Reviewerly/exHarmony huggingface dataset:
KazNewsDataset: Single Country Overall Digital Mass Media Publication Corpus

Mass media is one of the most important elements influencing the information environment of society. The mass media is not only a source of information about what is happening but is often the authority that shapes the information agenda, the boundaries, and forms of discussion on socially relevant topics. A multifaceted and, where possible, quantitative assessment of mass media performance is crucial for understanding their objectivity, tone, thematic focus and, quality. The paper presents a corpus of Kazakhstan media, which contains over 4 million publications from 36 primary sources (which has at least 500 publications). The corpus also includes more than 2 million texts of Russian media for comparative analysis of publication activity of the countries, also about 4000 sections of state policy documents. The paper briefly describes the natural language processing and multiple-criteria decision-making methods, which are the algorithmic basis of the text and mass media evaluation method, and describes the results of several research cases, such as identification of propaganda, assessment of the tone of publications, calculation of the level of socially relevant negativity, comparative analysis of publication activity in the field of renewable energy. Experiments confirm the general possibility of evaluating the socially significant news, identifying texts with propagandistic content, evaluating the sentiment of publications using the topic model of the text corpus since the area under receiver operating characteristics curve (ROC AUC) values of 0.81, 0.73 and 0.93 were achieved on abovementioned tasks. The described cases do not exhaust the possibilities of thematic, tonal, dynamic, etc., analysis of the considered corpus of texts. The corpus will be interesting to researchers considering both multiple publications and mass media analysis, including comparative analysis and identification of common patterns inherent in the media of different countries.
Optimization of Membership Function Parameters for Fuzzy Controllers in Cruise Control Problem Using the Multi-verse Optimizer

In this paper we propose the application of metaheuristics to optimize control systems that use fuzzy logic, involving the multi-verse optimizer in various situations that involve control. The systems that we study are benchmark fuzzy controllers, like the case of cruise control, which focuses on controlling the velocity that a vehicle has to achieve and maintain in an optimal environment for its implementation, without constraints of the outside world like air friction or the use of inclination. This is by using a simple one input-output fuzzy system that is being optimized in its membership functions parameters; also another system used to prove the functionality of the algorithm is a case of approximation for the tipper system, were the objective is to approximate the system using a two input-one output fuzzy system. The main goal is to introduce the multi-verse optimizer in control problems as a great choice for these systems.
Industrial intrusion detection based on the behavior of rotating machine

In this study, a new industrial intrusion detection method is introduced for the control system of rotating machines as critical assets in many industries. Data tampering is a major attack on the control systems which disrupts the functionality of the asset. Hence, our objective is to detect data manipulations in the system. We use the behavior of the rotating machine to propose new industrial intrusion detection for the control system of the rotating machine by machine learning techniques. The behavior is elicited by the data of sensors under all the conditions of the rotating machine operation. In this work, the nonlinear regression, novelty detection, outlier detection, and classification approaches are implemented to create behavioral model. On each implementation, online data are compared with the real data of behavior prediction model during the operation of the rotating machine to detect any abnormality. According to our experimental results, the accuracy of the behavioral models created by the One-classSVM novelty detection, k- Nearest Neighbor (kNN) outlier detection, decision tree classifier, k-Neighbors classifier, random forest classifier, and AdaBoost classifier is obtained as 0.98, 0.994, 0.999, 0.999, 0.999, and 0.999, respectively. The results indicate that the proposed industrial intrusion detection method is able to detect the data tampering attacks on the control system of the rotating machines very accurately.
IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks

Missing value imputation is a challenging and well- researched topic in data mining. In this paper, we propose IFGAN, a missing value imputation algorithm based on Feature- specific Generative Adversarial Networks (GAN). Our idea is intuitive yet effective: a feature-specific generator is trained to impute missing values, while a discriminator is expected to distinguish the imputed values from observed ones. The proposed architecture is capable of handling different data types, data distributions, missing mechanisms, and missing rates. It also improves post-imputation analysis by preserving inter-feature correlations. We empirically show on several real-life datasets that IFGAN outperforms current state-of-the-art algorithm under various missing conditions.
Bayesian inference on M/M/1 queue under asymmetric loss function using Markov Chain Monte Carlo method

