Reviewerly/exHarmony huggingface dataset:
Fracture network organization with continuation of injections in the Habanero field (Cooper Basin, Australia) disclosed by coda wave fluctuations

Hydraulic stimulation of a geothermal reservoir aims to increase the permeability of the rock formations by creating/enhancing a fracture network, to take advantage of the geothermal energy hosted by deep hot formations. A common side effect of the fracturing process is the small-in-magnitude but abundant induced seismicity. In this work, we study the seismicity induced during the hydraulic stimulation of the Habanero 4 well, in the Cooper Basin (Australia), where an extensive network of sensors has been deployed to monitor the stimulation process. The detailed monitoring of the induced seismic events enabled the study of the dynamic characteristics of the enhanced fracture network by making use of the seismic coda waves scattering properties. Since the Earth’s crust has inhomogeneities with fractal properties, manifested in the seismic coda waves, we address the fracture network as a complex, non-equilibrium system. In this line, the framework of Non-Extensive Statistical Physics (NESP) and the maximum entropy principle were used to study the fluctuations of the seismic coda waves and their possible variations during the hydraulic stimulation. The analysis shows that the probability density functions of the normalized increments of S-coda waves present heavy tails with asymptotic power-law behavior, enhancing the probabilities of large fluctuations. The results further indicate that as the stimulation progresses, this enhancement becomes even more pronounced. The observed scaling behavior can exactly be approximated with the q-Gaussian distribution, derived in the framework of NESP, showing a modest uprising trend of the entropic index (q-value) with continuation of injections. These results suggest that the system of fractures progressively becomes more organized, with stronger memory effects and long-range interactions, as the hydraulic stimulation progresses.
XOR-Based Detector of Different Decisions on Anomalies in the Computer Network Traffic

Anomaly-based intrusion detection systems are designed to scan computer network traffic for abnormal behavior. Binary classifiers based on supervised machine learning have proven to be highly accurate tools for classifying instances as normal or abnormal. Main disadvantages of supervised machine learning are the long processing time and large amount of training data required to ensure accurate results. Two preprocessing steps to reduce data sets are feature selection and feature scaling. In this article, we present a new hyperbolic tangent feature scaling approach based on the linearization of the tangent hyperbolic function and the damping strategy of the Levenberg-Marquardt algorithm. Experiments performed on the Kyoto 2006+ dataset used four high-precision binary classifiers: weighted k-nearest neighbors, decision tree, feedforward neural networks, and support vector machine. It is shown that hyperbolic tangent scaling reduces processing time by more than twofold. An XOR-based detector is proposed to determine conflicting decisions about anomalies. The decisions of the FNN and wk-NN models are compared. It is shown that decisions sometimes turn out differently. The percentage of the opposite decisions has been shown to vary and is not affected by dataset size.
MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online Games

Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks, but usually ignore to improve the interpretability of recommendation, which is still a challenging and crucial task, especially for some complicated scenarios such as large-scale online games. Some few previous attempts on explainable recommendation mostly depend on a large amount of a priori knowledge or user-provided review corpus, which is labor-consuming as well as often suffers from data deficiency. To relieve this issue, we propose a Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation (MHANER) for the case without enough a priori knowledge or corpus of user comments. Specifically, MHANER employs the attention mechanism to model players’ preference to in-game store items as the support for the explanation of recommendation. Then a graph neural network based method is designed to model players’ multi-source heterogeneous information, including the players’ historical behavior data, historical purchase data, and attributes of the player-controlled character, which is leveraged to recommend possible items for players to buy. Finally, the multi-level subgraph pattern mining is adopted to combine the characteristics of a recommendation list to generate corresponding explanations of items. Extensive experiments on three real-world datasets, two collected from JD and one from NetEase game, demonstrate that the proposed model MHANER outperforms state-of-the-art baselines. Moreover, the generated explanations are verified by human encoding comprised of hard-core game players and endorsed by experts from game developers.
Sentiment Analysis of Turkish Drug Reviews with Bidirectional Encoder Representations from Transformers

