Well-structured Representation Research Articles

Information Categorization for Canopy Mapping using Quality Control (QC) Tool – Affinity Diagram (KJ Method)

Canopy mapping involves the process of accurately delineating and assessing the distribution and extent of green spaces, trees, and vegetation within a designated area. Understanding the canopy's characteristics holds paramount importance, as it plays a critical role in supporting biodiversity, regulating microclimates, and mitigating the adverse impacts of urbanization on the environment. This research paper focuses on exploring the practical application of the KJ method and collaborative brainstorming to gather and organize relevant information for canopy mapping. The study engaged six undergraduate students, under the guidance of a faculty member specialized in geoinformatics, in a productive brainstorming session, generating twenty-one diverse ideas related to canopy mapping. Through a methodical process of iterative refinement and consensus-building, the students effectively grouped these ideas into four distinct categories: "Data Sources," "Canopy Estimation Process," "Canopy Map Development," and "Accuracy Assessment." The resulting Affinity Diagram served as a clear and well-structured representation of the research paper's key aspects, harnessing the collective intelligence of the team to organize complex information and facilitate the precise mapping of tree canopies. This collaborative approach proved instrumental in enhancing the project's efficiency and effectiveness, promoting a cooperative environment that fosters innovation and informed decision-making.

Machine understanding and deep learning representation

Practical ability manifested through robust and reliable task performance, as well as information relevance and well-structured representation, are key factors indicative of understanding in the philosophical literature. We explore these factors in the context of deep learning, identifying prominent patterns in how the results of these algorithms represent information. While the estimation applications of modern neural networks do not qualify as the mental activity of persons, we argue that coupling analyses from philosophical accounts with the empirical and theoretical basis for identifying these factors in deep learning representations provides a framework for discussing and critically evaluating potential machine understanding given the continually improving task performance enabled by such algorithms.

A Specific Kind of Representation: How Systematics May Ease Cognitive Overload

Multiple representations are beneficial for meaningful understanding. However, three or more representations may add to the cognitive overload of students, if not in interactive diagrams and dynamic geometry. How a well-known representation consisting of more than 3 or more representational registers may overcome the problem of cognitive overload without being too complicated. In this study, an old but well-structured representation that was used even over 40 years was analyzed. The critical points of a function, asymptotes, x / y-intercepts, inflection points, and graphing can be identified easily. It is prepared in the form of a table and the factors of the first derivative of the function and the second derivative and their roots indicate the function’s increasing and decreasing intervals and its graph. This representation is very systematic and it acts like a method to draw the function’s graph with no-fault possible. Yet, besides being used for many years, is still used for courses like Calculus, etc. We argue that cognitive overload theory cannot alter this representation due to its systematic nature. In content analysis, some examples of this representation are shared via the reader, and some qualitative aspects about it are analyzed. Finally, its systematicity, well-structured nature, and nature in reducing extraneous cognitive load are emphasized. The important thing here is that it is very strategic not to lose some representations for the sake of new ones if their value is already known but not discussed too much.

Effects of Age and Expertise on Mental Representation of the Throwing Movement Among 6- to 16-Year-Olds

The aim of this article was to assess the development of mental representation of the overhead throwing movement as a function of age and expertise. The mental representational structure of the overhead throwing movement was measured using the Structural Dimensional Analysis-Motoric (SDA-M) method that reflects the organization of basic action concepts (BACs). BACs are fundamental building blocks of mental representations, which comprise functional, sensory, spatiotemporal, and biomechanical characteristics of a movement (Schack, 2010). In this study, novices and handball athletes (N = 199) each were grouped according to the level of development in motor ontogenesis (in childhood, pubescence, and adolescents). Male and female handball athletes played in the highest leagues of their age groups. As a result, novices of all age groups showed the same unstructured mental representation. Athletes in the earliest age band resemble all novices’ groups and showed similar unstructured mental representation, whereas athletes within pubescence and adolescents showed functionally well-structured representations, which were similar to the structure of the reference group (N = 8). These results are consistent with a previous investigation of related quantitative and qualitative performance parameters of the overhead throwing movement (Gromeier et al., 2017). Without an increased training, neither the throwing performance nor the associated mental representation is unlikely to improve further by itself or automatically.

