title: Impact of Water on Methane Adsorption in Nanopores: A Hybrid GCMC-MD Simulation Study

text: Adsorbed methane is an important component of shale gas. Shale generally contains a certain amount of primary water, and isothermal adsorption experiments on wet samples show that water inhibits methane adsorption. Researches on methane adsorption mainly focus on the conditions of low pressure and water content. In this study, a hybrid GCMC-MD simulation method is proposed to study methane adsorption characteristics under high pressure and water content in pores of different sizes. This method can obtain the bulk pressure of the system while ensuring the simultaneous movement of methane and water molecules, and has high efficiency and reliability. It is found that the existence of water does not change the morphology of excess isotherm, and the relative decrease of adsorption capacity due to the existence of water is not sensitive to temperature. In ≤3 nm pores, water molecules form water clusters and partially occupy wall adsorption sites, and the adsorption amount decreases linearly with increasing water saturation. In the 5 nm wide pore with 40% water saturation, water films formed and methane adsorption is strongly suppressed. It is expected these findings could provide guidance for the evaluation of the amount of adsorbed methane with primary water.


title: Regression Methods for Detecting Anomalies in Flue Gas Desulphurization Installations in Coal-Fired Power Plants Based on Sensor Data

text: In the industrial world, the Internet of Things produces an enormous amount of data that we can use as a source for machine learning algorithms to optimize the production process. One area of application of this kind of advanced analytics is Predictive Maintenance, which involves early detection of faults based on existing metering. In this paper, we present the concept of a portable solution for a real-time condition monitoring system allowing for early detection of failures based on sensor data retrieved from SCADA systems. Although the data processed in systems, such as SCADA, are not initially intended for purposes other than controlling the production process, new technologies on the edge of big data and IoT remove these limitations and provide new possibilities of using advanced analytics. This paper shows how regression-based techniques can be adapted to fault detection based on actual process data from the oxygenating compressors in the flue gas desulphurization installation in a coal-fired power plant.


title: Fuzzy Intelligence in Monitoring Older Adults with Wearables

text: Monitoring older adults with wearable sensors and IoT devices requires collecting data from various sources and proliferates the number of data that should be collected in the monitoring center. Due to the large storage space and scalability, Clouds became an attractive place where the data can be stored, processed, and analyzed in order to perform the monitoring on large scale and possibly detect dangerous situations. The use of fuzzy sets in the monitoring and detection processes allows incorporating expert knowledge and medical standards while describing the meaning of various sensor readings. Calculations related to fuzzy processing and data analysis can be performed on the Edge devices which frees the Cloud platform from performing costly operations, especially for many connected IoT devices and monitored people. In this paper, we show a solution that relies on fuzzy rules while classifying health states of monitored adults and we investigate the computational cost of rules evaluation in the Cloud and on the Edge devices.


title: Autonomous Guided Vehicles for Smart Industries – The State-of-the-Art and Research Challenges

text: Autonomous Guided Vehicles (AGVs) are considered to be one of the critical enabling technologies for smart manufacturing. This paper focus on the application of AGVs in new generations of manufacturing systems including: (i) the fusion between AGVs and collaborative robots; (ii) the application of machine to machine communication for integrating AGVs with the production environment and (iii) AI-driven analytics that is focused on the data that is produced and consumed by AGV. This work aims to evoke discussion and elucidate the current research opportunities, highlight the relationship between different subareas and suggest possible courses of action.


title: Addressing the Robustness of Resource Allocation in the Presence of Application and System Irregularities via PEPA Based Modeling

text: Applications executing in heterogeneous parallel and/or distributed computing (PDC) environments are often prone to unpredictable runtime due to variations in problem, algorithm, and system characteristics. This serves as a key motivation towards a study of the robustness of resource allocations required to maintain and guarantee a desired level of performance. Performance modeling and evaluation is often utilized to understand and predict the behavior of the application and the computational system from a performance point of view. In prior work, performance modeling for evaluating response times of static resource allocations in PDC systems was introduced by the authors as a proof of concept for validating the use of the performance evaluation process algebra (PEPA) for analyzing the robustness of static resource allocations. Herein, the authors present numerical modeling of several static resource allocations to evaluate their robustness in the presence of compound perturbations generated as combinations of variations in application workload and machine availability. The novelty of the approach is to introduce the compound effect as the variability of both, application workload and processor/machine availability, into the performance modeling of the overall computational system. The performance is obtained as a parallel execution time via a numerical analysis of the modeled execution of applications on non-dedicated parallel computational resources. A significant improvement in the robustness value (up to 143%) among the mappings yielding equal parallel execution times has been demonstrated via the analysis of the results. This notable difference in the robustness values strongly indicates the benefit of selecting one mapping versus the other for guaranteeing the best execution performance.


title: Fitting Penalized Logistic Regression Models Using QR Factorization

text: The paper presents improvement of a commonly used learning algorithm for logistic regression. In the direct approach Newton method needs inversion of Hessian, what is cubic with respect to the number of attributes. We study a special case when the number of samples m is smaller than the number of attributes n, and we prove that using previously computed QR factorization of the data matrix, Hessian inversion in each step can be performed significantly faster, that is [Formula: see text] or [Formula: see text] instead of [Formula: see text] in the ordinary Newton optimization case. We show formally that it can be adopted very effectively to [Formula: see text] penalized logistic regression and also, not so effectively but still competitively, for certain types of sparse penalty terms. This approach can be especially interesting for a large number of attributes and relatively small number of samples, what takes place in the so-called extreme learning. We present a comparison of our approach with commonly used learning tools.


title: Evolving Long Short-Term Memory Networks

text: Machine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task.


title: Convex Polygon Packing Based Meshing Algorithm for Modeling of Rock and Porous Media

text: In this work, we propose new packing algorithm designed for the generation of polygon meshes to be used for modeling of rock and porous media based on the virtual element method. The packing problem to be solved corresponds to a two-dimensional packing of convex-shape polygons and is based on the locus operation used for the advancing front approach. Additionally, for the sake of simplicity, we decided to restrain the polygon rotation in the packing process. Three heuristics are presented to simplify the packing problem: density heuristic, gravity heuristic and the multi-layer packing. The decision made by those three heuristic are prioritizing on minimizing the area, inserting polygons on the minimum Y coordinate and pack polygons in multiple layers dividing the input in multiple lists, respectively. Finally, we illustrate the potential of the generated meshes by solving a diffusion problem, where the discretized domain consisted in polygons and spaces with different conductivities. Due to the arbitrary shape of polygons and spaces that are generated by the packing algorithm, the virtual element method was used to solve the diffusion problem numerically.


title: Optimal Location of Sensors for Early Detection of Tsunami Waves

text: Tsunami early detection systems are of great importance as they provide time to prepare for a tsunami and mitigate its impact. In this paper, we propose a method to determine the optimal location of a given number of sensors to report a tsunami as early as possible. The rainfall optimization algorithm, a population-based algorithm, was used to solve the resulting optimization problem. Computation of wave travel times was done by illustrating the kinematics of a wave front using a linear approximation of the shallow water equations.


title: Improving Accuracy and Speeding Up Document Image Classification Through Parallel Systems

text: This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much lighter model and present its transfer learning capabilities on a smaller in-domain dataset such as Tobacco3482. Moreover, we present an ensemble pipeline which is able to boost solely image input by combining image model predictions with the ones generated by BERT model on extracted text by OCR. We also show that the batch size can be effectively increased without hindering its accuracy so that the training process can be sped up by parallelizing throughout multiple GPUs, decreasing the computational time needed. Lastly, we expose the training performance differences between PyTorch and Tensorflow Deep Learning frameworks.


title: A Direct High-Order Curvilinear Triangular Mesh Generation Method Using an Advancing Front Technique

text: In this paper, we propose a novel method of generating high-order curvilinear triangular meshes using an advancing front approach. Our method relies on a direct approach to generate meshes on geometries with curved boundaries. Our advancing front method yields high-quality triangular elements in each iteration which omits the need for post-processing steps. We present several numerical examples of second-order curvilinear triangular meshes of patient-specific anatomical models generated using our technique on boundary meshes obtained from biomedical images.


title: Preconditioning Jacobian Systems by Superimposing Diagonal Blocks

text: Preconditioning constitutes an important building block for the solution of large sparse systems of linear equations. If the coefficient matrix is the Jacobian of some mathematical function given in the form of a computer program, automatic differentiation enables the efficient and accurate evaluation of Jacobian-vector products and transposed Jacobian-vector products in a matrix-free fashion. Standard preconditioning techniques, however, typically require access to individual nonzero elements of the coefficient matrix. These operations are computationally expensive in a matrix-free approach where the coefficient matrix is not explicitly assembled. We propose a novel preconditioning technique that is designed to be used in combination with automatic differentiation. A key element of this technique is the formulation and solution of a graph coloring problem that encodes the rules of partial Jacobian computation that determines only a proper subset of the nonzero elements of the Jacobian matrix. The feasibility of this semi-matrix-free approach is demonstrated on a set of numerical experiments using the automatic differentiation tool ADiMat.


