An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs

Development of an Optimal Replenishment Policy for Human Capital Inventory

A unique approach is developed for evaluating Human Capital (workforce) requirements. With this approach, new ways of measuring personnel availability are proposed and available to ensure that an organization remains ready to provide timely, relevant, and accurate products and services in support of its strategic objectives over its planning horizon. The development of this analysis and methodology was established as an alternative approach to existing studies for determining appropriate hiring and attrition rates and to maintain appropriate personnel levels of effectiveness to support existing and future missions. The contribution of this research is a prescribed method for the strategic analyst to incorporate a personnel and cost simulation model within the framework of Human Resources Human Capital forecasting which can be used to project personnel requirements and evaluate workforce sustainment, at least cost, through time. This will allow various personnel managers to evaluate multiple resource strategies, present and future, maintaining near “perfect” hiring and attrition policies to support its future Human Capital assets

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments

This book presents the collection of fifty papers which were presented in the Second International Conference on BUSINESS SUSTAINABILITY 2011 - Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments , held in Póvoa de Varzim, Portugal, from 22ndto 24thof June, 2011.The main motive of the meeting was growing awareness of the importance of the sustainability issue. This importance had emerged from the growing uncertainty of the market behaviour that leads to the characterization of the market, i.e. environment, as turbulent. Actually, the characterization of the environment as uncertain and turbulent reflects the fact that the traditional technocratic and/or socio-technical approaches cannot effectively and efficiently lead with the present situation. In other words, the rise of the sustainability issue means the quest for new instruments to deal with uncertainty and/or turbulence. The sustainability issue has a complex nature and solutions are sought in a wide range of domains and instruments to achieve and manage it. The domains range from environmental sustainability (referring to natural environment) through organisational and business sustainability towards social sustainability. Concerning the instruments for sustainability, they range from traditional engineering and management methodologies towards “soft” instruments such as knowledge, learning, and creativity. The papers in this book address virtually whole sustainability problems space in a greater or lesser extent. However, although the uncertainty and/or turbulence, or in other words the dynamic properties, come from coupling of management, technology, learning, individuals, organisations and society, meaning that everything is at the same time effect and cause, we wanted to put the emphasis on business with the intention to address primarily companies and their businesses. Due to this reason, the main title of the book is “Business Sustainability 2.0” but with the approach of coupling Management, Technology and Learning for individuals, organisations and society in Turbulent Environments. Also, the notation“2.0” is to promote the publication as a step further from our previous publication – “Business Sustainability I” – as would be for a new version of software. Concerning the Second International Conference on BUSINESS SUSTAINABILITY, its particularity was that it had served primarily as a learning environment in which the papers published in this book were the ground for further individual and collective growth in understanding and perception of sustainability and capacity for building new instruments for business sustainability. In that respect, the methodology of the conference work was basically dialogical, meaning promoting dialog on the papers, but also including formal paper presentations. In this way, the conference presented a rich space for satisfying different authors’ and participants’ needs. Additionally, promoting the widest and global learning environment and participation, in accordance with the Conference's assumed mission to promote Proactive Generative Collaborative Learning, the Conference Organisation shares/puts open to the community the papers presented in this book, as well as the papers presented on the previous Conference(s). These papers can be accessed from the conference webpage (http://labve.dps.uminho.pt/bs11). In these terms, this book could also be understood as a complementary instrument to the Conference authors’ and participants’, but also to the wider readerships’ interested in the sustainability issues. The book brought together 107 authors from 11 countries, namely from Australia, Belgium, Brazil, Canada, France, Germany, Italy, Portugal, Serbia, Switzerland, and United States of America. The authors “ranged” from senior and renowned scientists to young researchers providing a rich and learning environment. At the end, the editors hope, and would like, that this book to be useful, meeting the expectation of the authors and wider readership and serving for enhancing the individual and collective learning, and to incentive further scientific development and creation of new papers. Also, the editors would use this opportunity to announce the intention to continue with new editions of the conference and subsequent editions of accompanying books on the subject of BUSINESS SUSTAINABILITY, the third of which is planned for year 2013.info:eu-repo/semantics/publishedVersio

Declarative techniques for modeling and mining business processes..

