Pattern Instances Research Articles

High-order complex Fourier numerical manifold method for improving the optimization of cracked structures

In this paper, a combination of the high-order numerical manifold method with material interpolation is established with the goal of improving the optimization of cracked structures. Complex Fourier shape functions, known for their inherent advantages, are utilized as weight functions in the numerical manifold method. By implementing high-order analysis, the occurrence of checkerboard patterns is effectively avoided. In addition, a modified sensitivity filtering technique is introduced to address the issue of mesh dependency and minimum length scale in the final topology. This technique is based on the notion of a manifold element. Furthermore, a methodology is introduced to represent void regions within the design domain without the need for passive elements. This methodology involves describing an algorithm to accurately determine the manifold element and the corresponding element area within the final topology. Numerical problems of topology optimization are presented to examine all the aforementioned advantages. The findings indicate that the proposed algorithm for topology optimization does not exhibit any instances of checkerboard patterns. Moreover, the final topology of the curved void regions does not show any zigzag patterns. The above method demonstrates a commendable rate of convergence, and the final topology of the cracked regions can be conveniently simulated.

‘We’re not that Gullible!’ Revealing Dark Pattern Mental Models of 11-12 Year-Old Scottish Children

Deceptive techniques known as dark patterns specifically target online users. Children are particularly vulnerable as they might lack the skills to recognise and resist these deceptive attempts. To be effective, interventions to forewarn and forearm should build on a comprehensive understanding of children’s existing mental models. To this end, we carried out a study with 11-12 year old Scottish children to reveal their mental models of dark patterns. They were acutely aware of online deception, referring to deployers as being ‘up to no good’. Yet, they were overly vigilant and construed worst-case outcomes, with even a benign warning triggering suspicion. We recommend that rather than focusing on specific instances of dark patterns in awareness raising, interventions should prioritise improving children’s understanding of the characteristics of, and the motivations behind, deceptive online techniques. By so doing, we can help them to develop a more robust defence against these deceptive practices.

There Are Infinite Ways to Formulate Code: How to Mitigate the Resulting Problems for Better Software Vulnerability Detection

Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent ways to implement a particular functionality in a program. For instance, the naming of variables is often a matter of the personal style of programmers, and thus, the detection of vulnerability patterns in programs is made difficult. Current deep learning approaches to software vulnerability detection rely on the raw text of a program and exploit general natural language processing capabilities to address the problem of dealing with different naming schemes in instances of vulnerability patterns. Relying on natural language processing, and learning how to reveal variable reference structures from the raw text, is often too high a burden, however. Thus, approaches based on deep learning still exhibit problems generating a detector with decent generalization properties due to the naming or, more generally formulated, the vocabulary explosion problem. In this work, we propose techniques to mitigate this problem by making the referential structure of variable references explicit in input representations for deep learning approaches. Evaluation results show that deep learning models based on techniques presented in this article outperform raw text approaches for vulnerability detection. In addition, the new techniques also induce a very small main memory footprint. The efficiency gain of memory usage can be up to four orders of magnitude compared to existing methods as our experiments indicate.

Live software documentation of design pattern instances.

Approaches to documenting the software patterns of a system can support intentionally and manually documenting them or automatically extracting them from the source code. Some of the approaches that we review do not maintain proximity between code and documentation. Others do not update the documentation after the code is changed. All of them present a low level of liveness. This work proposes an approach to improve the understandability of a software system by documenting the design patterns it uses. We regard the creation and the documentation of software as part of the same process and attempt to streamline the two activities. We achieve this by increasing the feedback about the pattern instances present in the code, during development-i.e., by increasing liveness. Moreover, our approach maintains proximity between code and documentation and allows us to visualize the pattern instances under the same environment. We developed a prototype-DesignPatternDoc-for IntelliJ IDEA that continuously identifies pattern instances in the code, suggests them to the developer, generates the respective pattern-instance documentation, and enables live editing and visualization of that documentation. To evaluate this approach, we conducted a controlled experiment with 21 novice developers. We asked participants to complete three tasks that involved understanding and evolving small software systems-up to six classes and 100 lines of code-and recorded the duration and the number of context switches. The results show that our approach helps developers spend less time understanding and documenting a software system when compared to using tools with a lower degree of liveness. Additionally, embedding documentation in the IDE and maintaining it close to the source code reduces context switching significantly.

