COVERAGE: detecting and reacting to worm epidemics using cooperation and validation

Cooperative defensive systems communicate and cooperate in their response to worm attacks, but determine the presence of a worm attack solely on local information. Distributed worm detection and immunization systems track suspicious behavior at multiple cooperating nodes to determine whether a worm attack is in progress. Earlier work has shown that cooperative systems can respond quickly to day-zero worms, while distributed detection systems allow detectors to be more conservative (i.e., paranoid) about potential attacks because they manage false alarms efficiently. In this paper we present our investigation into the complex tradeoffs in such systems between communication costs, computation overhead, accuracy of the local tests, estimation of viral virulence, and the fraction of the network infected before the attack crests. We evaluate the effectiveness of different system configurations in various simulations. Our experiments show that distributed algorithms are better able to balance effectiveness against worms and viruses with reduced cost in computation and communication when faced with false alarms. Furthermore, cooperative, distributed systems seem more robust against malicious participants in the immunization system than earlier cooperative but non-distributed approaches.     

<Emphasis Type="Italic">HumanPT</Emphasis>: An Open-source,HumanPT Architecture-based,Robotic Application for Low Cost Robotic Tasks

In this paper, first HumanPT architecture for low cost robotic applications is presented. HumanPT architecture differs than other architectures because it is implemented on existing robotic systems (robot  robotic controller) and exploits the minimum communication facilities for real-time control that these systems provide. It is based on well-known communication methods like serial communication (USB, RS232, IEEE-1394) and windows sockets (server–client model) and permits an important number of different type of components like actuators, sensors and particularly vision systems to be connected in a robotic system. The operating system (OS) used is Microsoft Windows, the most widely spread OS. The proposed architecture exploits features of this OS that is not a real-time one, to ensure – in case that the robotic system provide such a facility – control and real time communication with the robotic system controller and to integrate by means of sensors and actuators an important number of robotic tasks and procedures. As implementation of this architecture, HumanPT robotic application and experimental results concerning its performance and its implementation in real tasks are provided. HumanPT robotic application, developed in Visual C++, is an integrated, but simultaneously an open-source software that can be adapted in different types of robotic systems. An important number of robotic tasks or procedures including sensors and particularly vision systems can be generated and executed. Small enterprises by means of the proposed architecture and the open source software can be automated at low cost enhancing in this way their production.     

An intelligent,uncertainty driven aggregation scheme for streams of ordered sets

Data streams management has attracted the attention of many researchers during the recent years. The reason is that numerous devices generate huge amounts of data demanding an efficient processing scheme for delivering high quality applications. Data are reported through streams and stored into a number of partitions. Separation techniques facilitate the parallel management of data while intelligent methods are necessary to manage these multiple instances of data. Progressive analytics over huge amounts of data could be adopted to deliver partial responses and, possibly, to save time in the execution of applications. An interesting research domain is the efficient management of queries over multiple partitions. Usually, such queries demand responses in the form of ordered sets of objects (e.g., top-k queries). These ordered sets include objects in a ranked order and require novel mechanisms for deriving responses based on partial results. In this paper, we study a setting of multiple data partitions and propose an intelligent, uncertainty driven decision making mechanism that aims to respond to streams of queries. Our mechanism delivers an ordered set of objects over a number of partial ordered subsets retrieved by each partition of data. We envision that a number of query processors are placed in front of each partition and report progressive analytics to a Query Controller (QC). The QC receives queries, assigns the task to the underlying processors and decides the right time to deliver the final ordered set to the application. We propose an aggregation model for deriving the final ordered set of objects and a Fuzzy Logic (FL) inference process. We present a Type-2 FL system that decides when the QC should stop aggregating partial subsets and return the final response to the application. We report on the performance of the proposed mechanism through the execution of a large set of experiments. Our results deal with the throughput of the QC, the quality of the final ordered set of objects and the time required for delivering the final response.     

Maintaining consistent results of continuous queries under diverse window specifications

Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving–yet restricted–set of tuples and thus provide timely and incremental results. Although sliding windows get frequently employed in many user requests, additional types like partitioned or landmark windows are also available in stream processing engines. In this paper, we set out to study the existence of monotonic-related semantics for a rich set of windowing constructs in order to facilitate a more efficient maintenance of their changing contents. After laying out a formal foundation for expressing windowed queries, we investigate update patterns observed in most common window variants as well as their impact on adaptations of typical operators (like windowed join, union or aggregation), thus offering more insight towards design and implementation of stream processing mechanisms. Furthermore, we identify syntactic equivalences in algebraic expressions involving windows, to the potential benefit of query optimizations. Finally, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window specifications, providing concrete evidence of its significance.     