False alarms during recognition of famous people from faces and voices in patients with unilateral temporal lobe resection and normal participants tested after anodal tDCS over the left or right ATL

Data gathered in the field of the experimental social psychology have shown that it is more difficult to recognize a person through his/her voice than through his/her face and that false alarms (FA) are produced more in voice than in face recognition. Furthermore, some neuropsychological investigations have suggested that in patients with damage to the right anterior temporal lobe (ATL) the number of FA could be higher for voice than for face recognition.In the present study we assessed FA during recognition of famous people from faces and voices in patients with unilateral ATL tumours and in normal participants tested after anodal transcranial direct current stimulation (tCDS), over the left or right ATL. The number of FA was significantly higher in patients with right than in those with left temporal tumours on both face and voice familiarity. Furthermore, lesion side did not differentially affect patient's sensitivity or response criterion when recognizing famous faces, but influenced both these measures on a voice recognition task. In fact, in this condition patients with right temporal tumours showed a lower sensitivity index and a lower response criterion than those with left-sided lesions. In normal subjects, the greater right sided involvement in voice than in face processing was confirmed by the observation that right ATL anodal stimulation significantly increased voice but only marginally influenced face sensitivity. This asymmetry between face and voice processing in the right hemisphere could be due to the greater complexity of voice processing and to the difficulty of forming stable and well-structured representations, allowing to evaluate if a presented voice matches or not with an already known voice.

Group Differences and Similarities in Mental Representation Structure of Tennis Serve.

A large number of studies have examined expertise and gender-related differences in the mental representation of motor skills in different sports, like throwing technique in judo, front loop in sailing, and integration of routines in the mental movement representation in volleyball. Also, tactical behavioral studies were conducted in futsal and football. In addition, studies were also carried out to support the motor learning process through mental training in golf. The Structural Dimensional Analysis-Motoric (SDA-M) method was also used in the medical sector for the rehabilitation of stroke patients. So far, few studies have investigated differences in the mental representation of a specific motor skill by experienced athletes of other related sports. The goal of the present study is to examine group differences and similarities in the mental representation of the tennis serve between experienced tennis, badminton, and handball athletes as well as a control group without any sport experience. We want to assess the quality of mental representation of technical-related overhead motion task expertise. For this purpose, we used the SDA-M to measure the mental representation of the tennis serve of four different groups (tennis, badminton, and handball athletes and a group of novices). As expected, badminton and handball athletes showed functionally well-structured representations, which were similar to the structure of the group of tennis athletes. Novices showed an unstructured mental representation. These outcomes confirm the relationship between mental representation and performance in the development of overhead motion. Furthermore, the results emphasize the importance of mental representations as an essential developmental aspect in learning motor skills, especially in learning technical-related motor skills.

WIZUALIZACJA DANYCH JAKOŚCIOWYCH Z WYKORZYSTANIEM PAKIETU EXTRACAT PROGRAMU R

Visualization in research process plays a crucial role. There are several advanced plots for visualizing categorical data, such as mosaic, association, double-decker, sieve or fourfold plot that are based on the graphical presentation of residuals in a contingency table. In this paper we present new methods for visualizing categorical data such as rmb, fluctile and scpcp plot available in extracat package in R. This package provides a well-structured representation of categorical data and allows for a detailed presentation of the relationship between categories in terms of proportions. We describe rmb, fluctile and cpcp. Those plots are based on the concept of multiple bar charts, a fluctuation diagram from a multidimensional table and parallel coordinates respectively. Such plots are mostly used for a visualization of a contingency table or a data frame; they can also be used for exploratory analysis and allows for a graphical presentation even for a high number of variables [Pilhofer, Unwin 2013]. All the calculations and plots are obtained using R software.

New Approaches in Visualization of Categorical Data:RPackageextracat

The R package extracat provides two new graphical methods for displaying categorical data extending the concepts of multiple barcharts and parallel coordinates plots. The first method called rmb plot uses a crossover of mosaicplots and multiple barcharts to display the frequencies of a data table split up into conditional relative frequencies of one target variable and the absolute frequencies of the corresponding combinations of the remaining explanatory variables. It provides a well-structured representation of the data which is easy to interpret and allows precise comparisons. The graphic can additionally be used as a generalization of spineplots or with barcharts for the conditional relative frequencies. Several options, including ceiling censored zooming, residual shadings and a choice of color palettes, are provided. An interactive version based on the R package iWidgets is also presented. The second graphic cpcp uses the interactive parallel coordinates plots in the iplots package to visualize categorical data. Sequences of points are used to represent each of the variable categories, while ordering algorithms are applied to represent a hierarchical structure in the data and keep the arrangement clear. This interactive graphic is well-suited for exploratory analysis and allows a visual interpretation even for a higher number of variables and a mixture of categorical and numeric scales.

Maximal Structuring of Acyclic Process Models

This article addresses the transformation of a process model with an arbitrary topology into an equivalent structured process model. In particular, this article studies the subclass of process models that have no equivalent well-structured representation but which, nevertheless, can be partially structured into their maximally-structured representation. The transformations are performed under a behavioral equivalence notion that preserves the observed concurrency of tasks in equivalent process models. The article gives a full characterization of the subclass of acyclic process models that have no equivalent well-structured representation, but do have an equivalent maximally-structured one, as well as proposes a complete structuring method. Together with our previous results, this article completes the solution of the process model structuring problem for the class of acyclic process models.