A Blockchain Approach to Extract Design Patterns From Source Codes

Design patterns are an effective technique for software development. However, lacking suitable documents often reduces traceability, and advantages remain unclear in source lines of code (SLOC). Design patterns are proven, reliable solutions to several frequent problems in object‐oriented software design. Detecting design patterns in a software system contributes to maintainability and reengineering and facilitates understanding codes. Furthermore, different design pattern implementations impede the detection of code pattern instances. A design pattern is a set of roles played by the code classes. Roles are the main details and determinants in patterns. A pattern can be detected in its defined role as it is an instance of a role. It is also of great importance in system documentation. The proposed method exploits blockchain‐based approaches for pattern mining and isolating design patterns in order to identify intercode relationships. The results demonstrate that the blockchain is effective and efficient in extracting the connections of features from source codes and adapting to design patterns with precision 97.2%.

A study of behavioral decay in design patterns

AbstractDesign patterns represent a means of communicating reusable solutions to common problems, provided they are implemented and maintained correctly. However, many design pattern instances erode as they age, sacrificing qualities they once provided. Identifying such instances of pattern decay is valuable because it allows for proactive attempts to extend the longevity and quality attributes of pattern components. Apart from structural decay, design patterns can exhibit symptoms of behavioral decay. We utilized a taxonomy that characterizes these negative behaviors and designed a case study wherein we measured structural and behavioral decay, hereafter referred to as pattern grime, as well as pattern quality and size, across pattern evolutions. We evaluated the relationships between structural and behavioral grime and found statistically significant cases of strong correlations between specific types of structural and behavioral grime. Furthermore, we extended the QATCH operational software quality model to incorporate design pattern evolution metrics and measured and correlated software quality to the presence of behavioral grime in software systems. Our results suggest a strong inverse relationship between software quality and behavioral grime.

Intramedullary Fibular Strut Allograft and a Small Fragment Nonlocking T-plate for Periprosthetic Lateral Femoral Condyle Fracture: A Case Report

Intramedullary Fibular Strut Allograft and a Small Fragment Nonlocking T-plate for Periprosthetic Lateral Femoral Condyle Fracture: A Case Report

A Review of Control Charts and Exploring Their Utility for Regional Environmental Monitoring Programs

Industrial control charts are used in manufacturing to quickly and robustly indicate the status of production and to prompt any necessary corrective actions. The library of tools available for these tasks has grown over time and many have been used in other disciplines with similar objectives, including environmental monitoring. While the utility of control charts in environmental monitoring has been recognized, and the tools have already been used in many individual studies, they may be underutilized in some types of programs. For example, control charts may be especially useful for reporting and evaluating data from regional surveillance monitoring programs, but they are not yet routinely used. The purpose of this study was to promote the use of control charts in regional environmental monitoring by surveying the literature for control charting techniques suitable for the various types of data available from large programs measuring multiple indicators at multiple locations across various physical environments. Example datasets were obtained for Canada’s Oil Sands Region, including water quality, air quality, facility production and performance, and bird communities, and were analyzed using univariate (e.g., x-bar) and multivariate (e.g., Hotelling’s T2) control charts. The control charts indicated multiple instances of unexpected observations and highlighted subtle patterns in all of the example data. While control charts are not uniquely able to identify potentially relevant patterns in data and can be challenging to apply in some monitoring analyses, this work emphasizes the broad utility of the tools for straightforwardly presenting the results from standardized and routine surveillance monitoring.

Sufficient Networks for Computing Support of Graph Patterns

Graph mining is the process of extracting and analyzing patterns from graph data. Graphs are a data structure that consists of a set of nodes and a set of edges that connect these nodes. Graphs are often used to represent real-world entities and the relationships between them. In a graph database, the importance of a pattern (also known as support) must be quantified using a counting function called a support measure. This function must adhere to several constraints, such as antimonotonicity that forbids a pattern to have support bigger than its sub-patterns. These constraints make the tasks of defining and computing support measures highly non-trivial and computationally expensive. In this paper, I use the previously discovered relationship between support measures in graph databases and flows in networks of subgraph appearances to simplify the process of computing support measures. I show that the network of pattern instances may be successfully pruned to contain just particular kinds of patterns and prove that any legitimate computing support measures in graph databases can adopt this strategy. When the suggested method is utilized, experimental evaluation demonstrates that network size reduction is significant.

