Ultra-dense planar metallic nanowire arrays with extremely large anisotropic optical and magnetic properties

A nanofabrication method for the production of ultra-dense planar metallic nanowire arrays scalable to wafer-size is presented. The method is based on an efficient template deposition process to grow diverse metallic nanowire arrays with extreme regularity in only two steps. First, III–V semiconductor substrates are irradiated by a low-energy ion beam at an elevated temperature, forming a highly ordered nanogroove pattern by a “reverse epitaxy” process due to self-assembly of surface vacancies. Second, diverse metallic nanowire arrays (Au, Fe, Ni, Co, FeAl alloy) are fabricated on these III–V templates by deposition at a glancing incidence angle. This method allows for the fabrication of metallic nanowire arrays with periodicities down to 45 nm scaled up to wafer-size fabrication. As typical noble and magnetic metals, the Au and Fe nanowire arrays produced here exhibited large anisotropic optical and magnetic properties, respectively. The excitation of localized surface plasmon resonances (LSPRs) of the Au nanowire arrays resulted in a high electric field enhancement, which was used to detect phthalocyanine (CoPc) in surface-enhanced Raman scattering (SERS). Furthermore, the Fe nanowire arrays showed a very high in-plane magnetic anisotropy of approximately 412 mT, which may be the largest in-plane magnetic anisotropy field yet reported that is solely induced via shape anisotropy within the plane of a thin film.

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Modelling co-operation in the design of knowledge production systems: The Made In'Coop method

This paper presents the latest developments of the MadeIn 'Coop method for modelling the human-machine and human-human co-operation process, and an application of this method for the design of a more co-operative version of the C³I System CHEOPS. We first consider that the design of software systems for organizations is tied more and more to the perspective of compound Knowledge Production Systems that link humans and machines engaged in a co-operative problem solving process. After exposing the four principles upon which MadeIn 'Coop rests for modelling co-operation, we present an artificial problem solving dialogue between CHEOPS and its users. Consistent with the Group Cognitive Processes Theory framework, we propose a dialogue analysis according to two complimentary points of view: the Collective Problem Solving model, and the Coordination model. This analysis should help system designers to identify new system functionalities to assist problem solving.(C³I) Command Control Communication Intelligence Systems     

Surrogate modeling approximation using a mixture of experts based on EM joint estimation

An automatic method to combine several local surrogate models is presented. This method is intended to build accurate and smooth approximation of discontinuous functions that are to be used in structural optimization problems. It strongly relies on the Expectation−Maximization (EM) algorithm for Gaussian mixture models (GMM). To the end of regression, the inputs are clustered together with their output values by means of parameter estimation of the joint distribution. A local expert is then built (linear, quadratic, artificial neural network, moving least squares) on each cluster. Lastly, the local experts are combined using the Gaussian mixture model parameters found by the EM algorithm to obtain a global model. This method is tested over both mathematical test cases and an engineering optimization problem from aeronautics and is found to improve the accuracy of the approximation.     

Removing web spam links from search engine results

Web spam denotes the manipulation of web pages with the sole intent to raise their position in search engine rankings. Since a better position in the rankings directly and positively affects the number of visits to a site, attackers use different techniques to boost their pages to higher ranks. In the best case, web spam pages are a nuisance that provide undeserved advertisement revenues to the page owners. In the worst case, these pages pose a threat to Internet users by hosting malicious content and launching drive-by attacks against unsuspecting victims. When successful, these drive-by attacks then install malware on the victims’ machines. In this paper, we introduce an approach to detect web spam pages in the list of results that are returned by a search engine. In a first step, we determine the importance of different page features to the ranking in search engine results. Based on this information, we develop a classification technique that uses important features to successfully distinguish spam sites from legitimate entries. By removing spam sites from the results, more slots are available to links that point to pages with useful content. Additionally, and more importantly, the threat posed by malicious web sites can be mitigated, reducing the risk for users to get infected by malicious code that spreads via drive-by attacks.     

Efficient on-line signature recognition based on multi-section vector quantization

This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used a database of 330 users which includes 25 skilled forgeries performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system obtains similar results as the state-of-the-art online signature recognition algorithm, Dynamic Time Warping, with a reduced computational requirement, around 47 times lower. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly because it is not possible to recover the original signature using the codebooks. Experimental results reveal that our proposed multi-section vector quantization achieves a 98% identification rate, minimum Detection Cost Function value equal to 2.29% for random forgeries and 7.75% for skilled forgeries.     

Ontology-driven analysis of UML-based collaborative processes using OWL-DL and CPN

A key ingredient in system and organization modeling is modeling business processes that involve the collaborative participation of different teams within and outside the organization. Recently, the use of the Unified Modeling Language (UML) for collaborative business modeling has been increasing, thanks to its human-friendly visual representation of a rich set of structural and behavioral views, albeit its unclear semantics. In the meantime, the use of the Web Ontology Language (OWL) has also been emerging, thanks to its clearly-defined semantics, hence being amenable to automatic analysis and reasoning, although it is less human friendly than, and also perhaps not as rich as, the UML notation — especially concerning processes, or activities. In this paper, we view the UML and the OWL as being complementary to each other, and exploit their relative strengths. We provide a mapping between the two, through a set of mapping rules, which allow for the capture of UML activity diagrams in an OWL-ontology. This mapping, which results in a formalization of collaborative processes, also sets a basis for subsequent construction of executable models using the Colored Petri Nets (CPN) formalism. For this purpose, we also provide appropriate mappings from OWL-based ontological elements into CPN elements. A case study of a mortgage granting system is described, along with the potential benefits and limitations of our proposal.     

A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems

For the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40× when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies.