Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Tesis por compendio[EN] The long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing. In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergy-based, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency. In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems. First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution. Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassign them to optimize the pheromone usage. The resulting algorithm is called EntropicAAF and it has been evaluated as one of the top-performing algorithms for P2P semantic searches under all conditions.[ES] La popular tendencia de distribución de carga y recursos en el ámbito de la computación se ha transmitido a las redes computacionales a través del concepto de la conectividad peer-to-peer (P2P). P2P es un modelo simétrico, en el cual a cada nodo de la red se le otorga un rango comparable de capacidades y recursos. Se trata de un fuerte contraste con el clásico y fuertemente asimétrico enfoque cliente-servidor. P2P, originalmente considerado solo como una estructura del lado del servidor complementaria al sencillo modelo cliente-servidor, ha demostrado tener un potencial considerable por sí mismo, con múltiples beneficios ampliamente conocidos: buena tolerancia a fallos y recuperación, escalabilidad satisfactoria y distribución de carga intrínseca. Sin embargo, al contrario que el modelo cliente-servidor, las redes P2P requieren de soluciones sofisticadas a todos los niveles, desde la organización de la red hasta la gestión y localización de recursos. Esta tesis aborda uno de los problemas principales de las redes P2P: la búsqueda eficiente de recursos de naturaleza semántica bajo condiciones dinámicas y realistas. Ha habido numerosas soluciones a este problema basadas en enfoques evolucionarios, estigmérgicos y simples, pero pocas han tratado de resolver el abanico completo de desafíos. En primer lugar, las redes P2P reales son raramente estáticas: los nodos se desconectan, reconectan y cambian de contenido. Además, la incorporación trivial de búsquedas semánticas en algoritmos conocidos causa un decremento significativo de la eficiencia de la búsqueda. En esta investigación se ha construido una solución de manera incremental, comenzando por el clásico Ant Colony System (ACS) basado en la metaheurística de Ant Colony Optimization (ACO). ACO es un framework algorítmico usado para búsquedas en grafos que encaja perfectamente con las condiciones del problema, aunque no provee una solución inmediata a las cuestiones mencionadas anteriormente. En primer lugar, se propone una variante eficiente de ACS para redes P2P estructuradas (con estructura de hipercubo) permitiendo el postprocesamiento de las rutas, al que hemos denominado Tabu Route Optimization (TRO). A continuación, se ha tratado de resolver el problema del dinamismo de la red mediante la difusión de la información a través de una estrategia compatible con ACO. En consecuencia, se ha tratado de incorporar el componente semántico de las búsquedas. Esta aproximación inicial al problema ha sido lograda permitiendo al ACS diferenciar entre tipos de búsquedas através de la idea de pheromone-per-concept. El resultado de esta fusión se ha denominado Routing Concept ACS (RC-ACS). RC-ACS es una implementación multiferomona estática y robusta de ACS. Sin embargo, a partir de esta implementación se ha podido concluir que el enfoque pheromone-per-concept ofrece solo escalabilidad limitada y que no puede ser considerado una solución global. Por lo tanto, para lograr una mejora a este respecto, se ha introducido al RC-ACS una novedosa idea: la creación dinámica de feromonas, que reemplaza la asignación estática uno a uno. En el algoritmo resultante, al que hemos denominado Angry Ant Framework (AAF), los nuevos niveles de feromona se crean conforme se necesitan y durante la búsqueda, en lugar de crearse antes de la misma. La mejora final se ha obtenido al permitir al AAF no solo crear niveles de feromona, sino también reasignarlos para optimizar el uso de la misma. El algoritmo resultante se denomina EntropicAAF y ha sido evaluado como uno de los algoritmos más exitosos para las búsquedas semánticas P2P bajo todas las condiciones.[CA] La popular tendència de distribuir càrrega i recursos en el camp de la computació s'ha estès cap a les xarxes d'ordinadors a través del concepte de connexions d'igual a igual (de l'anglès, peer to peer o P2P). P2P és un model simètric on cada node de la xarxa disposa del mateix nombre de capacitats i recursos. P2P, considerat originàriament només una estructura situada al servidor complementària al model client-servidor simple, ha provat tindre el suficient potencial per ella mateixa, amb múltiples beneficis ben coneguts: una bona tolerància a errades i recuperació, una satisfactòria escalabilitat i una intrínseca distribució de càrrega. No obstant, contràriament al client-servidor, les xarxes P2P requereixen solucions sofisticades a tots els nivells, que varien des de l'organització de la xarxa a la localització de recursos i la seua gestió. En aquesta tesi s'adreça un dels problemes clau de les xarxes P2P: ser capaç de realitzar eficientment cerques de recursos de naturalesa semàntica sota condicions realistes i dinàmiques. Existeixen nombroses solucions a aquest tema basades en la computació simple, evolutiva i també basades en l'estimèrgia (de l'anglès, stigmergy), però pocs esforços s'han realitzat per intentar resoldre l'ampli conjunt de reptes existent. En primer lloc, les xarxes P2P reals són rarament estàtiques: els nodes es connecten, desconnecten i canvien els seus continguts. A més a més, la incorporació trivial de cerques semàntiques als algorismes existents causa una disminució significant de l'eficiència de la cerca. En aquesta recerca s'ha construït una solució incremental, començant pel sistema clàssic de colònia de formigues (de l'anglés, Ant Colony System o ACS) dins de la metaheurística d'optimització de colònies de formigues (de l'anglès, Ant Colony Optimization o ACO). ACO és un entorn algorísmic utilitzat per cercar en grafs i que aborda el problema de forma satisfactòria, tot i que no proveeix d'una solució immediata a cap dels problemes anteriorment mencionats. Primer, s'ha proposat una variant eficient d'ACS en xarxes P2P estructurades (en forma d'hipercub) a través d'un algorisme de processament post-camí el qual s'ha anomenat en anglès Tabu Route Optimization (TRO). A continuació, s'ha procedit a resoldre el problema del dinamisme de les xarxes amb un enfocament de difusió d'informació compatible amb ACO. Com a conseqüència, s'ha intentat incorporar la component semàntica de les cerques. Aquest enfocament inicial al problema s'ha realitzat permetent a ACS diferenciar entre tipus de cerques amb la idea de ''feromona per concepte'', i s'ha anomenat a aquest producte Routing Concept ACS o RC-ACS. RC-ACS és una implementació multi-feromona robusta i estàtica d'ACS. No obstant, s'ha pogut concloure que l'enfocament de feromona per concepte ofereix només una escalabilitat limitada i no pot ser considerada una solució global. En aquest respecte s'ha realitzat progrés posteriorment introduint una nova idea a RC-ACS: la creació dinàmica de feromones, la qual reemplaça a l'assignació un a un de les mateixes. A l'algorisme resultant se l'ha anomenat en anglès Angry Ant Framework (AAF). En AAF es creen nous nivells de feromones a mesura que es necessiten durant la cerca, i no abans d'aquesta. El progrés final s'ha aconseguit quan s'ha permès a AAF, no sols crear nivells de feromones, sinó reassignar-los per optimitzar la utilització de feromones. L'algorisme resultant s'ha anomenat EntropicAAF i ha sigut avaluat com un dels algorismes per a cerques semàntiques P2P amb millors prestacions.Krynicki, KK. (2016). Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61293TESISPremios Extraordinarios de tesis doctoralesCompendi

