An Emergent Approach to Text Analysis Based on a Connectionist Model and the Web

In this paper, we present a method to provide proactive assistance in text checking, based on usage relationships between words structuralized on the Web. For a given sentence, the method builds a connectionist structure of relationships between word n-grams. Such structure is then parameterized by means of an unsupervised and language agnostic optimization process. Finally, the method provides a representation of the sentence that allows emerging the least prominent usage-based relational patterns, helping to easily find badly-written and unpopular text. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach and some experimental use

An Interval-Valued Approach to Business Process Simulation Based on Genetic Algorithms and the BPMN

Simulating organizational processes characterized by interacting human activities, resources, business rules and constraints, is a challenging task, because of the inherent uncertainty, inaccuracy, variability and dynamicity. With regard to this problem, currently available business process simulation (BPS) methods and tools are unable to efficiently capture the process behavior along its lifecycle. In this paper, a novel approach of BPS is presented. To build and manage simulation models according to the proposed approach, a simulation system is designed, developed and tested on pilot scenarios, as well as on real-world processes. The proposed approach exploits interval-valued data to represent model parameters, in place of conventional single-valued or probability-valued parameters. Indeed, an interval-valued parameter is comprehensive; it is the easiest to understand and express and the simplest to process, among multi-valued representations. In order to compute the interval-valued output of the system, a genetic algorithm is used. The resulting process model allows forming mappings at different levels of detail and, therefore, at different model resolutions. The system has been developed as an extension of a publicly available simulation engine, based on the Business Process Model and Notation (BPMN) standard

On the schedulability of deadline-constrained traffic in TDMA Wireless Mesh Networks

In this paper, we evaluate the schedulability of traffic with arbitrary end-to-end deadline constraints in Wireless Mesh Networks (WMNs). We formulate the problem as a mixed integer linear optimization problem, and show that, depending on the flow aggregation policy used in the network, the problem can be either convex or non-convex. We optimally solve the problem in both cases, and prove that the schedulability does depend on the aggregation policy. This allows us to derive rules of thumb to identify which policy improves the schedulability with a given traffic. Furthermore, we propose a heuristic solution strategy that allows good suboptimal solutions to the scheduling problem to be computed in relatively small times, comparable to those required for online admission control in relatively large WMNs

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

Due to growing endurance, safety and non-invasivity, small drones can be increasingly experimented in unstructured environments. Their moderate computing power can be assimilated into swarm coordination algorithms, performing tasks in a scalable manner. For this purpose, it is challenging to investigate the use of biologically-inspired mechanisms. In this paper the focus is on the coordination aspects between small drones required to perform target search. We show how this objective can be better achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone senses a potential target, by releasing digital pheromone on its location. Multiple pheromone deposits are aggregated, increasing in intensity, but also diffused, to be propagated to neighborhood, and lastly evaporated, decreasing intensity in time. As a consequence, pheromone intensity creates a spatiotemporal attractive potential field coordinating a swarm of drones to visit a potential target. Flocking occurs when drones are spatially organized into groups, whose members have approximately the same heading, and attempt to remain in range between them, for each group. It is an emergent effect of individual rules based on alignment, separation and cohesion. In this paper, we present a novel and fully decentralized model for target search, and experiment it empirically using a multi-agent simulation platform. The different combination strategies are reviewed, describing their performance on a number of synthetic and real-world scenarios

Model Checking Multithreaded Programs by Means of Reduced Models

n/

Enabling swarm aggregation of position data via adaptive stigmergy: a case study in urban traffic flows

Urban road congestion estimation is a challenge in traffic management. City traffic state can vary temporally and spatially between road links, depending on crossroads and lanes. In addition, congestion estimation requires some sort of tuning to “what is around” to trigger appropriate reactions. An adaptive aggregation mechanism of position data is therefore crucial for traffic control. We present a biologically-inspired technique to aggregate position samples coming from on-vehicle devices. In essence, each vehicle position sample is spatially and temporally augmented with digital pheromone information, locally deposited and evaporated. As a consequence, an aggregated pheromone concentration appears and stays spontaneously while many stationary vehicles and high density roads occur. Pheromone concentration is then sharpened to achieve a better distinction of critical phenomena to be triggered as detected traffic events. The overall mechanism can be actually enabled if structural parameters are correctly tuned for the given application context. Determining such correct parameters is not a simple task since different urban areas have different traffic flux and density. Thus, an appropriate tuning to adapt parameters to the specific urban area is desirable to make the estimation effective. In this paper, we show how this objective can be achieved by using differential evolution

Heuristic search for equivalence checking

Equivalence checking plays a crucial role in formal verification since it is a natural relation for expressing the matching of a system implementation against its specification. In this paper, we present an efficient procedure, based on heuristic search, for checking well-known bisimulation equivalences for concurrent systems specified through process algebras. The method tries to improve, with respect to other solutions, both the memory occupation and the time required for proving the equivalence of systems. A prototype has been developed to evaluate the approach on several examples of concurrent system specifications