title: COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

text: Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.


title: Are n-gram Categories Helpful in Text Classification?

text: Character n-grams are widely used in text categorization problems and are the single most successful type of feature in authorship attribution. Their primary advantage is language independence, as they can be applied to a new language with no additional effort. Typed character n-grams reflect information about their content and context. According to previous research, typed character n-grams improve the accuracy of authorship attribution. This paper examines their effectiveness in three domains: authorship attribution, author profiling and sentiment analysis. The problem of a very high number of features is tackled with distributed Apache Spark processing.


title: A New Coefficient of Rankings Similarity in Decision-Making Problems

text: Multi-criteria decision-making methods are tools that facilitate and help to make better and more responsible decisions. Their main objective is usually to establish a ranking of alternatives, where the best solution is in the first place and the worst in the last place. However, using different techniques to solve the same decisional problem may result in rankings that are not the same. How can we test their similarity? For this purpose, scientists most often use different correlation measures, which unfortunately do not fully meet their objective. In this paper, we identify the shortcomings of currently used coefficients to measure the similarity of two rankings in decision-making problems. Afterward, we present a new coefficient that is much better suited to compare the reference ranking and the tested rankings. In our proposal, positions at the top of the ranking have a more significant impact on the similarity than those further away, which is right in the decision-making domain. Finally, we show a set of numerical examples, where this new coefficient is presented as an efficient tool to compare rankings in the decision-making field.


Breaking New Ground


User-centric Design


"Die Landtechnik ist weltweit systemrelevant"


Nutzerzentriertes Design


"Agricultural technology is system-relevant, worldwide"


title: Selection for Liver Transplantation: Indications and Evaluation

text: PURPOSE OF REVIEW: Liver transplantation is an important therapeutic option for patients with life-limiting liver disease, which may present in the form of acute liver failure, end-stage chronic liver disease, primary hepatic cancers, or inborn metabolic disorders. While significant strides have been made with respect to liver transplantation outcomes, the practice is constrained by an organ supply/demand mismatch. The purpose of this review, therefore, is to review the general indications and contraindication to liver transplantation, and to provide an overview of the transplant evaluation process. These considerations ultimately shape the specific criteria for patient selection, which will continue to evolve as means are developed to expand the donor pool, improve surgical techniques, broaden indications for safe transplant, and extend the lifetime of a graft. RECENT FINDINGS: Selected patients with unresectable hilar cholangiocarcinoma may be candidates for liver transplantation. Patients over 65 years may be transplant candidates if they possess a favorable comorbidity profile. Patients at body mass index extremes (≥ 40 or < 18.5) have increased post-transplant mortality and require nutritional evaluation. SUMMARY: Liver transplantation may be life saving for patients with acute liver failure or end-stage liver disease. It is therefore critical for healthcare providers caring for patients with liver disease to be familiar with the general indications for transplantation and to know when it is appropriate or inappropriate to refer for transplant evaluation.


title: Perception and Reality

text: The paper highlights a lifetime experience of knowledge gained through interactions with Professor Hung T. Nguyen and the profound impact he has made in advancing the state-of-the-art in modeling, simulating, and understanding the dynamics of uncertain systems. His seminal works on Random Fuzzy Sets have (a) significantly impacted our perceptions of seeking methods and approaches to acquire and assimilate data or information from uncertain sources, (b) enhanced our understanding to develop strategies for deep learning the interconnections, implications, and consequences, thereby allowing the processing of data to its logical conclusions and (c) has infinitely broadened the horizon towards conceiving machine-based decision-making architectures that optimize our perceived understanding to build systems that can mimic human decision-making capabilities. These three universal attributes fit the breadth and scope of Soft Computing. The paper focuses on three distinct topics wherein we explore human perceptions that merge the conscious attributes combining facts, myths, traditions, culture, beliefs, and a sense of reasoning towards a higher level of consciousness, and provides avenues for their adoption and physical realizations. The first topic discusses the possibilities of harvesting vast amounts of hydrokinetic energy from low-head flows in the Mekong Delta, Vietnam. The Mekong River has a perceived power potential of between 50 and 70 Gigawatts (GW). The reality is only one-quarter of the perceived power is plausible for harvesting. We discuss the possibility of transforming existing concrete structures across the arteries and tributaries of the Mekong River in the Delta Region to mini-hydropower plants. One example shows the possibilities of harvesting up to 250 KW at an existing structure that can serve the energy needs of local businesses involved in aquaculture, local commerce, and residential needs. Since recognizing conventional hydropower is ecologically damaging, Vietnam has focused its energy policy solely on Solar and Wind, which are 3–4 times higher in cost than Water. Low-head energy, which is currently unused, could be perceived as a significant contributor to the renewable energy portfolio for Vietnam. The second topic discusses the possibility for Ayurvedic medicine, an ancient Indian practice of herbal medicine, as a complementary form of medication compared to modern medicine. The Tulsi plant is recognized for its life-saving properties that make Hindus believe it is a Holy plant. Scientific observations have shown the plant can produce substantial amounts of pure Oxygen by absorbing carbon dioxide from the ambient. Research has also shown that Tulsi can absorb fluorides from water. The discussions highlight the adoption of Ayurvedic medicine based on historical and traditional beliefs that Nature grown ingredients can induce natural body responses that aid in recovery from illnesses. Ayurvedic medicine exploits the power of the mind based on beliefs and traditions as a means for healing from illnesses. Finally, the third topic is a search for the meaning to the phrase “Pointing the finger at the Moon,” and leading to the discovery of a three-dimensional holographic image on the Moon. The discovery sheds light on a long-standing question of what The Buddha was pointing to on the Moon. Historical background based on art is combined with basic Moon facts and a sense of geometry to uncover a three-dimensional holographic image that symbolizes Gautama Buddha’s path to enlightenment. The form is identified as Abhaya Mudra, the Fearless Form. Among the many forms displayed in Buddhist sculptures of the meditation posture, the Abhaya Mudra is significant as it is a gesture for tolerance, fearlessness, and compassion. In Buddhist philosophy, the attributes of intolerance, fearfulness, and hatred are erased from the human mind through detachment.


Qualitätsgesicherte Informationen zu Diabetes und Prävention


Die Coronakrise zieht weite Kreise - Bestandsaufnahme und Ausblick


On The Cover


New international guidance on quality, safety and efficacy of DNA vaccines


title: Overview of Data Visualization

text: This chapter will first address data visualization and then discuss the relationship between data visualization and aesthetics. It discusses the definition of data and information and the forms and characteristics of traditional data visualization, emphasizes on understanding of meaning of data in effectiveness and efficiency. And then this chapter outlines some key data visualizations, which includes Trees, Scatter plots, Charts, Tables, Diagram, Graphic, Waveform, Simulation and Volume.


Antifungals/tacrolimus: Various toxicities: 2 case reports


Antifungals/immunosuppressants: Cryptococcus gattii Meningoencephalitis, immune reconstitution syndrome and prolonged QTc: case report


Immunosuppressants: COVID-2019 infections: 7 case reports


Methylprednisolone: Decrease in T cell count following off label use: case report


ISMP issues safety warnings for drug shortage replacement products


Azithromycin/hydroxychloroquine: Prolongation of the QT interval: case report


Antineoplastics: Drug induced lung injury, oral ulcer and viral pneumonia: 2 case reports


'I miss my dental colleagues massively'


From heroism to hardship: dental nurses during the pandemic


Schmerzmedizin in Zeiten von Corona: überflüssig?


Stärkere Ausrichtung auf die konservative Orthopädie


The management of periodontal and peri implant disease


Entschädigung für Praxisschließung wegen COVID-19-Pandemie


COVID-19: Was Rheumapatienten jetzt wissen sollten


The dento-legal implications of returning to practice


Awe-inspiring dental nurses


Gum Health Day 2020 says 'No' to bleeding gums


Whatever it takes ...