The “post-weanling’s conundrum”: exploring the impact of infant and child feeding practices on early mortality in the Bronze Age burial cave of Moro de Alins, north-eastern Iberia, through stable isotope analysis

The relationship between infant and child feeding practices and early mortality is difficult to address in past societies. Here, stable carbon (δ13C) and nitrogen (δ15N) isotope measurements of bulk bone and sequential dentine samples of deciduous second and/or permanent first molars of four younger children, one older child, one late adolescent, and two young adults (n = 8) from Moro de Alins cave, north-eastern Iberia, are used to explore the potential impact of early-life nutrition on mortality in the Bronze Age. Isotope results are compatible with generally short exclusive breastfeeding and standard weaning periods compared to other pre-modern populations. However, there are differences in exclusive breastfeeding mean δ13C values and in Δ13C trophic shifts between exclusive breastfeeding and immediate post-weaning isotope values for those individuals who survived into adolescence and adulthood and those who did not. While the former seem to be consistent with trophic distances published for modern mother–infant pairs, the latter are above most of them. This may suggest that individuals who consumed similar foods to their mothers or suffered from less physiological stress during or after weaning had greater chances of survival during early childhood and beyond. Post-weaning seems to have been a particularly stressful period of life, where a number of instances of patterns of opposing isotopic covariance compatible with catabolic changes, often preceding death among non-survivors, are detected. This outcome shows the key role of nutritional and/or physiological status in early-life morbidity and mortality among partially and especially fully weaned children from pre-antibiotic, pre-vaccination, and poor sanitation contexts and proposes that adult survival is rooted in early life experiences, in keeping with the developmental origins of health and disease.

Research on Design Pattern Detection Method Based on UML Model with Extended Image Information and Deep Learning

Detecting relevant design patterns from system design or source code helps software developers and maintainers understand the ideas behind the design of large-scale, highly complicated software systems, thereby improving the quality of software systems. Currently, design pattern detection based on machine learning has become a hot research direction. Scholars have proposed many design pattern detection methods based on machine learning. However, most of the existing literature only reports the utilization of traditional machine learning algorithms such as KNN, decision trees, ANN, SVM, etc., which require manual feature extraction and feature selection. It is very difficult to find suitable and effective features for the detection of design patterns. In the previous research, we have initially explored a design pattern detection method based on graph theory and ANN. Based on the research work done, we speculate that if we can realize the end-to-end design pattern detection from system design or source code to design pattern with the help of the powerful automatic feature extraction and other advantages of deep learning, the detection effect can be further improved. This paper intends to first explore a UML model that extends image information, called colored UML, so as to transform the design pattern detection problem into an image classification problem; on this basis, the positive and negative sample sets and the system to be recognized are all expressed in the form of colored UML models, the convolutional neural network VGGNet is used to train the data set to extract features, and the extracted features are trained by the SVM for binary classification to judge the pattern instances. Experiments were carried out on three open-source projects. We used three non-machine learning design pattern detection methods and five design pattern detection methods based on traditional machine learning algorithms, as well as the method in this paper. In general, the method proposed in this paper achieved higher precision and recall, and for different programs and their patterns, the precision and recall were stable at more than 85% in most cases. The experimental results demonstrate that this paper can achieve a better effect in recognizing design patterns. The research is, therefore, of both theoretical significance and application value.

Reusable Ontology Modelling Patterns for Biodiversity Data with Reasonable Ontology Templates (OTTR)

The Norwegian Biodiversity Information Centre (NBIC) is currently building a traitbank for Norwegian species. The purpose of the NBIC TraitBank is to enhance sharing of traits and other information about species to support conservation actions and ecological research. The traitbank will cover a subset of traits for all multicellular species and taxa that are found in Norway. Observations of traits, collected through citizen science as well as by experts, are connected to the NBIC TraitBank ontology, forming a knowledge base. We have modeled TraitBank’s ontology in accordance with NBIC’s data management requirements, focusing on the domain knowledge necessary for ontology-based data integration of internal databases and queries specified by use cases. Our initial steps in ontology construction included outlining competency questions, which are natural language sentences expressing the questions system users expect an ontology to answer (Bezerra et al. 2013, Ren et al. 2014). The ontology for the TraitBank is populated using expert input (manual entry) and through harvesting of traits from existing internal databases at NBIC. We will present our experiences with implementing Reasonable Ontology Templates (OTTR) (Skjæveland et al. 2021) as the means for modeling the ontology and populating the TraitBank ontology. OTTR is a language for formally representing and instantiating ontology modeling patterns and is designed to support knowledge base construction and interaction at a higher level of abstraction. In the case of the TraitBank, ontology patterns are edited and published using a Semantic MediaWiki (SMW) extension for OTTR (FloSchroeder 2022), thereby providing a tool for the domain expert to work directly with templates. We build the TraitBank ontology by instantiating the templates directly in SMW as wiki pages. We argue that templates are an effective means to support the integration and use of digital biodiversity data in transparent ways, leading to successful collaboration and reuse of data. Following the "Don't repeat yourself" (DRY) principle of software development (Hunt and Thomas 1999), OTTR templates lend themselves well to easier ontology maintenance, allowing updates to occur through changes in individual template definitions rather than to repeated statements spread throughout the ontology. OTTR reshapes how domain experts work with ontologies and the data connected to the knowledge base, lifting the biodiversity expert away from dealing directly with logical axioms and Web Ontology Language (OWL). The template libraries have the power to improve international collaboration, making it easier to exchange and reuse specific templates and suggest improvements. Our templates include mappings to standards developed by Biodiversity Information Standards (TDWG) and biodiversity-related ontologies, linking to the international community. Use of OTTR supports the principles of Findable, Accessible, Interoperable, and Reusable (FAIR) data and demonstrates a new technology that can support the creation of an extensive online network of knowledge. Example: Scientific Name The NBIC uses Scientific Name as the main identifier and means to track a species. The OTTR template shown below captures the NBIC’s modelling pattern for Scientific Name. The signature of the template specifies the Internationalized Resource Identifier (IRI) of the template (adb-t:ScientificName), and six parameters (where ?iri is the 1st parameter). The parameters are used in the body of the template and define how instances of the template are expanded to Resource Description Framework (RDF) statements. Template instance expansion is done in a recursive manner, similar to many macro programming languages. With the OTTR template definition given in Fig. 1, a template instance can be expanded, as shown in the example for Metopa glacialis Fig. 2. The benefits of using the OTTR framework is that modeling patterns are explicitly represented as an OTTR template, allowing for instances of patterns to be compactly and consistently captured. The format of template instances lends itself to instantiation from tabular data sources like spreadsheets and databases.