Agent‐oriented activity recognition in the event calculus: An application for diabetic patients

We present a knowledge representation framework on the basis of the Event Calculus that allows an agent to recognize complex activities from low‐level observations received by multiple sensors, reason about the life cycle of such activities, and take action to support their successful completion. Activities are multivalue fluents that change according to events that occur in the environment. The parameters of an activity consist of a unique label, a set of participants involved in the performing of the activity, and a unique goal associated with the activity revealing the activity's desired outcome. Our contribution is the identification of an activity life cycle describing how activities can be started, interrupted, suspended, resumed, or completed over time, as well as how these can be represented. The framework also specifies activity goals, their associated life cycle, and their relation with the activity life cycle. We provide the complete implementation of the framework, which includes an activity generator that automatically creates synthetic sensor data in the form of event streams that represent the everyday lifestyle of a type 1 diabetic patient. Moreover, we test the framework by generating very large activity streams that we use to evaluate the performance of the recognition capability and study its relative merits.     

Switching networked attitude control of an unmanned quadrotor

In this article, a switching networked attitude controller for an unmanned quadrotor over a wireless sensor network is presented. To deal with the network induced time varying delays, the quadrotor is being modeled as a switching time varying linear system, while the applied switching output feedback control scheme, is calculated based on Linear Matrix Inequalities, and is able to guarantee the stability of the quadrotor under arbitrary changes in the time delays.     

A new method preserving the positive definiteness of a second order tensor variable in flow simulations with application to viscoelastic turbulence

We present a new numerical method for the simulation of the flow of complex fluids with internal microstructure that is described by a second order, positive definite, internal structural parameter, so that its positive definiteness is preserved in the simulations. An example of such an internal structural parameter is the conformation tensor, used in the modeling of viscoelastic flows, which characterizes the flow-induced molecular deformation. Numerical methods that guarantee the preservation of the positive definiteness of the conformation tensor help both the physical interpretation of the results and the numerical stability of the simulations. The new method presented here is based on the log-conformation representation of a second order tensor. It is implemented through the application of the Cayley-Hamilton theorem for second order tensors. When necessary, we also use an additional mapping that ensures the boundness of the conformation tensor. This approach has been applied in Direct Numerical Simulations (DNS) of viscoelastic turbulent channel flow, which has received great attention since the mid 1990s with ultimate goal to understand the phenomenon of maximum drag reduction. The algorithm uses a full 3D spectral representation of the spatial dependence for the flow and conformation variables and a second order accurate backward differentiation formula for the integration of the governing equations in time. The key issue for the successful implementation of the proposed scheme is a second order finite difference multigrid diffusion applied to the conformation field. Numerical diffusion has been always added to the hyperbolic evolution equations for the conformation tensor in spectral DNS of viscoelastic turbulent flows in order to remove a-physical high wavenumber instabilities induced due to the chaotic nature of the flow. The main advantage of the finite-difference implementation (as opposed to a spectral implementation as done before) is that it allows for the preservation of the positive definiteness of the conformation tensor.     

A market-based approach to managing the risk of peer-to-peer transactions

Market-based principles can be used to manage the risk of distributed peer-to-peer transactions. This is demonstrated by Ptrim, a system that builds a transaction default market on top of a main transaction processing system, within which peers offer to underwrite the transaction risk for a slight increase in the transaction cost. The insurance cost, determined through market-based mechanisms, is a way of identifying untrustworthy peers and perilous transactions. The risk of the transactions is contained, and at the same time members of the peer-to-peer network capitalise on their market knowledge by profiting as transaction insurers. We evaluated the approach through trials with the deployed Ptrim prototype, as well as composite experiments involving real online transaction data and real subjects participating in the transaction default market. We examine the efficacy of our approach both from a theoretical and an experimental perspective. Our findings suggest that the Ptrim market layer functions in an efficient manner, and is able to support the transaction processing system through the insurance offers it produces, thus acting as an effective means of reducing the risk of peer-to-peer transactions. In our conclusions we discuss how a system like Ptrim assimilates properties of real world markets, and its potential exposure and possible countermeasures to events such as those witnessed in the recent global financial turmoil.