COVID-19 epidemic: Should ophthalmologists be aware of oculomotor paresis?()


COVID-19, a new opportunity for global health education()


title: Stroke ICU Patient Mortality Day Prediction

text: This article presents a study on development of methods for analysis of data reflecting the process of treatment of stroke inpatients to predict clinical outcomes at the emergency care unit. The aim of this work is to develop models for the creation of validated risk scales for early intravenous stroke with minimum number of parameters with maximum prognostic accuracy and possibility to calculate the time of “expected intravenous stroke mortality”. The study of experience in the development and use of medical information systems allows us to state the insufficient ability of existing models for adequate data analysis, weak formalization and lack of system approach in the collection of diagnostic data, insufficient personalization of diagnostic data on the factors determining early intravenous stroke mortality. In our study we divided patients into 3 subgroups according to the time of death - up to 1 day, 1 to 3 days, and 4 to 10 days. Early mortality in each subgroup was associated with a number of demographic, clinical, and instrumental-laboratory characteristics based on the interpretation of the results of calculating the significance of predictors of binary classification models by machine learning methods from the Scikit-Learn library. The target classes in training were “mortality rate of 1 day”, “mortality rate of 1–3 days”, “mortality rate from 4 days”. AUC ROC of trained models reached 91% for the method of random forest. The results of interpretation of decision trees and calculation of significance of predictors of built-in methods of random forest coincide that can prove to correctness of calculations.


title: An Analysis of Demographic Data in Irish Healthcare Domain to Support Semantic Uplift

text: Healthcare data in Ireland is often fragmented and siloed making it difficult to access and use, and of the data that is digitized, it is rarely standardised from the perspective of data interoperability. The Web of Data (WoD) is an initiative to make data open and interconnected, stored and shared across the World Wide Web. Once a data schema is described using an ontology and published, it resides on the web, and any data described using Linked Data can be associated with this ontology so that the semantics of the data are open and freely available to a global audience. In this article we explore the semantic uplift of demographic data in the Irish context through an analysis of Irish data catalogues, and explore how demographic data is represented in health standards internationally. Through this analysis we identify the Fast Healthcare Interoperability Resources (FHIR) ontology as a basis for managing demographic health care data in Ireland.


title: Application of the Stochastic Gradient Method in the Construction of the Main Components of PCA in the Task Diagnosis of Multiple Sclerosis in Children

text: Many different medical problems are characterized by quite large spatial dimensions, which causes the task of recognizing patterns to become troublesome. This is a well-known phenomenon called curse of dimensionality. These problems force the creation of various methods of reducing dimensionality. These methods are based on selection and extraction of features. The most commonly used method in literature, regarding the later, is the analysis of the main components of pca. The natural problem of this method is the possibility of applying it to linear space. It is a natural problem to develop the pca concept for cases of nonlinear feature spaces, optimization of feature selection for principal components and the inclusion of classes in the task of supervised learning. An important problem in the perspective of machine learning is not only a reduction of features and attributes but also separation of classes. The developed method was tested in two computer experiments using real data of multiple sclerosis in children. The discussed problem, even from the very nature of the data itself, is important because it can contribute to practical implementations in medical diagnostics. The purpose of the research is to develop a method of extracting features with the application of the stochastic gradient method in the task diagnosis of multiple sclerosis in children. This solution could contribute to the increasing quality of classification and thus may be the basis for building systems that support the medical diagnostics in recognition of multiple sclerosis in children.


title: Syntactic and Semantic Bias Detection and Countermeasures

text: Applied Artificial Intelligence (AAI) and, especially Machine Learning (ML), both had recently a breakthrough with high-performant hardware for Deep Learning [1]. Additionally, big companies like Huawei and Google are adapting their product philosophy to AAI and ML [2–4]. Using ML-based systems require always a training data set to achieve a usable, i.e. trained, AAI system. The quality of the training data set determines the quality of the predictions. One important quality factor is that the training data are unbiased. Bias may lead in the worst case to incorrect and unusable predictions. This paper investigates the most important types of bias, namely syntactic and semantic bias. Countermeasures and methods to detect these biases are provided to diminish the deficiencies.


title: On the Impact of Network Data Balancing in Cybersecurity Applications

text: Machine learning methods are now widely used to detect a wide range of cyberattacks. Nevertheless, the commonly used algorithms come with challenges of their own - one of them lies in network dataset characteristics. The dataset should be well-balanced in terms of the number of malicious data samples vs. benign traffic samples to achieve adequate results. When the data is not balanced, numerous machine learning approaches show a tendency to classify minority class samples as majority class samples. Since usually in network traffic data there are significantly fewer malicious samples than benign samples, in this work the problem of learning from imbalanced network traffic data in the cybersecurity domain is addressed. A number of balancing approaches is evaluated along with their impact on different machine learning algorithms.


title: Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network

text: Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction. Variational Autoencoder with Arbitrary Conditioning (VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as representatives of generative models, while the denoising autoencoder (DAE) represented non-generative models. Performance of the models is compared to traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein modification of GAIN, which turns out to be the best imputation model when the degree of missingness is less than or equal to [Formula: see text]. Experiments were performed on real-world and artificial datasets with continuous features where different percentages of features, varying from [Formula: see text] to [Formula: see text], were missing. Evaluation of algorithms was done by measuring the accuracy of the classification model previously trained on the uncorrupted dataset. The results show that GAIN and especially WGAIN are the best imputers regardless of the conditions. In general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.


title: Towards Prediction of Heart Arrhythmia Onset Using Machine Learning

text: Current study aims at prediction of the onset of malignant cardiac arrhythmia in patients with Implantable Cardioverter-Defibrillators (ICDs) using Machine Learning algorithms. The input data consisted of 184 signals of RR-intervals from 29 patients with ICD, recorded both during normal heartbeat and arrhythmia. For every signal we generated 47 descriptors with different signal analysis methods. Then, we performed feature selection using several methods and used selected feature for building predictive models with the help of Random Forest algorithm. Entire modelling procedure was performed within 5-fold cross-validation procedure that was repeated 10 times. Results were stable and repeatable. The results obtained (AUC = 0.82, MCC = 0.45) are statistically significant and show that RR intervals carry information about arrhythmia onset. The sample size used in this study was too small to build useful medical predictive models, hence large data sets should be explored to construct models of sufficient quality to be of direct utility in medical practice.


title: The Atrial Fibrillation Risk Score for Hyperthyroidism Patients

text: Thyrotoxicosis (TT) is associated with an increase in both total and cardiovascular mortality. One of the main thyrotoxicosis complications is Atrial Fibrillation (AF). Right AF predictors help medical personal prescribe the select patients with high risk of TAF for a closest follow-up or for an early radical treatment of thyrotoxicosis. The main goal of this study is creating a method for practical treatment and diagnostic AF. This study proposes a new method for assessing the risk of occurrence atrial fibrillation for patients with TT. This method considers both the features of the complication and the specifics of the chronic disease. A model is created based on case histories of patients with thyrotoxicosis. We used Machine Learning methods for creating several models. Each model has advantages and disadvantages depending on the diagnostic and medical purposes. The resulting models show high results in the different metrics of the prediction of a thyrotoxic AF. These models are interpreted and simple for use. Therefore, models can be used as part of the support and decision-making system (DSS) by medical specialists in the treatment AF.


title: Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?

text: Recent advances in machine learning (ML) and the surge in computational power have opened the way to the proliferation of ML and Artificial Intelligence (AI) in many domains and applications. Still, apart from achieving good accuracy and results, there are many challenges that need to be discussed in order to effectively apply ML algorithms in critical applications for the good of societies. The aspects that can hinder practical and trustful ML and AI are: lack of security of ML algorithms as well as lack of fairness and explainability. In this paper we discuss those aspects and provide current state of the art analysis of the relevant works in the mentioned domains.


title: Investigating Coordination of Hospital Departments in Delivering Healthcare for Acute Coronary Syndrome Patients Using Data-Driven Network Analysis

text: Healthcare systems are challenged to deliver high-quality and efficient care. Studying patient flow in a hospital is particularly fundamental as it demonstrates effectiveness and efficiency of a hospital. Since hospital is a collection of physically nearby services under one administration, its performance and outcome are shaped by the interaction of its discrete components. Coordination of processes at different levels of organizational structure of a hospital can be studied using network analysis. Hence, this article presents a data-driven static and temporal network of departments. Both networks are directed and weighted and constructed using seven years’ (2010–2016) empirical data of 24902 Acute Coronary Syndrome (ACS) patients. The ties reflect an episode-based transfer of ACS patients from department to department in a hospital. The weight represents the number of patients transferred among departments. As a result, the underlying structure of network of departments that deliver healthcare for ACS patients is described, the main departments and their role in the diagnosis and treatment process of ACS patients are identified, the role of departments over seven years is analyzed and communities of departments are discovered. The results of this study may help hospital administration to effectively organize and manage the coordination of departments based on their significance, strategic positioning and role in the diagnosis and treatment process which, in-turn, nurtures value-based and precision healthcare.


title: Ontology-Based Inference for Supporting Clinical Decisions in Mental Health

text: According to the World Health Organization (WHO), mental and behavioral disorders are increasingly common and currently affect on average 1/4 of the world’s population at some point in their lives, economically impacting communities and generating a high social cost that involves human and technological resources. Among these problems, in Brazil, the lack of a transparent, formal and standardized mental health information model stands out, thus hindering the generation of knowledge, which directly influences the quality of the mental healthcare services provided to the population. Therefore, in this paper, we propose a computational ontology to serve as a common knowledge base among those involved in this domain, to make inferences about treatments, symptoms, diagnosis and prevention methods, helping health professionals in clinical decisions. To do this, we initially carried out a literature review involving scientific papers and the most current WHO guidelines on mental health, later we transferred this knowledge to a formal computational model, building the proposed ontology. Also, the Hermit Reasoner inference engine was used to deduce facts and legitimize the consistency of the logic rules assigned to the model. Hence, it was possible to develop a semantic computational artifact for storage and generate knowledge to assist mental health professionals in clinical decisions.