A stationary M/M/1 queueing model satisfies the equilibrium condition that the arrival rate, λ is less than or equal to the service rate, µ. In this paper, we have discussed the Bayes estimation of queue parameters λ, µ and various queue characteristics using Mckay’s bivariate gamma prior distribution for (λ, µ) for a stationary M/M/1 queue as it satisfies the equilibrium order restriction, 0 < λ < µ. Using Markov Chain Monte Carlo method, we have numerically computed the Bayes estimates of queue parameters and various queue characteristics under different asymmetric loss functions.
A kernel path algorithm for general parametric quadratic programming problem

It is well known that the performance of a kernel method highly depends on the choice of kernel parameter. A kernel path provides a compact representation of all optimal solutions, which can be used to choose the optimal value of kernel parameter along with cross validation (CV) method. However, none of these existing kernel path algorithms provides a unified implementation to various learning problems. To fill this gap, in this paper, we first study a general parametric quadratic programming (PQP) problem that can be instantiated to an extensive number of learning problems. Then we provide a generalized kernel path (GKP) for the general PQP problem. Furthermore, we analyze the iteration complexity and computational complexity of GKP. Extensive experimental results on various benchmark datasets not only confirm the identity of GKP with several existing kernel path algorithms, but also show that our GKP is superior to the existing kernel path algorithms in terms of generalization and robustness.
Big Data Analysis of Massive PMU Datasets: A Data Platform Perspective

The discovery of `event signatures' and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.
An efficient JPEG-2000 based multimodal compression scheme

In this paper, a wavelet-based multimodal compression method is proposed. The method jointly compresses a medical image and an ECG signal within a single codec, i.e., JPEG-2000 in an effective and simple way. The multimodal scheme operates in two main stages: the first stage, consists of the encoder and involves a mixing function, aiming at inserting the samples of the signal in the image according to a predefined insertion pattern in the wavelet domain. The second stage represented by a separation function, consists of the extraction process of the ECG signal from the image after performing the decoding stage. Both the cubic spline and the median edge detection (MED) predictor have been adopted to conduct the interpolation process for estimating image pixels. Intensive experiments have been conducted to evaluate the performance of the multimodal scheme using objective distortion criteria. Results show clear superiority of the proposed scheme over the conventional separate compression approach involving two codecs: JPEG-2000 for images and ECG SPIHT-1D as well as other competing multimodal compression schemes in terms of both PRD and SNR at the signal decompression stage while maintaining good image quality and exhibiting a reduced computational complexity. Improvements in terms of average PRD and SNR values are as significant as 0.7 and 6 dB at low bit rates and 0.06 and 2 dB at higher bit rates on a number of test ECG signals and medical images.
Finding Better Subwords for Tibetan Neural Machine Translation

Subword segmentation plays an important role in Tibetan neural machine translation (NMT). The structure of Tibetan words consists of two levels. First, words consist of a sequence of syllables, and then a syllable consists of a sequence of characters. According to this special word structure, we propose two methods for Tibetan subword segmentation, namely syllable-based and character-based methods. The former generates subwords based on the Tibetan syllables, and the latter is based on Tibetan characters. In addition, we carry out experiments with these two subword segmentation methods on low-resource Tibetan-to-Chinese NMT, respectively. The experimental results show that both of them can improve translation performance, in which the subword segmentation based on character sequences can achieve better results. Overall, our proposed character-based subword segmentation is more simple and effective. Moreover, it can achieve better experimental results without paying much attention to the linguistic features of Tibetan.
Compact Incremental Random Weight Network for Estimating the Underground Airflow Quantity

Optimal operation of an actual mine main fan switchover process relies heavily on a good measurement of underground airflow quantity (UAQ). However, real-time measuring the UAQ is difficult using conventional measurement techniques. In this article, a novel randomized learning model, named compact incremental random weight network (CIRWN), is proposed to estimate the UAQ. Since the hidden parameters of the original IRWN are generated in a fixed scope with a random manner, which is prone to create redundant hidden nodes, a CIRWN with new inequality constraints is proposed. The inequality constraints have several attractive properties, including dynamically guiding the generation of hidden parameters, effectively enhancing the convergence rate, and successfully establishing a universal approximator. Experiments using four benchmark datasets and an industrial dataset show that the established model possesses a more compact network structure and better modeling accuracy as well as faster convergence rate compared with other methods.
Systematic Variation of Preattentive Attributes to Highlight Relevant Data in Information Visualization

