Reviewerly/exHarmony huggingface dataset:
Machine Learning Techniques for Extracting Relevant Features from Clinical Data for COVID-19 Mortality Prediction

The role of Machine Learning (ML) in healthcare is based on the ability of a machine to analyse the huge amounts of data available for each patient, like age, medical history, overall health status, test results, etc. With ML algorithms it is possible to learn models from data for the early identification of pathologies and their severity. Early identification is crucial to proceed as soon as possible with the necessary therapeutic actions. This work applies modern ML techniques to clinical data of either COVID-19 positive and COVID-19 negative patients with pulmonary complications, to learn mortality prediction models for both groups of patients, and compare results. We have focused on symbolic methods for building classifiers able to extract patterns from clinical data. This approach leads to predictive Artificial Intelligence (AI) systems working on medical data, and able to explain the reasons that lead the systems themselves to reach a certain conclusion.
Research on Emergency Communication Network Planning Method Based on MCTS

Network planning is a crucial process in the construction of emergency communication network. To make emergency communication network planning scientifically and effectively, with the combine of Mont Carlo tree search algorithm and deep learning, the paper proposes an intelligence planning method based on MCTS. The simulation results show that the intelligence network planning method based on MCTS can generate the network plans which with high rationality and efficiently.
Change that Respects Business Expertise: Stories as Prompts for a Conversation about Organisation Security

Leaders of organisations must make investment decisions relating to the security of their organisation. This often happens through consultation with a security specialist. Consultations may be regarded as conversations taking place in a trading zone between the two domains. We propose that supporting the trading zone is a route to sustainable, workable security change improvements. Prompts for such improvements are already in place, in the security stories that reach business leaders through news media, or anecdotes from trusted peers. However, a shift in perspective is needed to view these stories and anecdotes as prompts for individual decision makers to enter into the trading zone with security specialists. We illustrate how to facilitate this shift by recasting security ontology tools, previously centred around security-specific expertise, as a support device to enrich conversations between business expertise and security advice toward finding workable security choices. We frame our proposal within a broader view of community transformation, exploring the important principle of identifying practical opportunities to inform discussions about security solutions that are appropriate in the business context. Community-level discussions have potential to lead to more lasting, effective improvements than those instigated by one-way interventions from security specialists. We extend the view, applying the paradigm to articulate the importance of two-way conversations between business peers and security specialists.
Nonexistence of linear codes meeting the Griesmer bound

In this paper, we prove the nonexistence of [gq(5,d),5,d]q codes for q4−3q2−q+1≤d≤q4−3q2 and q4−2q2−3q+3≤d≤q4−2q2−2q with q≥5. We also prove the nonexistence of [gq(4,d),4,d]q codes for 2q3−rq2−3q+1≤d≤2q3−rq2 with 3≤r≤q−12 and q≥7.
The Need for Automated Analysis of Computer Systems’ Configuration

Modern computer systems are composed of many components, each of which contains from tens to thousands of configuration settings. Errors in their configurations are one of the primary causes of outages on production systems. This paper presents an overview of currently available methods of automated analysis of configurations of computer systems that could be used to diagnose such errors. It then proposes a set of features that such a method must have to be useful. Finally, it concludes that so far, no method meets these requirements.
FRAGAN-VSR: Frame-Recurrent Attention Generative Adversarial Network for Video Super-Resolution

Video super resolution (SR) is an important task, which recovers high-resolution (HR) frames from consecutive low-resolution (LR) couterparts. The most advanced works achieved good performance to this day. However, most of them has largely focussed on making a breakthrough in accuracy and speed, which has neglect that how to recover the finer texture details. Therefore, in this paper, we first present an Video SR model combined generative adversarial network and recurrent neural network (GAN-RNN) structure. It is forced by the self-attention mechanism to pay great attention to the high-frequency information of the LR frames. The perceptual loss is introduced to retain the high-frequency detail which is different from other video SR network. A great deal of evaluations and comparisons with previous methods have confirmed the merits of the proposed framework which can significantly outperform the current state of the art.
Analysis Skin Health Patterns in Highlands Area with Apriori and Bayes Contributions

