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• This paper presents a soft and hard hybrid qualitative balanced clustering (SHHQBC). • Qualitative balancing produces clusters with the lowest cardinality and highest value which are very practical. • Creating value criterion with the SHHQBC method enhanced quantitative and qualitative clustering criteria. K-means is a popular clustering method that has consistently failed to produce a balanced cluster structure. While changes in cardinality, variance, and density have arisen due to the importance of balancing in different fields, balancing has never been viewed from a qualitative viewpoint. The current paper takes a new look at cluster balancing by presenting a soft (balance-driven) and hard (balance-constrained) hybrid qualitative balanced clustering (SHHQBC). It starts by identifying and prioritizing key features using a random forest algorithm and the mean decrease in the Gini coefficient criterion. It then uses a weighted linear combination of features with the importance of above 25%, 50%, and 75% to construct a feature called value criterion. The developed clustering approach is then implemented to establish clusters with the highest value with the least cardinality or a value similar to other clusters. By implementing the SHHQBC on 14 different datasets, first soft clustering is implemented for all three cases and balanced conditions are checked. Hard clustering is then performed to make balanced conditions. Finally, the best-balanced case with the least objective function is selected. Formulating the value criterion facilitates the interpretation and labeling of clusters, and quantitative clustering criteria are improved. [ABSTRACT FROM AUTHOR]

• A new feature set composed of 46 features for phishing URLs detection were proposed. • An updated feature selection algorithm to obtain the most representative features. • An optimum subset composed of 9 features for phishing URLs detection was proposed. • Random Forest obtains the best Accuracy results using the feature subset proposed. • The Accuracy results obtained outperforms those reported in the literature. Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). Hence, it is advantageous to detect a phishing attack by analyzing URLs before accessing them. With the raising of the Internet of Things (IoT), phishing attacks are moving to this field due to the number of IoT devices and the amount of personal information they handle. Although several approaches were proposed for phishing attacks detection, the URL-based Machine Learning approaches obtain better performance results, but all of them are dependent on the feature set used. Contradictorily, only a few works on selecting the best-suited feature set for improving the phishing detection process have been published. The present research explores how to obtain a feature set that substantially enhances the phishing detection rate in IoT environments. Hence, a feature selection algorithm was adopted and extended for getting the most representative feature set. When Random Forest is used with the proposed data representation, the phishing URL attacks discovery rate is 99.57 %. [ABSTRACT FROM AUTHOR]

The increased amount of high-dimensional imbalanced data in online social networks challenges existing feature selection methods. Although feature selection methods such as principal component analysis (PCA) are effective for solving high-dimensional imbalanced data problems, they can be computationally expensive. Hence, an effortless approach for identifying meaningful features that are indicative of anomalous behaviour between humans and malicious bots is presented herein. The most recent Twitter dataset that encompasses the behaviour of various types of malicious bots (including fake followers, retweet spam, fake advertisements, and traditional spambots) is used to understand the behavioural traits of such bots. The approach is based on Benford's law for predicting the frequency distribution of significant leading digits. This study demonstrates that features closely obey Benford's law on a human dataset, whereas the same features violate Benford's law on a malicious bot dataset. Finally, it is demonstrated that the features identified by Benford's law are consistent with those identified via PCA and the ensemble random forest method on the same datasets. This study contributes to the intelligent detection of malicious bots such that their malicious activities, such as the dissemination of spam, can be minimised. [ABSTRACT FROM AUTHOR]

