Taxonomy Of Patterns Research Articles

Development of a spatial dimension-based taxonomy for classifying the defect patterns in a wafer bin map

A wafer bin map (WBM) represents the locational information of defective chips on the wafer. The spatial correlation of defects on the wafer provides crucial information for the root cause diagnosis of defects in wafer fabrication. The spatial correlation is classified as a defect pattern for efficient diagnostics. A defect pattern taxonomy should be defined in advance for coherent classification of defect patterns. Various taxonomies are used in previous studies, but they share common limitations in that the differentiation among defect patterns is unclear, the set of predefined defect patterns is insufficient, and they cannot accommodate newly-emerged defect patterns. A concept of spatial dimension-based defect pattern taxonomy and its development procedure are proposed. Defect patterns are defined by three spatial dimensions, namely, Shape, Size, and Location. The development procedure is applied to a major NAND flash memory semiconductor manufacturer for two years. Results show that spatial dimension-based taxonomy can improve the performance of the defect pattern classification system by alleviating common existing limitations. Moreover, meaningful defect patterns for diagnostics are retained through the engineers’ involvement in the development procedure.

The United Kingdom’s Human Rights Act as a Catalyst of Constitutional Migration: Patterns and Limitations of Rights Importation by Design

The United Kingdom Human Rights Act 1998 – Constitutional migration – Impacts of legislative design and process on the internalisation of international standards – A taxonomy of migratory patterns under the Human Rights Act – Constitutional migration as a source of constitutional instability – Proposals for a British Bill of Rights

Taxonomy of Damage Patterns in Composite Materials, Measuring Signals, and Methods for Automated Damage Diagnostics.

Due to the increasing use of the different composite materials in lightweight applications, such as in aerospace, it becomes crucial to understand the different damages occurring within them during life cycle and their possible inspection with different inspection techniques in different life cycle stages. A comprehensive classification of these damage patterns, measuring signals, and analysis methods using a taxonomical approach can help in this direction. In conjunction with the taxonomy, this work addresses damage diagnostics in hybrid and composite materials, such as fibre metal laminates (FMLs). A novel unified taxonomy atlas of damage patterns, measuring signals, and analysis methods is introduced. Analysis methods based on advanced supervised and unsupervised machine learning algorithms, such as autoencoders, self-organising maps, and convolutional neural networks, and a novel z-profiling method, are implemented. Besides formal aspects, an extended use case demonstrating damage identification in FML plates using X-ray computer tomography (X-ray CT) data is used to elaborate different data analysis techniques to amplify or detect damages and to show challenges.

A Novel Approach for Discovering the Patterns by using PDBD Model in Big Data

Big data is a term that refers to information collected from a wide range of sources, including transaction processing systems, sensors, digital photographs, internet click stream logs, movies, and social media. The goal of text mining is to reveal new patterns and relationships that could lead to the discovery of previously unknown sources. It provides ample scope for interpretation of genuine user intent or contextual meanings as the case be. This research focuses on the creation of a novel approach for discovering patterns in big data using the Pattern Discovery in Big Data (PDBD) model in text mining. From textual sources, text-mining techniques extract a range of patterns and important facts. Sequential pattern mining helps to extract informative elements from a set of sequences based on the frequency of occurrences. The Pattern Discovery in Big Data (PDBD) model study has four primary contributions to uncovering patterns uniquely and effectively: Semantic information extraction, pattern improvements, LDA Topic Modeling, and cluster assignments. The proposed work compared the Pattern Taxonomy Model (Inner Pattern Evolving) pattern inner evolving model with other existing similar benchmark models to evaluate the model performance. The empirical results prove that the proposed model outperformed the compared models with significant improvement in accuracy Improvement in this Pattern Discovery in Big Data (PDBD) model yields better results in discovering the patterns.

SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs

One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. To overcome these problems, in this paper, we propose a novel sequential word parsing-based method to construct and refine an uncertain question graph that is disambiguated directly over the knowledge graph. Instead of relying on the syntactic dependency relations and some predefined rules that recognize the relations and their arguments, we consider the identified entities and variables in the NLQ as well as their corresponding place in the structure of a query graph pattern to build question triples. First, by leveraging the ordered dependency tree of an NLQ, sentence words are reordered. Then the question graph structure is constructed by parsing the new sequence backward, starting from the identified items. Subsequently, the question graph is refined by eliminating the useless elements. Additionally, to improve the relation similarity measure in the graph similarity process, we exploit the knowledge hidden in a relation pattern taxonomy. Experimental studies over several benchmarks demonstrate that our proposed approach is effective as it achieves promising results in answering the complex NLQs.

Towards a Collection of Security and Privacy Patterns

Security and privacy (SP)-related challenges constitute a significant barrier to the wider adoption of Internet of Things (IoT)/Industrial IoT (IIoT) devices and the associated novel applications and services. In this context, patterns, which are constructs encoding re-usable solutions to common problems and building blocks to architectures, can be an asset in alleviating said barrier. More specifically, patterns can be used to encode dependencies between SP properties of individual smart objects and corresponding properties of orchestrations (compositions) involving them, facilitating the design of IoT solutions that are secure and privacy-aware by design. Motivated by the above, this work presents a survey and taxonomy of SP patterns towards the creation of a usable pattern collection. The aim is to enable decomposition of higher-level properties to more specific ones, matching them to relevant patterns, while also creating a comprehensive overview of security- and privacy-related properties and sub-properties that are of interest in IoT/IIoT environments. To this end, the identified patterns are organized using a hierarchical taxonomy that allows their classification based on provided property, context, and generality, while also showing the relationships between them. The two high-level properties, Security and Privacy, are decomposed to a first layer of lower-level sub-properties such as confidentiality and anonymity. The lower layers of the taxonomy, then, include implementation-level enablers. The coverage that these patterns offer in terms of the considered properties, data states (data in transit, at rest, and in process), and platform connectivity cases (within the same IoT platform and across different IoT platforms) is also highlighted. Furthermore, pointers to extensions of the pattern collection to include additional patterns and properties, including Dependability and Interoperability, are given. Finally, to showcase the use of the presented pattern collection, a practical application is detailed, involving the pattern-driven composition of IoT/IIoT orchestrations with SP property guarantees.

A proposed taxonomy for population-level prescription use patterns.

Recent and increasing discussion of prescription price transparency highlights the importance of defining, measuring and communicating prescription drug value. To help advance these goals, the authors propose a taxonomy of population-level prescription drug use patterns. The taxonomy assigns prescription use to one of five categories according to likely population-level health impact. The categories include effective, potentially discretionary, potentially harmful, wasteful, and lifestyle. The authors hope the proposed taxonomy will inform discussion of prescription drug value by providing estimates of population impact, especially the balance of anticipated benefit and harm.

Incongruity-resolution humorous strategies in image macro memes

Abstract In previous research (Yus 2016, 2017), a classification of jokes was proposed depending on how the humorous incongruity-resolution strategy was achieved. Twelve cases were isolated resulting from the combination of several parameters: (1) a differentiation between discourse-based incongruities and frame-based incongruities; (2) the location of the incongruity-triggering element (setup or punchline); and (3) three types of resolution: discourse-, frame- and implication-based. This paper proposes a similar taxonomy of incongruity-resolution patterns for a specific type of internet discourse: the image macro meme. The resulting seven-case taxonomy inherits some of the features of that were proposed for verbal jokes, albeit exhibiting the extent to which the image plays a specific role in the successful meme-centred humorous strategy.

Ladder Safety: A Taxonomy of Limb-Movement Patterns for Three Points of Control.