The skin is a sensitive part of the human body and is most often noticed by humans, especially women. The condition of the skin itself varies significantly for each person. This is due to the influence of age, profession, both those who spend a lot of time in the field or who spend a lot of time in the AC room, as well as the domicile of the area where they live. This study is devoted to analyzing the skin health patterns of people in highland areas, where highland areas are often in hot weather conditions but cold and humid air and strong winds. This dramatically affects the state of the skin. Researchers are trying to educate the people of the highlands that with the environmental conditions in the highlands, the community must be more active in taking care of their skin. The analysis was carried out using a priori and Bayes algorithms. Where a priori will form a pattern of rules from the results of the data obtained, and Bayes will calculate the value of confidence or the value of trust from the regulations that are formed. After the analysis was carried out, one of the rules was that people with an adult age of 20 to 60 years who lived in Tiga Arrow Village with a profession as a farmer and had a moisturizer level on their skin <30%, then the skin condition tended to be very dry with a group of confidence, namely the confidence value. By 80.6% with the support of 68.3% of the total data.
ANFIS Identification applied to a reservoir level liquid system

This work applies a methodology for identification of a double tank system with conic shape by using an Adaptive-network-based fuzzy inference system (ANFIS). Considering that the cross section of the tank is no longer constant, it rather changes with the height of the liquid, a nonlinear modelling method becomes more suitable. A comparison is made with the phenomenological model, which shows that such approach does not show appropriate adherence to the data collected from the system, even for the nonlinear model obtained from geometry. ANFIS on the other hand, presents a considerable improved fitness as it captures far more modes of the system, being tested for five different regression vector structures and for three evaluation criteria, namely root mean square error (RMSE), the adjusted determination coefficient $R^{2}_{a\, j}$ and Akaike Information Criterion (AIC). A trade off between complexity and fitness was used to choose the ANFIS model 3 with ${RMSE}=0.0702, R^{2}_{aj} \quad =0.7135$ and ${AIC}=2147.2974$ for the validation collected data. The findings show the effectiveness of the presented methodology herein.
A Data Set for US Horror Film Trailers

Abstract This article presents a new data set comprising audio, colour, motion, and shot length data of trailers for the fifty highest grossing horror films at the US box office from 2011 to 2015. This data set is one of the few available for computational film analysis that includes data on multiple elements of film style and is the only existing data set for motion picture trailers suitable for formal analyses. Data is stored in csv files available under a Creative Commons Attribution 4.0 International license on Zenodo: www.doi.org/10.5281/zenodo.4479068 .
An Overabundance of Signifiers: Derridean <i>Play</i> in Hans-Joachim Hespos’ <i>Weiβschatten</i>

An interwoven reading of Hans Joachim Hespos’ Weiβschatten and Jacques Derrida’s ‘Structure, Sign, and Play in the Discourse of the Human Sciences’ (Writing and Difference, 1967). I examine the impact that improvisation has on performance and consider how an information overload creates shifting layers of reference. I explore the ways in which Hespos combines information systems, and infinite, or ‘overabundant’, chains of signifiers, which allow the space for collaboration and performer agency, thereby keeping the performer/composer/audience interface in constant movement.
A Novel approach to identify Build Failure Causes involving Machine Learning Techniques

One of the important tasks in text mining is Text Categorization for classification of data. Text classification plays an important role to identify errors among software builds. There are several different supervised learning methods for text classification and popular among them is Naïve Bayes. For the selected features assigning the weight is an important task as it directly impacts the strength of the model designed. In the weight calculation approach instead of having binary formula in assigning the values for the features, calculating and assigning the weight to the attributes in a distributed manner provides more dividend in not only strengthening the model but also improves accuracy in classification. In our proposed approach we have not only extracted the required features but also sub-features are identified based on Natural Language Processing (NLP). The weight is calculated for a Feature by distributing the weight evenly across the sub conditions. Threshold value is set for each sub condition and the weight is summated based on the passing condition of each sub condition for a feature. Our approach when compared to existing approaches provides more accuracy while classification.
3D Reconstruction of Carotid Artery from Ultrasound Images

3D reconstruction is an important area in computer vision, which can be applied to assist in medical diagnosis. Compared to observing 2D ultrasound images, 3D models are more suitable for diagnostic interpretation. In this paper, we describe an approach for 3D reconstruction of the carotid artery utilizing ultrasound images from the transverse and longitudinal views. We implement a human-computer interface to ensure the accuracy of the segmentation results by involving superpixels and ellipse fitting techniques. This approach is expected to achieve better accuracy to assist diagnostics in the future.
Memory in Terms of Logic and Semiotics

