Woodworking marking tool structuration based on craft traditions

PurposeBased on profound information lacking in compiled information materials, the risks of losing knowledge related to the values of traditional woodworking processes are increasing. The purpose of this article is to collect and structure diverse marking tool data into a comprehensive, understandable and clear design schematic view, which serves as a basis for the accumulation and preservation of diverse marking objects and shows woodworking marking tool relation in the group and subgroup levels.Design/methodology/approachA method for marking tools structuring and analysis are described, including breaking down a set of objects into groups of marking objects, and assigning one or more attributes to the parcelled objects by arranging them into hierarchic levels. Research is based on marking tools used by carpenters, joiners and woodcarvers mainly in the Baltic region.FindingsCollected data, object analyses and comparison within-group and subgroup levels are based on written and visual sources, museum and museum funds visits, and participation in the local craftsmen events. The created structure is expandable in group and subgroup levels. The most comprehensive way for object structuring is chosen as a base to reveal a diversity of the objects.Originality/valueStructure schemes of woodworking marking tools are important in scientific, educative and cultural levels based on their wide range and use. Aggregated information of the woodworking tools serves as a base for existing tool studies and improvement, new tool and wood product creation as well as complements the structure of the upcoming woodworking hand tool database and book.

Implementasi Algoritma FG-Growth untuk Sistem Rekomendasi Penjualan Produk

The variety of stationery marketed, makes business competition increasingly fierce in order to provide the best service to customers. Abundant sales transaction data, triggering piles of data so that it requires data mining processing techniques, namely association rule mining using the FP-Growth algorithm. Algorithm that generates frequent itemset used in the process of determining the rules that can produce an option by taking a product sales transaction data object. The test results show a rule that has the best confidence value and lift ratio of 100%, as well as 80% support with the rules that every purchase of a ballpoint product can be sure to buy a notebook from the dataset used as a sample data in the system trial (50 names). goods and 7 transaction data) with minimum support (5% = 0.05) and minimum confidence (30% = 0.3).

Visualizing dynamic data with heat triangles

In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels. Graphic abstract

Building Information Modelling (BIM): Benefits for Small Scale Construction Industry

While time passes and life changes, the development of technology is taking place in every part of our life quickly, also it affects daily life. it creates new tools, procedures, and methods for all sectors, and simplifies many operations. Nowadays, design tools that depend on computers have been used in the construction industry, it has a direct effect on the whole project life, and it has made a revolution in the construction sector. Building information modeling (BIM) simply refers to the development of a building model generated by using the computer, that model is rich of data, object-oriented, smart and also a parametric digital representation of the building. This paper discusses the advantages of implementation of BIM technology for the small scale construction industry, it will mainly focus on quantity takeoff and accuracy of projects, firstly it presents the definition and the main concept of BIM. Then, a case study of a 3-storey building project in Baghdad is done to evaluate the effect of BIM quantity takeoff, tendering, and other effects on the project. At the end, results and challenges will be discussed.

multiomics: A user-friendly multi-omics data harmonisation R pipeline

Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.

Clustering Analysis using an Unsupervised Machine Learning Method

Artificial Intelligence (AI) and Machine Learning (ML), which are becoming a part of interest rapidly for various researchers. ML is the field of Computer Science study, which gives capability to learn without being absolutely programmed. This work focuses on the standard k-means clustering algorithm and analysis the shortcomings of the standard k-means algorithm. The k-means clustering algorithm calculates the distance between each data object and not all cluster centres in every iteration, which makes the efficiency of clustering is high. In this work, we have to try to improve the k-means algorithm to solve simple data to store some information in every iteration, which is to be used in the next interaction. This method avoids computing distance of data object to the cluster centre repeatedly, saving the running time. An experimental result shows the enhanced speed of clustering, accuracy, reducing the computational complexity of the k-means. In this, we have work on iris dataset extracted from Kaggle.

A Density-Based Method for the Identification of Non-Disjoint Clusters With Arbitrary and Non-Spherical Shapes

Overlapping clustering is an important challenge in unsupervised learning applications while it allows for each data object to belong to more than one group. Several clustering methods were proposed to deal with this requirement by using several usual clustering approaches. Although the ability of these methods to detect non-disjoint partitioning, they fail when data contain groups with arbitrary and non-spherical shapes. We propose in this work a new density based overlapping clustering method, referred to as OC-DD, which is able to detect overlapping clusters even having non-spherical and complex shapes. The proposed method is based on the density and distances to detect dense regions in data while allowing for some data objects to belong to more than one group.Experiments performed on articial and real multi-labeled datasets have shown the effectiveness of the proposed method compared to the existing ones.

Learning-Based Dissimilarity for Clustering Categorical Data

Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several different ways. Most existing category dissimilarity measures characterize the distance among the values an attribute may take using precisely the number of different values the attribute takes (the attribute space) and the frequency at which they occur. These kinds of measures overlook attribute interdependence, which may provide valuable information when capturing per-attribute object dissimilarity. In this paper, we introduce a novel object dissimilarity measure that we call Learning-Based Dissimilarity, for comparing categorical data. Our measure characterizes the distance between two categorical values of a given attribute in terms of how likely it is that such values are confused or not when all the dataset objects with the remaining attributes are used to predict them. To that end, we provide an algorithm that, given a target attribute, first learns a classification model in order to compute a confusion matrix for the attribute. Then, our method transforms the confusion matrix into a per-attribute dissimilarity measure. We have successfully tested our measure against 55 datasets gathered from the University of California, Irvine (UCI) Machine Learning Repository. Our results show that it surpasses, in terms of various performance indicators for data clustering, the most prominent distance relations put forward in the literature.