Reviewerly/exHarmony huggingface dataset:
Machine learning for risk prediction of in-hospital mortality in adult cardiac surgery

Abstract Background Risk prediction plays a central role in clinical decision-making for patients undergoing cardiac surgery. The logistic EuroSCORE has demonstrated a dangerous calibration drift with the changing patient case-mix, resulting in a significant overestimation of mortality and risk-averse practice. Despite these limitations, it continues being used in the United Kingdom due to a lack of alternative validated models. It is urgent to find a replacement for EuroSCORE with a better calibrated prediction model. Machine learning models are increasingly used for risk prediction in medicine due to their potential of overcoming limitations of regression models. Precisely quantifying the risk of in-hospital mortality may better inform patient-centred decision-making and direct targeted quality improvement interventions. Methods This is a retrospective monocentric cohort study using prospectively collected fully anonymised data from the National Adult Cardiac Surgery Audit database, restricted to patients undergoing adult cardiac surgery at our institute from 1996 to 2017 (n=28,761). The aim was to develop a predictive model with improved discriminatory power and calibration using machine learning methods. Model calibration was assessed using the calibration belt method. Discrimination power of each model (area under the receiver operating characteristic curve [AUC]) was compared with the logistic EuroSCORE using the De Long's test. Results A time series of the observed:expected (O:E) ratio for the logistic EuroSCORE showed a linear decrease with a slope of −7.4x10–3. The calibration belt showed a significant risk overestimation across all risk categories (p&amp;lt;0.001). Model discrimination was excellent over time, with a marginal but significant linear trend in reducing the AUC (p=0.03). Although miscalibration was detected for all models (p&amp;lt;0.05), neural network achieved the best calibration with a test statistic of 13.3, followed by logistic regression (18.0), and EuroSCORE (228.7). The neural network achieved the highest AUC (0.82, 95% CI 0.78–0.85) of all models and was marginally non-significantly higher than that of the logistic EuroSCORE (0.79, 95% CI 0.75–0.83, p=0.056). Conclusion Our neural network model of cardiac surgery in-hospital mortality achieves slightly improved discriminatory power and significantly better calibration compared to that of EuroSCORE, making it more appropriate for dealing with the changing patient case-mix. Further model training on larger datasets with larger demographics is necessary. Clinical implementation of such models may reduce risk of overestimation of mortality. Funding Acknowledgement Type of funding source: None
Evaluating Dutch Named Entity Recognition and De-Identification Methods in the Human Resource Domain

The human resource (HR) domain contains various types of privacy-sensitive textual data, such as e-mail correspondence and performance appraisal. Doing research on these documents brings several challenges, one of them anonymisation. In this paper, we evaluate the current Dutch text de-identification methods for the HR domain in three steps. First, by updating one of these methods with the latest named entity recognition (NER) models. The result is that the NER model based on the CoNLL 2002 corpus in combination with the BERTje transformer give the best combination for suppressing persons (recall 0.94) and locations (recall 0.82). For suppressing gender, DEDUCE is performing best (recall 0.53). Second NER evaluation is based on both strict de-identification of entities (a person must be suppressed as a person) and third evaluation on a loose sense of de-identification (no matter what how a person is suppressed, as long it is suppressed).
Comparison of Feedforward and Recurrent Neural Network in Forecasting Chaotic Dynamical System

Artificial neural networks are commonly accepted as a very successful tool for global function approximation. Because of this reason, they are considered as a good approach to forecasting chaotic time series in many studies. For a given time series, the Lyapunov exponent is a good parameter to characterize the series as chaotic or not. In this study, we use three different neural network architectures to test capabilities of the neural network in forecasting time series generated from different dynamical systems. In addition to forecasting time series, using the feedforward neural network with single hidden layer, Lyapunov exponents of the studied systems are forecasted.
Prediksi Understandability Pada CK Metrics Object Oriented Menggunakan Fuzzy Inference System

