Reviewerly/exHarmony huggingface dataset:
Mining Entropy Optimized Parameter based Precise positioning Episode Rules from Event Sequences

Episode Rule Mining is utilized in various fields like telecommunication alarm management, stock market, traffic data, customer transactions etc. Episode rule mining is the extraction of important and useful patterns or episodes from large event databases. The main objective of the existing technique Mining Precise-Positioning Episode Rules (MIPER) is to mine episodes from event sequences. For resolving the enormous energy consumption, the proposed methodology ensures selection of parameter setting as well as a simple and effective technique to correct the fixed-gap. This is done by using bat optimization mechanism and this mechanism generates fixed-gap episodes. The proposed study focuses on two major aspects which are correlation between entropy and optimal generation of parameter selection. These are achieved by the proposed approach Mining Entropy Optimized Parameter-based Precise-Positioning Episode Rules (MEOPPER). In this approach, each event's occurrence time has been clearly specified. To mine Precise-Positioning Episode Rules (PER), a trie-based data structure is used. The proposed algorithm's efficiency has been analyzed and studied based on three datasets Retail, Kosarak and MSNBC. The experimental results of the proposed work achieves better performance when compared with the existing algorithms like Mining Precise-Positioning-Enumeration (MIP-ENUM), Mining Precise-Positioning-Trie(Depth First Search) (MIP-TRIE(DFS)) and Mining Precise-Positioning-TRIE(Pruning) (MIP-TRIE(PRU) algorithms.
A Comparative Analysis of Soft computing techniques for Password Strength Classification

Passwords are a very important sensitive aspect in computer security. Password is the important identitiy in authenticating any individual in different domains. It plays a vital role in accessing any device or any software. Users around the world are consistently told about the necessity of a strong password to protect their access with unauthorized users. In this paper, we propose a comparative analysis of soft computing techniques such as Back propagation neural network (BPN), Logistic regression (LR), Hopfield neural network (HNN), Brain state in a box (BSB) and associative memory network (BAM), Convolution neural network (CNN) system for strong password classification. The dataset contains only a single attribute of passwords with 3 classes as weak, medium and strong. The experimental results analysis shows that for such a datset, the simple logistic regression model yields better output when compared with Convolution Neural networks, Back propagation neural network, Logistic regression, Hopfield neural network, Brain state in a box and Bidirectional Associative Memory. It can be concluded that logistic regression is adapted for such an application and also plays as a better prediction system for such a dataset.
An Image Watermarking Approach Based on Artificial Fish Swarm Algorithm

In this paper, an artificial fish swarm algorithm was developed to select a proper basis of wavelet packet transform used in the robust image watermark. In the proposed algorithm, watermark is viewed as a vector. To embed the watermark, a vector composed of wavelet coefficients selected from wavelet packet transform of an image will then be modified. The performance measure is the quality of the watermarked images. Both artificial fish swarm and the watermark embedding process will try to optimize the image quality, so the performance will be better than directly embedding the watermark into wavelet domain coefficients. Simulation results obtained from the approach based on the artificial fish swarm algorithm are compared with the heuristic based approach. The experimental results show that the proposed approach in terms of image quality is encouraging when compared to the heuristic approach.
Systematic framework for short text classification based on improved TWE and supervised MCFS topic merging strategy

Text classification task can help people to discover valuable information hidden in the text set. Many previous studies have achieved excellent results in traditional text classification tasks. With the development of new social media, a large number of short texts appear on the Internet. Due to the sparsity of the short text, many classification algorithms which achieve excellent results on long texts hardly achieve satisfactory results on short texts. Therefore, it is important to find a method for calculating effective vector representations of words and overcoming the feature sparseness problem. Based on the above, we carry out the work from improving the quality of word vector representation and enhancing the effect of classification. This paper proposes a systematic framework for improving short text classification performance. In our framework, we first build a topic model with Latent Dirichlet Allocation (LDA) on a universal dataset from Wikipedia and use this model to perform topic inference on short texts. Then, an improved scheme of topical word embedding (TWE) is proposed to learn the vector representations of both words and topics, which use the word in the current word-topic pair to predict the contextual words and the topic in the same word-topic pair to predict its contextual topics. In addition, the supervised Multi-Cluster Feature Selection algorithm (MCFS) is employed to execute topic selection, and we propose a topic merging strategy that is based on the MCFS. At the end of topic selection and merging, short text matrices are generated using the vector representations of both words and topics, and these matrices are fed into a convolution neural network (CNN). On an open short text classification dataset, we compared the proposed framework with various baselines, and the experimental results indicate the effectiveness of our method.
Siamese Network with Phash for Video Vibration Detection

