Numerical Attributes Research Articles

Factors that promote the repulsion effect in preferential choice

Abstract Inclusion of a decoy alternative dominated by a target option, but not its competitor, typically leads to increased choice for the target over the competitor, known as the attraction effect. However, the reverse sometimes occurs, known as the repulsion effect. This research tested factors that moderate the repulsion effect in preferential choice scenarios with numerical attributes. Experiment 1 used a between-subjects design with a small set of consumer products and demonstrated robust repulsion effects that did not depend on the relative similarity of the decoy and target. Experiments 2 and 4 used a more powerful within-subjects design along with an expanded set of products and showed that repulsion effects were generally enhanced when the decoy and target had more similar attributes; however, the moderating effect of decoy–target similarity appeared to be fragile and sensitive to stimulus presentation factors. These findings provided mixed support for the hypothesis that the target is tainted by its proximity to the decoy. Experiments 3 and 5 tested whether the extremity of values on the attribute favoring the target moderates the repulsion effect. The results demonstrated that repulsion is more likely when all the alternatives have extremely high values on the target’s better attribute. Extremity of attribute values on the dimension favoring the target may result in a categorical assessment along that dimension and shift focus to the attribute favoring the competitor as one way to foster the repulsion effect.

Large Language Models for Transforming Categorical Data to Interpretable Feature Vectors.

When analyzing heterogeneous data comprising numerical and categorical attributes, it is common to treat the different data types separately or transform the categorical attributes to numerical ones. The transformation has the advantage of facilitating an integrated multi-variate analysis of all attributes. We propose a novel technique for transforming categorical data into interpretable numerical feature vectors using Large Language Models (LLMs). The LLMs are used to identify the categorical attributes' main characteristics and assign numerical values to these characteristics, thus generating a multi-dimensional feature vector. The transformation can be computed fully automatically, but due to the interpretability of the characteristics, it can also be adjusted intuitively by an end user. We provide a respective interactive tool that aims to validate and possibly improve the AI-generated outputs. Having transformed a categorical attribute, we propose novel methods for ordering and color-coding the categories based on the similarities of the feature vectors.

Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV

Stunting is a growth and development disorder caused by malnutrition characterized by a child's height less than the standard deviation set by WHO. In 2022, stunting cases in Indonesia are considered a high prevalence rate, reaching 21.6%. There are several factors that can cause stunting in children, namely maternal and antenatal care factors, home environment factors, breastfeeding practices, and feeding factors during toddlerhood. There are several impacts that occur when children are stunted, namely increased risk of child mortality, susceptibility to illness, impaired brain development, physical disorders and metabolic disorders. Currently, deep learning has been widely used for disease classification and prediction, one of the deep learning methods is Multi-Layer Perceptron (MLP). The purpose of this research is to classify stunting disease using a deep learning method, namely MLP. The dataset used consists of 8 attributes, namely gender, age, birth weight, birth length, body weight, body length, breastfeeding and stunting with a total of 10,000 records. The encoding process is carried out to convert categorical data into numeric attributes of gender, breastfeeding, and stunting. This research produces a higher accuracy value than previous research which used the C4.5 algorithm with an accuracy of 61.82%, whereas in this study using MLP which was integrated with the GridSearchCV hyperparameter it obtained an accuracy of 82.37%. This proves that the MLP method is successful in classifying stunting compared to previous research algorithms.

Research on the construction of weaponry indicator system and intelligent evaluation methods

To decrease subjective interference and improve the construction efficiency of the traditional weapon and equipment index system, an index system construction method based on target detection is proposed in combination with the equipment test video data. The three-level index system of combat effectiveness of a certain type of equipment is established, and various intelligent assessment methods are proposed. Firstly, an optimaized IPSO-BP network model is proposed, in which dynamic weights are set to improve the particle search network, and adaptive learning factors are introduced to optimize the update speed. Secondly, an improved DS evidence-parallel neural network assessment method is proposed, setting multiple parallel neural networks with different parameters, and improving the angle cosine to weaken the numerical nonlinear attributes in DS evidence fusion and increase the model's assessment operation stability. Thirdly, the three types of view features corresponding to the index item images are extracted to train the base classifiers. The integrated CNN network based multi-view feature integration assessment model is constructed and the improved residual network block is introduced to optimize the network gradient. Comparison with existing evaluation methods shows that the proposed methods achieve efficient and intelligent construction and evaluation of the indicator system and enrich the evaluation of indicator data.