The ability of the software to easily understand it is very important, to evaluate the performance of a system or process a certain mechanism is needed. So that the achievement level of software development is obtained, the assessment of code quality and design in the metrics have different contributions to the quality properties of software design. The assessment of software characteristics is described in a model based on the Chidamber Kemerer (CK) Entropy Metrics design property indicator. Measure specific properties of the software by mapping them into numbers according to objective and well-defined measurement rules. To be able to help perform the software quality calculation process based on CK metrics on the eclipse equinox data by using the Mamdani fuzzy inference system, pointing the classification into low which can be interpreted as having good quality. And the value affirmation of the deffuzzyfication method in this case is done by using the COA (center of area) method, to determine the crisp x value, obtained from the membership function formed from the composition process of all outputs.
Culture and paralinguistic features ~!^^:-): East meets West in a virtual exchange between South Korea and England

This paper investigates whether cultural differences are apparent in the paralinguistic features used by culturally diverse interactants online. Paralinguistic features are used pervasively in digital discourse (Herring &amp; Androutsopoulos, 2015), therefore they play a pivotal role in online communication skills. Paralinguistic features such as the innovative use of punctuation and typographical features as well as emoticons and emojis are used to add nuance, emotional tone, and to manage discourse in online communication. However, the effectiveness of these paralinguistic features is dependent upon a shared understanding of their functions. This study seeks to explore any potential cultural manifestations in the use of paralinguistic features during a semester-long virtual exchange between 21 South Korean students and 25 students studying at a university in England. The dataset of 20,379 words generated during the virtual exchange was examined for cultural manifestations in paralinguistic features. As this study examines potential cultural manifestations online, it adheres to a culturally relativist perspective, therefore an inductive approach to the analysis of the data was taken. The analysis of the data revealed culturally specific paralinguistic features with the emergence of a feature that, to the best of my knowledge, has not been recorded in previous virtual exchange research.
Tigrigna language spellchecker and correction system for mobile phone devices

This paper presents on the implementation of spellchecker and corrector system in mobile phone devices, such as a smartphone for the low-resourced Tigrigna language. Designing and developing a spell checking for Tigrigna language is a challenging task. Tigrigna script has more than 32 base letters with seven vowels each. Every first letter has six suffixes. Word formation in Tigrigna depends mainly on root-and-pattern morphology and exhibits prefixes, suffixes, and infixes. A few project have been done on Tigrigna spellchecker on desktop application and the nature of Ethiopic characters. However, in this work we have proposed a systems modeling for Tigrigna language spellchecker, detecting and correction: a corpus of 430,379 Tigrigna words has been used. To indication the validity of the spellchecker and corrector model and algorithm designed, a prototype is developed. The experiment is tested and accuracy of the prototype for Tigrigna spellchecker and correction system for mobile phone devices achieved 92%. This experiment result shows clearly that the system model is efficient in spellchecking and correcting relevant suggested correct words and reduces the misspelled input words for writing Tigrigna words on mobile phone devices.
Vehicle detection and tracking for traffic management

&lt;span lang="EN-US"&gt;The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.&lt;/span&gt;
Bat Algorithm with CNN Parameter Tuning for Lung Nodule False Positive Reduction

Lung cancer, an uncontrolled development of abnormal cells in one or both lungs has been one of the primary causes of cancer related deaths worldwide. Detecting it in the earlier stage is the only solution to reduce lung cancer deaths. The most common tests to look for cancerous cells include X-ray, CT scan, Sputum cytology and biopsy test. CT scan is recognized as one of the effective tools in recognizing it in the earlier stage. Detecting the lung nodules (lesions) sometimes seems to be very difficult in Computer Aided Detection (CAD) systems. Because of the fact that the lung nodules have similar contrast with other structure, there might be a chance in generating numerous false positives. The performance of Convolutional Neural Network (CNN) mainly depends on the hyper parameters selected for a problem. The main motive of the proposed work is to use Bat algorithm to optimize the network hyper parameters such as number of filters in convolution layers, number of neurons and filter size in the CNN to enhance the network performance thereby eliminating the requirement of manual search for optimal hyper parameters. The methodology is validated using important performance validation metrics such as accuracy, sensitivity and specificity. The result shows that CNN in conjunction with Bat algorithm provides better results in the classification of nodules and non-nodules with minimal false positive rate.
A study on r-edge regular intuitionistic fuzzy graphs