MODELLING OF PEDAGOGICAL PATTERNS THROUGH E-LEARNING OBJECTS

Software platforms for e-learning support various options for presenting educational content. One of the ways to organize and structure it is via so-called pedagogical patterns. They are a method for describing and sharing knowledge and practical experience. Pedagogical patterns are used to describe pedagogical situations that occur repeatedly in the learning process. In the context of e-learning systems, there are various approaches to digitalization of pedagogical patterns. The purpose of the paper is to show how to build instances of pedagogical patterns using e-learning pedagogical objects, which can be easily and conveniently used as models in an adaptive e-learning environment. An e-learning pedagogical object is an abstract concept that can be presented in the concrete form of an e-learning object, an e-learning methodological object, an e-learning object for monitoring and diagnostics or an e-learning object with learning outcomes. These objects are building blocks for constructing instances of pedagogical patterns. This paper thoroughly discusses the issue of creating instances of pedagogical patterns of the four types of e-learning pedagogical objects. The instance of a pedagogical pattern is meant to serve to create subsections of educational topics. The instances of the patterns built of e-learning objects are learning units that are used depending on the context of a particular pedagogical situation. The e-learning pedagogical objects and the pedagogical pattern instances are intended to be applied in an adaptive e-learning environment as teaching aids. Their theoretical models are applied in Moodle LMS in line with the tendency for software to assist and replace some of the teacher functions, while the teacher’s role is raised to a higher organizational, pedagogical and methodological level. Three instances of pedagogical patterns have been created through e-learning pedagogical objects: “Early Feedback”, “Feedback Sandwich” and “Consistent Metaphor” in LMS Moodle, which have been tested in the training course “Modeling of training courses in Moodle” during the autumn trimester of the academic year 2021/2022 at the Faculty of Mathematics and Informatics of Paisii Hilendarski University of Plovdiv, Bulgaria.

Specification and Verification of Timing Properties in Interoperable Medical Systems

To support the dynamic composition of various devices/apps into a medical system at point-of-care, a set of communication patterns to describe the communication needs of devices has been proposed. To address timing requirements, each pattern breaks common timing properties into finer ones that can be enforced locally by the components. Common timing requirements for the underlying communication substrate are derived from these local properties. The local properties of devices are assured by the vendors at the development time. Although organizations procure devices that are compatible in terms of their local properties and middleware, they may not operate as desired. The latency of the organization network interacts with the local properties of devices. To validate the interaction among the timing properties of components and the network, we formally specify such systems in Timed Rebeca. We use model checking to verify the derived timing requirements of the communication substrate in terms of the network and device models. We provide a set of templates as a guideline to specify medical systems in terms of the formal model of patterns. A composite medical system using several devices is subject to state-space explosion. We extend the reduction technique of Timed Rebeca based on the static properties of patterns. We prove that our reduction is sound and show the applicability of our approach in reducing the state space by modeling two clinical scenarios made of several instances of patterns.