Traditional guidance on ladder safety emphasizes training workers on the use of three points of contact. More recent guidance is to train workers to use three points of control. What is lacking is empirical information about what limb-movement patterns effectively support the use of three points of control. This project was conducted to establish a taxonomy of possible limb-movement patterns and a means for comparing relative safety. Prior to the experiment, a taxonomy of six possible limb-movement patterns was established. A sample of 20 undergraduate students performed four tasks each without any instructions on limb-movement pattern. The tasks were ascending and descending a straight ladder and a portable ladder, once each, while being videotaped. Out of 80 observed tasks, 59 of the subjects were using rungs rather than rails. Analysis of rung users identified the use of all six patterns. An innovative measure of safe performance was developed and used to compare the patterns. Statistical analysis did not find significant differences in the patterns based on the safety performance measure.

Personality neuroscience and psychopathology: should we start with biology and look for neural-level factors?

"Personality is an abstraction used to explain consistency and coherency in an individual's pattern of affects, cognitions, desires and behaviors [ABCDs]" (Revelle, 2007, p. 37). But personality research currently provides more a taxonomy of patterns than theories of fundamental causes. Psychiatric disorders can be viewed as involving extremes of personality but are diagnosed via symptom patterns not biological causes. Such surface-level taxonomic description is necessary for science, but consistent predictive explanation requires causal theory. Personality constructs, and especially their clinical extremes, should predict variation in ABCD patterns, with parsimony requiring the lowest effective causal level of explanation. But, even biologically inspired personality theories currently use an intuitive language-based approach for scale development that lacks biological anchors. I argue that teleonomic "purpose" explains the organisation and outputs of conserved brain emotion systems, where high activation is adaptive in specific situations but is otherwise maladaptive. Simple modulators of whole-system sensitivity evolved because the requisite adaptive level can vary across people and time. Sensitivity to a modulator is an abstract predictive personality factor that operates at the neural level but provides a causal explanation of both coherence and occasional apparent incoherence in ABCD variation. Neuromodulators impact all levels of the "personality hierarchy" from metatraits to aspects: stability appears altered by serotonergic drugs, neuroticism by ketamine and trait anxiety by simple anxiolytic drugs. Here, the tools of psychiatry transfer to personality research and imply both interaction between levels and oblique factor mappings to ABCD. On this view, much psychopathology reflects extremes of neural-level personality factors, and we can view much pharmacotherapy as temporarily altering personality. So, particularly for personality factors linked to basic emotions and their disorders, I think we should start with evolutionary biology and look directly at conserved neural-level modulators for our explanatory personality constructs and only invoke higher order, emergent, explanations when neural-level explanation fails.

R2DCLT: retrieving relevant documents using cosine similarity and LDA in text mining

The availability of digital documents over web has increased exponentially and hence there is a need for effective methods to retrieve and organise. Since data is dispersed globally and unorganised, a number of algorithms have been proposed based on relevance calculations. However, it is found that there is a gap between user's search intention and retrieved results. In this paper, we propose a framework for retrieving relevant documents using cosine similarity (CS) and LDA in text mining (R2DCLT). The uniqueness of this approach is that LDA is applied for the documents and extracted patterns like unigram, bigram and trigram. Documents are ranked based on the CS score. Experiments are conducted on Reuters Corpus volume and custom news dataset. It is observed that R2DCLT outperforms pattern taxonomy and relevance feature discovery models by providing high quality relevant documents with improved response time and dynamically updated document set.