For an intuitionistic fuzzy set S = {< a, S µ (a), S λ (a) >; a ∈ A} of a universe set A, we discuss and study properties on an intuitionistic fuzzy graph like r-edge regular intuitionistic fuzzy graph and strongly regular intuitionistic fuzzy graph.In this paper, we describe variously related to these graphic's.Also, we have provided many examples to explain these concepts.
BALANCING INNOVATION AND VULNERABILITY: WATER SECURITY IN AN AGE OF CYBER-WARFARE

Through technological and organisational innovations, water services are increasingly finding new pathways to reduce water insecurity.For instance, the real-time detection of water pollution through the deployment of novel sensors can mitigate contamination problems before they become widespread.While these tools open up new forms of water security, they simultaneously create new forms of vulnerability as the connectivity and digitalisation of these infrastructures create remote pathways to control water system behaviours.The cyber-warfare capabilities of state and non-state actors are becoming increasingly sophisticated, and attacks have successfully infiltrated water systems with worrying potential.Indeed, in 2018, the US Department of Homeland Security and Federal Bureau of Investigations highlighted the threat of cyber-attacks from hostile countries on water systems, demonstrating the very real nature of these threats.This paper assesses the vulnerabilities arising from increasing interconnectivity and digitalisation in water infrastructures, paying particular attention to demographics at risk of insecurity.The paper starts by reviewing cyber-warfare practices relating to infrastructure, including their increasing frequency and sophistication.This is overlaid with a current demographic understanding of water insecurity and potential vulnerabilities to cyber-attack to identify what intersectionalities appear as new threats emerge.The paper then explores the necessary structure and value of ethical impact assessments in the design of innovative technology and practices in the water sector.In order to foster sensitivity to vulnerabilities and create avenues for incorporating scalable preventative and mitigating measures into design, a practical framework (in the form of a list of questions) is outlined.This paper finds that our understanding of water insecurity must adapt to the challenges posed by cyber-attacks.Sensitivity to the existence of these threats must be fostered and a practical framework developed to attune stakeholders to cyber-threats and assist those engaged with new technologies in the water sector.
Clustering using kernel entropy principal component analysis and variable kernel estimator

Clustering as unsupervised learning method is the mission of dividing data objects into clusters with common characteristics. In the present paper, we introduce an enhanced technique of the existing EPCA data transformation method. Incorporating the kernel function into the EPCA, the input space can be mapped implicitly into a high-dimensional of feature space. Then, the Shannon’s entropy estimated via the inertia provided by the contribution of every mapped object in data is the key measure to determine the optimal extracted features space. Our proposed method performs very well the clustering algorithm of the fast search of clusters’ centers based on the local densities’ computing. Experimental results disclose that the approach is feasible and efficient on the performance query.
Emotion Recognition in Sentences - A Recurrent Neural Network Approach

This paper presents a Long short-term memory (LSTM) model to automate the tagging of sentences with a dominant emotion. LSTM is a type of artificial recurrent neural network (RNN) architecture. A labeled corpus of news headlinesc [1] and textual descriptions [3] are used to train our model. The data set is annotated for a set of six basic emotions: Joy, Sadness, Fear, Anger, Disgust and Surprise and also takes into consideration a 7 $$^\mathrm{th}$$ emotion, Neutral, to adequately represent sentences with incongruous emotions. Our model takes into account that one sentence can represent a conjunct of emotions and resolves all such conflicts to bring out one dominant emotion that the sentence can be categorized into. Furthermore, this can be extended to categorize an entire paragraph into a particular emotion. Our model gives us an accuracy of 85.63% for the prediction of emotion when trained on the above mentioned data set and an accuracy of 91.6% for the prediction of degree of emotion for a sentence. Additionally, every sentence is associated with a degree of the dominant emotion. One can infer that a degree of emotion means the extent of the emphasis of an emotion. Although, more than one sentence conveys the same emotion, the amount of emphasis of the emotion itself can vary depending on the context. This feature of determining the emphasis of an emotion, that is, degree of an emotion, is also taken care of in our model.
Upaya Meningkatkan Hasil Belajar Melalui Penggunaan Media Power Point pada Pembelajaran Tematik Siswa Kelas III

&lt;p&gt;&lt;em&gt;The purpose of this study is to improve student learning outcomes in thematic learning. This research is a classroom action research (PTK) which is carried out in three cycles, each cycle consisting of planning, implementing, observing, and reflecting stages. The subjects of this study were students of class III SD Negeri Kedawung 02, Kroya District, Cilacap Regency in the academic year 2020/2021, totaling 22 students. Data collection techniques using tests and observations. This study shows that the use of power point media can improve learning outcomes in thematic learning of class III SD Negeri Kedawung 02 in the 2020/2021 academic year as evidenced by the percentage of completeness of learning outcomes in cycle I of 77.3%, cycle II of 90.1% , and cycle III reaches 100%.&lt;/em&gt;&lt;/p&gt;
Modeling and Analyzing Graph Algorithms by Means of Graph Transformation Units.

