An Embedded Microprocessor for Intelligent Control

The conventional approach for the implementation of the knowledge base of a planning agent, on an intelligent embedded system, is solely of software nature. It requires the existence of a compiler that transforms the initial declarative logic program, specifying the knowledge base, to its equivalent procedural one, to be programmed to the embedded systems microprocessor. This practice increases the complexity of the final implementation (the declarative to sequential transformation adds a great amount of software code for simulating the declarative execution) and reduces the overall systems performance (logic derivations require the use of a stack and a great number of jump instructions for their evaluation). The design of specialized hardware implementations, which are only capable of supporting logic programs, in an effort to resolve the aforementioned problems, introduces limitations in their use in applications where logic programs need to be intertwined with traditional procedural ones in a desired application. In this paper, we exploit HW/SW codesign methods to present a microprocessor, capable of supporting hybrid applications using both programming approaches. We take advantage of the close relationship between attribute grammar (AG) evaluation and knowledge engineering methods to present a programmable hardware parser that performs logic derivations and combine it with an extension of a conventional RISC microprocessor that performs the unification process to report the success or failure of logic derivations. The extended RISC microprocessor is still capable of executing conventional procedural programs, thus hybrid applications can be implemented. The presented implementation increases the performance of logic derivations for the control inference process (experimental analysis yields an approximate 1000% – 10 times increase in performance) and reduces the complexity of the final implemented code through the introduction of an extended C language called C-AG that simplifies the programming of hybrid procedural-declarative applications.     

On-line Optimal Control of a Class of Discrete Event Systems with Real-Time Constraints

We consider Discrete Event Systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. It has been shown that the off-line version of this problem can be efficiently solved by the Critical Task Decomposition Algorithm (CTDA) (Mao et al., IEEE Trans Mobile Comput 6(6):678–688, 2007). In the on-line version, random task characteristics (e.g., arrival times) are not known in advance. To bypass this difficulty, worst-case analysis may be used. This, however, does not make use of probability distributions and results in an overly conservative solution. In this paper, we develop a new approach which does not rely on worst-case analysis but provides a “best solution in probability” efficiently obtained by estimating the probability distribution of sample-path-optimal solutions. We introduce a condition termed “non-singularity” under which the best solution in probability leads to the on-line optimal control. Numerical examples are included to illustrate our results and show substantial performance improvements over worst-case analysis.     

Context-based caching and routing for P2P web service discovery

In modern heterogeneous environments, such as mobile, pervasive and ad-hoc networks, architectures based on web services offer an attractive solution for effective communication and inter-operation. In such dynamic and rapidly evolving environments, efficient web service discovery is an important task. Usually this task is based on the input/output parameters or other functional attributes, however this does not guarantee the validity or successful utilization of retrieved web services. Instead, non-functional attributes, such as device power features, computational resources and connectivity status, that characterize the context of both service providers and consumers play an important role to the quality and usability of discovery results. In this paper we introduce context-awareness in web service discovery, enabling the provision of the most appropriate services at the right location and time. We focus on context-based caching and routing for improving web service discovery in a mobile peer-to-peer environment. We conducted a thorough experimental study, using our prototype implementation based on the JXTA framework, while simulations are employed for testing the scalability of the approach. We illustrate the advantages that this approach offers, both by evaluating the context-based cache performance and by comparing the efficiency of location-based routing to broadcast-based approaches. Recommended by: Zakaria Maamar     

A complex study of Etna's volcanic plume from ground‐based,in situ and space‐borne observations

Two periods of transboundary transport of volcanic aerosols and debris following recent eruptions of Mount Etna, Italy, were examined using ground‐based and satellite spectrophotometric measurements together with Light Detection And Ranging (LiDAR) and aerosol filter observations in Athens and Thessaloniki, Greece. Independent columnar SO₂ measurements from ground and space identified peaks at Greek sites after the volcanic eruptions. LiDAR measurements of the aerosol extinction at Thessaloniki and Athens performed in July 2001 have shown the height of the volcanic plume to be about 3.5 km asl and the optical thickness of the dust layer to be of the order of 3×10^?3 at 532 nm. Strong ozone depletion observed at the volcano plume level by using ozonesonde ascents may be attributed to the in‐plume processes that generate reactive halogens, which in turn destroy ozone. The chemical and elemental composition of aerosol samples, taken at the Earth's surface, was analysed and confirmed the volcanic origin of the dust.     

Optimal Influence Strategies in Social Networks

This paper suggests a modeling framework to investigate the optimal strategy followed by a monopolistic firm aiming to manipulate the process of opinion formation in a social network. The monopolist and a set of consumers communicate to form their beliefs about the underlying product quality. Since the firm’s associated optimization problem can be analytically solved only under specific assumptions, we rely on the sequential quadratic programming computational approach to characterize the equilibrium. When consumers’ initial beliefs are uniform, the firm’s optimal influence strategy always involves targeting the most influential consumer. For the case of non-uniform initial beliefs, the monopolist might target the less influential consumer if the latter’s initial opinion is low enough. The probability of investing more in the consumer with the lower influence increases with the distance between consumers’ initial beliefs and with the degree of trust attributed on consumers by the firm. The firm’s profit is minimized when consumers’ influences become equal, implying that the firm benefits from the presence of consumers with divergent strategic locations in the network. In the absence of a binding constraint on total investment, the monopolist’s incentives to manipulate the network decrease with consumers’ initial beliefs and might either increase or decrease with the trust put in consumers’ opinion by the firm. Finally, the firm’s strategic motivation to communicate persistently high beliefs during the opinion formation process is positively associated with the market size, with the available budget and with the direct influence of the most influential consumer on the other but negatively associated with consumers’ initial valuation of the good.     

A framework for designing cloud forensic-enabled services (CFeS)

Requirements Engineering - Cloud computing is used by consumers to access cloud services. Malicious actors exploit vulnerabilities of cloud services to attack consumers. The link between these two...     

Perturbation Analysis and Optimization of Multiclass Multiobjective Stochastic Flow Models

Stochastic Flow Models (SFMs) are stochastic hybrid systems that abstract the dynamics of many complex discrete event systems and provide the basis for their control and optimization. SFMs have been used to date to study systems with a single user class or some multiclass settings in which performance metrics are not class-dependent. In this paper, we develop a SFM framework for multiple classes and class-dependent performance objectives, where competing classes employ threshold control policies and service is provided on a First Come First Serve (FCFS) basis. In this framework, we analyze new phenomena that result from the interaction of the different classes and give rise to a new class of “induced” events that capture delays in the SFM dynamics. We derive Infinitesimal Perturbation Analysis (IPA) estimators for derivatives of various class-dependent objectives, and use them as the basis for on-line optimization algorithms that apply to the underlying discrete event system (not the SFM). This allows us to contrast system-centric and user-centric objectives, thus putting the resource contention problem in a game framework. The unbiasedness of IPA estimators is established and numerical results are provided to illustrate the effectiveness of our method for the case where there are no constraints on the controllable thresholds and to demonstrate the gap between the results of system-centric optimization and user-centric optimization.     

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.