Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine

This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined

Online Diagnosis based on Chronicle Recognition of a Coil Winding Machine

This paper falls under the problems of the diagnosis of Discrete Event System (DES) such as coil winding machine. Among the various techniques used for the on-line diagnosis, we are interested in the chronicle recognition and fault tree. The Chronicle can be defined as temporal patterns that represent system possible evolutions of an observed system. Starting from the model of the system to be diagnosed, the proposed method based on the P-time Petri net allows to generate the chronicles necessary to the diagnosis. Finally, to demonstrate the effectiveness and accuracy of the monitoring approach, an application to a coil winding unit is outlined

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Integrating Diagnostic and Repair to Ensure the Quality of a Composition of Web Services

Service-Oriented Computing is based on dynamic composition of web services to meet the demand of a user. A major challenge in conditioning actual use of web services is to monitor their performance and enable them to react to unexpected malfunctioning. This can be done using the mechanisms of exception handling. But they do react in a predefined manner and local issues have to be planned at the services design time. However, in dynamic environments like the Internet, web services may be subject to unexpected malfunctioning which may not be handled with repair mechanisms defined at design time. In addition, local management ignores errors during the interactions between services, which limit their effectiveness. Such failures may also propagate through the services before being detected, and the key is to find the problem at the source of the malfunction and repair the service. In this context, this work is dedicated to study a distributed but coordinated and dynamic management of repair mechanisms. The difficulty is that repairs are carried out locally, but a global approach must be ensured to take into account interactions between different services. Our objective is to propose a diagnostic-repair architecture and mechanisms for this feature in detail

Uncoupling inequality: Reflections on the ethics of benchmarks for digital media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion

Literatures of urban development: World bank literature and the chronicles of Rio de Janeiro and Mexico City

Contextualized within the setting of urban development, this dissertation considers contemporary manifestations of international development policy alongside the work of chroniclers in Latin America. Specifically, this project studies the World Bank's involvement in Mexico City and Rio de Janeiro over the last thirty years in relation to chronicles--literary-journalistic writings distributed primarily through newspapers and magazines--written in and about these two cities. Examining documents produced by the World Bank's staff alongside the work of chroniclers residing in these cities provides an informed locale from which to analyze the World Bank's approach to issues confronting the urban spaces within Latin America. This project also argues that a common genre of the chronicle exists in these two cities, regardless of the language in which it is written. The first chapter contextualizes the project within a history of the World Bank and the genre of the chronicle in Mexico and Brazil. The second chapter focuses on Mexico, studying the chronicles of Elena Poniatowska and Carlos Monsiváis along side the work of the World Bank as they respond to the earthquake of 1985 in Mexico City. The third chapter considers urban poverty in Brazil's Rio de Janeiro and pulls from the writings of Carlos Drummond de Andrade, Luis Fernando Veríssimo, Affonso Romano de Sant'Anna, Marina Colasanti and Diogo Mainardi. The fourth chapter examines public transportation in Mexico City as well as the favelas of Rio de Janeiro, pulling from chronicles by Carlos Monsiváis and Diogo Mainardi. I argue that the World Bank must approach its lending decisions with a nuanced understanding of whom its loans and projects will affect. When designing projects with such cultural and social knowledge, the priorities addressed by World Bank align closely with the concerns forwarded by the chroniclers considered in this study. Moreover, this approach to project design and implementation enables the World Bank to honor its stated mission of "using [its] financial resources, staff and extensive experience to help developing countries reduce poverty, increase economic growth and improve their quality of life" (World Bank Group Brochure 3)

Digital dying in personal information management towards thanotosensitive information management

Tese de mestrado. Multimédia. Universidade do Porto. Faculdade de Engenharia. 201