title: Performance Analysis of Binarization Strategies for Multi-class Imbalanced Data Classification

text: Multi-class imbalanced classification tasks are characterized by the skewed distribution of examples among the classes and, usually, strong overlapping between class regions in the feature space. Furthermore, frequently the goal of the final system is to obtain very high precision for each of the concepts. All of these factors contribute to the complexity of the task and increase the difficulty of building a quality data model by learning algorithms. One of the ways of addressing these challenges are so-called binarization strategies, which allow for decomposition of the multi-class problem into several binary tasks with lower complexity. Because of the different decomposition schemes used by each of those methods, some of them are considered to be better suited for handling imbalanced data than the others. In this study, we focus on the well-known binary approaches, namely One-Vs-All, One-Vs-One, and Error-Correcting Output Codes, and their effectiveness in multi-class imbalanced data classification, with respect to the base classifiers and various aggregation schemes for each of the strategies. We compare the performance of these approaches and try to boost the performance of seemingly weaker methods by sampling algorithms. The detailed comparative experimental study of the considered methods, supported by the statistical analysis, is presented. The results show the differences among various binarization strategies. We show how one can mitigate those differences using simple oversampling methods.


title: Time Expressions Identification Without Human-Labeled Corpus for Clinical Text Mining in Russian

text: To obtain accurate predictive models in medicine, it is necessary to use complete relevant information about the patient. We propose an approach for extracting temporary expressions from unlabeled natural language texts. This approach can be used for the first analysis of the corpus, for data labeling as the first stage, or for obtaining linguistic constructions that can be used for a rule-based approach to retrieve information. Our method includes the sequential use of several machine learning and natural language processing methods: classification of sentences, the transformation of word bag frequencies, clustering of sentences with time expressions, classification of new data into clusters and construction of sentence profiles using feature importances. With this method, we derive the list of the most frequent time expressions and extract events and/or time events for 9801 sentences of anamnesis in Russian. The proposed approach is independent of the corpus language and can be used for other tasks, for example, extracting an experiencer of a disease.


title: Analyses of Public Health Databases via Clinical Pathway Modelling: TBWEB

text: One of the purposes of public health databases is to serve as repositories for storing information regarding the treatment of patients. TBWEB (TuBerculose WEB) is an epidemiological surveillance system for tuberculosis cases in the state of São Paulo, Brazil. This paper proposes an analysis of the TBWEB database with the use of clinical pathways modelling. Firstly, the database was analysed in order to find the interventions registered on the database. The clinical pathways were obtained from the database by the use of process mining techniques. Similar pathways were grouped into clusters in order to find the most common treatment sequences. Each cluster was characterised and the risk of bad outcomes associated with each cluster was discovered. Some clusters had an association with the risk of negative outcomes. This method can be applied to other databases, serve as a base for decision-making systems and can be used to monitor public health databases.


title: Employing One-Class SVM Classifier Ensemble for Imbalanced Data Stream Classification

text: The classification of imbalanced data streams is gaining more and more interest. However, apart from the problem that one of the class is not well represented, there are problems typical for data stream classification, such as limited resources, lack of access to the true labels and the possibility of occurrence of the concept drift. Possibility of concept drift appearing enforces design in the method adaptation mechanism. In this article, we propose the OCEIS classifier (One-Class support vector machine classifier Ensemble for Imbalanced data Stream). The main idea is to supply the committee with one-class classifiers trained on clustered data for each class separately. The results obtained from experiments carried out on synthetic and real data show that the proposed method achieves results at a similar level as the state of the art methods compared with it.


title: Machine Learning Approach for the Early Prediction of the Risk of Overweight and Obesity in Young People

text: Obesity is a major global concern with more than 2.1 billion people overweight or obese worldwide which amounts to almost 30% of the global population. If the current trend continues, the overweight and obese population is likely to increase to 41% by 2030. Individuals developing signs of weight gain or obesity are also at a risk of developing serious illnesses such as type 2 diabetes, respiratory problems, heart disease and stroke. Some intervention measures such as physical activity and healthy eating can be a fundamental component to maintain a healthy lifestyle. Therefore, it is absolutely essential to detect childhood obesity as early as possible. This paper utilises the vast amount of data available via UK’s millennium cohort study in order to construct a machine learning driven model to predict young people at the risk of becoming overweight or obese. The childhood BMI values from the ages 3, 5, 7 and 11 are used to predict adolescents of age 14 at the risk of becoming overweight or obese. There is an inherent imbalance in the dataset of individuals with normal BMI and the ones at risk. The results obtained are encouraging and a prediction accuracy of over 90% for the target class has been achieved. Various issues relating to data preprocessing and prediction accuracy are addressed and discussed.


title: On Model Evaluation Under Non-constant Class Imbalance

text: Many real-world classification problems are significantly class-imbalanced to detriment of the class of interest. The standard set of proper evaluation metrics is well-known but the usual assumption is that the test dataset imbalance equals the real-world imbalance. In practice, this assumption is often broken for various reasons. The reported results are then often too optimistic and may lead to wrong conclusions about industrial impact and suitability of proposed techniques. We introduce methods (Supplementary code related to techniques described in this paper is available at: https://github.com/CiscoCTA/nci_eval) focusing on evaluation under non-constant class imbalance. We show that not only the absolute values of commonly used metrics, but even the order of classifiers in relation to the evaluation metric used is affected by the change of the imbalance rate. Finally, we demonstrate that using subsampling in order to get a test dataset with class imbalance equal to the one observed in the wild is not necessary, and eventually can lead to significant errors in classifier’s performance estimate.


title: Constructing Holistic Patient Flow Simulation Using System Approach

text: Patient flow often described as a systemic issue requiring a systemic approach because hospital is a collection of highly dynamic, interconnected, complex, ad hoc and multi-disciplinary sub-processes. However, studies on holistic patient flow simulation following system approach are limited and/or poorly understood. Several researchers have been investigating single departments such as ambulatory care unit, Intensive Care Unit (ICU), emergency department, surgery department or patients’ interaction with limited resources such as doctor, endoscopy or bed, independently. Hence, this article demonstrates how to achieve system approach in constructing holistic patient flow simulation, while maintaining the balance between the complexity and the simplicity of the model. To this end, system approach, network analysis and discrete event simulation (DES) were employed. The most important departments in the diagnosis and treatment process are identified by analyzing network of hospital departments. Holistic patient flow simulation is constructed using DES following system approach. Case studies are conducted and the results illustrate that healthcare systems must be modeled and investigated as a complex and interconnected system so that the real impact of changes on the entire system or parts of the system could be observed at strategic as well as operational levels.


title: Mapping the Port Influence Diffusion Patterns: A Case Study of Rotterdam, Antwerp and Singapore

text: Ports play a vital role in global oil trade and those with significant influence implicitly have better control over global oil transportation. To provide a better understanding of port influence, it is necessary to analyze the development of the mechanisms underlying port influence. In this study, we adopt a port influence diffusion model to modelling diffusion patterns using vessel trajectory data from 2009 to 2016. The results of the case study of Rotterdam, Antwerp and Singapore ports shows: 1) ports with a strong direct influence control their neighboring ports, thereby building a direct influence area; 2) directly influenced ports show path-dependent characteristics, reflecting the importance of geographical distance; 3) the indirect influence of the initial diffusion port creates hierarchical diffusion, with directly influenced ports affected by previous diffusion-influenced ports. 4) a port’s indirect influence and efficiency can be increased via an increase in the number of significant ports it influences directly or by increasing its influence on significant ports in an earlier diffusion stage.


title: Preliminary Results on Pulmonary Tuberculosis Detection in Chest X-Ray Using Convolutional Neural Networks

text: Tuberculosis (TB), is an ancient disease that probably affects humans since pre-hominids. This disease is caused by bacteria belonging to the mycobacterium tuberculosis complex and usually affects the lungs in up to 67% of cases. In 2019, there were estimated to be over 10 million tuberculosis cases in the world, in the same year TB was between the ten leading causes of death, and the deadliest from a single infectious agent. Chest X-ray (CXR) has recently been promoted by the WHO as a tool possibly placed early in screening and triaging algorithms for TB detection. Numerous TB prevalence surveys have demonstrated that CXR is the most sensitive screening tool for pulmonary TB and that a significant proportion of people with TB are asymptomatic in the early stages of the disease. This study presents experimentation of classic convolutional neural network architectures on public CRX databases in order to create a tool applied to the diagnostic aid of TB in chest X-ray images. As result the study has an AUC ranging from 0.78 to 0.84, sensitivity from 0.76 to 0.86 and specificity from 0.58 to 0.74 depending on the network architecture. The observed performance by these metrics alone are within the range of metrics found in the literature, although there is much room for metrics improvement and bias avoiding. Also, the usage of the model in a triage use-case could be used to validate the efficiency of the model in the future.