An efficient Simaese network(SiaNet) with Phash for video vibration detection in the paper were formed, such a network can transform the images to feature vectors and by caculating the distance between two consecutive picture vectors to judge whether the video is in stable or vibrating state. The proposed SiaNet takes the network with excellent feature extraction performance such as vgg as the base network branch, Uses Phash to preprocess the images that previous frame and current frame of the input network to screen out the simple samples, and adds the SPP layer before the decision layer to increase the applicability and robustness of the network. Then, a technique called Contrasive Loss of Loss function was used in this article to reduce the distance between positive sample pairs and increase the distance between negative sample pairs. Finally, some experiments for the the SiaNet on 16000 electric meter pictures which were collected in the subway station machine room are utilized to demonstrate the validity of the proposed SiaNet with Phash.
Real‐Time Earthquake Location Based on the Kalman Filter Formulation

Abstract Seismic location is an essential task for earthquake monitoring. The general practice is to locate earthquakes using arrival times from all recorded stations. However, this is not well suited for real‐time applications such as the earthquake early warning, where earthquake locations need to be determined and updated as more stations are triggered. Here we have developed a real‐time linear location method based on a Kalman filter formulation. It updates location and its uncertainty whenever a station is triggered. We have demonstrated its effectiveness with synthetic and real data sets in Parkfield, California in a retrospective mode. The tests show that we can obtain relatively accurate locations and reliable uncertainties for earthquakes with 4 or 5 stations triggered. In particular, without considering the station latency, accurate earthquake locations can be achieved retrospectively in about 3.3 and 2.7 s for the 2003 San Simeon Mw6.6 earthquake and 2004 Parkfield Mw6.0 earthquake, respectively.
A Metric Model for the Functional Architecture of the Visual Cortex

The purpose of this work is to construct a model for the functional architecture of the primary visual cortex (V1), based on a structure of metric measure space induced by the underlying organization of receptive profiles (RPs) of visual cells.In order to account for the horizontal connectivity of V1 in such a context, a diffusion process compatible with the geometry of the space is defined following the classical approach of K.-T.Sturm [39].The construction of our distance function does neither require any group parameterization of the family of RPs, nor involve any differential structure.As such, it adapts to non-parameterized sets of RPs, possibly obtained through numerical procedures; it also allows to model the lateral connectivity arising from non-differential metrics such as the one induced on a pinwheel surface by a family of filters of vanishing scale.On the other hand, when applied to the classical framework of Gabor filters, this construction yields a distance approximating the sub-Riemannian structure proposed as a model for V1 by Citti and Sarti [7], thus showing itself to be consistent with existing cortex models.
Multi-Target Tracking by Associating and Fusing the Multi-Bernoulli Parameter Sets

The Cardinality Balanced MeMBer (CBMeMBer) filter is a single sensor multi-target tracking method based on the random finite set. Compared with the single sensor system, the multi-sensor system can achieve more stable and better performance in tracking targets. However, some problems exist in multi-sensor system based on the CBMeMBer filter. Tracks in the CBMeMBer filter are described by parameter sets which may be generated by miss-detection, targets and clutters. It is difficult to associate the parameter sets correctly because of their complex forms and various types. Moreover, the filter reacts slowly to disappeared targets, which leads to a cardinality overestimation. This problem may be more serious in the multi-sensor system. To deal with the above problems, the parameter sets association and fusion methods are presented in this paper. By three association processes with the adaptive thresholds selection approaches, parameter sets corresponding to the same target are grouped into one parameter set partition. Parameter sets have different association thresholds because of their different accuracies. The fusion method considers the types and relationships of parameter sets in the partition simultaneously and uses a joint credibility to accelerate changes in existence probability. The cardinality estimation decreases rapidly when the target disappears. The theoretical analysis and experiment results of different tracking scenarios show that the proposed methods perform well in both state estimation and cardinality estimation.
A Radial Visualisation for Model Comparison and Feature Identification