In information visualization (InfoVis), the Visualizers (graph designers and creators) have to consider multiple parameters, such as colors and graphic symbols, to obtain a chart that correctly represents a data set. Along with this, visualizers must adequately select the combination of these range of parameters to drive the observers' attention to the relevant data. When a visualizer drives the attention to relevant aspects of the information, she is providing a starting point to read the graph; this focus point might help the observer to complete the task faster and more efficiently, minimizing distraction from unimportant information. Contemporary tools for InfoVis help visualizers to a certain extent, but most of them currently do not provide insights or suggestions about the modifications needed to drive data attention. This article presents the preliminary results of an exploratory approach to draw the attention to some specific data subset selected by the graph creator, through a systematic variation of some preattentive attributes (i.e., color, texture and orientation). As a first simple method to validate the feasibility of the approach, a set of charts is created from the same source data, with exhaustive variations on preattentive attributes. All generated charts are then automatically evaluated using a salience map algorithm for data analysis images, to identify their focus attention point. After that, the algorithm chooses the chart that best emphasizes the data subset initially specified by the visualizer. To validate our approach, we have implemented a prototype tool, and preliminary results confirm that it is possible to systematically change the attention area of a chart.
Stabilization Via Feedback Switching for Quantum Stochastic Dynamics

We propose a new method for pure-state and subspace preparation in quantum systems, which employs the output of a continuous measurement process and switching dissipative control to improve convergence speed, as well as robustness with respect to the initial conditions. In particular, we prove that the proposed closed-loop strategy makes the desired target globally asymptotically stable both in mean and almost surely, and we show it compares favorably against a time-based and a state-based switching control law, with significant improvements in the case of faulty initialization.
Comparison of Pairwise Similarity Distance Methods for Effective Hashing

Abstract Content-based image retrieval (CBIR) methods search for points with the most similar content to query features from within a large dataset. The most notable approach for this purpose is an approximate nearest neighbor (ANN) searching. The main properties expected from a retrieval system can be listed as follows; low storage requirement, high retrieve speed, and high average precision. Hashing, which can generate discriminative and low-dimensional binary codes, is one of today’s most effective ANN searching methods. Although there are various hashing approaches in the literature, almost all hashing approaches consist of low-dimension feature representation and binarization sections. This study focuses on the low-dimension feature representation. Hand-crafted or deep learning based approaches are used for feature extraction in hashing methods. These features are the main components that affect the performance in creating binary codes. Contrastive loss is often used in the literature to update the learnable parameters of these feature extraction algorithms. The distance parameter of these data points critical to calculating contrastive loss. In this study, contrastive loss performance is tested using five different distance methods (Euclidean, Manhattan, Cosine, Minkowski, Chebyshev) for more effective feature representation. Retrieval performance is tested using low-dimensional feature vectors produced by these methods in MNIST and CIFAR-10 datasets. It is thought that the information obtained from this study is very useful for new researchers.
Credit Card Fraud Detection Using Spark and Machine Learning Techniques

To run a property business, merchants got to create a profit, that is what's left when deducting the prices of doing business from a company's revenue. Therefore, a business tolerance for payment fraud may be operation of, among different things, its margin of profit (sell value - value of products sold). The lower the margin, the lower the tolerance for payment fraud. In observance, once fraud happens, the cardholder disputes the charge and also the debit is typically off, which suggests either the cardholders bank or the bourgeois absorbs the loss (see Aleskerov, E., Freisleben, B., Rao, B.: Cardwatch: a neural network- based database mining system for credit card fraud detection. In: Processdings of the IEEE/IAFE on CIFE, pp. 220–226 (1997) for additional details). Cumulatively, fraud represents a big monetary risk to the bourgeois and also the provision bank. to scale back fraud, 3D secure, fraud detection techniques, chip and pin technologies are used. However, if 3D secure and chip and pin technology exist, why is fraud detection used? There are two main reasons. First, the full value of 3D secure and chip and pin technology is comparatively high compared to the value of fraud detection. e.g., whereas online merchants care concerning conversion, 3D secure reduces it by many percent (>5%). Hence, once they have the choice, several online merchants conceive to deactivate 3D secure and manage the chance of payment fraud themselves. Second, adding additional security layers to the shopping method greatly reduces checkout rate and, in turn, convenience for the client. Whereas convenience for consumers might appear as if a fuzzy conception initially, for corporations like Amazon, that pioneered one-click checkout, its a selling argument and a method to convert and grow revenues. The objectives of MasterCard fraud detection are to scale back losses because of fraud payments for each provision banks and merchants and revenue opportunities for merchants increases.
The turmite-based cryptographic algorithm