Cyber security Reinforcement through Firewall Log Analysis and Machine Learning

Firewalls play a crucial role as a primary protective measure in safeguarding network security, effectively mitigating risks posed by external vulnerabilities and internal security breaches. This study presents a new framework that utilizes firewall log data to classify incoming data packets as either permitted or forbidden. The dataset utilized in this research is obtained from Department of CS&IT, MANU University and is subjected to a thorough data pre-processing procedure. This procedure includes several tasks such as managing missing values, encoding categorical variables, standardizing numerical attributes, and guaranteeing data coherence. In order to mitigate the issue of class imbalance within the target variable, we utilize a range of machine learning models and assess their efficacy through the examination of fundamental metrics such as accuracy, precision, recall, and F1-score. The results of our study demonstrate that the AdaBoost model has superior performance compared to other models, achieving a remarkable accuracy rate of 99.00%. This study demonstrates the application of machine learning methods to automatically identify the activities indicated in firewall logs, thereby improving the security of corporate networks. Through the implementation of automation, we facilitate a more dependable and efficient method of detecting and addressing possible risks, thereby strengthening network security measures and protecting valuable corporate information.

Statistical Significance and Utility of Data-Driven Functional Dependencies of Wine Quality Data of Numerical Attributes

There has been a lot of research work to find out functional dependencies algorithmically from databases. But, when the databases consist of numerical attributes, some of the found functional dependencies might not be real functional dependencies, because numerical attributes can have a variety of values. On the other hand, regression analysis is an analysis method in which a model of the observed continuous or numerical variables is obtained and the degree of fit is measured. In this paper, we show how we can determine whether the found functional dependencies of numerical attributes have explanatory power by doing multivariate linear regression tests. We can check their explanatory power by way of adjusted R-squared, as well as other statistics like multicollinearity, the Durbin-Watson test for independence, and the F value for suitability of the regression models. For the experiment, we used the wine quality data set of Vinho Verde in the UCI machine learning library, and we found out that only 48.7% and 30.7% of functional dependencies found by the algorithm called FDtool have explanatory power for the red wine and white wine data set respectively. So, we can conclude that we should be careful when we want to apply the functional dependencies found by the algorithm. In addition, as a possible application of the found functional dependencies in the conditional attributes of the data sets, we have generated a series of random forests by dropping redundant attributes that appear on the right-hand side of the explanatory functional dependencies and acquired good results. So, we can also conclude that we may reduce our efforts by not collecting the data of the redundant attribute to check the wine quality because we can use samples with as few attribute values as possible in mass-produced wines like Vinho Verde.

Time series numerical association rule mining variants in smart agriculture

Numerical association rule mining offers a very efficient way of mining association rules, where algorithms can operate directly with categorical and numerical attributes. These methods are suitable for mining different transaction databases, where data are entered sequentially. However, little attention has been paid to the time series numerical association rule mining, which offers a new technique for extracting association rules from time series data. This paper presents a new algorithmic method for time series numerical association rule mining and its application in smart agriculture. We offer a concept of a hardware environment for monitoring plant parameters and a novel data mining method with practical experiments. The practical experiments showed the method’s potential and opened the door for further extension.

Eris: efficiently measuring discord in multidimensional sources

AbstractData integration is a classical problem in databases, typically decomposed into schema matching, entity matching and data fusion. To solve the latter, it is mostly assumed that ground truth can be determined. However, in general, the data gathering processes in the different sources are imperfect and cannot provide an accurate merging of values. Thus, in the absence of ways to determine ground truth, it is important to at least quantify how far from being internally consistent a dataset is. Hence, we propose definitions of concordant data and define a discordance metric as a way of measuring disagreement to improve decision-making based on trustworthiness. We define the discord measurement problem of numerical attributes in which given a set of uncertain raw observations or aggregate results (such as case/hospitalization/death data relevant to COVID-19) and information on the alignment of different conceptualizations of the same reality (e.g., granularities or units), we wish to assess whether the different sources are concordant, or if not, use the discordance metric to quantify how discordant they are. We also define a set of algebraic operators to describe the alignments of different data sources with correctness guarantees, together with two alternative relational database implementations that reduce the problem to linear or quadratic programming. These are evaluated against both COVID-19 and synthetic data, and our experimental results show that discordance measurement can be performed efficiently in realistic situations.

Graph-Based Dissimilarity Measurement for Cluster Analysis of Any-Type-Attributed Data.