Machine Learning (ML) plays a key role in various intelligent systems, and building an effective ML model for a data set is a difficult task involving various steps including data cleaning, feature definition and extraction, ML algorithms development, model training and evaluation as well as others. One of the most important steps in the process is to compare generated substantial amounts of ML models to find the optimal one for the deployment. It is challenging to compare such models with dynamic number of features. This paper proposes a novel visualisation approach based on a radial net to compare ML models trained with a different number of features of a given data set while revealing implicit dependent relations. In the proposed approach, ML models and features are represented by lines and arcs respectively. The dependence of ML models with dynamic number of features is encoded into the structure of visualisation, where ML models and their dependent features are directly revealed from related line connections. ML model performance information is encoded with colour and line width in the innovative visualisation. Together with the structure of visualization, feature importance can be directly discerned to help to understand ML models.
Tandem Language Learning: Research Experience in Russian Universities Context

The article is devoted to the intercultural communication in the process of learning foreign languages in tandem groups. Cross-cultural issues of language learning are analyzed and their solutions are proposed. The effectiveness of tandem projects was investigated. The main methods of the research are the analysis of the scientific literature in the field of foreign language teaching methodology, psychology and pedagogy; the study and generalization of the experience in teaching foreign languages in tandem groups, as well as the analysis of the students' educational activity products; teacher's observation, questionnaires and interviews. The tandem network is aimed at building communication, it is focused on the mastery of the native language of a tandem partner and building intercultural competence in the process of mutual learning with the help of new information and communication technologies. The considered articles are of practical importance for foreign language teachers since they contain recommendations for conducting tandem projects and requirements for their participants.
Trigger Event and Hate Content: Insights from Twitter Analytics

The problematic act damages the target and seeds the fear in the neighborhood. Social media sites are used for planning and coordinating problematic acts. The problematic act is a trigger event which influences hatred feeling. The objective of the paper is to analyze the aftermath of a recent problematic incident in the southern part of the Asian continent from Twitter content. After the problematic incident, citizens used to share their views over social media sites. A total of 48,819 opinions shared through Twitter social media are collected and analyzed using the software developed in the R programming language. The results show hatred against the problematic act through different related emotions. Results also contain more negative tweets which are almost thrice the positive tweets. Fear and anger emotions exhibit a high degree of emotions than the other.
A New Term Weight Scheme and Ensemble Technique for Authorship Identification

A few of the previous studies on authorship identification have applied term weighting to features. The present study introduced a new term weight scheme, called 1/sigma, that rescales the values of a feature set to a mean of zero and a standard deviation of one. In other words, the 1/sigma scheme standardizes the values of a feature set. Three experiments showed the robustness of the proposed term weight scheme from different perspectives. These experiments showed that the proposed term weight scheme worked perfectly with different feature sets and classifiers in comparison to two popular term weight scemes: TF and TF-IDF. Furthermore, 1/sigma was shown to work successfully with the following different types of datasets: literary texts (fiction) and online messages (blogs, emails, and tweets). Although these experiments did not directly examine the effects of the numbers of documents and authors, the results indicated that these factors did not have any effects because the numbers of documents and authors vary from dataset to dataset.
Partial disentanglement of hierarchical variational auto‐encoder for texture synthesis

Multiple research studies have recently demonstrated deep networks can generate realistic‐looking textures and stylised images from a single texture example. However, they suffer from some drawbacks. Generative adversarial networks are in general difficult to train. Multiple feature variations, encoded in their latent representation, require a priori information to generate images with specific features. The auto‐encoders are prone to generate a blurry output. One of the main reasons is the inability to parameterise complex distributions. The authors present a novel texture generative model architecture extending the variational auto‐encoder approach. It gradually increases the accuracy of details in the reconstructed images. Thanks to the proposed architecture, the model is able to learn a higher level of details resulting from the partial disentanglement of latent variables. The generative model is also capable of synthesising complex real‐world textures. The model consists of multiple separate latent layers responsible for learning the gradual levels of texture details. Separate training of latent representations increases the stability of the learning process and provides partial disentanglement of latent variables. The experiments with proposed architecture demonstrate the potential of variational auto‐encoders in the domain of texture synthesis and also tend to yield sharper reconstruction as well as synthesised texture images.
Artificial Electric Field Algorithm for Solving Real Parameter CEC 2017 Benchmark Problems