The phenomenon of memory is considered as a component implicitly present in the process of information communication. A short typology of memory kinds that form a referential field around so-called semantic memory is given. Through the approaches of Yu. Lotman and R. Jacobson, the classical notion of time is considered through the relationship "Past-Present-Future", which is closely related to the problems of memory. The focus is on how could the memory be considered within logic and by means of logic? As one of the ways of solving the point, to apply the tools of tense logic to the problem through the analysis of the works of Anatoly Ishmuratov (1946–2017), a prominent Ukrainian logician, is proposed. The classifications of the tenses by O. Jespersen, H. Reichenbach, W. Bull are considered. The subjective and objective axes of orientation, which form the basis of calendars, charts and scales, as fragments of time, are analyzed. According to Ishmuratov, these instruments – schemes, diagrams, etc. can be considered languages. The possibility of language objectification of meaning determines the relationship of logical inference, and thus the structuring of semantic areas, which are memories, by means the language of logic and in accordance with its structures. Through the study of logical and cognitive conditions of action, A. Ishmuratov continued to develop ideas of tense logic and their application to explain the psychological perception of objective time. He construed a scheme of semantic connections of memory as a mental act that reproduces the life path of the individual; he distinguished between memories and "pseudo-memories", which together influence the reassessment of past events, shape the individual`s experience and his ability to construe alternatives to the future and reasoning about it. A special place in A. Ishmuratov's research is occupied by his explication of temporal three-valued logic and application of temporal modalities to the analysis of so-called transient states. Further study of such approaches could help to make sense of rational explication of memory, testimonies, reminiscences of past events and give interesting results.
Forensic Handwriting Identification System for the Arabic Language Based on Stationary Wavelet Transform (SWT) Fusion Technique

Forensic-handwriting text analysis aims to link two similar texts, an input text and a stored handwritten text, from a specific suspect using distinctive features, such as motion, hand pressure, and character shape. There are various forensic handwriting text analysis systems that aim to analyze English handwritten text. However, scarcely any of them are proposed to analyze the Arabic handwritten texts. Hence, the examiners or inspectors are forced to manually analyze the handwritten text. This can be tedious and time-consuming for inspectors. This study proposes an offline multistage forensic handwriting identification system for the Arabic language based on Stationary Wavelet Transform (SWT) Fusion Technique to facilitate and reduce the time required for the inspectors or forensic examiners to find similarities in handwritten texts. The proposed system has four main processes: Normalization and preprocessing feature extraction using Truncated Singular Value Decomposition (TSVD), Sparse Random Projection (SRP), and feature fusion using SWT, recognition using Polynomial, Linear and Gaussian SVM classifiers. The accuracy of the proposed system is evaluated using the IFN/ENIT dataset of handwritten Arabic text using Polynomial, Linear, and Gaussian SVM classifiers. Moreover, the accuracy result of the proposed system is compared with the accuracy result produced by a state-of-the-art HATRS, which is based on Local Binary Pattern and SVM classifiers using several normalization sizes of Arabic text images. The experiential result shows the effectiveness of the proposed system compared to the HATRS model. The best classification accuracy of the proposed system (98.83%) is obtained using the Gaussian SVM classifier.
Pressure stress modeling on expanded polystyrene materials using genetic programming

Abstract In the production of expanded polystyrene, the standards are very high in terms of thermal, fire, dimensional, and mechanical characteristics, because each of the characteristics is a condition for achieving quality that allows competitiveness in the market. To ensure high-quality products, it is necessary to achieve optimal performance and product quality through carefully adjusted input parameters of production. Since the production of expanded polystyrene is specific in several ways, an experimental study was conducted in which the basic parameters affecting product quality were detected and through which a series of experiments were performed to prove product quality. Experimental research for this work was conducted on three types of expanded polystyrene samples whose purpose is to insulate floors exposed to pressure. The samples were made of the same material of different densities and aging times for which the pressure stress at a deformation of 10% was tested. After the experimental phase, the modeling of the output parameters was performed. Modeling involved the development of a model that describes a given problem and the obtained modeled values were analyzed and compared with the experimental one. The modeling method used genetic programming using the GPdotNET software package. The goal of modeling with the GpdotNET tool is to obtain a realistic model that would give the value of the compression stress at a deformation of 10% as an output variable in materials made of expanded polystyrene.
Comparison of Siamese neural network architectures in the problem of similar code sequences search

The paper considers the problem of finding similar code sequences (functions) in executable files. Within the framework of the previously proposed original search method based on the use of a Siamese neural network, the influence on its efficiency of several types of the neural network architecture is investigated. The results of experimental studies of the similar code sequences search efficiency for each of them are presented.
The technology of organising the search activity of students of a technical university for the analysis of solutions to applied mathematical problems