Heterogeneous attribute data composed of attributes with different types of values are quite common in a variety of real-world applications. As data annotation is usually expensive, clustering has provided a promising way for processing unlabeled data, where the adopted similarity measure plays a key role in determining the clustering accuracy. However, it is a very challenging task to appropriately define the similarity between data objects with heterogeneous attributes because the values from heterogeneous attributes are generally with very different characteristics. Specifically, numerical attributes are with quantitative values, while categorical attributes are with qualitative values. Furthermore, categorical attributes can be categorized into nominal and ordinal ones according to the order information of their values. To circumvent the awkward gap among the heterogeneous attributes, this article will propose a new dissimilarity metric for cluster analysis of such data. We first study the connections among the heterogeneous attributes and build graph representations for them. Then, a metric is proposed, which computes the dissimilarities between attribute values under the guidance of the graph structures. Finally, we develop a new k -means-type clustering algorithm associated with this proposed metric. It turns out that the proposed method is competent to perform cluster analysis of datasets composed of an arbitrary combination of numerical, nominal, and ordinal attributes. Experimental results show its efficacy in comparison with its counterparts.

Attribute-weighted outlier detection for mixed data based on parallel mutual information

Outlier detection plays an important role in data mining because it can improve the performance of data analysis. Most outlier detection algorithms focus on numerical or categorical attributes; however, data typically have a mixture of numerical and categorical attributes. We addressed this problem by developing an attribute-weighted outlier detection algorithm, PMIOD, for high-dimensional and massive mixed data. The PMIOD algorithm adopts mutual information to measure attribute correlations and provides an attribute-weighting method for mixed data. Based on this, an attribute-weighted outlier detection method for mixed data was developed. Moreover, to improve the efficiency of mutual information computing for high-dimensional mixed data, the mutual information computing was parallelized on the Spark platform. We evaluated the proposed algorithm using ten UCI datasets and four synthetic datasets in comparison with widely used algorithms. Experiments were conducted to demonstrate the superiority of the results produced by the proposed algorithm.

Interdependence analysis on heterogeneous data via behavior interior dimensions

Interdependent dimensions including categorical and continuous variables can be seen commonly as heterogeneous behavioral data in the real world. Mixed-type objects are more or less associated in terms of certain coupling relationships. The usual representation of such behavioral data is an information table with explicit behavior exterior dimensions (i.e. the original attributes to describe data heterogeneity), assuming the independence of dimensions and the independence of objects. However, both variables and objects are actually very often interdependent on one another either explicitly or implicitly in functional and semantic manners. Limited research has been done in analyzing such interactions among dimensions and those relationships among objects, leading to the learning results to be more local than global. This paper proposes the interdependence analysis to capture the functional multifarious relationships among attributes and among objects in heterogeneous data by addressing the coupling context and coupling weights in unsupervised learning. Such global couplings consider the interactions within discrete dimensions, within numerical attributes and across them, as well as the relationships within an individual object and between multiple objects, to form the attribute-based and object-based coupled data representation schemes based on feature conversion and neighborhood calculation. In addition, we interpret both the representation models via implicit behavior interior dimensions (i.e. the newly defined attributes to model data interdependence) to explain the intrinsic rationales for the superiority of our proposed methods. This work explicitly models the coupling of multiple attributes and the coupling of multiple objects for heterogeneous data sets, demonstrated by various data mining and machine learning applications, such as cluster structure analysis, data clustering evaluation, and data density comparison. Moreover, the sensitivity study is carried out to tune the neighborhood parameter and weight parameter, and the scalability analysis is explored to test the robustness of both models. Extensive experiments on a series of synthetic data sets and multiple UCI data sets show that our proposed framework can effectively capture the global couplings of both heterogeneous variables and mixed-type objects, and is superior to the traditional way as well as the state-of-the-art approaches, which is also verified by statistical analysis.

A Design of Fuzzy Rule-Based Models With Data Privacy

One of the currently visible requirement of data analysis is to cope with data privacy requirements. The quest for privacy triggers design challenges in the construction of Machine Learning models. In this study, we propose a novel and systematic design methodology of fuzzy rule-based models completed in the following privacy-driven environment. Multidimensional data are available for model design in two different formats: those attributes that are privacy sensitive are provided at the higher non-numeric level of abstraction represented in the form of information granules while the remaining ones are available directly. It is shown that the granular manifestation of data becomes beneficial in the formation of the conditions of the rules whereas numeric attributes are available for the construction of the conclusions of the rules. The generic development scheme is discussed. Some generalization is introduced to cope with a number of data islands associated with corresponding data owners. It is shown that a formation of the data structure calls for a mechanism of collaborative clustering. A way of handling information granules at different levels of information granularity is discussed. Some illustrative examples are presented.