An ACO-based personalized learning technique in support of people with acquired brain injury

This is the author’s version of a work that was accepted for publication in Applied Soft Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing 47 (2016) 316–331. DOI 10.1016/j.asoc.2016.04.039The ever-increasing cases of acquired brain injury (ABI), especially among young people, have prompted a rapid progress in research involving neurological disorders. One important path is the concept of relearning, which attempts to help people regain basic motor and cognitive skills lost due to illness or accident. The goals of relearning are twofold. First, there must exist a way to properly assess the necessities of an affected person, leading to a diagnosis, followed by a recommendation regarding the exercises, tests and tasks to perform; and second, there must be a way to confirm the results obtained from these recommendations in order to fine-tune and personalize the relearning process. This presents a challenge, as there is a deeply-rooted duality between the personalized and the generalized approach. In this work we propose a personalization algorithm based on the ant colony optimization (ACO), which is a bio-inspired meta-heuristic. As we show, the stochastic nature of ants has certain similarities to the human learning process. We combine the adaptive and exploratory capabilities of ACO systems to respond to rapidly changing environments and the ubiquitous human factor. Finally, we test the proposed solution extensively in various scenarios, achieving high quality results. © 2016 Elsevier B.V. All rights reservedThis research has been funded by the Spanish Ministry of Economy and Competitiveness and by the FEDER funds of the EU under the project SUPEREMOS (TIN2014-60077-R) and insPIre (TIN2012-34003). Kamil Krynicki is supported by the FPI fellowship from Universitat Politecnica de Valencia.Krynicki, K.; Jaén Martínez, FJ.; Navarro, E. (2016). An ACO-based personalized learning technique in support of people with acquired brain injury. Applied Soft Computing. 47:316-331. doi:10.1016/j.asoc.2016.04.039S3163314