title: Applicability of Machine Learning Methods to Multi-label Medical Text Classification

text: Structuring medical text using international standards allows to improve interoperability and quality of predictive modelling. Medical text classification task facilitates information extraction. In this work we investigate the applicability of several machine learning models and classifier chains (CC) to medical unstructured text classification. The experimental study was performed on a corpus of 11671 manually labeled Russian medical notes. The results showed that using CC strategy allows to improve classification performance. Ensemble of classifier chains based on linear SVC showed the best result: 0.924 micro F-measure, 0.872 micro precision and 0.927 micro recall.


title: Grammatical Inference by Answer Set Programming

text: In this paper, the identification of context-free grammars based on the presentation of samples is investigated. The main idea of solving this problem proposed in the literature is reformulated in two different ways: in terms of general constrains and as an answer set program. In a series of experiments, we showed that our answer set programming approach is much faster than our alternative method and the original SAT encoding method. Similarly to a pioneer work, some well-known context-free grammars have been induced correctly, and we also followed its test procedure with randomly generated grammars, making it clear that using our answer set programs increases computational efficiency. The research can be regarded as another evidence that solutions based on the stable model (answer set) semantics of logic programming may be a right choice for complex problems.


title: Standard Decision Boundary in a Support-Domain of Fuzzy Classifier Prediction for the Task of Imbalanced Data Classification

text: Many real classification problems are characterized by a strong disturbance in a prior probability, which for the most of classification algorithms leads to favoring majority classes. The action most often used to deal with this problem is oversampling of the minority class by the smote algorithm. Following work proposes to employ a modification of an individual binary classifier support-domain decision boundary, similar to the fusion of classifier ensembles done by the Fuzzy Templates method to deal with imbalanced data classification without introducing any repeated or artificial patterns into the training set. The proposed solution has been tested in computer experiments, which results shows its potential in the imbalanced data classification.


title: Gait Abnormality Detection in People with Cerebral Palsy Using an Uncertainty-Based State-Space Model

text: Assessment and quantification of feature uncertainty in modeling gait pattern is crucial in clinical decision making. Automatic diagnostic systems for Cerebral Palsy gait often ignored the uncertainty factor while recognizing the gait pattern. In addition, they also suffer from limited clinical interpretability. This study establishes a low-cost data acquisition set up and proposes a state-space model where the temporal evolution of gait pattern was recognized by analyzing the feature uncertainty using Dempster-Shafer theory of evidence. An attempt was also made to quantify the degree of abnormality by proposing gait deviation indexes. Results indicate that our proposed model outperformed state-of-the-art with an overall [Formula: see text] of detection accuracy (sensitivity [Formula: see text], and specificity [Formula: see text]). In a gait cycle of a Cerebral Palsy patient, first double limb support and left single limb support were observed to be affected mainly. Incorporation of feature uncertainty in quantifying the degree of abnormality is demonstrated to be promising. Larger value of feature uncertainty was observed for the patients having higher degree of abnormality. Sub-phase wise assessment of gait pattern improves the interpretability of the results which is crucial in clinical decision making.


title: A Machine Learning Approach to Short-Term Body Weight Prediction in a Dietary Intervention Program

text: Weight and obesity management is one of the emerging challenges in current health management. Nutrient-gene interactions in human obesity (NUGENOB) seek to find various solutions to challenges posed by obesity and over-weight. This research was based on utilising a dietary intervention method as a means of addressing the problem of managing obesity and overweight. The dietary intervention program was done for a period of ten weeks. Traditional statistical techniques have been utilised in analyzing the potential gains in weight and diet intervention programs. This work investigates the applicability of machine learning to improve on the prediction of body weight in a dietary intervention program. Models that were utilised include Dynamic model, Machine Learning models (Linear regression, Support vector machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN)). The performance of these estimation models was compared based on evaluation metrics like RMSE, MAE and R2. The results indicate that the Machine learning models (ANN and RF) perform better than the other models in predicting body weight at the end of the dietary intervention program.


title: Risk-Based AED Placement - Singapore Case

text: This paper presents a novel risk-based method for Automated External Defibrillator (AED) placement. In sudden cardiac events, availability of a nearby AED is crucial for the surviving of cardiac arrest patients. The common method uses historical Out-of-Hospital Cardiac Arrest (OHCA) data for AED placement optimization. But historical data often do not cover the entire area of investigation. The goal of this work is to develop an approach to improve the method based on historical data for AED placement. To this end, we have developed a risk-based method which generates artificial OHCAs based on a risk model. We compare our risk-based method with the one based on historical data using real Singapore OHCA occurrences from Pan-Asian Resuscitation Outcome Study (PAROS). Results show that to deploy a large number of AEDs the risk-based method outperforms the method purely using historical data on the testing dataset. This paper describes our risk-based AED placement method, discusses experimental results, and outlines future work.


title: Detecting Rumours in Disasters: An Imbalanced Learning Approach

text: The online spread of rumours in disasters can create panic and anxiety and disrupt crisis operations. Hence, it is crucial to take measure against such a distressing phenomenon since it can turn into a crisis by itself. In this work, the automatic rumour detection in natural disasters is addressed from an imbalanced learning perspective due to the rumour dearth versus non-rumour abundance in social networks. We first provide two datasets by collecting and annotating tweets regarding the Hurricane Florence and Kerala flood. We then capture the properties of rumours and non-rumours in those disasters using 83 theory-based and early-available features, 47 of which are proposed for the first time. The proposed features show a high discrimination power that help us distinguish rumours from non-rumours more reliably. Next, We build the rumour identification models using imbalanced learning to address the scarcity of rumours compared to non-rumour. Additionally, to replicate the rumour detection in the real-world situation, we practice cross-incident learning by training the classifier with the samples of one incident and test it with the other one. In the end we measure the impact of imbalanced learning using Bayesian Wilcoxon Signed-rank test and observe a significant improvement in the classifiers performance.


title: Pattern Recognition Model to Aid the Optimization of Dynamic Spectrally-Spatially Flexible Optical Networks

text: The following paper considers pattern recognition-aided optimization of complex and relevant problem related to optical networks. For that problem, we propose a four-step dedicated optimization approach that makes use, among others, of a regression method. The main focus of that study is put on the construction of efficient regression model and its application for the initial optimization problem. We therefore perform extensive experiments using realistic network assumptions and then draw conclusions regarding efficient approach configuration. According to the results, the approach performs best using multi-layer perceptron regressor, whose prediction ability was the highest among all tested methods.


title: Sentiment Analysis for Fake News Detection by Means of Neural Networks

text: The problem of fake news has become one of the most challenging issues having an impact on societies. Nowadays, false information may spread quickly through social media. In that regard, fake news needs to be detected as fast as possible to avoid negative influence on people who may rely on such information while making important decisions (e.g., presidential elections). In this paper, we present an innovative solution for fake news detection that utilizes deep learning methods. Our experiments prove that the proposed approach allows us to achieve promising results.


title: Analyzing the Spatial Distribution of Acute Coronary Syndrome Cases Using Synthesized Data on Arterial Hypertension Prevalence

text: In the current study, the authors demonstrate the method aimed at analyzing the distribution of acute coronary syndrome (ACS) cases in Saint Petersburg. The employed approach utilizes a synthetic population of Saint Petersburg and a statistical model for arterial hypertension prevalence. The number of ACS–related emergency services calls in an area is matched with the population density and the prospected number of individuals with arterial hypertension, which makes it possible to find locations with excessive ACS incidence. Three categories of locations, depending on the joint distribution of the above-mentioned indicators, are proposed as a result of data analysis. The method is implemented in Python programming language, the visualization is made using QGIS open software. The proposed method can be used to assess the prevalence of certain health conditions in the population and to match them with the corresponding severe health outcomes.


title: Evaluation of the Costs of Delayed Campaigns for Limiting the Spread of Negative Content, Panic and Rumours in Complex Networks

text: Increasing the performance of information spreading processes and influence maximisation is important from the perspective of marketing and other activities within social networks. Another direction is suppressing spreading processes for limiting the coverage of misleading information, spreading information helping to avoid epidemics or decreasing the role of competitors on the market. Suppressing action can take a form of spreading competing content and it’s performance is related to timing and campaign intensity. Presented in this paper study showed how the delay in launching suppressing process can be compensated by properly chosen parameters and the action still can be successful.