Describe the house and I will tell you the price: House price prediction with textual description data

Abstract House price prediction is an important problem that could benefit home buyers and sellers. Traditional models for house price prediction use numerical attributes such as the number of rooms but disregard the house description text. The recent developments in text processing suggest these can be valuable attributes, which motivated us to use house descriptions. This paper focuses on the house asking/advertising price and studies the impact of using house description texts to predict the final house price. To achieve this, we collected a large and diverse set of attributes on house postings, including the house advertising price. Then, we compare the performance of three scenarios: using only the house description, only numeric attributes, or both. We processed the description text through three word embedding techniques: TF-IDF, Word2Vec, and BERT. Four regression algorithms are trained using only textual data, non-textual data, or both. Our results show that by using exclusively the description data with Word2Vec and a Deep Learning model, we can achieve good performance. However, the best overall performance is obtained when using both textual and non-textual features. An $R^2$ of 0.7904 is achieved by the deep learning model using only description data on the testing data. This clearly indicates that using the house description text alone is a strong predictor for the house price. However, when observing the RMSE on the test data, the best model was gradient boosting using both numeric and description data. Overall, we observe that combining the textual and non-textual features improves the learned model and provides performance benefits when compared against using only one of the feature types. We also provide a freely available application for house price prediction, which is solely based on a house text description and uses our final developed model with Word2Vec and Deep Learning to predict the house price.

Instance-specific algorithm configuration via unsupervised deep graph clustering

Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense instance embeddings unsupervisedly, which facilitates clustering heterogeneous instances into homogeneous clusters for training instance-specific configurations. Experimental results on multiple benchmarks show that the proposed method can improve the solving efficiency of CPLEX on highly heterogeneous instances, and outperform existing instance specific AC methods.

EAQ: Enabling Authenticated Complex Query Services in Sustainable-Storage Blockchain

The data query service is urgently required in sustainable-storage blockchain, where full nodes store the entire transaction data while light nodes only store block headers. Queries invariably seek data with multiple attributes. However, no existing method provides a unified authenticated data structure (ADS) to support complex query operators (e.g., range queries and data object queries) on multiattribute blockchain data. In this paper, we propose a framework EAQ that effectively supports both fast data queries on multiple attributes and authentication of the query result. We propose a new ADS, called the MR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\mathit{Bloom}$</tex-math></inline-formula> -tree, based on the Bloom filter (BF) and Merkle R-tree. We prove the decomposability of BFs, which enables the BF to be seamlessly incorporated with the Merkle R-tree. This ADS enables range-level search using multidimensional attribute ranges and object-level search using BFs. This ADS also supports querying and proving inexistent data objects. To reduce storage overhead, we improve the MR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\mathit{Bloom}$</tex-math></inline-formula> -tree using the suppressed BF structure, which constructs only one BF independent of the number of attributes. To manage string attributes, we transform them into discrete numerical attributes using density-based clustering to represent similar items with close numerical values. Experiments show that the proposed framework achieves promising results.

한반도와 중국 동북지방의 골촉 연구

This thesis presents a comprehensive analysis of bone arrowheads discovered in prehistoric and early historic sites in Korean Peninsula and Northeast China. It tries to reanalyze the function of arrowheads that had been thought to be simple replacement of iron arrowheads and to find out the background of their use, the reason for its popularity, and the background of the extinction of bone arrowheads. This paper also compares bone arrowheads of Korean peninsula with those of Northeast China and discusses their similarities and differences.&#x0D; First of all, their nominal and numeric attributes are analyzed in order to understand their formal characteristics of bone arrowheads from Korea and Northeast China. Bone arrowheads of Korea can be divided by the presence or absence of the tang and can be divided into steptanged/non-step-tanged subtypes. Next, the relationship between the cross section of the bone arrowheads and the raw material was reviewed, and whether the cross section of the bone arrowheads reflects temporality was discussed. The width of most of the bone arrowheads discovered in Korea is below 1.1cm and is narrower than their contemporary stone and iron arrowheads. This difference may be related to their function. Bone arrowheads of Northeast China can be divided by the presence/absence of the tang and can be divided by the shape of their tips. The flat-tip-tanged arrowheads are most numerous subtype and may be related to the local environment of Northeast China where bamboo shaft was difficult to acquire. The width of most of the bone arrowhead discovered in Northeast China is below 1.1cm as in Korea and is narrower than their contemporary stone and iron arrowheads.&#x0D; Compared to the wide stone and iron arrowheads, the bone arrowheads is suitable for strike vital parts of the game such as heart or lung. The number of arrowheads with triangular cross section increased over time and the total length of bone arrowheads increases over time. This seems to contain an intention to increase penetrative power. The prevalence of the use of bone arrowheads after stone arrowheads is related to the spread of metal tools. It is concluded that bone arrowheads were not replacement of metal ones but improvement over earlier stone arrowheads as major types of arrowheads due to their better efficiency. Finally, by comparing the bone arrowheads of Korea and Northeast China, it is also suggested that environmental factors influenced mostly on the shape of bone arrowheads and cultural factors rather influenced on their prevalent time.

