Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional processes are themselves evolutionary practices of mutual learning in which actors, regardless of their initial political positions, continuously interact with each other, demonstrating differences and making alliances regarding different topics. In this article, we develop Tree Augmented Naive Bayes (TAN) classifiers to model the behavior of constituent agents. According to the nature of the constituent dynamics, weights are learned by the model from the data using an evolution strategy to obtain a good classification performance. For our analysis, we used the constituent agents’ communications on Twitter during the installation period of the Constitutional Convention (July–October 2021). In order to differentiate political positions (left, center, right), we applied the developed algorithm to obtain the scores of 882 ballots cast in the first stage of the convention (4 July to 29 September 2021). Then, we used k-means to identify three clusters containing right-wing, center, and left-wing positions. Experimental results obtained using the three constructed datasets showed that using alternative weight values in the TAN construction procedure, inferred by an evolution strategy, yielded improvements in the classification accuracy measured in the test sets compared to the results of the TAN constructed with conditional mutual information, as well as other Bayesian network classifier construction approaches. Additionally, our results may help us to better understand political behavior in constitutional processes and to improve the accuracy of TAN classifiers applied to social, real-world data.

Research on Default Prediction for Credit Card Users Based on XGBoost-LSTM Model

The credit card business has become an indispensable financial service for commercial banks. With the development of credit card business, commercial banks have achieved outstanding results in maintaining existing customers, tapping potential customers, and market share. During credit card operations, massive amounts of data in multiple dimensions—including basic customer information; billing, installment, and repayment information; transaction flows; and overdue records—are generated. Compared with preloan and postloan links, user default prediction of the on-loan link has a huge scale of data, which makes it difficult to identify signs of risk. With the recent growing maturity and practicality of technologies such as big data analysis and artificial intelligence, it has become possible to further mine and analyze massive amounts of transaction data. This study mined and analyzed the transaction flow data that best reflected customer behavior. XGBoost, which is widely used in financial classification models, and Long-Short Term Memory (LSTM), which is widely used in time-series information, were selected for comparative research. The accuracy of the XGBoost model depends on the degree of expertise in feature extraction, while the LSTM algorithm can achieve higher accuracy without feature extraction. The resulting XGBoost-LSTM model showed good classification performance in default prediction. The results of this study can provide a reference for the application of deep learning algorithms in the field of finance.

Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

Background Deep learning is an active bioinformatics artificial intelligence field that is useful in solving many biological problems, including predicting altered epigenetics such as DNA methylation regions. Deep learning (DL) can learn an informative representation that addresses the need for defining relevant features. However, deep learning models are computationally expensive, and they require large training datasets to achieve good classification performance. Results One approach to addressing these challenges is to use a less complex deep learning network for feature selection and Machine Learning (ML) for classification. In the current study, we introduce a hybrid DL-ML approach that uses a deep neural network for extracting molecular features and a non-DL classifier to predict environmentally responsive transgenerational differential DNA methylated regions (DMRs), termed epimutations, based on the extracted DL-based features. Various environmental toxicant induced epigenetic transgenerational inheritance sperm epimutations were used to train the model on the rat genome DNA sequence and use the model to predict transgenerational DMRs (epimutations) across the entire genome. Conclusion The approach was also used to predict potential DMRs in the human genome. Experimental results show that the hybrid DL-ML approach outperforms deep learning and traditional machine learning methods.

Penerapan Algoritme Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN) Untuk Klasifikasi Hasil Kinerja Pegawai Negeri Sipil

<p class="Abstrak">Keberhasilan sebuah perusahaan terjadi karena dapat mengelola sumber daya manusianya dengan baik begitu juga sebaliknya. Salah satu instansi yang mengelola sumber daya manusia menggunakan Manajemen Talenta adalah Badan Kepegawaian Daerah (BKD) kota Malang, dengan mengevaluasi pegawainya setiap tahunnya setelah pekerjaan selesai dilakukan. Hal ini menyebabkan hasil pekerjaan yang telah dilakukan tidak optimal, sehingga perlu identifikasi dini pegawai yang memiliki kinerja dibawah rata – rata sehingga dapat dievaluasi dan meminimalisir hasil pekerjaan yang tidak optimal dengan menggunakan teknik klasifikasi. Penelitian ini menggunakan teknik klasifikasi <em>Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance</em> (LMKHNCN). Metode ini merupakan metode modifikasi dari metode <em>K-Nearest Neighbor</em> (KNN) dan dibuktikan memiliki performa lebih baik dibandingkan dengan metode aslinya KNN. Dilakukan pengujian <em>F1-Score</em> dan akurasi menggunakan <em>K-Fold Cross Validation</em> untuk mengetahui persebaran akurasi dan juga pengujian mengenai pengaruh normalisasi karena tidak ada informasi normalisasi pada penelitian sebelumnya. Metode pada kasus ini menghasilkan performa klasifikasi yang baik, dibuktikan bahwa hasil akurasi dan <em>F1-Score</em> oleh metode ini berturut – turut ialah mencapai 98,8% dan 98,1%.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p><em>The success of company occurs because is manage human resources well and vice versa. One of institute that mange human resource using Talent Management is Malang city Badan Kepegawaian Daerah (BKD), which evaluates its employee annually after the work is completed. This can cause not optimal work result, so it necessary to early identification of employees who have performance below average performance so that can be evaluated and minimize not optimal result. This study is use classification technique Nearest Centroid Neighbor Classifier Based on K Local Means Using Harmonic Mean Distance (LMKHNCN). This method is modified base algorithm of K-Nearest Neighbor (KNN). F1-Score and Accuracy using K-Fold Cross Validation to measure performance of this method and normalization testing due to no any information about that in previous study. This method is proven to have better performance compared to it original algorithm KNN. The method in this study has produced good classification performance. The result of classification accuracy and F1-Score by this method reach </em><em>98,8% dan 98,1%</em>.</p>