In big data era, some data, becoming meaningless or illegal over time and space, need to be deleted from historical knowledge. It is a challenging problem, called machine unlearning, to efficiently forget the information of those outdated data from historical models. Some unlearning techniques have been proposed in loss-well-defined classification models, such as SVM, Random Forest, and Federated learning model. Yet, it is under study to remove outdated data from learned feature selection in fuzzy rough philosophy. To narrow this gap, we propose a fuzzy rough unlearning model for feature selection. Specifically, the outdated information is first identified in a compact set, called unlearning set, that remarkably shrinks the search space of feature selection. Then, the unlearning mechanisms containing two main theorems are proposed by leveraging unlearning set, followed by a feature selection unlearning algorithm. Theoretical analyses verify that the proposed unlearning algorithm is equivalent to the traditional algorithm retraining on the remaining data with the beginning of historical results. Experimentally, extensive results on 20 datasets demonstrate that our proposed unlearning methods perform effectively with remarkably less time cost. For the source code of ARU, please refer to: https://github.com/yuxin370/ARU. [ABSTRACT FROM AUTHOR]

Current automatic data-driven vulnerability detection in smart contracts selects and processes features of interest under black box settings without empirical justification. In this paper, we propose a smart contract testing methodology that bestows developers with flexible, practical and customizable strategies to detect vulnerabilities. Our work enforces strong whitebox knowledge to a series of supervised multimodal tasks under static analysis. Each task encapsulates a vulnerability detection branch test and pipelines feature selection, dimension unification, feature fusion, model training and decision-making. We exploit multiple features made up of code and graph embeddings at the single modality level (intramodal settings) and across individual modalities (intermodal settings). We assign each task to either intramodal or intermodal settings, and show how to train state-of-the-art self-attentive bi-LSTM, textCNN, and random forest (RF) models to extract a joint multimodal feature representation per task. We evaluate our framework over 101,082 functions extracted from the SmartEmbed dataset, and rank each multimodal vulnerability mining strategy in terms of detection performance. Extensive experiments show that our work outperforms existing schemes, and the highest performance reaches 99.71%. [ABSTRACT FROM AUTHOR]

This paper introduces an approach aiming to determine head pose healthiness of computer users. The main contributions of this paper are: 1) Image Quality Assessment (IQA) based Stacked Autoencoder (referred to as IQASAE) which adjusts the value of learning rate based on the quality of images; 2) Head Pose Healthiness Prediction (HPHP) framework which leverages the proposed IQASAE algorithm in combination with image processing operations; 3) A set of features suitable for face analysis applications; 4) Ontology-driven semantic framework which enables further exploiting pose estimation results within applications in synergy with healthcare expert domain knowledge about pose healthiness. Our framework was evaluated on both offline (BIWI and AFLW) and online (our own, collected using Arduino) datasets. Furthermore, it was compared to several state-of-art methods, including Multi-Layer Perceptron (MLP), CART, Random Forest, Convolutional Neural Networks (CNN), Temporal Deep Learning Model (TDLM), hybrid CNN with Support Vector Machine (SVM), Quatnet and Trinet. According to the achieved experimental results, it reaches accuracy up to 79.63% outperforming all of them, except Quatnet and Trinet. However, the main advantages of IQASAE compared to state-of-art methods are: 1) it does not require selection of features, so the processing time is reduced, 2) utilizing angle between chin and mouth reduces training time for SAE, 3) leveraging vector-based feature set to create training data resulted in a significant improvement, especially in offline facial images. [ABSTRACT FROM AUTHOR]

• A general nonparametric feature selection algorithm is proposed. • The theoretical properties of the proposed algorithm are established under regularity conditions. • The proposed algorithm outperforms its alternatives in terms of detecting useful features and avoiding useless ones. • The proposed algorithm can be widely applied in other machine learning methods. Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features. Herein, we propose a nonparametric feature selection algorithm that incorporates random forests and deep neural networks, and its theoretical properties are also investigated under regularity conditions. Using different synthetic models and a real-world example, we demonstrate the advantage of the proposed algorithm over other alternatives in terms of identifying useful features, avoiding useless ones, and the computation efficiency. Although the algorithm is proposed using standard random forests, it can be widely adapted to other machine learning algorithms, as long as features can be sorted accordingly. [ABSTRACT FROM AUTHOR]