Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

A multi-agent system with distributed bayesian reasoning for network fault diagnosis

In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.

Bayesian retrieval of complete posterior PDFs of oceanic rain rate from microwave observations

A new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) over the ocean is presented, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge. While other rain-rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes’s theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate. To advance the understanding of theoretical benefits of the Bayesian approach, sensitivity analyses have been conducted based on two synthetic datasets for which the “true” conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism, but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak owing to saturation effects. It is also suggested that both the choice of the estimators and the prior information are crucial to the retrieval. In addition, the performance of the Bayesian algorithm herein is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.

B2DI a bayesian BDI agent model with causal belief updating based on MSBN

In this paper, we introduce B2DI model that extends BDI model to perform Bayesian inference under uncertainty. For scalability and flexibility purposes, Multiply Sectioned Bayesian Network (MSBN) technology has been selected and adapted to BDI agent reasoning. A belief update mechanism has been defined for agents, whose belief models are connected by public shared beliefs, and the certainty of these beliefs is updated based on MSBN. The classical BDI agent architecture has been extended in order to manage uncertainty using Bayesian reasoning. The resulting extended model, so-called B2DI, proposes a new control loop. The proposed B2DI model has been evaluated in a network fault diagnosis scenario. The evaluation has compared this model with two previously developed agent models. The evaluation has been carried out with a real testbed diagnosis scenario using JADEX. As a result, the proposed model exhibits significant improvements in the cost and time required to carry out a reliable diagnosis.

Crowdsourcing without a crowd : Reliable online species identification using Bayesian models to minimize crowd size

Acknowledgment This research is supported by an award made by the RCUK Digital Economy program to the University of Aberdeen’s dot.rural Digital Economy Hub (ref. EP/G066051/1).