The changes taking place in modern society and production naturally have an impact on the education system, making new requirements for it, which are associated not only with the professional qualities of a university graduate, but also with personal ones. These requirements are fixed in the federal state educational standards of higher education in the form of universal and professional competences. One of the directions that will contribute to the formation of personal qualities of a graduate is related to the training of students in search activities. We consider the issues of organising the search activity of students of a technical university in the process of studying higher mathematics. To do this, we have developed a technology for organising students' search activity based on the analysis of solutions to applied mathematical problems. We have identified three stages in the developed technology – preparatory, basic, and final. The goal of each stage is formulated both from the position of the student and from the position of the pedagogue. Also in the article we show an example of the practical implementation of the developed technology of organising the search activity of students of a technical university for the analysis of solutions to applied mathematical problems, which are reduced to calculating a certain integral. The proposed technology of organising students' search activities contributes to the development of their communication skills – students gain experience in preparing a report, presentation, public speaking, learn to answer additional questions, defend and argue their point of view.
Review on de Bruijn shapes in one, two and three dimensions

Abstract Working with ever growing datasets may be a time consuming and resource exhausting task. In order to try and process the corresponding items within those datasets in an optimal way, de Bruijn sequences may be an interesting option due to their special characteristics, allowing to visit all possible combinations of data exactly once. Such sequences are unidimensional, although the same principle may be extended to involve more dimensions, such as de Bruijn tori for bidimensional patterns, or de Bruijn hypertori for tridimensional patterns, even though those might be further expanded up to infinite dimensions. In this context, the main features of all those de Bruijn shapes are going to be exposed, along with some particular instances, which may be useful in pattern location in one, two and three dimensions.
Intelligent control of electric automation instrumentation based on blockchain

Abstract With the development of society and the continuous improvement of residents' living standards, residents have higher and higher requirements for the technical level of power system. This paper mainly studies how to apply intelligent technology to the research of instrument electrical control. Nowadays, the traditional electrical instruments cannot meet the needs of the public, so the research on intelligent instruments is the most important problem at present. This article uses the RSA public key encryption algorithm based on the public blockchain. In the experimental results of this article, big data shows that from 2016 to mid‐2017, there were 50 merchants using non‐smart technologies in electrified dashboards in 2016. There are 40 merchants using smart technology in electrified dashboards, which is lower than the number of non‐smart control technologies. However, in 2017, the number of merchants using non‐intelligent control was 35, and by November 2017, it was 120, a decrease of 5% from 2016. The number of merchants using smart technology in electrified dashboards is increasing every month, from 45 in January 2016 to 285 in November 2017, an increase of 13% in the same year. This shows that intelligent control is more and more widely used in electrical automation instrument panels. Therefore, the research of a more intelligent control system of electrical automation instruments is a problem that has attracted much attention today.
HLS-centric DSE and Optimization for Dynamically Reconfigurable Elliptic Curve Cryptography (ReCC)

Asymmetric cryptography is frequently used for key exchanges and signatures in today's secure data transmissions, for instance, Elliptic Curve Cryptography (ECC). This paper describes our ECC-based cryptographic algorithms implementation on a low-end All Programmable System-on-Chip (APSoC) imposed upon its Dynamic Partial Reconfiguration (DPR) due to the target platform's limited resource. Our asymmetric cryptosystem is based on elliptic curves defined over prime fields and utilizes a dynamic change of multiple key lengths. High-Level Synthesis (HLS) is the basis of our Design Space Exploration (DSE) and hardware implementation. The design is adapted to be algorithmically robust against numerous existing types of attacks. Referring to various possible key lengths, the provided modes are 192, 256, 384, and 512 bits during the design time. An optimized result in design time shows an implementation of the key length of 512 bits is inapplicable due to FPGA resources' plethora in our targeted APSoC. An optimized design is implemented and compared to particularly related works. Indeed, DPR usage brings an advantage that resources can be reused for ECC with various key lengths, other implementable crypto algorithms, or non-crypto designs.
Attention Residual Connection Based Graph Convolution Networks for Skeleton-based Action Recognition

In the human skeleton-based action recognition area, most researches aim to extract and fuse the spatial and temporal information by exploiting Graph Convolution Networks (GCNs), which only consider the information propagation of nodes in small neighborhoods, ignoring the impact on the global graph. Therefore, the actions with complex spatial-temporal relationships are not effectively expressed. In this paper, we propose a novel Attention Residual Connection Based Graph Convolution Networks (ARGCNs) to further integrate spatial-temporal features of the global graph with graph attention mechanism and adaptive feature enhancement. Graph attention mechanism focus on identify the key points of every action, since a few joints are more critical than others of the whole body in some action. As the residual connection of ARGCNs, the graph attention mechanism can also increase the flexibility and generalization of model. Experiments demonstrate that the proposed model is effective, and is competitive compared with state-of-the-art methods.