‘Let There Be Light!’ Dark Patterns Under the Lens of the EU Legal Framework

Dark patterns can be found both offline and online in various shapes and sizes. They can take the form of aesthetically coloured ‘Accept’ buttons capable of cajoling users into accepting terms and conditions, trick questions and default settings which lure users into sharing more personal data, or even the manner in which supermarkets choose to stack packs of water in their back shelves while displaying sweets in the front at the checkout counter. These practices go by the name of dark patterns, as they systematically and covertly aim to influence individuals into making choices by inhibiting the control of their decision-making. Dark patterns can be considered unethical since they do not take into account the interests of individuals, who, following the deployment of dark patterns become a means to an end. Relying only on ethics or social norms to prevent the use of dark patterns, however, is not effective given the difficulty in enforcing them. Thus, this paper will explore the underlying legal aspects of dark patterns. Chapter 1 sets the scene by introducing the central research question of determining whether the EU legal framework needs a specific law to prevent and sanction the use of dark patterns. The first half of Chapter 2 reveals a taxonomy of dark patterns and further defines this concept before the second half delves into a legal analysis of the laws applicable to dark patterns. From privacy and data protection to consumer protection, media law, competition law or contract law, this legal analysis unveils the existence of a broad legal framework which can protect individuals’ decision-making process against deceptive, manipulative, coercive or excessively persuasive dark patterns. Finally, Chapter 3 reveals the conclusions and recommendations of this paper. While the EU legal framework does not require a specific law to prevent and sanction the use of dark patterns, there is still a lack of awareness and enforcement. The solution, therefore, lies in a three-fold approach (individuals, companies, and authorities), which would leverage the best results in increasing awareness and enforcement in relation to dark patterns.

Industry Business Model Patterns for Business Model Innovation–The Case of the Electricity Industry

This paper addresses the question of how a pattern taxonomy of activities in an industry can be used to innovate business models. At the example of the electricity industry – an industry that is facing several changes due to digital technologies, liberalization, and renewable energy technologies – we present a systematic process of building a business model pattern taxonomy using a modified Delphi card sorting approach. In doing so, this paper contributes to understanding how patterns and pattern taxonomies can be used in business model innovation and provide a methodology to catalogue emerging new business models in traditional industries.

The sustainable business model pattern taxonomy—45 patterns to support sustainability-oriented business model innovation

The literature on sustainable business models (SBMs) offers different classifications of the available kinds of SBM. Our careful reading of this literature reveals that the received classifications have developed ad-hoc from multiple divergent perspectives. As a consequence, the proposed classifications are only partly overlapping and difficult to reconcile, thus hampering cumulative progress. Building on this premise, we offer a synthesis and consolidation of the available knowledge about SBMs. Following the notion of patterns as problem–solution combinations, we developed, tested, and applied a new multi-method and multi-step approach centred on an expert review process that combines literature review, Delphi survey, and physical card sorting to identify and validate the currently existing SBM patterns. Ten international experts participated in this process. They classified 45 SBM patterns, assigned these patterns to 11 groups along ecological, social, and economic dimensions of sustainability and evaluated their potential to contribute to value creation. The resulting taxonomy can serve as a basis for more unified and comparable studies of SBMs and for new business model tools that can be used in various disciplines and industries to analyse and develop sustainability-oriented business models in a consistent manner.

Strategy patterns and corporate competitive advantage in hypercompetition

Action aggressiveness is considered as a firm response to the phenomenon of temporary advantage. Firms able to respond quickly to market demands strengthen their market power and generate advantages; but those that can be even faster, will generate even greater market power and advantage over its competitors. However, there is no guarantee that competitive advantage achieved today will remain unchanged in the long run. The focus is on being prepared to take an action, i.e. the extent to which the firm is willing to participate with competitors and act quickly in the involvement and participation. The dynamics of top management is a very important component of the ability of the firm competitive behavior. The top management team is the kingpin that coordinates and mobilizes organizational resources and efforts for firms’ aggressive competitive engagement. The assumption of being more aggressive at the market and collaborative with competitors is the integration of top management of the firm that depends primarily on compatible traits and members’ communication skills. With a special focus on top management teams, this paper explores the extent to which firms’ certain strategic behavior in hypercompetitive industry can be related to gaining temporary competitive advantage, measured through improving its firm performance. The central goal of this research is to theoretically and empirically define and examine firms’ strategic behavior in hypercompetition through defining new taxonomy of strategy patterns, i.e. firm specific strategic behavior that provides and raises the probability of gaining the competitive advantage in hypercompetitive industries. An important contribution of this research is also reflected in the development of the model that analyze the influence of specific characteristics of top management team (TMT) on pattern firm uses, as well as the relationship between firms’ market position on specific strategic behavior.