title: Maintenance and Security System for PLC Railway LED Sign Communication Infrastructure

text: LED marking systems are currently becoming key elements of every Smart Transport System. Ensuring proper level of security, protection and continuity of failure-free operation seems to be not a completely solved issue. In the article, a system is present allowing to detect different types of anomalies and failures/damage in critical infrastructure of railway transport realized by means of Power Line Communication. There is also described the structure of the examined LED Sign Communications Network. Other discussed topics include significant security problems and maintenance of LED sign system which have direct impact on correct operation of critical communication infrastructure. A two-stage method of anomaly/damage detection is proposed. In the first step, all the outlying observations are detected and eliminated from the analysed network traffic parameters by means of the Cook’s distance. So prepared data is used in stage two to create models on the basis of autoregressive neural network describing variability of the analysed LED Sign Communications Network parameters. Next, relations between the expected network traffic and its real variability are examined in order to detect abnormal behaviour which could indicate an attempt of an attack or failure/damage. There is also proposed a procedure of recurrent learning of the exploited neural networks in case there emerge significant fluctuations in the real PLC traffic. A number of scientific research was realized, which fully confirmed efficiency of the proposed solution and accuracy of autoregressive type of neural network for prediction of the analysed time series.


title: Early Signs of Critical Slowing Down in Heart Surface Electrograms of Ventricular Fibrillation Victims

text: Ventricular fibrillation (VF) is a dangerous type of cardiac arrhythmia which, without intervention, almost always results in sudden death. Implantable automatic defibrillators are among the most successful devices to prevent sudden death by automatically applying a shock to the heart when fibrillation occurs. However, the electric shock is very painful and could lead to dangerous situations when a patient is, for example, driving or biking. An early warning signal for VF could reduce the risk in such situations or, in the future, reduce the need for defibrillation altogether. Here, we test for the presence of critical slowing down (CSD), which has proven to be an early warning indicator for critical transitions in a range of different systems. CSD is characterized by a buildup of autocorrelation; we therefore study the residuals of heart surface electrocardiograms (ECGs) of patients that suffered VF to investigate if we can measure positive trends in autocorrelation. We consider several methods to extract these residuals from the original signals. For three out of four VF victims, we find a significant amount of positive autocorrelation trends in the residuals, which might be explained by CSD. We show that these positive trends may not be measurable from the original body surface ECGs, but only from certain areas around the heart surface. We argue that additional experimental studies involving heart surface ECG data of subjects that did not suffer VF are required to quantify the prediction accuracy of the promising results we get from the data of VF victims.


title: Fingerprinting of URL Logs: Continuous User Authentication from Behavioural Patterns

text: Security of computer systems is now a critical and evolving issue. Current trends try to use behavioural biometrics for continuous authorization. Our work is intended to strengthen network user authentication by a software interaction analysis. In our research, we use HTTP request (URLs) logs that network administrators collect. We use a set of full-convolutional autoencoders and one authentication (one-class) convolutional neural network. The proposed method copes with extensive data from many users and allows to add new users in the future. Moreover, the system works in a real-time manner, and the proposed deep learning framework can use other user behaviour- and software interaction-related features.


title: Clustering and Weighted Scoring in Geometric Space Support Vector Machine Ensemble for Highly Imbalanced Data Classification

text: Learning from imbalanced datasets is a challenging task for standard classification algorithms. In general, there are two main approaches to solve the problem of imbalanced data: algorithm-level and data-level solutions. This paper deals with the second approach. In particular, this paper shows a new proposition for calculating the weighted score function to use in the integration phase of the multiple classification system. The presented research includes experimental evaluation over multiple, open-source, highly imbalanced datasets, presenting the results of comparing the proposed algorithm with three other approaches in the context of six performance measures. Comprehensive experimental results show that the proposed algorithm has better performance measures than the other ensemble methods for highly imbalanced datasets.


title: Towards Network Anomaly Detection Using Graph Embedding

text: In the face of endless cyberattacks, many researchers have proposed machine learning-based network anomaly detection technologies. Traditional statistical features of network flows are manually extracted and rely heavily on expert knowledge, while classifiers based on statistical features have a high false-positive rate. The communications between different hosts forms graphs, which contain a large number of latent features. By combining statistical features with these latent features, we can train better machine learning classifiers. Therefore, we propose a novel network anomaly detection method that can use latent features in graphs and reduce the false positive rate of anomaly detection. We convert network traffic into first-order and second-order graph. The first-order graph learns the latent features from the perspective of a single host, and the second-order graph learns the latent features from a global perspective. This feature extraction process does not require manual participation or expert knowledge. We use these features to train machine learning algorithm classifiers for detecting network anomalies. We conducted experiments on two real-world datasets, and the results show that our approach allows for better learning of latent features and improved accuracy of anomaly detection. In addition, our method has the ability to detect unknown attacks.


title: Experiencer Detection and Automated Extraction of a Family Disease Tree from Medical Texts in Russian Language

text: Text descriptions in natural language are an essential part of electronic health records (EHRs). Such descriptions usually contain facts about patient’s life, events, diseases and other relevant information. Sometimes it may also include facts about their family members. In order to find the facts about the right person (experiencer) and convert the unstructured medical text into structured information, we developed a module of experiencer detection. We compared different vector representations and machine learning models to get the highest quality of 0.96 f-score for binary classification and 0.93 f-score for multi-classification. Additionally, we present the results plotting the family disease tree.


title: From Generality to Specificity: On Matter of Scale in Social Media Topic Communities

text: Research question stated in current paper concerns measuring significance of interest topic to a person on the base of digital footprints, observed in on-line social media. Interests are represented by on-line social groups in VK social network, which were marked by topics. Topic significance to a person is supposed to be related to the fraction of representative groups in user’s subscription list. We imply that for each topic, depending on its popularity, relation to geographical region, and social acceptability, there is a value of group size which is significant. In addition, we suppose, that professional clusters of groups demonstrate relatively higher inner density and unify common groups. Therefore, following groups from more specific clusters indicate higher personal involvement to a topic – in this way, representative topical groups are marked. We build social group similarity graph, which is based on the number of common followers, extract subgraphs related to a single topic, and analyse bins of groups, build with increase of group sizes. Results show topics of general interests have higher density at larger groups in contrast to specific interests, which is in correspondence with initial hypothesis.


title: A Correction Method of a Base Classifier Applied to Imbalanced Data Classification

text: In this paper, the issue of tailoring the soft confusion matrix classifier to deal with imbalanced data is addressed. This is done by changing the definition of the soft neighbourhood of the classified object. The first approach is to change the neighbourhood to be more local by changing the Gaussian potential function approach to the nearest neighbour rule. The second one is to weight the instances that are included in the neighbourhood. The instances are weighted inversely proportional to the a priori class probability. The experimental results show that for one of the investigated base classifiers, the usage of the KNN neighbourhood significantly improves the classification results. What is more, the application of the weighting schema also offers a significant improvement.


title: Entropy-Based Measure for Influence Maximization in Temporal Networks

text: The challenge of influence maximization in social networks is tackled in many settings and scenarios. However, the most explored variant is looking at how to choose a seed set of a given size, that maximizes the number of activated nodes for selected model of social influence. This has been studied mostly in the area of static networks, yet other kinds of networks, such as multilayer or temporal ones, are also in the scope of recent research. In this work we propose and evaluate the measure based on entropy, that investigates how the neighbourhood of nodes varies over time, and based on that and their activity ranks, the nodes as possible candidates for seeds are selected. This measure applied for temporal networks intends to favor nodes that vary their neighbourhood highly and, thanks to that, are good spreaders for certain influence models. The results demonstrate that for the Independent Cascade Model of social influence the introduced entropy-based metric outperforms typical seed selection heuristics for temporal networks. Moreover, compared to some other heuristics, it is fast to compute, thus can be used for fast-varying temporal networks.


title: Cooperation for Public Goods Under Uncertainty

text: Everyone wants clean air, peace and other public goods but is tempted to freeride on others’ efforts. The usual way out of this dilemma is to impose norms, maintain reputations and incentivize individuals to contribute. In situations of high uncertainty, however, such as confrontations of protesters with a dictatorial regime, the usual measures are not feasible, but cooperation can be achieved nevertheless. We use an Ising model with asymmetric spins that represent cooperation and defection to show numerically how public goods can be realized. Under uncertainty, people use the heuristic of conformity. The turmoil of a confrontation causes some individuals to cooperate accidentally, and at a critical level of turmoil, they entail a cascade of cooperation. This critical level is much lower in small networks.


title: Branch-and-Bound Search for Training Cascades of Classifiers

text: We propose a general algorithm that treats cascade training as a tree search process working according to the branch-and-bound technique. The algorithm allows to reduce the expected number of features used by an operating cascade—a key quantity we focus on in the paper. While searching, we observe suitable lower bounds on partial expectations and prune tree branches that cannot improve the best-so-far result. Both exact and approximate variants of the approach are formulated. Experiments pertain to cascades trained to be face or letter detectors with Haar-like features or Zernike moments being the input information, respectively. Results confirm shorter operating times of cascades obtained owing to the reduction in the number of extracted features.


