Reviewerly/exHarmony huggingface dataset:
Wireless Transmission and Wavelets

The final chapter considers two of the most important topics in modern signal processing, namely wavelets and wireless transmissions. The origin of wavelets lies in the work of Haar and Paley on orthogonal series of functions, and the Haar wavelet was introduced as an orthonormal basis for L 2[0, 1] with properties that Paley realized were remarkable. The Haar system was interesting in its own right, and was studied as a model of an orthonormal system which was apparently simpler than Fourier series. The work of Haar and Paley started a course of study that led to martingales and the sought-after results about Fourier series in the 1960s and 1970s. It was in the 1980s that the study of wavelets really sprung to life, and completely transformed signal processing. In this chapter we look at one wavelet, associated with the sinc function, which is known as Shannon’s wavelet.
A Deep Learning Based Approach to Automate Clinical Coding of Electronic Health Records

The medical records in different electronic formats, such as handwritten notes, diagnosis summaries, lab reports, electronic pdfs, etc., contain valuable information that can be used for various medical purposes. These health records are currently coded manually or semi-automated to assign clinical codes (ICD-codes) for clinical research and analytics. This process is very time-consuming, expensive, and error-prone. This paper presents a method for automated clinical coding of electronic health records (EHRs) given the patient diagnosis summary and other medical-related documents. The presented method uses natural language processing (NLP) techniques, which capture knowledge from the free-text diagnosis descriptions, do the text matching and semantic mapping, and translate diagnosis descriptions into clinical codes. We develop one baseline Word2vec and cosine similarity hybrid model, a transformer encoder model, and a BERT (Bidirectional Encoder Representations from Transformers) model for the automated clinical coding. The presented models are evaluated using a publicly available Medical Information Mart for Intensive Care III (MIMIC-III) dataset. The used dataset consists of various patient diagnosis descriptions and corresponding ICD-9 codes. The experimental results show that the presented BlueBERT based automated clinical coding model produced an AUC (area under ROC curve) value of 98.9% for the top-10 ICD codes prediction. On the full MIMIC-III dataset, the transformer model produced an accuracy of 76.8%, a precision of 61.02%, a recall of 47.22%, a f1-score of 53.2%, and an AUC value of 92.1%. The hybrid baseline model and another used transformer encoder model also showed promising results.
Analysis of Task Comparability in Digital Environment by the Case of Metacognitive Skills

&lt;p&gt;This article discusses the problem of task comparability with the help of scenario-based tasks for metacognitive skills. Using the data of &amp;ldquo;&amp;laquo;4С&amp;rdquo;&amp;raquo; tool for measuring critical thinking (N=500), the comparability of two scenarios within an identical digital environment with one set of indicators was investigated. The main difference in the scenarios lies in the contextual characteristics. The measurement invariance analysis of the instrument using confirmatory factor analysis was conducted. The results show that even with the equivalent construct structure and tasks&amp;rsquo; characteristics, the context of the scenario has an effect on the student`&amp;rsquo;s performance. The main differences in results were recorded for tasks involving interaction with the environment, where the test-taker created an object with elements. Tasks involving working with text in a digital environment can be considered comparable in case of elements content change. The possible reasons behind the observed differences in scenarios are discussed.&lt;/p&gt;
FastTIRP: Efficient Discovery of Time-Interval Related Patterns

Finding frequent patterns in discrete sequences of symbols or events can be useful to understand data, support decision-making and make predictions. However, many studies on analyzing event sequence do not consider the duration of events, and thus the complex time relationships between them (e.g. an event may start at the same time as another event but end before). To find frequent sequential patterns in data where events have a start and end time, an emerging topic is time-interval related pattern (TIRP) mining. Several algorithms have been proposed for this task but efficiency remains a major issue due to the very large search space. To provide a more efficient algorithm for TIRP mining, this paper presents a novel algorithm called FastTIRP. It utilizes a novel Pair Support Pruning (PSP) optimization to reduce the search space. Experiments show that FastTIRP outperforms the state-of-the-art VertTIRP algorithm in terms of runtime on four benchmark datasets.
Copying data

