Dynamics and Complexity of Computrons

Finite State Machines (FSMs) are widely used for analysis and synthesis of digital components of control systems. In order to take into account time aspects, timed FSMs are considered. In this paper, we address the problem of deriving a parallel composition of two types of Timed Finite State Machines (TFSM), namely, FSMs with timeouts and FSMs with timed guards. These two TFSM types are not interchangeable and are particular cases of a more general TFSM model that has timeouts and timed guards. We also assume that all of considered TFSMs have output delays (output timeouts). When considering the parallel composition, component FSMs work in the dialog and the composition produces an external output when interaction between components is finished. In this work, it is shown that in the general case, unlike classical FSMs, a slow environment and the absence of livelocks are not enough for describing the behavior of a composition by a complete deterministic FSM with a single clock. The latter occurs when inputs can be applied to TFSMs not only at integer time instances but also at rational. A class of systems for which the behavior can be described by a complete deterministic TFSM is specified. Those are systems where both component TFSMs are participating in the dialog when an external input is applied; a sequential composition of TFSMs is a particular case of such composition.

Mutation testing from probabilistic and stochastic finite state machines

Many signal processing systems make use of event driven mechanisms-typically based on finite state machines (FSMs)-to control the operation of the computationally intensive (data flow) parts. The state machines in turn are often fueled by external inputs as well as by feedback from the signal processing portions of the system. Packet-based transmission systems are a good example for such a close interaction between data and control flow. For a smooth design flow with a maximum degree of modularity it is of crucial importance to be able to model the complete functionality of the system, containing both control and data flow, within one single design environment. While the degree of abstraction should be sufficiently high to model and simulate efficiently, the link to implementation has to be fully supported. For these reasons we developed a computational model that integrates the specification of control and data flow. It combines the notion of multirate dynamic data flow graphs with event driven process activation. Thus, it maintains the flexibility and expressive power of data flow representations while enabling designers to efficiently control these operations by incorporating control automata that may have been designed using protocol compilers or state machine tools.

Cloud service is a new trend in constructing enterprise applications. However, implementing cloud services is time consuming, costly, and error prone. Moreover, there are concerns about the isolation and flexibility of multitenancy cloud services. To solve these problems, we propose a novel and easy approach: CodingBlind, which automatically generates cloud services from the most commonly used printed forms and graphical business process modeling notation (BPMN). CodingBlind provides a natural solution for the deployment and migration of systems in clouds. First, printed forms are mapped onto entity-relationship models (ER models) to generate data services. States of data and a finite state machine (FSM) are constructed from the BPMN. Based on the FSM and the ER models, quadruple models for cloud services are generated. Then, a complete system package is generated according to the quadruple model. Finally, all of the components are packaged and deployed to a cloud. CodingBlind is easy to use because it only takes printed forms and BPMNs as inputs. Self-defining data and business logic make the generated cloud services flexible. Moreover, the business logic is strictly controlled by the FSM, which provides natural isolation for cloud applications. Case studies and experiments are conducted to assess the proposed approach, and the results show that it is feasible, convenient, and flexible.

We have built an intelligent multimodal interface to capture, understand and correct in real time a complex hand/arm gestures performed inside its workspace. The interface is formed by a commercial vision tracking system, a commercial PC and feedback devices: 3D sound system, a cave like VE and a pair of wireless vibrotactile devices. The interface can capture the upper part limbs kinematics of the user independently of the user's size and high. The interface recognizes complex gestures due a novel recognition methodology based on several machine-learning techniques such as: dynamic k-means, probabilistic neural networks and finite state machines. This methodology is the main contribution of this research Human Hand Computer Interaction research area, its working principle is simple: a gesture is split in several states (a state is an ensemble of variables that define an static position or configuration), the key is obtain the optimal number of states that define