Abstract The novel turmite-based cryptography algorithm has been designed and implemented. The turmites ability to generate pseudo-random number series makes them promising for cryptographic applications. At the same time, most turmites-related researches concentrate on their mathematical properties and generally don't consider possible applications. Lack of effective implementations of turmites-based cryptographic algorithms makes this research topical. The properties of the proposed algorithm have been examined. The frequency analysis resistance and avalanche criterion have been estimated. The results demonstrate that turmites-based algorithms may be used in cryptography and this application deserves attention and further examination.
The analysis method of security vulnerability based on the knowledge graph

Given the increasingly prominent network security issues, it is of great significance to deeply analyze the vulnerability of network space software and hardware resources. Although the existing Common Vulnerabilities and Exposures (CVE) security vulnerability database contains a wealth of vulnerability information, the information is poorly readable, the potential correlation is difficult to express intuitively, and the degree of visualization is insufficient. To solve the current problems, a method of constructing a knowledge graph of CVE security vulnerabilities is proposed. By acquiring raw data, ontology modeling, data extraction and import, the knowledge graph is imported into the Neo4j graph database to complete the construction of the CVE knowledge graph. Based on the knowledge graph, the in-depth analysis is performed from the cause dimension, time dimension and association dimension, and the results are displayed visually. Experiments show that this analysis method can intuitively and effectively mine the intrinsic value of CVE security vulnerability data.
Can Boosted Randomness Mimic Learning Algorithms of Geometric Nature? Example of a Simple Algorithm That Converges in Probability to Hard-Margin SVM

In the light of the general question posed in the title, we write down a very simple randomized learning algorithm, based on boosting, that can be seen as a nonstationary Markov random process. Surprisingly, the decision hyperplanes resulting from this algorithm converge in probability to the exact hard-margin solutions of support vector machines (SVMs). This fact is curious because the hard-margin hyperplane is not a statistical solution, but a purely geometric one-driven by margin maximization and strictly dependent on particular locations of some data points that are placed in the contact region of two classes, namely the support vectors. The proposed algorithm detects support vectors probabilistically, without being aware of their geometric definition. We give proofs of the main convergence theorem and several auxiliary lemmas. The analysis sheds new light on the relation between boosting and SVMs and also on the nature of SVM solutions since they can now be regarded equivalently as limits of certain random trajectories. In the experimental part, correctness of the proposed algorithm is verified against known SVM solvers: libsvm, liblinear, and also against optimization packages: cvxopt (Python) and Wolfram Mathematica.
A Trade-off between ML and DL Techniques in Natural Language Processing

Abstract The domain of Natural Language Processing covers various tasks, such as classification, text generation, and language model. The data processed using word embeddings, or vectorizers, is then trained using Machine Learning and Deep Learning algorithms. In order to observe the tradeoff between both these types of algorithms, with respect to data available, accuracy obtained and other factors, a binary classification is undertaken to distinguish between insincere and regular questions on Quora. A dataset called Quora Insincere Questions Classification was used to train various machine learning and deep learning models. A Bidirectional-Long Short Term Network (LSTM) was trained, with the text processed using Global Vectors for Word Representation (GloVe). Machine Learning algorithms such as Extreme Gradient Boosting classifier, Gaussian Naive Bayes, and Support Vector Classifier (SVC), by using the TF-IDF vectorizer to process the text. This paper also presents an evaluation of the above algorithms on the basis of precision, recall, f1 score metrics.