Exploring transparent approaches to the authentication of signatures on artwork

This thesis is composed of three main parts. The first consists of a state of the art of the different notions that are significant to understand the elements surrounding art authentication in general, and of signatures in particular, and that the author deemed them necessary to fully grasp the microcosm that makes up this particular market. Individuals with a solid knowledge of the art and expertise area, and that are particularly interested in the present study are advised to advance directly to the fourth Chapter. The expertise of the signature, it's reliability, and the factors impacting the expert's conclusions are brought forward. The final aim of the state of the art is to offer a general list of recommendations based on an exhaustive review of the current literature and given in light of all of the exposed issues. These guidelines are specifically formulated for the expertise of signatures on paintings, but can also be applied to wider themes in the area of signature examination. The second part of this thesis covers the experimental stages of the research. It consists of the method developed to authenticate painted signatures on works of art. This method is articulated around several main objectives: defining measurable features on painted signatures and defining their relevance in order to establish the separation capacities between groups of authentic and simulated signatures. For the first time, numerical analyses of painted signatures have been obtained and are used to attribute their authorship to given artists. An in-depth discussion of the developed method constitutes the third and final part of this study. It evaluates the opportunities and constraints when applied by signature and handwriting experts in forensic science. A brief summary covering each chapter allows a rapid overview of the study and summarizes the aims and main themes of each chapter. These outlines presented below summarize the aims and main themes addressed in each chapter. Part I - Theory Chapter 1 exposes legal aspects surrounding the authentication of works of art by art experts. The definition of what is legally authentic, the quality and types of the experts that can express an opinion concerning the authorship of a specific painting, and standard deontological rules are addressed. The practices applied in Switzerland will be specifically dealt with. Chapter 2 presents an overview of the different scientific analyses that can be carried out on paintings (from the canvas to the top coat). Scientific examinations of works of art have become more common, as more and more museums equip themselves with laboratories, thus an understanding of their role in the art authentication process is vital. The added value that a signature expertise can have in comparison to other scientific techniques is also addressed. Chapter 3 provides a historical overview of the signature on paintings throughout the ages, in order to offer the reader an understanding of the origin of the signature on works of art and its evolution through time. An explanation is given on the transitions that the signature went through from the 15th century on and how it progressively took on its widely known modern form. Both this chapter and chapter 2 are presented to show the reader the rich sources of information that can be provided to describe a painting, and how the signature is one of these sources. Chapter 4 focuses on the different hypotheses the FHE must keep in mind when examining a painted signature, since a number of scenarios can be encountered when dealing with signatures on works of art. The different forms of signatures, as well as the variables that may have an influence on the painted signatures, are also presented. Finally, the current state of knowledge of the examination procedure of signatures in forensic science in general, and in particular for painted signatures, is exposed. The state of the art of the assessment of the authorship of signatures on paintings is established and discussed in light of the theoretical facets mentioned previously. Chapter 5 considers key elements that can have an impact on the FHE during his or her2 examinations. This includes a discussion on elements such as the skill, confidence and competence of an expert, as well as the potential bias effects he might encounter. A better understanding of elements surrounding handwriting examinations, to, in turn, better communicate results and conclusions to an audience, is also undertaken. Chapter 6 reviews the judicial acceptance of signature analysis in Courts and closes the state of the art section of this thesis. This chapter brings forward the current issues pertaining to the appreciation of this expertise by the non- forensic community, and will discuss the increasing number of claims of the unscientific nature of signature authentication. The necessity to aim for more scientific, comprehensive and transparent authentication methods will be discussed. The theoretical part of this thesis is concluded by a series of general recommendations for forensic handwriting examiners in forensic science, specifically for the expertise of signatures on paintings. These recommendations stem from the exhaustive review of the literature and the issues exposed from this review and can also be applied to the traditional examination of signatures (on paper). Part II - Experimental part Chapter 7 describes and defines the sampling, extraction and analysis phases of the research. The sampling stage of artists' signatures and their respective simulations are presented, followed by the steps that were undertaken to extract and determine sets of characteristics, specific to each artist, that describe their signatures. The method is based on a study of five artists and a group of individuals acting as forgers for the sake of this study. Finally, the analysis procedure of these characteristics to assess of the strength of evidence, and based on a Bayesian reasoning process, is presented. Chapter 8 outlines the results concerning both the artist and simulation corpuses after their optical observation, followed by the results of the analysis phase of the research. The feature selection process and the likelihood ratio evaluation are the main themes that are addressed. The discrimination power between both corpuses is illustrated through multivariate analysis. Part III - Discussion Chapter 9 discusses the materials, the methods, and the obtained results of the research. The opportunities, but also constraints and limits, of the developed method are exposed. Future works that can be carried out subsequent to the results of the study are also presented. Chapter 10, the last chapter of this thesis, proposes a strategy to incorporate the model developed in the last chapters into the traditional signature expertise procedures. Thus, the strength of this expertise is discussed in conjunction with the traditional conclusions reached by forensic handwriting examiners in forensic science. Finally, this chapter summarizes and advocates a list of formal recommendations for good practices for handwriting examiners. In conclusion, the research highlights the interdisciplinary aspect of signature examination of signatures on paintings. The current state of knowledge of the judicial quality of art experts, along with the scientific and historical analysis of paintings and signatures, are overviewed to give the reader a feel of the different factors that have an impact on this particular subject. The temperamental acceptance of forensic signature analysis in court, also presented in the state of the art, explicitly demonstrates the necessity of a better recognition of signature expertise by courts of law. This general acceptance, however, can only be achieved by producing high quality results through a well-defined examination process. This research offers an original approach to attribute a painted signature to a certain artist: for the first time, a probabilistic model used to measure the discriminative potential between authentic and simulated painted signatures is studied. The opportunities and limits that lie within this method of scientifically establishing the authorship of signatures on works of art are thus presented. In addition, the second key contribution of this work proposes a procedure to combine the developed method into that used traditionally signature experts in forensic science. Such an implementation into the holistic traditional signature examination casework is a large step providing the forensic, judicial and art communities with a solid-based reasoning framework for the examination of signatures on paintings. The framework and preliminary results associated with this research have been published (Montani, 2009a) and presented at international forensic science conferences (Montani, 2009b; Montani, 2012).

Contextual Priming for Object Detection

There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. Here we introduce a simple probabilistic framework for modeling the relationship between context and object properties based on the correlation between the statistics of low-level features across the entire scene and the objects that it contains. The resulting scheme serves as an effective procedure for object priming, context driven focus of attention and automatic scale-selection on real-world scenes.

Application of Agent Technology for Fault Diagnosis of Telecommunication Networks