Reply to Jakovac: “COVID-19 and vitamin D—Is there a link and an opportunity for intervention?”


title: Universal Measure for Medical Image Quality Evaluation Based on Gradient Approach

text: In this paper, a new universal measure of medical images quality is proposed. The measure is based on the analysis of the image by using gradient methods. The number of isolated peaks in the examined image, as a function of the threshold value, is the basis of the assessment of the image quality. It turns out that for higher quality images the curvature of the graph of the said function has a higher value for lower threshold values. On the basis of the observed property, a new method of no-reference image quality assessment has been created. The experimental verification confirmed the method efficiency. The correlation between the arrangement depending on the image quality done by an expert and by using the proposed method is equal to 0.74. This means that the proposed method gives a correlation of higher than the best methods described in the literature. The proposed measure is useful to maximize the image quality while minimizing the time of medical examination.


title: Hybrid Text Feature Modeling for Disease Group Prediction Using Unstructured Physician Notes

text: Existing Clinical Decision Support Systems (CDSSs) largely depend on the availability of structured patient data and Electronic Health Records (EHRs) to aid caregivers. However, in case of hospitals in developing countries, structured patient data formats are not widely adopted, where medical professionals still rely on clinical notes in the form of unstructured text. Such unstructured clinical notes recorded by medical personnel can also be a potential source of rich patient-specific information which can be leveraged to build CDSSs, even for hospitals in developing countries. If such unstructured clinical text can be used, the manual and time-consuming process of EHR generation will no longer be required, with huge person-hours and cost savings. In this article, we propose a generic ICD9 disease group prediction CDSS built on unstructured physician notes modeled using hybrid word embeddings. These word embeddings are used to train a deep neural network for effectively predicting ICD9 disease groups. Experimental evaluation showed that the proposed approach outperformed the state-of-the-art disease group prediction model built on structured EHRs by 15% in terms of AUROC and 40% in terms of AUPRC, thus proving our hypothesis and eliminating dependency on availability of structured patient data.


title: An Information-Theoretic and Dissipative Systems Approach to the Study of Knowledge Diffusion and Emerging Complexity in Innovation Systems

text: The paper applies information theory and the theory of dissipative systems to discuss the emergence of complexity in an innovation system, as a result of its adaptation to an uneven distribution of the cognitive distance between its members. By modelling, on one hand, cognitive distance as noise, and, on the other hand, the inefficiencies linked to a bad flow of information as costs, we propose a model of the dynamics by which a horizontal network evolves into a hierarchical network, with some members emerging as intermediaries in the transfer of knowledge between seekers and problem-solvers. Our theoretical model contributes to the understanding of the evolution of an innovation system by explaining how the increased complexity of the system can be thermodynamically justified by purely internal factors. Complementing previous studies, we demonstrate mathematically that the complexity of an innovation system can increase not only to address the complexity of the problems that the system has to solve, but also to improve the performance of the system in transferring the knowledge needed to find a solution.


title: Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data

text: In the paper we revisit the problem of fitting logistic regression to positive and unlabelled data. There are two key contributions. First, a new light is shed on the properties of frequently used naive method (in which unlabelled examples are treated as negative). In particular we show that naive method is related to incorrect specification of the logistic model and consequently the parameters in naive method are shrunk towards zero. An interesting relationship between shrinkage parameter and label frequency is established. Second, we introduce a novel method of fitting logistic model based on simultaneous estimation of vector of coefficients and label frequency. Importantly, the proposed method does not require prior estimation, which is a major obstacle in positive unlabelled learning. The method is superior in predicting posterior probability to both naive method and weighted likelihood method for several benchmark data sets. Moreover, it yields consistently better estimator of label frequency than other two known methods. We also introduce simple but powerful representation of positive and unlabelled data under Selected Completely at Random assumption which yields straightforwardly most properties of such model.


title: A Comparison of Generalized Stochastic Milevsky-Promislov Mortality Models with Continuous Non-Gaussian Filters

text: The ability to precisely model mortality rates [Formula: see text] plays an important role from the economic point of view in healthcare. The aim of this article is to propose a comparison of the estimation of the mortality rates based on a class of stochastic Milevsky-Promislov mortality models. We assume that excitations are modeled by second, fourth and sixth order polynomials of outputs from a linear non-Gaussian filter. To estimate the model parameters we use the first and second moments of [Formula: see text]. The theoretical values obtained in both cases were compared with theoretical [Formula: see text] based on a classical Lee-Carter model. The obtained results confirm the usefulness of the switched model based on the continuous non-Gaussian processes used for modeling [Formula: see text].


title: From Population to Subject-Specific Reference Intervals

text: In clinical practice, normal values or reference intervals are the main point of reference for interpreting a wide array of measurements, including biochemical laboratory tests, anthropometrical measurements, physiological or physical ability tests. They are historically defined to separate a healthy population from unhealthy and therefore serve a diagnostic purpose. Numerous cross-sectional studies use various classical parametric and nonparametric approaches to calculate reference intervals. Based on a large cross-sectional study (N = 60,799), we compute reference intervals for subpopulations (e.g. males and females) which illustrate that subpopulations may have their own specific and more narrow reference intervals. We further argue that each healthy subject may actually have its own reference interval (subject-specific reference intervals or SSRIs). However, for estimating such SSRIs longitudinal data are required, for which the traditional reference interval estimating methods cannot be used. In this study, a linear quantile mixed model (LQMM) is proposed for estimating SSRIs from longitudinal data. The SSRIs can help clinicians to give a more accurate diagnosis as they provide an interval for each individual patient. We conclude that it is worthwhile to develop a dedicated methodology to bring the idea of subject-specific reference intervals to the preventive healthcare landscape.


title: Dynamic Classifier Selection for Data with Skewed Class Distribution Using Imbalance Ratio and Euclidean Distance

text: Imbalanced data analysis remains one of the critical challenges in machine learning. This work aims to adapt the concept of Dynamic Classifier Selection (dcs) to the pattern classification task with the skewed class distribution. Two methods, using the similarity (distance) to the reference instances and class imbalance ratio to select the most confident classifier for a given observation, have been proposed. Both approaches come in two modes, one based on the k-Nearest Oracles (knora) and the other also considering those cases where the classifier makes a mistake. The proposed methods were evaluated based on computer experiments carried out on [Image: see text] datasets with a high imbalance ratio. The obtained results and statistical analysis confirm the usefulness of the proposed solutions.


title: Fusion Learning: A One Shot Federated Learning

text: Federated Learning is an emerging distributed machine learning technique which does not require the transmission of data to a central server to build a global model. Instead, individual devices build their own models, and the model parameters are transmitted. The server constructs a global model using these parameters, which is then re-transmitted back to the devices. The major bottleneck of this approach is the communication overhead as all the devices need to transmit their model parameters at regular intervals. Here we present an interesting and novel alternative to federated learning known as Fusion Learning, where the distribution parameters of the client’s data along with its local model parameters are sent to the server. The server regenerates the data from these distribution parameters and fuses all the data from multiple devices. This combined dataset is now used to build a global model that is transmitted back to the individual devices. Our experiments show that the accuracy achieved through this approach is in par with both a federated setup and a centralized framework needing only one round of communication to the central server.


title: Quantifying Overfitting Potential in Drug Binding Datasets

text: In this paper, we investigate potential biases in datasets used to make drug binding predictions using machine learning. We investigate a recently published metric called the Asymmetric Validation Embedding (AVE) bias which is used to quantify this bias and detect overfitting. We compare it to a slightly revised version and introduce a new weighted metric. We find that the new metrics allow to quantify overfitting while not overly limiting training data and produce models with greater predictive value.


title: The Challenge of Onboard SAR Processing: A GPU Opportunity

text: Data acquired by a Synthetic Aperture Radar (SAR), onboard a satellite or an airborne platform, must be processed to produce a visible image. For this reason, data must be transferred to the ground station and processed through a time/computing-consuming focusing algorithm. Thanks to the advances in avionic technology, now GPUs are available for onboard processing, and an opportunity for SAR focusing opened. Due to the unavailability of avionic platforms for this research, we developed a GPU-parallel algorithm on commercial off-the-shelf graphics cards, and with the help of a proper scaling factor, we projected execution times for the case of an avionic GPU. We evaluated performance using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 on both NVIDIA Kepler and Turing architectures.


title: An Agent-Based Simulation of the Spread of Dengue Fever

text: Vector-borne diseases (VBDs) account for more than 17% of all infectious diseases, causing more than 700,000 annual deaths. Lack of a robust infrastructure for timely collection, reporting, and analysis of epidemic data undermines necessary preparedness and thus posing serious health challenges to the general public. By developing a simulation framework that models population dynamics and the interactions of both humans and mosquitoes, we may enable epidemiologists to analyze and forecast the transmission and spread of an infectious disease in specific areas. We extend the traditional SEIR (Susceptible, Exposed, Infectious, Recovered) mathematical model and propose an Agent-based model to analyze the interactions between the host and the vector using: (i) our proposed algorithm to compute vector density, based on the reproductive behavior of the vector; and (ii) agent interactions to simulate transmission of virus in a spatio-temporal environment, and forecast the spread of the disease in a given area over a period of time. Our simulation results identify several expected dengue cases and their direction of spread, which can help in detecting epidemic outbreaks. Our proposed framework provides visualization and forecasting capabilities to study the epidemiology of a certain region and aid public health departments in emergency preparedness.