Representation of grasp postures and anticipatory motor planning in children

In this study, we investigated anticipatory motor planning and the development of cognitive representation of grasp postures in children aged 7, 8, and 9years. Overall, 9-year-old children were more likely to plan their movements to end in comfortable postures, and have distinct representational structures of certain grasp postures, compared to the 7- and 8-year old children. Additionally, the sensitivity toward comfortable end-states (end-state comfort) was related to the mental representation of certain grasp postures. Children with grasp comfort related and functionally well-structured representations were more likely to have satisfied end-state comfort in both the simple and the advanced planning condition. In contrast, end-state comfort satisfaction for the advanced planning condition was much lower for children whose cognitive representations were not structured by grasp comfort. The results of the present study support the notion that cognitive action representation plays an important role in the planning and control of grasp postures.

Prospective Adaptation in the Use of External Representations

An important element of adaptive expertise involves stepping away from a routine to retool one's knowledge or environment. The current study investigated two forms of this adaptive pattern: fault-driven adaptations, which are reactions to a difficulty, and prospective adaptations, which are proactive reformulations. Graduate and undergraduate students with no medical training engaged in a medical diagnosis task that involved complex information management. The graduate students, who were relative experts in information management and data analysis, uniformly made prospective adaptations by taking the time to create external representations of the available information before they diagnosed a single patient. In contrast, the undergraduate students only made representations reactively, when experimental manipulations made their default behaviors impractical. Graduate students tolerated the time lost creating representations in favor of future benefits—well-structured representations led to more optimal diagnostic choices. Overall, the results indicate that long-term educational experiences are correlated with prospective adaptation, even in a novel task domain that is not explicitly a part of those educational experiences. This research provides new metrics for evaluating educational interventions designed to move students along a trajectory toward adaptive expertise.

A Biologically Inspired System for Classification of Natural Images

A system for visual information analysis and classification based on a biologically inspired visual selective attention model with knowledge structuring is presented. The system is derived from well-known analogous processes in the visual system of primates and inference procedures of the human brain. It consists of three main units: biologically inspired visual selective attention, knowledge structuring, and clustering of visual information. The biologically inspired visual selective attention unit closely follow the mechanisms of the visual what pathway and where pathway in the primates' brain. It uses a bottom-up approach to generate a salient area based on low-level features extracted from natural images. The scale selection to determine suitable size of salient areas uses a maximum entropy approach. This unit also contains a low-level top-down selective attention module that performs decisions on interesting objects by human interaction. In this module, a reinforcement/inhibition mechanism is exploited. The knowledge structuring unit automatically creates a relevance map from salient image areas generated by the biologically inspired unit. It also derives a set of well-structured representations from low-level descriptions to drive the final classification. The knowledge structuring unit relys on human knowledge to produce suitable links between low-level descriptions and high-level representation on a limited training set. The backbone of this unit is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology representation module based on a growing cell structure network. The third unit of the system classification is achieved by simulating high-level top-down visual information perception and clustering using an incremental Bayesian parameter estimation method. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available

SWEET-DB: an attempt to create annotated data collections for carbohydrates.

Complex carbohydrates are known as mediators of complex cellular events. Concerning their structural diversity, their potential of information content is several orders of magnitude higher in a short sequence than any other biological macromolecule. SWEET-DB (http://www.dkfz.de/spec2/sweetdb/) is an attempt to use modern web techniques to annotate and/or cross-reference carbohydrate-related data collections which allow glycoscientists to find important data for compounds of interest in a compact and well-structured representation. Currently, reference data taken from three data sources can be retrieved for a given carbohydrate (sub)structure. The sources are CarbBank structures and literature references (linked to NCBI PubMed service), NMR data taken from SugaBase and 3D co-ordinates generated with SWEET-II. The main purpose of SWEET-DB is to enable an easy access to all data stored for one carbohydrate structure entering a complete sequence or parts thereof. Access to SWEET-DB contents is provided with the help of separate input spreadsheets for (sub)structures, bibliographic data, general structural data like molecular weight, NMR spectra and biological data. A detailed online tutorial is available at http://www.dkfz.de/spec2/sweetdb/nar/.

Time–cost evaluation of alternative system/process designs

Techniques for representing system/process designs are a common feature of most design methodologies. These techniques can be enhanced to provide valuable information regarding a specific design structure’s trade-off between task completion times and cost. In this paper, we present a mathematical model and a solution algorithm which can be used to upgrade any well-structured representation technique to provide this trade-off. Having this information readily available allows a design team to focus on less quantifiable features of specific designs, thereby improving the overall design process.