Sentiment analysis of user generated product or service reviews is significant to enhance quality. Healthcare related computational linguistics studies particularly analysis of drug based user criticisms have principal importance above all. Sentiment analysis of healthcare reviews reveal the relations between patients, doctors and healthcare services. More specifically, sentiment analysis of drug reviews may be used to acquire relations such as adverse drug reactions (ADRs), diagnosis-treatment assist, and personalized therapy recommendations. Most of the drug review sentiment studies are in English. Though Turkish is a widely spoken language, there is limited research conducted on medical domain and there is particularly no study related to drug review sentiment analysis. In this study, we generated a Turkish drug review dataset and we evaluated the generated dataset in detail against (i)traditional machine learning algorithms with language pre-processing steps, stemming and feature selection, (ii)deep learning algorithms with word2vec embedding language model and (iii)various bidirectional encoder representations from transformers (BERT) models in terms of sentiment analysis. The experiments show that neural transformers are promising in Turkish drug review sentiment identification. In particular, Turkish dedicated BERT (BERTurk) resulted in 95.1% weighted-F1 score as the best drug review sentiment prediction performance.
Feature Disentanglement Network: Multi-Object Tracking Needs More Differentiated Features

To reduce computational redundancies, a common approach is to integrate detection and re-identification (Re-ID) into a single network in multi-object tracking (MOT), referred to as “tracking by detection.” Most of the previous research has focused on resolving the conflict between the detection and Re-ID branches, considering it a simple coupling. In our work, we uncover that the entangled state between the detection and Re-ID tasks is much more complex than previous idea, resulting in a form of competition that degrades performance. To address the preceding issue, we propose a feature disentanglement network that deeply disentangles the intricately interwoven latent space of features and provides differentiated feature maps for each individual task. Furthermore, considering the demand for shallow semantic features in the feature re-ID branch, we also introduce a feature re-globalization module to enrich the shallow semantics. By integrating two distinct networks into a one-shot online MOT method, we develop a robust MOT tracker (named HDGTrack ). We conduct extensive experiments on a number of benchmarks, and our experimental results demonstrate that our method significantly outperforms state-of-the-art MOT methods. Besides, HDGTrack is efficient and can run at 13.9 (MOT17) and 8.7 (MOT20) frames per second.
The Phistogram

This article introduces a new kind of histogram-based representation for univariate random variables, named the phistogram because of its perceptual qualities. The technique relies on shifted groupings of data, creating a color-gradient zone that evidences the uncertainty from smoothing and highlights sampling issues. In this way, the phistogram offers a deep and visually appealing perspective on the finite sample peculiarities, being capable of depicting the underlying distribution as well, thus, becoming an useful complement to histograms and other statistical summaries. Although not limited to it, the present construction is derived from the equal-area histogram, a variant that differs conceptually from the traditional one. As such a distinction is not greatly emphasized in the literature, the graphical fundamentals are described in detail, and an alternative terminology is proposed to separate some concepts. Additionally, a compact notation is adopted to integrate the representation's metadata into the graphic itself.
Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning

High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks. For real robots, it is challenging to collect enough qualified data either as demonstrations for IL or experiences for RL due to safety consideration in environments with obstacles. We target this challenge by proposing the self-imitation learning by planning plus (SILP+) algorithm, which efficiently embeds experience-based planning into the learning architecture to mitigate the data-collection problem. The planner generates demonstrations based on successfully visited states from the current RL policy, and the policy improves by learning from these demonstrations. In this way, we relieve the demand for human expert operators to collect demonstrations required by IL and improve the RL performance as well. Various experimental results shows that SILP+ achieves better training efficiency, higher and more stable success rate in complex motion planning tasks compared to several other methods. Extensive tests on physical robots illustrate the effectiveness of SILP+ in a physical setting, retaining a success rate of 90% where the next-best contender drops from 87% to 75% in the Sim2Real transition.
Predicting line of therapy transition via similar patient augmentation

Analyzing large EHR databases to predict cancer progression and treatments has become a hot trend in recent years. An increasing number of modern deep learning models have been proposed to find the milestones of essential patient medical journey characteristics to predict their disease status and give healthcare professionals valuable insights. However, most of the existing methods are lack of consideration for the inter-relationship among different patients. We believe that more valuable information can be extracted, especially when patients with similar disease statuses visit the same doctors. Towards this end, a similar patient augmentation-based approach named SimPA is proposed to enhance the learning of patient representations and further predict lines of therapy transition. Our experiment results on a real-world multiple myeloma dataset show that our proposed approach outperforms state-of-the-art baseline approaches in terms of standard evaluation metrics for classification tasks.
Organization and Application of Statistical Modeling

The object of this chapter is principles of organization of statistical modeling and form of its applications. In this reason, the first section discusses the essence of the statical approach and presents the main form for its application. A summary of published research is made to clarify the features of the statistical models and their role in scientific research. The second part is devoted to mathematical statistics, with a brief presentation of basic concepts such as simple and statistical order, statistical estimates, numerical characteristics, and basic methods used in statistical approach. Particular attention is paid to the method of least squares applied in determining the statistical estimates. In the third part, the characteristic surface is presented and the two main groups of statistical models—univariate and multivariate—are discussed. Realization of statistical experiments is discussed in the last part, starting with presentation of a program application for statistical analysis organization and continuing with several concrete examples of regression models.
Mathematical Formalization and Abstract Description

The chapter is dedicated to the possibilities of formalization of an investigated object and its abstract description with appropriate mathematical means. At the beginning, the need to decompose complex systems is discussed to facilitate the following model study. The appropriate elements of a standard formal theory, applied in mathematical formalization and the abstract description of the investigated object, are formulated. A major place is devoted to the application of discrete and stochastic structures for the formalization of computer systems and processes for the purposes of further modeling. In the part of discrete means, the possibilities of formalization based on set theory, binary and weighted graphs, theory of finite automata are discussed. Some examples of specific applications are also given. The discussion of stochastic means is mainly focused on the application of elements of probability theory (random processes, streams of random events), the theory of Markov processes (in particular the application of Markov chains), and the theory of queues.
A Case Study of Cyber Subversion Attack based Design Flaw in Service Oriented Component Application Logic

ABSTRACTModern e-commerce systems are more likely focused on mechanisms of security, such as secure transactional protocols, cryptographic schemes and parameter sanitization, and it is assumed that putting these in place will guarantee a secure e-commerce application. However, vulnerabilities in the business application logic itself are often ignored which can make the effect of these security mechanisms null and void. Essentially, the weakest link can be at the server rather than client because of business logic and insecure server-side business components, its security ignoring is another factor, which is done at developer's peril. This paper focuses on the weakest link (component's logic subversion) in the e-commerce system. We outline a logical attack (subversion attack, class Design Flaw) that would not be prevented by the deployment of the mechanisms commonly used in e-commerce systems. To further investigate this problem, we propose a security assurance methodology for service component-oriented application that will be practiced through threat modeling and component fault detection model with further modeling component and its application using unified modeling language secure-design approach with a valid technique (verification, validation model for security-by-design testing) for design flaw detection to avoid the business logic problem in component-based e-commerce applications from existing application logic.KEYWORDS: Design flawssubversion attacke-commerce systemservice component architecture assurance AcknowledgmentsThe authors would like to thank Prof. Jianming Yong for support and cooperation.Disclosure statementNo potential conflict of interest was reported by the authors.Availability of data and materialNo data have been provided for that project as it is not allowed.Additional informationFundingNo funding is granted for this particular project.
Improved YOLOv5-based target detection algorithm on water

Target detection tasks on water have been affected by water surface fluctuations, light changes, occlusions, overlaps and targets that are too small, resulting in poor recognition accuracy and detection speed. To solve this problem, this paper proposes an improved water target detection algorithm based on YOLOv5. Firstly, recursive gated convolution module were used instead of ordinary convolution to achieve high-order spatial interactions and improve model performance and confidence. Secondly, in order to solve the problems of difficulty in detecting small targets, the input size of the image is increased and P6 detection heads are introduced to improve the detection accuracy of small targets. Finally, to alleviate the situation of poor overlapping target detection, coordinate attention(CA) mechanism is introduced and incorporated into the backbone to extract large range features, and reduce the number of parameters and computational overhead. Compared with the original model of YOLOv5s, the improved algorithm model improves the recognition accuracy by 4.6% and the recall by 3.4%, reaching 69.2% and 67.6% respectively. The running speed reaches 208FPS, meeting the requirements of real-time detection.
Exploring exotic configurations with anomalous features with deep learning: Application of classical and quantum-classical hybrid anomaly detection