The strength of a dynamic language is also its weakness: run-time flexibility comes at the cost of compile-time predictability. Many of the hallmarks of dynamic languages such as closures, continuations, various forms of reflection, and a lack of static types make many programmers rejoice, while compiler writers, tool developers, and verification engineers lament. The dynamism of these features simply confounds statically reasoning about programs that use them. Consequently, static analyses for dynamic languages are few, far between, and seldom sound. The "abstracting abstract machines" (AAM) approach to constructing static analyses has recently been proposed as a method to ameliorate the difficulty of designing analyses for such language features. The approach, so called because it derives a function for the sound and computable approximation of program behavior starting from the abstract machine semantics of a language, provides a viable approach to dynamic language analysis since all that is required is a machine description of the interpreter. The AAM recipe as originally described produces finite state abstractions: the behavior of a program is approximated as a finite state machine. Such a model is inherently imprecise when it comes to reasoning about the control stack of the interpreter: a finite state machine cannot faithfully represent a stack. Recent advances have shown that higher-order programs can be approximated with pushdown systems. However, such models, founded in automata theory, either breakdown or require significant engineering in the face of dynamic language features that inspect or modify the control stack. In this paper, we tackle the problem of bringing pushdown flow analysis to the domain of dynamic language features. We revise the abstracting abstract machines technique to target the stronger computational model of pushdown systems. In place of automata theory, we use only abstract machines and memoization. As case studies, we show the technique applies to a language with closures, garbage collection, stack-inspection, and first-class composable continuations.

In earlier work, the problem of generating a preset distinguishing sequence from a finite state machine (FSM) was converted into a Boolean formulae to be fed into a SAT solver, with experiments suggesting that such approaches are required as the size of input alphabet grows. In this paper we extend the approach to the minimum height adaptive distinguishing sequence construction problem for partially specified FSMs (PSFMSs), which is known to be an NP-Hard problem. The results of experiments with randomly generated PSFSMs and case studies from the literature show that SAT solvers can perform better than a previously proposed brute-force algorithm.KeywordsModel based testingFinite state machinesAdaptive distinguishing sequence

On the dynamics of chaotic circuits based on memristive diode-bridge with variable symmetry: A case study

Correlated neuronal activity is a natural consequence of network connectivity and shared inputs to pairs of neurons, but the task-dependent modulation of correlations in relation to behavior also hints at a functional role. Correlations influence the gain of postsynaptic neurons, the amount of information encoded in the population activity and decoded by readout neurons, and synaptic plasticity. Further, it affects the power and spatial reach of extracellular signals like the local-field potential. A theory of correlated neuronal activity accounting for recurrent connectivity as well as fluctuating external sources is currently lacking. In particular, it is unclear how the recently found mechanism of active decorrelation by negative feedback on the population level affects the network response to externally applied correlated stimuli. Here, we present such an extension of the theory of correlations in stochastic binary networks. We show that (1) for homogeneous external input, the structure of correlations is mainly determined by the local recurrent connectivity, (2) homogeneous external inputs provide an additive, unspecific contribution to the correlations, (3) inhibitory feedback effectively decorrelates neuronal activity, even if neurons receive identical external inputs, and (4) identical synaptic input statistics to excitatory and to inhibitory cells increases intrinsically generated fluctuations and pairwise correlations. We further demonstrate how the accuracy of mean-field predictions can be improved by self-consistently including correlations. As a byproduct, we show that the cancellation of correlations between the summed inputs to pairs of neurons does not originate from the fast tracking of external input, but from the suppression of fluctuations on the population level by the local network. This suppression is a necessary constraint, but not sufficient to determine the structure of correlations; specifically, the structure observed at finite network size differs from the prediction based on perfect tracking, even though perfect tracking implies suppression of population fluctuations.