Evolutionary Selection of a Set of Association Rules Considering Biological Constraints Describing the Prevalent Elements in Bacterial Vaginosis

Bacterial Vaginosis is a common disease and recurring public health problem. Additionally, this infection can trigger other sexually transmitted diseases. In the medical field, not all possible combinations among the pathogens of a possible case of Bacterial Vaginosis are known to allow a diagnosis at the onset of the disease. It is important to contribute to this line of research, so this study uses a dataset with information from sexually active women between 18 and 50 years old, including 17 numerical attributes of microorganisms and bacteria with positive and negative results for BV. These values were semantically categorized for the Apriori algorithm to create the association rules, using support, confidence, and lift as statistical metrics to evaluate the quality of the rules, and incorporate those results in the objective function of the DE algorithm. To guide the evolutionary process we also incorporated the knowledge of a human expert represented as a set of biologically meaningful constraints. Thus, we were able to compare the performance of the rand/1/bin and best/1/bin versions from Differential Evolution to analyze the results of 30 independent executions. Therefore the experimental results allowed a reduced subset of biologically meaningful association rules by their executions, dimension, and DE version to be selected.

Transforming Numerical Feature Models into Propositional Formulas and the Universal Variability Language

Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features have not only boolean values that satisfy boolean constraints but also have numeric attributes that satisfy arithmetic constraints. An essential operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting and sampling.Nemo (Numbers, features, models) is a tool that supports NFMs by bit-blasting, the technique that encodes arithmetic expressions as boolean clauses. The newest version, Nemo2, translates NFMs to propositional formulas and the Universal Variability Language (UVL). By doing so, products can be counted efficiently by #SAT and Binary Decision Tree solvers, enabling finding near-optimal products. This article evaluates Nemo2 with a large set of synthetic and colossal real-world NFMs, including complex arithmetic constraints and counting and sampling experiments. We empirically demonstrate the viability of Nemo2 when counting and sampling large and complex SPLs.

How applicable are attribute-based approaches for human-centered ranking creation?

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, lack flexibility in expressing various types of user preferences, or do not support all mandatory steps in the ranking-creation workflow. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. The iterative design process resulted in different visual interfaces that enable users to formalize their preferences based on a taxonomy of attribute scoring functions. RankASco enables broad user groups to (a) select attributes of interest, (b) express preferences on attributes through interactively tailored scoring functions, and (c) analyze and refine item ranking results. We validate RankASco in a user study with 24 participants in comparison to a general purpose tool. We report on commonalities and differences with respect to usefulness and usability and ultimately present three personas that characterize common user behavior in ranking-creation. On the human factors side, we have also identified a series of interesting behavioral variables that have an influence on the task performance and may shape the design of human-centered ranking solutions in the future.

Comparison of tabular synthetic data generation techniques using propensity and cluster log metric

In the 21st-century, data is as valuable as gold. Many data-centric applications are generating a vast amount of data. Businesses can use this generated data to pinpoint the various sources of problems, if any. In addition, the data can help enterprises to identify connections between what is happening in different areas, departments, and systems. However, having more data is not enough; the data should also be of high quality. For example, taking action based on unfamiliar evidence, speculative ideas, or observations could lead to the wastage of resources. Whereas using high-quality data will help achieve correct results. Synthetic data is artificially generated data. Synthetic data is generated by an algorithm and used to represent real-world data, test datasets, perform mathematical model validation, and, most importantly, for training of machine learning models. Synthetic data can also be used to preserve data privacy. It is considered a safe way to transfer sensitive data because it creates a transaction database that does not contain any confidential information. This paper compares the tabular synthetic data generation techniques using various datasets, viz. balanced datasets, unbalanced datasets, datasets with numerical attributes only, datasets with categorical attributes only and mixed datasets. The utility of the generated synthetic data is measured using the Propensity score metric and Cluster-Log metric. The main finding of this paper is that the Classification And Regression Tree (CART) model provides the best results for all types of datasets. At the same time, Generative Adversarial Networks (GANs) give subpar or mediocre results at best. This contradicts the common belief that GANs are the go-to models for producing synthetic data.