A diffusion-based ACO resource discovery framework for dynamic p2p networks

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe Ant Colony Optimization (ACO) has been a very resourceful metaheuristic over the past decade and it has been successfully used to approximately solve many static NP-Hard problems. There is a limit, however, of its applicability in the field of p2p networks; derived from the fact that such networks have the potential to evolve constantly and at a high pace, rendering the already-established results useless. In this paper we approach the problem by proposing a generic knowledge diffusion mechanism that extends the classical ACO paradigm to better deal with the p2p's dynamic nature. Focusing initially on the appearance of new resources in the network we have shown that it is possible to increase the efficiency of ant routing by a significant margin.Kamil Krynicki is supported by a FPI fellowship from the Universitat Politècnica de València with reference number 3117. This work received financial support from the Spanish Ministry of Education under the National Strategic Program of Research and Project TSI2010-20488.Krynicki, KK.; Jaén Martínez, FJ.; Catalá Bolós, A. (2013). A diffusion-based ACO resource discovery framework for dynamic p2p networks. En 2013 IEEE Congress on Evolutionary Computation. IEEE. 860-867. https://doi.org/10.1109/CEC.2013.6557658S86086

On the performance of ACO-based methods in p2p resource discovery

Over the recent years peer-to-peer (p2p) systems have become increasingly popular. As of today most ofthe internet IP traffic is already transmitted in this format and still it is said to double in volume till 2014.Most p2p systems, however, are not pure serverless solutions, nor is the searching in those networkshighly efficient, usually achieved by simple flooding. In order to confront with the growing traffic wemust consider more elaborate search mechanisms and far less centralized environments. An effectiveproposal to this problem is to solve it in the domain of ant colony optimization metaheuristics. In thispaper we present an overview of ACO algorithms that offer the best potential in this field, under the strictrequirements and limitations of a pure p2p network. We design several experiments to serve as an evalu-ation platform for the mentioned algorithms to conclude the features of a high quality approach. Finally,we consider two hybrid extensions to the classical algorithms, in order to examine their contribution tothe overall quality robustness.© 2013 Elsevier B.V. All rights reserved.This work was funded by the Spanish Ministry of Education and Science and Innovation under the National Strategic Program of Scientific Research, Development and Technological Innovation (I+D+i) project TIN2010-20488. Kamil Krynicki is supported by a FPI fellowship from Universidad Politecnica de Valencia.Krynicki, KK.; Jaén Martínez, FJ.; Mocholí Agües, JA. (2013). On the performance of ACO-based methods in p2p resource discovery. Applied Soft Computing. 13(12):4813-4831. https://doi.org/10.1016/j.asoc.2013.07.022S48134831131

Ant colony optimisation for resource searching in dynamic peer-to-peer grids

The applicability of peer-to-peer (p2p) in the domain of grid computing has been an important subject over the past years. Nevertheless, the sole merger between p2p and the concept of grid is not sufficient to guarantee non-trivial efficiency. Some claim that ant colony optimisation (ACO) algorithms might provide a definite answer to this question. However, the use of ACO in grid networks causes several problems. The first and foremost stems out of the fact that ACO algorithms usually perform well under the conditions of static networks, solving predetermined problems in a known and bound space. The question that remains to be answered is whether the evolutive component of these algorithms is able to cope with changing conditions; and by those we mean changes both in the positive sense, such as the appearance of new resources, but also in the negative sense, such as the disappearance or failure of fragments of the network. In this paper we study these considerations in depth, bearing in mind the specificity of the peer-to-peer nature.This work was funded by the Spanish Ministry of Education and Science and Innovation under the National Strategic Programme of Scientific Research, Development and Technological Innovation (I+D+i) and project TIN 2010-20488. Kamil Krynicki is supported by the FPI Fellowship from Universitat Politecnica de Valencia.Krynicki, K.; Jaén Martínez, FJ.; Mocholí Agües, JA. (2014). Ant colony optimisation for resource searching in dynamic peer-to-peer grids. International Journal of Bio-Inspired Computation. 6(3):153-165. https://doi.org/10.1504/IJBIC.2014.062634S1531656

Automatic English phoneme recognition from articulatory data generated by EPG systems with grid and anatomical layout of contact sensors

The aim of the study was to conduct automatic phoneme identification from articulatory data that accompanied the production of these phonemes in continuous speech. The articulatory data were obtained from 2 electropalatographic systems, Palatometer by Complete Speech and Linguagraph by Rose-Medical. Palatometer was used with the artificial palate containing 124 contact sensors in a grid layout, including 2 sensors monitoring the lip contact. The palate included a vacuum-thermoformed flexible printed circuit. Linguagraph was used with the acrylic artificial palate designed and developed for the purpose of this study, containing 62 electrodes in anatomical layout. Palatometer was used by one native of General American and Linguagraph by one native of General British, each reading 140 phonetically balanced sentences that included Harvard Sentences and TIMIT prompts. The EPG data were parametrised into dimensionality reduction indexes, which were analysed by means of linear discriminant analysis and a probabilistic neural network. The results of classifications are discussed.National Science Centre (grant no. 2013/11/B/HS2/03151

TSACO: Extending a context-aware recommendation system with Allen temporal operators

