Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs

Multiobjective Evolutionary Optimization for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs

Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems

Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems

Multi-Objective Evolutionary Optimisation for Prototype-Based Fuzzy Classifiers

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification, while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimise the training error of the models on historical training data or alternatively, to iteratively minimise the intra-cluster variance of the clusters obtained via online data partitioning. This recognises the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimising the training error may potentially lead to overfitting, whilst minimising the intra-cluster variance does not necessarily ensure the optimised prototype-based models to attain improved classification outcomes. To achieve better classification performance whilst avoiding overfitting for zero-order EISs, this paper presents a novel multi-objective optimisation approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final non-dominated set of the resulting optimised models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimisation approach in improving the classification performance of zero-order EISs

INSTITUTIONAL LANGUAGE AND DISCOURSE IN INTERNATIONAL ORGANIZATIONS

ABSTRACT INSTITUTIONAL LANGUAGE AND DISCOURSE IN INTERNATIONAL ORGANIZATIONS To date institutional discourse has been little explored by analysts, who have rather focused on academic, corporate and professional settings in their discourse and genre analytical studies. This research analyzes the institutional discourse turned out in written form by three international organizations \u2013 the Food and Agriculture Organization, the World Health Organization and the World Trade Organization \u2013 in the form of periodical \u2013 mostly annual \u2013 reports detailing problematic areas, providing up-dates and illustrating programmes implemented by the International Community with a view to dealing with the great evils afflicting mankind, from extreme poverty and malnutrition, to ill health and commercial isolation. A corpus has been collected on the basis of typological and chronological criteria, running into approximately 2 million four hundred thousand tokens. It consists of three subcorpora, which coincide with the discursive production of each organization, starting in the late 1990s or early 2000s. Corpus collection has been stopped at the end of 2011. The research questions underlying the research are an exploration of the discursive strategies adopted in the texts under analysis, with a focus on their generic integrity and on the communicative purposes they serve. A discourse analytical approach integrating text grammar (Werlich 1983) and genre analysis (Bhatia 19993; 2004; 2010; Swales 1990), Critical Discourse Analysis (Dijk van 1998; 2000; Fairclough 1993; 1995a; 1995b; 2001; 2003) and functional\u2013systemic grammar (Halliday / Matthiessen 2004), has been selected as the most appropriate for a complex object of research, which needs a multi-perspective study. Recourse to automatic queries has been combined with a close reading of texts in an attempt to uncover the ideational and the interpersonal dimensions of the meaning-making process (Halliday / Matthiessen 2004). To this end the corpus has been scanned through both quantitative and qualitative analytical tools, with a focus on the text type profile (Werlich 1983) and the alternation of propositions and proposals (Halliday / Matthiessen 2004), as well as on the handling of person (Benveniste 1966) and modality (Coates 1983; Palmer 1979; 1986). Texts have been examined from both a synchronic and a diachronic perspective aiming to indentify regularities and signs of change in the handling of discursive strategies, while the pattern of rhetorical moves emerging from texts in the three subcorpora has been analyzed with a view to delineating the generic integrity of institutional reports, whose interdiscursive nature can be explored through a genre analytical approach. Finally, a smaller corpus has been selected for a close-up on fine linguistic choices such as the handling of \u201csemantic prosodies\u201d (Sinclair 1991) and the transitive / ergative structures underlying the letter-like Forewords and Messages prefacing the reports proper, which have been chosen for closer scrutiny on account of their ability to epitomize the longer texts from which they have been extracted and because of the role they play along both the ideational and the interpersonal dimensions of the meaning-making process

The future of manufacturing in Somerville

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 66-67).As traditional industrial uses in the U.S. have declined, Somerville, Massachusetts has similarly seen a decline in active industrial uses, together with a loss of living wage jobs. Somerville, like many New England cities, is now struggling to establish its identity in this "post-industrial" world. The City's large manufacturers have, for the most part, left the City leaving behind an abundance of old and irregular industrial building stock. Mayor Curtatone is on record as being interested in biotechnology, green, and clean energy businesses and City staff are actively seeking opportunities in these areas. At the same time, the City is increasingly becoming a regional destination for small-scale artisanal and food manufacturing. Products being manufactured locally include bicycles, guitars and chocolate. However, this sector faces many challenges such as limited growth and intense competition. Yet despite these challenges, this sector provides economic development returns by bringing new revenue into Somerville and providing lower skill residents with a higher wage than their alternatives in the retail and restaurant industry. What's more, this sector is appropriate for Somerville's land availability and building stock, and it significantly contributes to Somerville's creative brand and therefore its ability to attract more Creative Class residents and businesses. To bolster this sector, Somerville should embark on sector-specific strategy to strengthen the existing consumer goods sector. The cornerstone of this effort will be the creation of an umbrella organization that will provide technical assistance, marketing, and financial assistance to local manufacturers. To support this work, Somerville will also make infrastructure investments and targeted land use policies. With these policies in place, Somerville will create jobs in Somerville for Somerville residents, extract more value from the existing land, and strengthen its "brand" as a "City of Makers." What can other cities learn about manufacturing as an economic development activity from Somerville? When considering a manufacturing strategy, a city must consider: 1) its role within the region; 2) land availability; 3) existing building stock, and; 4) existing and nascent industry networks. Sound economic development strategies should not only attempt to foster profitable uses (tax revenues), but also uses that leverage a city's relative strengths and are aligned with their larger community and economic development goals including a range of jobs, quality of life, and perhaps most importantly, creating a strong identity and pride of place.by Anne Emig.M.C.P

Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning

River level estimation is a critical task required for the understanding of flood events, and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river camera images to estimate the river levels, but currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We develop an approach using an automated water semantic segmentation method to ease the process of river level estimation from river camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the Severn and Avon rivers, UK (21 November - 5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91%. Then, we apply our approach to year-long image series from the same cameras observing the Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's Correlation Coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations

Biodiversität - Vielfalt für die Zukunft: sozialwissenschaftliche Aspekte der biologischen Vielfalt

Anlässlich der UN-Naturschutzkonferenz vom 19. bis 30. Mai 2008 in Bonn bündelt die Mai-Ausgabe der Reihe Recherche Spezial aktuelle Literatur- und Forschungsnachweise zum Thema "Biodiversität". Die "Vielfalt der Arten" rückt mit dem vermehrten Aussterben ganzer Tier- und Pflanzenarten deutlicher denn je in den Fokus der Weltöffentlichkeit. Schließlich gilt die biologische Vielfalt als Grundpfeiler für die Stabilität eines Ökosystems. Doch das auf der UN-Naturschutz vordergründig diskutierte Thema "Biodiversität" umfasst nicht nur den Grad des Artenreichtums innerhalb eines Gebietes, sondern auch die genetische Vielfalt innerhalb einer Population. Auch jene Vielfalt ist durch Umweltverschmutzung, künstliche Genmanipulation und andere Einflüsse häufig bedroht. Die Folgewirkungen des menschlichen Einflusses auf die Biodiversität wirken freilich auf die Lebensbedingungen für den Menschen zurück. Abgesehen von dem immensen ökonomischen Schaden, die verödende Ökosysteme als indirekte Konsequenz verursachen, wird auch die Existenzgrundlage früher oder später in Gefahr geraten. Die vorliegende Ausgabe der Recherche Spezial beschäftigt sich mit solchen Folgen auf gesellschaftlicher und politischer Ebene. Aktuelle Literatur- und Forschungsnachweise beleuchten die sozialwissenschaftlichen Aspekte der Biodiversität und ihrer Gefährdung.The May issue of Research Special offers, on the occasion of the UN Convention on Biological Diversity held from 19-30 May, 2008 in Bonn, current literature and research references on the topic of biodiversity. Species diversity is in the world public eye, as the number of entire species of animals and plants threatened with extinction continues to rise. Biological diversity is viewed as a cornerstone of the stability of an ecosystem. The main topic of discussion at the UN Convention on Biological Diversity comprises not only the degree of biodiversity within an area, but also the genetic diversity within a population. All diversity is thus frequently endangered by environmental pollution, genetic engineering and other influences. The consequences of the human influence on biodiversity certainly have reciprocal effects on human living conditions. There is not only the ecological damage caused by increasingly barren ecosystems, but as a consequence our own existence will sooner or later be endangered. This issue of Research Special deals with such societal and political consequences. Current literature and research references illuminate the social science aspects of biodiversity and its endangerment. The project and literature references stern from the GESIS-databases SOLIS and SOFIS and the international CSA-databases

Der gläserne Bürger - personenbezogene Daten zwischen Forschung und Schwarzmarkt

Die Meldungen von neuen, immer weiter reichenden Datendiebstählen scheinen nicht abzureißen: Im Herbst 2008 eignete sich der Bundesverband der Verbraucherzentralen sechs Millionen Datensätze an, davon vier Millionen mit Kontonummern, um zu beweisen, dass die Beschaffung illegaler Daten ein Leichtes sei. Bereits im Jahr 2006 waren Datensätze von mehr als 17 Millionen Mobilfunkkunden der Telekom entwendet worden. Personenbezogene Daten sind inzwischen eine heißbegehrte Ware. Um dem Missbrauch von Daten vorzubeugen, hat die Bundesregierung eine neue Gesetzesinitiative gestartet, die die Verwendung personenbezogener Daten zu Werbezwecken oder zur Markt- und Meinungsforschung künftig nur noch mit ausdrücklicher Zustimmung zulassen möchte. Während die Datenschützer zustimmend jubilieren, bangt die Meinungsforschung um ihre Arbeitsgrundlage. Die vorliegende März-Ausgabe 2009 der Reihe Recherche Spezial beschäftigt sich mit dem Spannungsverhältnis zwischen informationeller Selbstbestimmung und Informationsfreiheit, in dessen Mitte sich der Datenschutz seit jeher bewegt. In fünf thematisch untergliederten Kapiteln werden aktuelle Literatur- und Forschungsnachweise mit bibliographischen Angaben und jeweils einer kurzen, inhaltlichen Zusammenfassung aufgelistet.Reports of new, ever more far-reaching data thefts appear unstoppable: In Fall of 2008, the Federation of German Consumer Organizations appropriated 6 million sets of data, 4 million of these with account numbers, to prove that it was relatively easy to obtain data illegally. Already in 2006 the data from more than 17 million Telekom cell phone customers was stolen. Personal information is, in the meantime, a highly sought after commodity. To stem the misuse of data the federal government has begun a new legislative initiative which would allow future usage of personal information for advertising purposes or for marketing and opinion research only with expressed permission. While "data protectionists" approvingly celebrate, opinion researchers fear for the basis of their work. The March 2009 edition of Research Special deals with the tense relationship between information self-determination and freedom of information in the midst of which is where the issue of data protection can be found, as always. Five chapters, arranged according to topic, list current literature and research references with bibliographic info along with a short content synopsis for each