Realization of Presentation layer information of Legacy Java Enterprise Applications Through Design Pattern’s Recovery

The presentation layer is the outermost layer of an application that provides user interface and communication services. This layer is responsible for session management, controlling client access, and validations within data from the client .In legacy enterprise applications like Java Enterprise Edition Platform (Java EE),the design considerations of the presentation layer are spread over different design patterns and cross-language constructs. Resultantly, the analysis of such applications becomes quite challenging due to their heterogeneity, essentially required for the extraction of design-level information and further modernization. In this research ,a flexible technique is presented to extract presentation tier information based on customizable feature types by recovering instances of presentation tier patterns of the Java Enterprise Edition Platform. The proposed approach is evaluated on well-operative open-source Enterprise Applications. The validation results demonstrate the extraction of presentation tier information through Design Pattern’s recovery.This prototype is validated on the repository of source code of Java applications as well on open source java applications.

Modeling of Pedagogical Patterns in an E-learning System

Pedagogical patterns describe teaching ideas that can be applied in different ways in teaching in different disciplines, and for different types of students. They are a tool for sharing experience and good practices between teachers. The use of pedagogical patterns in online learning is a challenge that can be met by using an appropriate software system for learning management. The article proposes a model of educational objects, suitable for software imple-mentation, which we call pedagogical pattern instances, or for short - in-stances. One instance combines specific learning content with additional fea-tures. Learning content can have different “views” that present knowledge in different ways, for example, through text files of presentations, audio or video content, interactive content, etc. Logical categories of characteristics and activities form the “aspects” of the instance, such as methodology, adap-tivity, assessment, etc. The proposed pattern instance model is flexible. It can be expanded with new features and adapted to specific goals and designs. The paper also outlines a conceptual framework of an e-learning software system using the presented model of a pattern instance.

MLCPM-UC:A Multi-level Co-location Pattern Mining Algorithm Based on Uniform Coefficient of Pattern Instance Distribution

MLCPM-UC:A Multi-level Co-location Pattern Mining Algorithm Based on Uniform Coefficient of Pattern Instance Distribution

Theorie und Methode der Gattungsgeschichtsschreibung.Mediävistische Perspektiven

Theorie und Methode der Gattungsgeschichtsschreibung.<b>Mediävistische Perspektiven</b>

Exploring Instances of Deleuzian Rhizomatic Patterns in Students’ Writing and in Online Student Interactions

Globally, it is a standard practice to study students’ academic writing by using linear academic-writing models. This study investigated instances of Deleuzian rhizomatic patterns in students’ writing and in online student interactions at an open and distance e-learning (ODeL) institution in South Africa. A convenience sample of 13 students’ paragraph writing samples and of 370 first-year students was used. All the participants were enrolled in a level-one module, ENG1503, in the second semester of 2020. The study followed a mixed-method approach, and utilized AntConc and AntMover to analyse the students’ writing samples, as well as Microsoft Power Business Intelligence (MS Power BI) and Gephi, in order to analyse and visualise online student interactions. When students’ writing samples were analysed in terms of keywords (e.g., key themes) by using the software applications employed in this study, various rhizomatic patterns were detected in the students’ text files. For example, the key-word frequencies of key themes, such as religion and cult, showed that these two key themes were used differently at the end of each concordance spectrum, thereby underscoring their varying rhizomatic patterns of usage in students’ respective text files. Online student interactions on both myUnisa’s ODF and MS Teams were visualized rhizomatically. The findings of this study underscore the importance of investigating and analysing students’ writing – not only from linear models, but also from non-linear perspectives, such as a rhizomatic approach. Additionally, they underline the significance of leveraging the opportunities offered by students’ writing analysis technologies, such as those employed in this study.

Figurative language in multilingual students’ L2 Swedish – a usage-based perspective

Abstract The aim of the current paper is to reinterpret some results of two previous studies on the mastery of figurative expressions from the perspective of usage-based linguistics. The reanalysis aims to shed more light on the learning and use of figurative language by multilingual students by exploring the complex interplay of linguistic creativity, expressivity, and conventionality in figurative expressions. The reinterpretation shows that many of the examples that were previously categorized as novel figurative expressions used in students’ writing, can be analyzed as instances of regular patterns, i.e. constructions, with certain lexical idiosyncrasies. Modifications of conventionalized figurative expressions are discussed and reinterpreted in terms of strength of entrenchment of links between form and meaning within certain constructions or links between constructions and conventionalized pragmatic information in the multilinguals’ mental construction. Implications for the treatment of Swedish figurative expressions in the second language class room are, in line with previous research, that focusing on regularity might reduce unpredictability, often seen as the core difficulty in the learning of such expressions in an L2. The paper also offers some directions for further investigation of the socio-cognitive processes involved in the learning of figurative language in an additional language.