The optimality ability of anyAnita optimization algorithm for different problems is a very important aspect. In this article, we check the optimality ability of newly designed population-based artificial electric fieldYadav, Anupam algorithm (AEFA) over 30 unconstrained optimization problems of CEC 2017 benchmark set. The computational results of AEFA are compared with other existing algorithms along with their statisticalKumar, Nitin validation. We also performed the numerical convergence of AEFA over the selected benchmark set which ensure the fast convergence of AEFA over other existing algorithms. This article demonstrates the superiority of AEFA over other existing algorithms.
Trajectory planning for an articulated probe

We consider a new trajectory planning problem involving a simple articulated probe. The probe is modeled as two line segments ab and bc, with a joint at the common point b, where bc is of fixed length r and ab is of arbitrarily large length. Initially, ab and bc are collinear. Given a set of obstacles in the form of n line segments and a target point t, the probe is to first be inserted in straight line, followed possibly by a rotation of bc, so that in the final configuration c coincides with t, all while avoiding intersections with the obstacles. We prove that a feasible probe trajectory can be determined in O(n2log⁡n) time using O(nlog⁡n) space (in fact, our algorithm finds a set of "extremal" feasible configurations). We also show that, for any constant δ>0, a feasible probe trajectory with a clearance δ can be determined in O(n2log⁡n) time using O(n2) space. Furthermore, we demonstrate that our algorithms can be extended to the case of h polygonal obstacles, where we can find a feasible solution in O(n2+h2log⁡n) time using O(nlog⁡n) space when there is no clearance requirement or O(n2) space otherwise. In the process of describing the algorithms, we address and solve some other interesting problems, such as circular sector emptiness queries and a special case of circular arc ray shooting queries for line segments and circular arcs in the plane.
Connectionist based models for solving Diophantine equation

Diophantine equation is an important concept while studying number theory. But there is as such no standard method or procedure to find the solution of the Diophantine equation. In this article, the numerical solution of Diophantine equation has been discussed using the concept of Artificial Neural Network (ANN). A four layer ANN architecture has been constructed for solving Diophantine eqution. Detail ANN procedure with few example problems and their network architectures have been addressed. Convergence plots and/or convergence tables for different example problems have also been included to validate the proposed method.
CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters with Different Densities

Density-based clustering is well-known for finding clusters that have different shapes and sizes, but they have unsatisfactory results on adjacent clusters with different densities. In this paper, we propose a novel algorithm that combines DBSCAN with centroid-based algorithms to address this issue. Our algorithm uses DBSCAN to form mini-clusters, which will be merged based on their density and center distances. We test the new algorithm on synthetic and real datasets to show the significant improvement in the results.
GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.
Comparative Analysis of Optimisations of Antecedents and Consequents of Fuzzy Inference System Rules Lists Using Genetic Algorithm Operations

Researches have pointed to the fact that fuzzy logic controls (FLC) largely rely on the inputs, rules lists specifying logical notation and outputs for its inference applications. The accuracy of these inference systems outputs is directly influenced by the quantity of rules constituting the Rule Bases (RBs), which are often redundant, poorly mapped and inconsistent, arising from human experts’ construction errors. This paper is investing the effectiveness of optimising antecedents and consequents of Rules Bases separately using genetic algorithms operations. The resultant rules lists realised from both genetic procedures are used to construct FLC. The training and evaluation of FLCs were carried out using DangoteCem PLC shares prices data set including opening, highest and closing prices. The paper found that the optimisation of RB-antecedents minimised the rules list redundancy to five (5) rules. Whereas, the optimisation of RB-consequents minimised the rules list to seven (7) rules when compared to nine (9) rules of initial human experts’ rules. However, the FLC constructed with five rules produced better forecasting outcomes as against that with seven (7) rules using parameters such as MSE, RMSE, RAE and MAPE.