La presente tesis doctoral contribuye al problema del diagnóstico autonómico de fallos en redes de telecomunicación. En las redes de telecomunicación actuales, las operadoras realizan tareas de diagnóstico de forma manual. Dichas operaciones deben ser llevadas a cabo por ingenieros altamente cualificados que cada vez tienen más dificultades a la hora de gestionar debidamente el crecimiento exponencial de la red tanto en tamaño, complejidad y heterogeneidad. Además, el advenimiento del Internet del Futuro hace que la demanda de sistemas que simplifiquen y automaticen la gestión de las redes de telecomunicación se haya incrementado en los últimos años. Para extraer el conocimiento necesario para desarrollar las soluciones propuestas y facilitar su adopción por los operadores de red, se propone una metodología de pruebas de aceptación para sistemas multi-agente enfocada en simplificar la comunicación entre los diferentes grupos de trabajo involucrados en todo proyecto de desarrollo software: clientes y desarrolladores. Para contribuir a la solución del problema del diagnóstico autonómico de fallos, se propone una arquitectura de agente capaz de diagnosticar fallos en redes de telecomunicación de manera autónoma. Dicha arquitectura extiende el modelo de agente Belief-Desire- Intention (BDI) con diferentes modelos de diagnóstico que gestionan las diferentes sub-tareas del proceso. La arquitectura propuesta combina diferentes técnicas de razonamiento para alcanzar su propósito gracias a un modelo estructural de la red, que usa razonamiento basado en ontologías, y un modelo causal de fallos, que usa razonamiento Bayesiano para gestionar debidamente la incertidumbre del proceso de diagnóstico. Para asegurar la adecuación de la arquitectura propuesta en situaciones de gran complejidad y heterogeneidad, se propone un marco de argumentación que permite diagnosticar a agentes que estén ejecutando en dominios federados. Para la aplicación de este marco en un sistema multi-agente, se propone un protocolo de coordinación en el que los agentes dialogan hasta alcanzar una conclusión para un caso de diagnóstico concreto. Como trabajos futuros, se consideran la extensión de la arquitectura para abordar otros problemas de gestión como el auto-descubrimiento o la auto-optimización, el uso de técnicas de reputación dentro del marco de argumentación para mejorar la extensibilidad del sistema de diagnóstico en entornos federados y la aplicación de las arquitecturas propuestas en las arquitecturas de red emergentes, como SDN, que ofrecen mayor capacidad de interacción con la red. ABSTRACT This PhD thesis contributes to the problem of autonomic fault diagnosis of telecommunication networks. Nowadays, in telecommunication networks, operators perform manual diagnosis tasks. Those operations must be carried out by high skilled network engineers which have increasing difficulties to properly manage the growing of those networks, both in size, complexity and heterogeneity. Moreover, the advent of the Future Internet makes the demand of solutions which simplifies and automates the telecommunication network management has been increased in recent years. To collect the domain knowledge required to developed the proposed solutions and to simplify its adoption by the operators, an agile testing methodology is defined for multiagent systems. This methodology is focused on the communication gap between the different work groups involved in any software development project, stakeholders and developers. To contribute to overcoming the problem of autonomic fault diagnosis, an agent architecture for fault diagnosis of telecommunication networks is defined. That architecture extends the Belief-Desire-Intention (BDI) agent model with different diagnostic models which handle the different subtasks of the process. The proposed architecture combines different reasoning techniques to achieve its objective using a structural model of the network, which uses ontology-based reasoning, and a causal model, which uses Bayesian reasoning to properly handle the uncertainty of the diagnosis process. To ensure the suitability of the proposed architecture in complex and heterogeneous environments, an argumentation framework is defined. This framework allows agents to perform fault diagnosis in federated domains. To apply this framework in a multi-agent system, a coordination protocol is defined. This protocol is used by agents to dialogue until a reliable conclusion for a specific diagnosis case is reached. Future work comprises the further extension of the agent architecture to approach other managements problems, such as self-discovery or self-optimisation; the application of reputation techniques in the argumentation framework to improve the extensibility of the diagnostic system in federated domains; and the application of the proposed agent architecture in emergent networking architectures, such as SDN, which offers new capabilities of control for the network.

Towards Qualitative Approaches to Bayesian Evidential Reasoning

A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.

Probabilistic reasoning with a bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique which performs Bayesian inference. The model will take single stranded DNA as input data, representing the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease playing with a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single stranded DNA species whose relative proportion represents the application of Bayes? Law: the conditional probability of the disease given the signal. The models presented in this paper can empower the application of probabilistic reasoning in genetic diagnosis in vitro.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

On Qualitative Probabilities for Legal Reasoning about Evidence

A crucial aspect of evidential reasoning in crime investigation involves comparing the support that evidence provides for alternative hypotheses. Recent work in forensic statistics has shown how Bayesian Networks (BNs) can be employed for this purpose. However, the specification of BNs requires conditional probability tables describing the uncertain processes under evaluation. When these processes are poorly understood, it is necessary to rely on subjective probabilities provided by experts. Accurate probabilities of this type are normally hard to acquire from experts. Recent work in qualitative reasoning has developed methods to perform probabilistic reasoning using coarser representations. However, the latter types of approaches are too imprecise to compare the likelihood of alternative hypotheses. This paper examines this shortcoming of the qualitative approaches when applied to the aforementioned problem, and identifies and integrates techniques to refine them.