Organisaties worden vandaag de dag geconfronteerd met een schijnbare tegenstelling. Hoewel ze aan de ene kant veel geld geïnvesteerd hebben in informatiesystemen die hun bedrijfsprocessen automatiseren, lijken ze hierdoor minder in staat om een goed inzicht te krijgen in het verloop van deze processen. Een gebrekkig inzicht in de bedrijfsprocessen bedreigt hun flexibiliteit en conformiteit. Flexibiliteit is belangrijk, omdat organisaties door continu wijzigende marktomstandigheden gedwongen worden hun bedrijfsprocessen snel en soepel aan te passen. Daarnaast moeten organisaties ook kunnen garanderen dan hun bedrijfsvoering conform is aan de wetten, richtlijnen, en normen die hun opgelegd worden. Schandalen zoals de recent aan het licht gekomen fraude bij de Franse bank Société Générale toont het belang aan van conformiteit en flexibiliteit. Door het afleveren van valse bewijsstukken en het omzeilen van vaste controlemomenten, kon één effectenhandelaar een risicoloze arbitragehandel op prijsverschillen in futures omtoveren tot een risicovolle, speculatieve handel in deze financiële derivaten. De niet-ingedekte, niet-geautoriseerde posities bleven lange tijd verborgen door een gebrekkige interne controle, en tekortkomingen in de IT beveiliging en toegangscontrole. Om deze fraude in de toekomst te voorkomen, is het in de eerste plaats noodzakelijk om inzicht te verkrijgen in de operationele processen van de bank en de hieraan gerelateerde controleprocessen. In deze tekst behandelen we twee benaderingen die gebruikt kunnen worden om het inzicht in de bedrijfsprocessen te verhogen: procesmodellering en procesontginning. In het onderzoek is getracht technieken te ontwikkelen voor procesmodellering en procesontginning die declaratief zijn. Procesmodellering process modeling is de manuele constructie van een formeel model dat een relevant aspect van een bedrijfsproces beschrijft op basis van informatie die grotendeels verworven is uit interviews. Procesmodellen moeten adequate informatie te verschaffen over de bedrijfsprocessen om zinvol te kunnen worden gebruikt bij hun ontwerp, implementatie, uitvoering, en analyse. De uitdaging bestaat erin om nieuwe talen voor procesmodellering te ontwikkelen die adequate informatie verschaffen om deze doelstelling realiseren. Declaratieve procestalen maken de informatie omtrent bedrijfsbekommernissen expliciet. We karakteriseren en motiveren declaratieve procestalen, en nemen we een aantal bestaande technieken onder de loep. Voorts introduceren we een veralgemenend raamwerk voor declaratieve procesmodellering waarbinnen bestaande procestalen gepositioneerd kunnen worden. Dit raamwerk heet het EM-BrA�CE raamwerk, en staat voor `Enterprise Modeling using Business Rules, Agents, Activities, Concepts and Events'. Het bestaat uit een formele ontolgie en een formeel uitvoeringsmodel. Dit raamwerk legt de ontologische basis voor de talen en technieken die verder in het doctoraat ontwikkeld worden. Procesontginning process mining is de automatische constructie van een procesmodel op basis van de zogenaamde event logs uit informatiesystemen. Vandaag de dag worden heel wat processen door informatiesystemen in event logs geregistreerd. In event logs vindt men in chronologische volgorde terug wie, wanneer, welke activiteit verricht heeft. De analyse van event logs kan een accuraat beeld opleveren van wat er zich in werkelijkheid afspeelt binnen een organisatie. Om bruikbaar te zijn, moeten de ontgonnen procesmodellen voldoen aan criteria zoals accuraatheid, verstaanbaarheid, en justifieerbaarheid. Bestaande technieken voor procesontginning focussen vooral op het eerste criterium: accuraatheid. Declaratieve technieken voor procesontginning richten zich ook op de verstaanbaarheid en justifieerbaarheid van de ontgonnen modellen. Declaratieve technieken voor procesontginning zijn meer verstaanbaar omdat ze pogen procesmodellen voor te stellen aan de hand van declaratieve voorstellingsvormen. Daarenboven verhogen declaratieve technieken de justifieerbaarheid van de ontgonnen modellen. Dit komt omdat deze technieken toelaten de apriori kennis, inductieve bias, en taal bias van een leeralgoritme in te stellen. Inductief logisch programmeren (ILP) is een leertechniek die inherent declaratief is. In de tekst tonen we hoe proces mining voorgesteld kan worden als een ILP classificatieprobleem, dat de logische voorwaarden leert waaronder gebeurtenis plaats vindt (positief event) of niet plaatsvindt (een negatief event). Vele event logs bevatten van nature geen negatieve events die aangeven dat een bepaalde activiteit niet kon plaatsvinden. Om aan dit probleem tegemoet te komen, beschrijven we een techniek om artificiële negatieve events te genereren, genaamd AGNEs (process discovery by Artificially Generated Negative Events). De generatie van artificiële negatieve events komt neer op een configureerbare inductieve bias. De AGNEs techniek is geïmplementeerd als een mining plugin in het ProM raamwerk. Door process discovery voor te stellen als een eerste-orde classificatieprobleem op event logs met artificiële negatieve events, kunnen de traditionele metrieken voor het kwantificeren van precisie (precision) en volledigheid (recall) toegepast worden voor het kwantificeren van de precisie en volledigheid van een procesmodel ten opzicht van een event log. In de tekst stellen we twee nieuwe metrieken voor. Deze nieuwe metrieken, in combinatie met bestaande metrieken, werden gebruikt voor een uitgebreide evaluatie van de AGNEs techniek voor process discovery in zowel een experimentele als een praktijkopstelling.