Deep learning has become an increasingly heated topic as artificial intelligence is on the rise. At the same time, hardware restrictions in real applications have driven the investigation of combining another rising technique, stochastic computing (SC), with deep learning systems to achieve low power costs. By far, operations successfully implemented include addition, multiplication, inner product, and other more complicated nonlinear functions such as hyperbolic tangent (tanh) with linear finite state machines (FSM). The inner product implementation realizes convolution, a core of neural networks, therefore encouraging SC- based deep learning neural network implementations. Meanwhile, extremely long bitstream lengths are needed to achieve satisfying accuracy, especially for large-scale deep learning systems, causing latency issues. The integration of parallelism is thus considered in an attempt to alleviate the latency issue. In this paper, an optimization to stochastic computing based deep learning system is proposed by introducing parallel FSM implementations to replace serial ones generally used in previous works. Substituting serial linear FSMs with several parallel linear FSMs of the same size yet with shorter bitstream length, parallel FSM aims at trading hardware for processing latency. The accuracy performance of a sample parallel FSM unit is evaluated against its counterpart in serial implementation before a case study verifies that the replacement sacrifices little accuracy, while reducing computing time exponentially in actual deep learning system realizations.

Purpose – External inputs are critical for organisational creativity. In order to bridge different thought worlds and cross-organisational barriers, managers must initiate and motivate boundary spanning processes. The purpose of this paper is to explore how boundary spanners manage creativity projects across organisational boundaries. Design/methodology/approach – The authors link to previous literature and present findings from a comparative case study of managerial practices for managing creativity projects. Data were collected through interviews, secondary materials, site visits and observation. Findings – Three meta-practices used by managers to manage boundary-spanning creative projects are presented: defining the creative space, making space for creativity and acting in the creative space. These practices are detailed in seven case studies of creative projects. Research limitations/implications – The analysis focuses on boundary spanning as a management process rather than a capability for organisat...

This article provides a computational framework to model self‐adaptive expert systems using the Petri net (PN) formalism. Self‐adaptive expert systems are understood here as expert systems with the ability to autonomously learn from external inputs, like monitoring data. To this end, the Bayesian learning principles are investigated and also combined with the Plausible PNs (PPNs) methodology. PPNs are a variant within the PN paradigm, which are efficient to jointly consider the dynamics of discrete events, like maintenance actions, together with multiple sources of uncertain information about a state variable. The manuscript shows the mathematical conditions and computational procedure where the Bayesian updating becomes a particular case of a more general basic operation within the PPN execution semantics, which enables the uncertain knowledge being updated from monitoring data. The approach is general, but here it is demonstrated in a novel computational model acting as expert system for railway track inspection management taken as a case study using published data from a laboratory simulation of train loading on ballast. The results reveal self‐adaptability and uncertainty management as key enabling aspects to optimize inspection actions in railway track, only being adaptively and autonomously triggered based on the actual learnt state of track and other contextual issues, like resource availability, as opposed to scheduled periodic maintenance activities.

Greenhouse gas emission of pastoralism is lower than combined extensive/intensive livestock husbandry: A case study on the Qinghai-Tibet Plateau of China

Currently, smartphone Virtual Reality (VR) has simple input methods and not able to track hand positions effectively. This limits the user's interaction in VR. This paper presents the use of multiple marker tracking methods to solve the problem while using welding as the use case study. VR Welding Kit is a VR application that uses a smartphone's camera and two external inputs: a tracked handheld controller integrated with multiple markers technique called welding torch to track user's hand position in real-time and a paper marker called welding position. VR Welding Kit simulates real-life welding training for trainees to gain basic psychomotor skills. The VR Welding Kit application is installed into two different mobile VR devices, smartphone VR and a standalone VR, for tracked handheld input accuracy comparison. 24 participants were involved in this study. Ideal welding variables of travel speed, work angle, and travel angle were used as the benchmark data. The evaluation was conducted by using the Root Mean Square Error (RMSE) method. The study shows that smartphone VR using multiple markers tracking method has higher accuracy than standalone VR.

Out-of-order enabled operating system for uncertain planning, scheduling and execution in aviation maintenance