with environmental perturbations. Instead, alternate freezing and freeing of the degrees of freedom is required. Such finding is consistent with observations made during transitional periods in acquisition of skills in infants.

Human sound systems are invariably phonemically coded. Furthermore, phoneme inventories follow very particular tendancies. To explain these phenomena, there existed so far three kinds of approaches : ``Chomskyan''/cognitive innatism, morpho-perceptual innatism and the more recent approach of ``language as a complex cultural system which adapts under the pressure of efficient communication''. The two first approaches are clearly not satisfying, while the third, even if much more convincing, makes a lot of speculative assumptions and did not really bring answers to the question of phonemic coding. We propose here a new hypothesis based on a low-level model of sensory-motor interactions. We show that certain very simple and non language-specific neural devices allow a population of agents to build signalling systems without any functional pressure. Moreover, these systems are phonemically coded. Using a realistic vowel articulatory synthesizer, we show that the inventories of vowels have striking similarities with human vowel systems.

The global dynamics, a novel concept for design of human/humanoid behavior is proposed. The principle of this concept is to exploit the body dynamics and apply control input only where it is necessary. Within the phase space of the body dynamics, there are many stable and unstable mani-folds coexist. Then if we analysed its structure and obtained a map in sufficient resolution, it may be possible to realise a motion by exploiting stable regions for reducing control input and unstable regions for switching between stable regions. Also, we expect an emergence of symbols within the dynamics, as the series of points where control input should be adopted. This feature realises higher level description and makes adaptation behavior easier. We are studying from two aspects, the motion capture experiment and dynamical simulation of simple elastic robot. The former supports that above assumption and the latter supports the exploiting the dynamical stability is useful.

We provide the first classification of different types of Random Boolean Networks (RBNs). We study the differences of RBNs depending on the degree of synchronicity and determinism of their updating scheme. For doing so, we first define three new types of RBNs. We note some similarities and differences between different types of RBNs with the aid of a public software laboratory we developed. Particularly, we find that the point attractors are independent of the updating scheme, and that RBNs are more different depending on their determinism or non-determinism rather than depending on their synchronicity or asynchronicity. We also show a way of mapping non-synchronous deterministic RBNs into synchronous RBNs. Our results are important for justifying the use of specific types of RBNs for modelling natural phenomena.

Traditionally, cognition has been considered as a ``mental processes'' only domain. Recently, however, there is a growing conscensus that cognition should be ``embodied'', i.e. it emerges from physical interaction with the world through a body with given perceptual and motoric abilities. Terms like ``emergence'', ``enaction'', ``grounding'', and ``situatedness'' are often used, but little attention is being paid to actually understanding the neural dynamics correlates of an emergence of cognition. Nor is hardly being investigated how the structure of the body-environment coupling is perceived and manipulated by our brain. It is as if talking about neural dynamics would somehow throw us backwards to the old cognitivist approach. In this paper we present a balanced view, in which we try to keep things in their respective place.

THERAPEUTIC IMPLICATIONS OF COMPUTER MODELS OF BRAIN ACTIVITY FOR ALZHEIMER DISEASE.

This paper addresses an original scheme based on feedforward neural networks, aimed at modelling queueing systems fed with bursty traffic. A neural network is trained to anticipate the average number of waiting cells, the cell loss rate and the coefficient of variation of the cell inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the cell inter-arrival time. Our long-term goal is the design of a preventive control strategy in B-ISDN networks based on distributed neural networks modelling each queueing system located at the input and output ports of the switching facilities. To illustrate the potential of neural networks for modelling queueing systems, a neural network is successfully trained to model OnOff/D/1/c, OnOff/OnOff/1/c and multi-OnOff/D/1/c queueing systems.