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition

A computer graphics approach to logistics strategy modelling

This thesis describes the development and application of a decision support system for logistics strategy modelling. The decision support system that is developed enables the modelling of logistics systems at a strategic level for any country or area in the world. The model runs on IBM PC or compatible computers under DOS (disk operating system). The decision support system uses colour graphics to represent the different physical functions of a logistics system. The graphics of the system is machine independent. The model displays on the screen the map of the area or country which is being considered for logistic planning. The decision support system is hybrid in term of algorithm. It employs optimisation for allocation. The customers are allocated by building a network path from customer to the source points taking into consideration all the production and throughput constraints on factories, distribution depots and transshipment points. The system uses computer graphic visually interactive heuristics to find the best possible location for distribution depots and transshipment points. In a one depot system it gives the optimum solution but where more than one depot is involved, the optimum solution is not guaranteed. The developed model is a cost-driven model. It represents all the logistics system costs in their proper form. Its solution very much depends on the relationship between all the costs. The locations of depots and transshipment points depend on the relationship between inbound and outbound transportation costs. The model has been validated on real world problems, some of which are described here. The advantages of such a decision support system for the formulation of a problem are discussed. Also discussed is the contribution of such an approach at the validation and solution presentation stages

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

A vision-based optical character recognition system for real-time identification of tractors in a port container terminal

Automation has been seen as a promising solution to increase the productivity of modern sea port container terminals. The potential of increase in throughput, work efficiency and reduction of labor cost have lured stick holders to strive for the introduction of automation in the overall terminal operation. A specific container handling process that is readily amenable to automation is the deployment and control of gantry cranes in the container yard of a container terminal where typical operations of truck identification, loading and unloading containers, and job management are primarily performed manually in a typical terminal. To facilitate the overall automation of the gantry crane operation, we devised an approach for the real-time identification of tractors through the recognition of the corresponding number plates that are located on top of the tractor cabin. With this crucial piece of information, remote or automated yard operations can then be performed. A machine vision-based system is introduced whereby these number plates are read and identified in real-time while the tractors are operating in the terminal. In this paper, we present the design and implementation of the system and highlight the major difficulties encountered including the recognition of character information printed on the number plates due to poor image integrity. Working solutions are proposed to address these problems which are incorporated in the overall identification system.postprin