Most Web servers, and computers in general, routinely make a number of extra copies of received and sent messages. Ultimately, if there are k copies involved in processing a message in a Web server, the throughput of the Web server can be k times slower. This chapter focusses on removing the obvious waste involved in such unnecessary copies. Eliminating redundant copies allows the software to come closer to realizing the potential of the hardware, one of the goals of network algorithmics. This chapter surveys techniques for reducing the costs of data manipulation without sacrificing modularity and without major changes to operating system design. Section 5.1 describes why and how extra data copies occur. Section 5.2 describes a series of techniques to avoid copies by local restructuring of the operating system and network code at an endnode. Section 5.3 shows how to avoid both copy and control overhead for large transfers, using remote DMA techniques that involve protocol changes. Section 5.4 broadens the discussion to consider the file system in, say, a Web server, and it shows how to avoid wasteful copies between the file cache and the application. Section 5.5 broadens the discussion to consider other operations that touch all the data, such as checksumming and encryption, and introduces a well-known technique called integrated layer processing. Section 5.6 broadens the discussion beyond copying to show that without careful consideration of cache effects, instruction cache effects can swamp the effects of copying for small messages. The world has changed since the first edition of this book and this chapter reflects some of these changes. The changes include the emergence of servers with multicore CPUs with NUMA (non-uniform memory access, where groups of cores share an L3 cache), the rapid emergence of 100 Gbps links, the ubiquity of solid state disks (NVM) that can saturate network links, and recent trends in remote DMA techniques such as the popular RoCE (RDMA over converged Ethernet). We will fit these trends into our framework, showing that the main ideas of the first edition still hold. Although this is the first chapter of the book that is devoted to techniques for overcoming a specific bottleneck, the techniques are based on the principles described in Part 1 of the book.
Managing access control issues in the choose your own device environment

Enterprise mobility is taking the business to another level. Organizations are nowadays managing to adopt varying enterprise mobility that helps run the transactions conducted in a bigger and better way. Despite their huge assistance, most enterprise mobilities are causing mayhem to most organizations that have integrated them as the sole strategy to keep things running. They are becoming inevitable and unstoppable trends that pose new security risks and challenges in controlling and managing corporate data and networks. If not well monitored, they can unwontedly lead to disclosing vital information, disruption of services, legal implications, financial issues, loss of productivity, and modify access policies. One such largely implemented enterprise mobility is choose your own de-vice. The intention of this paper is to propose a new framework that would help in managing the access control issues in the environment of choose your own device. The main aim of the proposed architecture is to reduce restrictions and enforce access control policies in choose your own device and the cloud.
Heterogeneous Multi-unit Control with Curriculum Learning for Multi-agent Reinforcement Learning

Heterogeneous Multi-unit control is one of the most concerned topic in multi-agent system, which focuses on controlling agents of different type of functions. Methods that utilize parameter or replay-buffer sharing are able to address the problem of combinatorial explosion under isomorphism assumption, but may lead to divergence under heterogeneous setting. This work use curriculum learning to bypass the barrier of a needle in a haystack that is faced by either joint-action learner or independent learner. According to the experiment on heterogeneous force combat engagements, the independent learner outperforms the baseline learner by 10% of evaluation metrics with curriculum learning, which empirically shows that curriculum learning is able to discover a novel learning trajectory that is not followed by conventional multi-agent learners.
Ad-hoc Searches on Image Databases

Searching for ad-hoc objects in image and video datasets can be expensive and time consuming. As image data is more common, we increasingly need systems to help us query it. In this talk, we describe related work and then explore two approaches: ExSample [14] and SeeSaw [15] which target different scenarios of image and video search.
The Main Approaches and Concepts of Creative Thinking

&lt;p&gt;The research is aimed at systematizing the most effective approaches to the study of creative thinking in foreign and domestic science. The following directions of studying creative thinking are considered: on the basis with which it is associated; on the paradigm that underlies it; on essential grounds; on the typology of creative achievements; on the basis of the approach developed within a certain scientific school; on the study of the features of the activity process; on developing practices; on adherence to the main factor influencing creative thinking; based on the allocation of components in the structure; based on the differences between creative and non-creative thinking; compensation of undeveloped components of creative thinking by strengthening more developed ones; neuropsychological approach (cognitive), from the point of view of self-actualization of personality. After the analysis, we tend to believe that the multicomponent consideration of creative thinking is the most effective one. When studying and developing creative thinking, it is important to pay attention not only to the operational component of creative thinking, but also semantic, motivational, emotional components (interest, joy from engaging in creative activity), which determine moral meanings, attitudes, states that ensure the manifestation of creative thinking in activity. Only with the simultaneous development of semantic, activity and emotional components of creative thinking, its development will be the most harmonious one, being carried out with the less effort on the part of students and a teacher.&lt;/p&gt;
Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases

Noncommunicable diseases are among the most significant health threats in our society, being cardiovascular diseases (CVD) the most prevalent. Because of the severity and prevalence of these illnesses, early detection and prevention are critical for reducing the worldwide health and economic burden. Though machine learning (ML) methods usually outperform conventional approaches in many domains, class imbalance can hinder the learning process. Oversampling techniques on the minority classes can help to overcome this issue. In particular, in this paper we apply oversampling methods to categorical data, aiming to improve the identification of risk factors associated with CVD. To conduct this study, questionnaire data (categorical) obtained by the Norwegian Centre for E-health Research associated with healthy and CVD patients are considered. The goal of this work is two-fold. Firstly, evaluating the influence of combining oversampling techniques and linear/nonlinear supervised ML methods in binary tasks. Secondly, identifying the most relevant features for predicting healthy and CVD cases. Experimental results show that oversampling and FS techniques help to improve CVD prediction. Specifically, the use of Generative Adversarial Networks and linear models usually achieve the best performance (area under the curve of 67%), outperforming other oversampling techniques. Synthetic data generation has proved to be beneficial for both identifying risk factors and creating models with reasonable generalization capability in the CVD prediction.
Information devices as means of dictance learning of the school biology сource

This paper considers the problem of using information devices in the process of Biology studying in secondary schools. The appropriateness of moving to a mixed form of learning that involves the creation of a polysubjective educational environment (teacher, student, information devices) has been justified. It has been examined how well the scientific problem is developed in pedagogical theory and educational practice. The methodology of using information devices in the process of Biology studying in a basic school, which involves the use of the Play Market server applications, Smart technologies and a website, has been created. After the analyses of the Play Market server content, there have been found several free of charge applications, which can be used while studying Biology in a basic school. Among them are the following: «Anatomy 4D», «Animal 4D+», «Augmented Reality Dinosaurs – my ARgalaxy», «BioInc – Biomedical Plague, BioInc», «Plan+Net». Their choice is caused by the specifics of the object of biological cognition (life in all its manifestations) and the concept of bio(eco)centrism, which recognizes the life of any living system as the highest value. The paper suggests the original approach for homework checking, which involves besides computer control of students’ learning outcomes, the use of Miracast wireless technology. This demands the owning of a smartphone, a multimedia projector, and a Google Chromecast type adapter. The methodology of conducting a mobile front-line survey at the lesson on the learned or current material in Biology in the test form, with the help of the free Plickers application, has been presented. The expediency of using the website builder Ucoz.ru for creation of a training website in Biology has been substantiated. The methodology of organizing the educational process in Biology in a basic school using the training website has been developed. Recommendations for using a Biology training website have been summarized. According to the results of the forming experiment, the effectiveness of the proposed methodology of using information devices in the process of Biology studying in a basic school has been substantiated.
N-Dimensional Structure: A Data Structure with Fast Access and Sorting of Integers

Various Data Structures have been introduced till date that aim at either to improve them in terms of time or space complexity or they are particularly designed for some special applications. This work aims at developing a new data structure that takes the idea of plotting each digit to another dimension and thereby reducing the access time for keys stored in it. In order to make it practical to implement, a tree-based approach has been used to store it in the memory. The asymptotic time complexity is O(nu) for extracting the keys in sorted order where u is the size of universe. In practical testing, the procedure took much less time to complete as compared to Merge Sort which is the best Comparison based Sorting Technique in terms of worst-case time complexity. This work discusses the concept, implementation and algorithms associated with the data structure. Then it evaluates it against various test scenarios with other sorting techniques.
Adaptive Path Following for a Differential Drive Robot with EKF-based Localization

For a mobile robot, uncertainties in its operating environment and system setup are common challenges. The robot may need to follow a new path in order to avoid collision with a previously unknown object in the environment. Additionally, the robot does not usually have precise knowledge of its own pose. To compensate for these uncertainties, we develop an adaptive path following scheme for a differential drive robot with localization capabilities. We employ a least-squares adaptive law to estimate an unknown circular path. With measurements to landmarks in the environment, we use an extended Kalman filter to estimate the robot's pose. Furthermore, a cascaded control scheme is used to track the estimated path. Simulation and experimental results are provided to verify the developed adaptive path following scheme and demonstrate its capabilities.
Self-organizing Maps of Artificial Neural Classifiers - A Brain-Like Pin Factory