title: Asynchronous Actor-Based Approach to Multiobjective Hierarchical Strategy

text: Hierarchical Genetic Strategy (HGS) is a general-purpose optimization metaheuristic based on multi-deme evolutionary-like optimization, while demes are parts of adaptive dynamically changing tree. The paper focuses on adaptation of the classic HGS algorithm for multi-criteria optimization problems, coupling the HGS with Particle Swarm Optimization demes. The main contribution of the paper is showing the efficacy and efficiency of the actor-based implementation of this metaheuristic algorithm.


title: Simulation of Neurotransmitter Flow in Three Dimensional Model of Presynaptic Bouton

text: In this paper a geometrical model for simulation of the nerve impulses inside the presynaptic bouton is designed. The neurotransmitter flow is described by using partial differential equation with nonlinear term. The bouton is modeled as a distorted geosphere and the mitochondrion inside it as a highly modified cuboid. The quality of the mesh elements is examined. The changes of the amount of neurotransmitter during exocytosis are simulated.


title: Detecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model

text: Every individual text or document is generated for specific purpose(s). Sometime, the text is deployed to convey a specific message about an event or a product. Other occasions, it may be communicating a scientific breakthrough, development or new model and so on. Given any specific objective, the creators and the users of documents may like to know which part(s) of the documents are more influential in conveying their specific messages or achieving their objectives. Understanding which parts of a document has more impact on the viewer’s perception would allow the content creators to design more effective content. Detecting the more impactful parts of a content would help content users, such as advertisers, to concentrate their efforts more on those parts of the content and thus to avoid spending resources on the rest of the document. This work uses a regularized attention-based method to detect the most influential part(s) of any given document or text. The model uses an encoder-decoder architecture based on attention-based decoder with regularization applied to the corresponding weights.


title: Interval Adjoint Significance Analysis for Neural Networks

text: Optimal neural network architecture is a very important factor for computational complexity and memory footprints of neural networks. In this regard, a robust pruning method based on interval adjoints significance analysis is presented in this paper to prune irrelevant and redundant nodes from a neural network. The significance of a node is defined as a product of a node’s interval width and an absolute maximum of first-order derivative of that node’s interval. Based on the significance of nodes, one can decide how much to prune from each layer. We show that the proposed method works effectively on hidden and input layers by experimenting on famous and complex datasets of machine learning. In the proposed method, a node is removed based on its significance and bias is updated for remaining nodes.


title: Classification of Lung Diseases Using Deep Learning Models

text: In this paper we address the problem of medical data scarcity by considering the task of detection of pulmonary diseases from chest X-Ray images using small volume datasets with less than thousand samples. We implemented three deep convolutional neural networks (VGG16, ResNet-50, and InceptionV3) pre-trained on the ImageNet dataset and assesed them in lung disease classification tasks using transfer learning approach. We created a pipeline that segmented chest X-Ray (CXR) images prior to classifying them and we compared the performance of our framework with the existing ones. We demonstrated that pre-trained models and simple classifiers such as shallow neural networks can compete with the complex systems. We also validated our framework on the publicly available Shenzhen and Montgomery lung datasets and compared its performance to the currently available solutions. Our method was able to reach the same level of accuracy as the best performing models trained on the Montgomery dataset however, the advantage of our approach is in smaller number of trainable parameters. Furthermore, our InceptionV3 based model almost tied with the best performing solution on the Shenzhen dataset despite being computationally less expensive.


title: Learning Functions Using Data-Dependent Regularization: Representer Theorem Revisited

text: We introduce a data-dependent regularization problem which uses the geometry structure of the data to learn functions from incomplete data. We show another proof of the standard representer theorem when introducing the problem. At the end of the paper, two applications in image processing are used to illustrate the function learning framework.


title: Using Machine Learning in Accuracy Assessment of Knowledge-Based Energy and Frequency Base Likelihood in Protein Structures

text: Many aspects of the study of protein folding and dynamics have been affected by the accumulation of data about native protein structures and recent advances in machine learning. Computational methods for predicting protein structures from their sequences are now heavily based on machine learning tools and on approaches that extract knowledge and rules from data using probabilistic models. Many of these methods use scoring functions to determine which structure best fits a native protein sequence. Using computational approaches, we obtained two scoring functions: knowledge-based energy and likelihood of base frequency, and we compared their accuracy in measuring the sequence structure fit. We compared the machine learning models’ accuracy of predictions for knowledge-based energy and likelihood values to validate our results, showing that likelihood is a more accurate scoring function than knowledge-based energy.


title: Tree Based Advanced Relative Expression Analysis

text: This paper presents a new concept for biomarker discovery and gene expression data classification that rises from the Relative Expression Analysis (RXA). The basic idea of RXA is to focus on simple ordering relationships between the expression of small sets of genes rather than their raw values. We propose a paradigm shift as we extend RXA concept to tree-based Advanced Relative Expression Analysis (ARXA). The main contribution is a decision tree with splitting nodes that consider relative fraction comparisons between multiple gene pairs. In addition, to face the enormous computational complexity of RXA, the most time-consuming part which is scoring all possible gene pairs in each splitting node is parallelized using GPU. This way the algorithm allows searching for more tailored interactions between sub-groups of genes in a reasonable time. Experiments carried out on 8 cancer-related datasets show not only significant improvement in accuracy and speed of our approach in comparison to various RXA solutions but also new interesting patterns between subgroups of genes.


title: Hybrid SWAN for Fast and Efficient Practical Wave Modelling - Part 2

text: In the Netherlands, for coastal and inland water applications, wave modelling with SWAN on structured computational grids has become a main ingredient. However, computational times are relatively high. Benchmarks showed that the MPI version of SWAN is not that efficient as the OpenMP version within a single node. Therefore, in a previous paper [5] a hybrid version of SWAN was proposed for computations on structured computational grids. It combines the efficiency of the OpenMP version on shared memory with the capability of the MPI version to distribute memory over nodes. In the current paper we extend this approach by an improved implementation, verification of the model performance with a testbed, and extensive benchmarks of its parallel performance. With these benchmarks for important real life applications we show the significance of this hybrid version. We optimize the approach and illustrate the behavior for larger number of nodes. Parallel I/O will be subject of future research.


title: Bootstrap Bias Corrected Cross Validation Applied to Super Learning

text: Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation; however, this technique is very expensive computationally. It has been proposed by Tsamardinos et al., that nested cross validation can be replaced by resampling for tuning hyper-parameters of the learning algorithms. The main contribution of this study is to apply this idea to verification of super learner. We compare the new method with other verification methods, including nested cross validation. Tests were performed on artificial data sets of diverse size and on seven real, biomedical data sets. The resampling method, called Bootstrap Bias Correction, proved to be a reasonably precise and very cost-efficient alternative for nested cross validation.


title: Design of Loss Functions for Solving Inverse Problems Using Deep Learning

text: Solving inverse problems is a crucial task in several applications that strongly affect our daily lives, including multiple engineering fields, military operations, and/or energy production. There exist different methods for solving inverse problems, including gradient based methods, statistics based methods, and Deep Learning (DL) methods. In this work, we focus on the latest. Specifically, we study the design of proper loss functions for dealing with inverse problems using DL. To do this, we introduce a simple benchmark problem with known analytical solution. Then, we propose multiple loss functions and compare their performance when applied to our benchmark example problem. In addition, we analyze how to improve the approximation of the forward function by: (a) considering a Hermite-type interpolation loss function, and (b) reducing the number of samples for the forward training in the Encoder-Decoder method. Results indicate that a correct design of the loss function is crucial to obtain accurate inversion results.


title: Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique

text: Interaction information is a model-free, non-parametric measure used for detection of interaction among variables. It frequently finds interactions which remain undetected by standard model-based methods. However in the previous studies application of interaction information was limited by lack of appropriate statistical tests. We study a challenging problem of testing the positiveness of interaction information which allows to confirm the statistical significance of the investigated interactions. It turns out that commonly used chi-squared test detects too many spurious interactions when the dependence between the variables (e.g. between two genetic markers) is strong. To overcome this problem we consider permutation test and also propose a novel HYBRID method that combines permutation and chi-squared tests and takes into account dependence between studied variables. We show in numerical experiments that, in contrast to chi-squared based test, the proposed method controls well the actual significance level and in many situations detects interactions which are undetected by standard methods. Moreover HYBRID method outperforms permutation test with respect to power and computational efficiency. The method is applied to find interactions among Single Nucleotide Polymorphisms as well as among gene expression levels of human immune cells.