An Overview of Models of Bilingual Education

Bilingual education is considered to be an effective way of learning a new language and in many countries of the world, it represents an increasingly popular pedagogic approach. In Europe, bilingual education using predominantly the Content and Language Integrated Learning (CLIL) methodology has evolved as a response to the commonly recognized need for the development of plurilingual competence. In this article, we present forms of bilingual education that are recognized according to worldwide research as well as varying purposes behind using different languages as the medium of instruction in schools. We begin by outlining the emergence of bilingual education in general and by stating the different purposes that lead to the occurrence of various actual school systems of bilingual education. The paper focuses on a taxonomy of common patterns of bilingual education, based on worldwide research sources. DOI: 10.5901/mjss.2015.v6n6s1p186

Similarity in difference: marriage in Europe and Asia, 1700-1900

A study of marriage in preindustrial Europe and Asia that goes beyond the Malthusian East-West dichotomy to find variation within regions and commonality across regions. Since Malthus, an East-West dichotomy has been used to characterize marriage behavior in Asia and Europe. Marriages in Asia were said to be early and universal, in Europe late and non-universal. In Europe, marriages were supposed to be the result of individual choices but, in Asia, decided by families and communities. This book challenges this binary taxonomy of marriage patterns and family systems. Drawing on richer and more nuanced data, the authors compare the interpretations based on aggregate demographic patterns with studies of individual actions in local populations. Doing so, they are able to analyze simultaneously the influence on marriage decisions of individual demographic features, socioeconomic status and composition of the household, and local conditions, and the interactions of these variables. They find differences between East and West but also variation within regions and commonality across regions. The book studies local populations in Sweden, Belgium, Italy, Japan, and China. Rather than a simple comparison of aggregate marriage patterns, it examines marriage outcomes and determinants of local populations in different countries using similar data and methods. The authors first present the results of comparative analyses of first marriage and remarriage and then offer chapters each of which is devoted to the results from a specific country. Similarity in Difference is the third in a prizewinning series on the demographic history of Eurasia, following Life under Pressure (2004) and Prudence and Pressure (2009), both published by the MIT Press.

Pattern Discovery Text Mining for Document Classification

In text documents data mining techniques have been discovered for mining useful patterns. But there are some questions, how to properly use and update discovered patterns is still an open research issue, specifically in the text mining. Therefore most existing text mining methods have used term-based approaches but they all suffer from the problems of polysemy (multiple meaning word) and synonymy(same meaning word).This is the literature survey paper with proposed system develops innovative and successful patternbased technique which contains the processes of pattern taxonomy, pattern deploying and gradually developing pattern ,to improve the effectiveness of using and updating researched patterns for finding applicable and interesting data with effectual patterns as per the users requirements. In this paper user is also getting the meaningful information without wrong meaning problem. General Terms Text mining, information filtering, Pattern Mining, Data Mining, pattern evolving, Text Classification.

Olfactory tubercle neurons exhibit slow-phasic firing patterns during cocaine self-administration

Olfactory tubercle neurons exhibit slow-phasic firing patterns during cocaine self-administration

Classification of Documents using Effective Pattern Taxonomy

Text mining is a technique helps users in extracting useful information from large amount of database available digitally on web or text data. Pattern Taxonomy based model containing sequential pattern used to perform the task. EPT (Effective Pattern Taxonomy) method helps in extracting useful patterns in the text documents by classifying them in Positive and Negative documents. Pattern-based method outperforms keyword based methods. Pattern based method is best way to eliminate meaningless as well as closed sequential patterns thus saving computational time and increases effectiveness of the system.