Most machine learning algorithms are based on the formulation of an optimization problem using a global loss criterion. The essence of this formulation is a top-down engineering thinking that might have some limitations on the way towards a general artificial intelligence. In contrast, self-organizing maps use cooperative and competitive bottom-up rules to generate low-dimensional representations of complex input data. Following similar rules to SOMs, we develop a self-organization approach for a system of classifiers that combines top-down and bottom-up principles in a machine learning system. We believe that such a combination will overcome the limitations with respect to autonomous learning, robustness and self-repair that exist for pure top-down systems. Here we present a preliminary study using simple subsystems with limited learning capacities. As proof of principle, we study a network of simple artificial neural classifiers on the MNIST data set. Each classifier is able to recognize only one single digit. We demonstrate that upon training, the different classifiers are able to specialize their learning for a particular digit and cluster according to the digits. The entire system is capable of recognizing all digits and demonstrates the feasibility of combining bottom-up and top-down principles to solve a more complex task, while exhibiting strong spontaneous organization and robustness.
Ethical Aspects of Computational Modelling in Science, Decision Support and Communication

Abstract The development of data science, the increase of computational power, the availability of the internet infrastructure for data exchange and the urgency for an understanding of complex systems require a responsible and ethical use of computational models in science, communication and decision-making. Starting with a discussion of the width of different purposes of computational models, we first investigate the process of model construction as an interplay of theory and experimentation. We emphasise the different aspects of the tension between model variables and experimentally measurable observables. The resolution of this tension is a prerequisite for the responsible use of models and an instrumental part of using models in the scientific processes. We then discuss the impact of models and the responsibility that results from the fact that models support and may also guide experimentation. Further, we investigate the difference between computational modelling in an interdisciplinary science project and computational models as tools in transdisciplinary decision support. We regard the communication of model structures and modelling results as essential; however, this communication cannot happen in a technical manner, but model structures and modelling results must be translated into a “narrative.” We discuss the role of concepts from disciplines such as literary theory, communication science, and cultural studies and the potential gains that a broader approach can obtain. Considering concepts from the liberal arts, we conclude that there is, besides the responsibility of the model author, also a responsibility of the user/reader of the modelling results.
Evolutionary Music: Statistical Learning and Novelty for Automatic Improvisation

In this work we combine aspects of implicit learning with novelty search in an evolutionary algorithm with the aim to automatically generate melodies with improvisational flavour. Using Markov chains, the technique we present combines implicit statistical knowledge, extracted from musical corpora, with an adaptive novelty search mechanism. The algorithm is described along with the main design choices. Preliminary results are shown in two different musical contexts: Irish music and counterpoint compositions.
Psychological Models and Instruments Employed to Identify Personality Traits of Software Developers: A Systematic Mapping Study

Over the years, a significant amount of research has been done to discover links and/or effects of human personality in the field of software engineering (SI). It has been shown that personality has a positive influence on the different tasks and/or processes in this field. Related studies helped to identify a suitable personality type for each profile in the SI. However, contradictions have also been found in the results that question their validity, therefore, generalizing and applying these results may be misleading. This is because one of the key and complex factors is evaluating the personality of engineers or software developers from a psychological point of view. Through a systematic mapping study, an attempt was made to characterize the most widely used, reliable and valid psychological instruments/models. However, most of the articles considered for this study were based solely on the data available in the literature; making purely conceptual comparisons. A more exhaustive comparative analysis between said instruments/models was expected. Despite this, it was possible to identify that MBTI is the most popular to examine the personality of software developers. However, the Big Five and its psychometric instruments such as BFI and NEO-FFI have gained more prominence in SI research in recent years. It is necessary to highlight and call on the scientific community to carry out this type of research in a more rigorous way and that it be supported and guided by specialized areas in human behavior, which allows for minimizing contradictions for future research.
The Good, the Bad and the Ugly: Droplet Recognition by a “Shootout”-Heuristics

Abstract We explain how to optimize the image analysis of mixed clusters of red and green droplets in solvents with various degrees of sharpness, brightness, contrast and density. The circular Hough Transform is highly efficient for separated circles with reasonable background contrast, but not for large amounts of partially overlapping shapes, some of them blurred, as in the images of our dense droplet suspensions. We explain why standard approaches for image improvement fail and present a “shootout” approach, where already detected circles are masked, so that the removal of sharp outlines improves the relative optical quality of the remaining droplets. Nevertheless, for intrinsic reasons, there are limits to the accuracy of data which can be obtained on very dense clusters.