PBDT: Python Backdoor Detection Model Based on Combined Features

Application security is essential in today’s highly development period. Backdoor is a means by which attackers can invade the system to achieve illegal purposes and damage users’ rights. It has posed a serious threat to network security. Thus, it is urgent to take adequate measures to defend such attacks. Previous research work was mainly focused on numerous PHP webshells, with less research on Python backdoor files. Language differences make the method not entirely applicable. This paper proposes a Python backdoor detection model named PBDT based on combined features. The model summarizes the common functional modules and functions in the backdoor files and extracts the number of calls in the text to form sample features. What is more, we consider the text’s statistical characteristics, including the information entropy, the longest string, etc., to identify the obfuscated Python code. Besides, the opcode sequence is used to represent code characteristics, such as TF-IDF vector and FastText classifier, to eliminate the influence of interference items. Finally, we introduce the Random Forest algorithm to build a classifier. Covering most types of backdoors, some samples are obfuscated, the model achieves an accuracy of 97.70%, and the TNR index is as high as 98.66%, showing a good classification performance in Python backdoor detection.

Biometric Identification Based on Eye Movement Dynamic Features

The paper presents studies on biometric identification methods based on the eye movement signal. New signal features were investigated for this purpose. They included its representation in the frequency domain and the largest Lyapunov exponent, which characterizes the dynamics of the eye movement signal seen as a nonlinear time series. These features, along with the velocities and accelerations used in the previously conducted works, were determined for 100-ms eye movement segments. 24 participants took part in the experiment, composed of two sessions. The users’ task was to observe a point appearing on the screen in 29 locations. The eye movement recordings for each point were used to create a feature vector in two variants: one vector for one point and one vector including signal for three consecutive locations. Two approaches for defining the training and test sets were applied. In the first one, 75% of randomly selected vectors were used as the training set, under a condition of equal proportions for each participant in both sets and the disjointness of the training and test sets. Among four classifiers: kNN (k = 5), decision tree, naïve Bayes, and random forest, good classification performance was obtained for decision tree and random forest. The efficiency of the last method reached 100%. The outcomes were much worse in the second scenario when the training and testing sets when defined based on recordings from different sessions; the possible reasons are discussed in the paper.

Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification

Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become the de-facto standard for HSI classification. It seems that the traditional neural networks such as multi-layer perceptron (MLP) are not competitive for HSI classification. However, in this study, we try to prove that the MLP can achieve good classification performance of HSI if it is properly designed and improved. The proposed Modified-MLP for HSI classification contains two special parts: spectral–spatial feature mapping and spectral–spatial information mixing. Specifically, for spectral–spatial feature mapping, each input sample of HSI is divided into a sequence of 3D patches with fixed length and then a linear layer is used to map the 3D patches to spectral–spatial features. For spectral–spatial information mixing, all the spectral–spatial features within a single sample are feed into the solely MLP architecture to model the spectral–spatial information across patches for following HSI classification. Furthermore, to obtain the abundant spectral–spatial information with different scales, Multiscale-MLP is proposed to aggregate neighboring patches with multiscale shapes for acquiring abundant spectral–spatial information. In addition, the Soft-MLP is proposed to further enhance the classification performance by applying soft split operation, which flexibly capture the global relations of patches at different positions in the input HSI sample. Finally, label smoothing is introduced to mitigate the overfitting problem in the Soft-MLP (Soft-MLP-L), which greatly improves the classification performance of MLP-based method. The proposed Modified-MLP, Multiscale-MLP, Soft-MLP, and Soft-MLP-L are tested on the three widely used hyperspectral datasets. The proposed Soft-MLP-L leads to the highest OA, which outperforms CNN by 5.76%, 2.55%, and 2.5% on the Salinas, Pavia, and Indian Pines datasets, respectively. The proposed Modified-MLP, Multiscale-MLP, and Soft-MLP are tested on the three widely used hyperspectral datasets (i.e., Salinas, Pavia, and Indian Pines). The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the MLP-based methods are still competitive for HSI classification.

Deep ensemble learning for skin lesions classification with convolutional neural network

<span lang="EN-US">One type of skin cancer that is considered a malignant tumor is melanoma. Such a dangerous disease can cause a lot of death in the world. The early detection of skin lesions becomes an important task in the diagnosis of skin cancer. Recently, a machine learning paradigm emerged known as deep learning (DL) utilized for skin lesions classification. However, in some previous studies by using seven class images diagnostic of skin lesions classification based on a single DL approach with CNNs architecture does not produce a satisfying performance. The DL approach allows the development of a medical image analysis system for improving performance, such as the deep convolutional neural networks (DCNNs) method. In this study, we propose an ensemble learning approach that combines three DCNNs architectures such as Inception V3, Inception ResNet V2 and DenseNet 201 for improving the performance in terms of accuracy, sensitivity, specificity, precision, and F1-score. Seven classes of dermoscopy image categories of skin lesions are utilized with 10015 dermoscopy images from well-known the HAM10000 dataset. The proposed model produces good classification performance with 97.23% accuracy, 90.12% sensitivity, 97.73% specificity, 82.01% precision, and 85.01% F1-Score. This method gives promising results in classifying skin lesions for cancer diagnosis.</span>

Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.