We present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configurations with anomalous features. We consider the Anderson model as a prototype, where we define two types of anomalies---a high conductance in the presence of strong impurity and a low conductance in the presence of weak impurity---as a function of random impurity distribution. Such anomalous outcome constitutes an imperceptible fraction of the data set and is not a part of the training process. These exotic configurations, which can be a source of rich new physics, usually remain elusive to conventional classification or regression methods and can be tracked only with a suitable anomaly detection scheme. We also present a systematic study of the performance of the classical and the quantum-classical hybrid anomaly detection method and show that the inclusion of a quantum circuit significantly enhances the performance of anomaly detection, which we quantify with suitable performance metrics. Our approach is quite generic in nature and can be used for any system that relies on a large number of parameters to find their new configurations, which can hold exotic new features.
Profilometry by zero-order interference fringe identification in the Commission Internationale de l’Eclairage system

AbstractIn this study, a numerical procedure is proposed to determine the optical contact (peak of the coherence function) in a scanning white light interferential microscope. The Red, Green and Blue image of the correlogram is transformed to the Commission Internationale de l’Eclairage chromatic system with x,y,z coordinates. The key element is to track the orientation of the curve (trajectory) which exhibits changes as it passes through the optical contact. The path described by the interferogram in this chromatic space makes it possible to localize, without ambiguity, the zero-order fringe and from this information to reconstruct the topography of an object of the order of a few tens of nanometres.KEYWORDS: Scanning white light interferometryCIExyz colour systemlow-coherence interferometrycolour CCD Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingCML thanks Ministerio de Ciencia, Tecnología e Innovación (Minciencia-Colombia) for its financial support.
MesoGraph: Automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score.
Simultaneous estimation and clustering with finite mixture of nonparanormal graphical models

AbstractGraphical mixture models provide a powerful tool to visually depict conditional independencies or dependencies between heterogeneous high-dimensional data. When the random variables corresponding to the vertices are continuous, mixture component densities are assumed to be multivariate normal with different covariance matrices, leading to introduction of the Gaussian graphical mixture model (GGMM). The nonparanormal graphical mixture model (NGMM) replaces the restrictive normal assumption with a semiparametric Gaussian copula, which extends the nonparanormal graphical model and mixture models. Such an extension allows us to simultaneously estimate cluster assignments and cluster-specific graphical model structure. To enable such analyses, we propose a regularized estimation scheme with two forms of ℓ1 penalty function (conventional and unconventional) via the expectation-maximization algorithm to learn a finite mixture of nonparanormal graphical models. We illustrate the performance of our method through a simulation study under both ideal and noisy settings. We also apply the proposed methodology to a breast cancer data set to diagnose malignant or benign tumors in patients. The results showed that the combination of NGMM together with unconventional penalty, which was named as NGMM1 during this study, provides the most efficient approach in clustering and estimating the graphical model structure.Keywords: EM algorithmMixture graphical modelsModel-based clusteringNonparanormal model Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe real dataset analyzed during the current study is available at http://archive.ics.uci.edu/ml/datasets/Breast±Cancer ± Wisconsin±(Diagnostic). Also, the complete data generation process for the simulation study is included in Subsec. 4.1.1.Notes1 In practice, this means that the data is assumed to be normalized.2 The model they introduced as the nonparanormal graphical mixture model is actually the nonparanormal mixture distribution, whereas we introduce the nonparanormal graphical mixture model in Sec. 3.1.3 http://cran.r-project.org/web/packages/huge.4 A linear transformation on normal data does not exchange the normality distribution of data.