In this paper we present our research to extend a recommender system based on a semantic multicriteria ant colony algorithm to allow the use of Allen temporal operators. The system utilizes user’s learnt routes, including their associated context information, in order to predict the most likely route a user is following, given his current location and context data. The addition of temporal operators will increase the level of expressiveness of the queries the system can answer what will allow, in turn, more fine-tuned predictions. This more refined knowledge could then be used as the basis for offering services related to his current (or most likely future) context in the vicinity of the path the user is followingThis work has been supported by the Centre for the Development of Industrial Technology (CDTI) under the funding project CENIT-MIO! CENIT-2008 1019.Mocholí Agües, JA.; Jaén Martínez, FJ.; Krynicki, KK.; Catalá Bolós, A. (2012). TSACO: Extending a context-aware recommendation system with Allen temporal operators. En Ubiquitous Computing and Ambient Intelligence. Springer. 253-260. https://doi.org/10.1007/978-3-642-35377-2_35S253260Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowledge Data Eng. 17(6), 734–749 (2005)Picón, A., Rodríguez-Vaamonde, S., Jaén, J., Mocholi, J.A., García, D., Cadenas, A.: A statistical recommendation model of mobile services based on contextual evidences. Expert Systems with Applications 39(1), 647–653 (2012)Mocholi, J.A., Jaen, J., Krynicki, K., Catala, A., Picón, A., Cadenas, A.: Learning semantically-annotated routes for context-aware recommendations on map navigation systems. Applied Soft Computing 12(9), 3088–3098 (2012)Linn, Z.Z., Hla, K.H.S.: Temporal Database Queries for Recommender System using Temporal Logic. In: Intl. Symposium on Micro-NanoMechatronics and Human Science, pp. 1–6 (2006)Ullah, F., Sarwar, G., Lee, S.C., Park, Y.K., Moon, K.D., Kim, J.T.: Hybrid recommender system with temporal information. In: Intl. Conf. on Information Networking, pp. 421–425 (2012)Shakshuki, E., Trudel, A., Xu, Y., Li, B.: A Probabilistic Temporal Interval Algebra Based Multi-agent Scheduling System. In: International Joint Conference on Artificial Intelligence Workshop in Multi-Agent Information Retrieval and Recommender Systems, pp. 62–69 (2005)Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Trans. Systems, Man and Cybernetics, Part B 26, 29–34 (1996)Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: Algorithms, applications and advances. In: Glover, F., Kochen-berger, G. (eds.) Handbook of Metaheuristics, pp. 251–285. Kluwer Academic Publishers (2003)Khan, L., McLeod, D.: Audio structuring and personalized retrieval using ontologies. IEEE Advances in Digital Libraries (2000)Liang, Y.C., Smith, A.E.: An Ant Colony Approach to the Orienteering Problem. Journal of the Chinese Institute of Industrial Engineers 23(5), 403–414 (2003

Automatic English phoneme recognition from articulatory data generated by EPG systems with grid and anatomical layout of contact sensors

The aim of the study was to conduct automatic phoneme identification from articulatory data that accompanied the production of these phonemes in continuous speech. The articulatory data were obtained from 2 electropalatographic systems, Palatometer by Complete Speech and Linguagraph by Rose-Medical. Palatometer was used with the artificial palate containing 124 contact sensors in a grid layout, including 2 sensors monitoring the lip contact. The palate included a vacuum-thermoformed flexible printed circuit. Linguagraph was used with the acrylic artificial palate designed and developed for the purpose of this study, containing 62 electrodes in anatomical layout. Palatometer was used by one native of General American and Linguagraph by one native of General British, each reading 140 phonetically balanced sentences that included Harvard Sentences and TIMIT prompts. The EPG data were parametrised into dimensionality reduction indexes, which were analysed by means of linear discriminant analysis and a probabilistic neural network. The results of classifications are discussed.National Science Centre (grant no. 2013/11/B/HS2/03151

Learning semantically-annotated routes for context-aware recommendations on map navigation systems

Modern technology has brought many changes to our everyday lives. Our need to be in constant touch with others has been met with the cellphone, which has become our companion and the convergence point of many technological advances. The combination of capabilities such as browsing the Internet and GPS reception has multiplied the services and applications based on the current location of the user. However, providing the user with these services has certain drawbacks. Although map navigation systems are the most meaningful way of displaying this information, the user still has to manually set up the filter in order to obtain a non-bloated visualization of the map and the available services. To tackle this problem, we present here a semantic multicriteria ant colony algorithm capable of learning the user's routes, including associated context information, and then predicting the most likely route a user is following, given his current location and context data. This knowledge could then be used as the basis for offering services related to his current (or most likely future) context data close to the path he is following. Our experimental results show that our algorithm is capable of obtaining consistent solutions sets even when multiple objective ontological terms are included in the process