Graph transformation units are rule-based devices to specify processes on graphs and the dynamics of informationprocessing systems with graphs as states.In this article, we propose graph transformation units as a framework to model graph algorithms, to prove their correctness and to analyze their complexity.A specific emphasis is laid on the improvement of the efficiency of graph algorithms by massive parallelism.
Tracking and Extracting Action Trajectory of Athlete Based on Hierarchical Features

The feature extraction from athlete action images is a research hotspot. To accurately evaluate athlete actions, it is necessary to partition the original image into refined blocks, and extract different levels of image features. However, the traditional feature extraction algorithms can only roughly divide action images into several classes, failing to acquire the accurate feature sets of the actions. This leads to relatively poor performance of feature extraction from action images. To overcome the defect of the traditional methods, this paper puts forward a feature extraction method for the action images of badminton players based on hierarchical features. The underlying image features were analyzed based on the techniques of badminton players, and mapped to the feature space of the corresponding dimension. Simulation results show that the proposed method can accurately extract the features from athlete action images.
Overview on Security Issues in Cloud Computing

Inside the ongoing decade, significant developments in technology have arisen that possibly add more accommodation to everyday life rehearses on an enterprise-level as well as on a singular level also. Cloud Computing technology has seen huge advances in its execution and becomes broadly received by one or the other private or public areas. It was clear as of late that many associations and enterprises are moving their outstanding tasks at hand to the cloud. In any case, Security is a significant worry for the cloud computing administrations, which depends on the Internet association that makes it powerless against numerous kinds of attacks. Despite the fact that that the security estimates executed over cloud computing are building up each spending year, Security still a test. In this paper, we led a review concentrate on cloud computing and tended to various kinds of attacks and potential dangers to this arising technology, just as assurance strategies and existing answers for such attacks.
EFL Students’ Perception on the Use of “Rhetorical Précis” as a Summarizing Template

For university students, a skill in writing a good summary of articles, books and other sources is very important. Rhetorical Précis, a summarizing template that consists of four dense and direct sentences, can be used to compose a precise summary. This research aims at exploring EFL students’ perceptions of the use of Rhetorical Précis. Using a survey study, a five-degree questionnaire was spread out to 47 students who had attended the Critical Reading course at a reputable university in Surabaya, Indonesia. The data from the questionnaire were analyzed quantitatively. In addition, a semi-structured interview was held with four of the students. The interview was recorded and transcribed to meet the need of this study. The findings of this study showed that most of the students had positive perceptions of each element of Rhetorical Précis. In short, the students perceived that the Rhetorical Précis summarizing template could be used by the students to make a summary of the original texts precisely and accurately.
The Relevance of Reading Materials Presentation in Indonesian Textbook for Grade Vii Junior High School: Learner Characteristics Review

This study aims to: (1) identify the presentation of reading material in grade VII Indonesian textbook and (2) describe the relevance of the presentation of reading material in grade VII Indonesian textbook based on the characteristics of the learners.The research approach used is descriptive qualitative.The data used in this study is the presentation of description text in the seventh-grade Indonesian textbook published by Erlangga.The data source of this research was a document, namely Indonesian grade VII Indonesian textbook.The data collection technique of this study used the documentation and listening methods and continued with the note-taking technique.Analysis of the data of this study used the content analysis method.The validity test of the data in this study used the triangulation theory.The results showed that from the 60 reading material data (1) 11 data are not relevant to students because of characteristics relating to initial abilities, such as intellectual ability, thinking ability, and movement ability.(2) 46 data are not relevant to students because of characteristics relating to social-cultural background and status.(3) 3 data are not relevant to students because of characteristics relating to personality differences, such as nature, attitudes, feelings, interests, and so on.This shows the need for material adjustments to the characteristics of students' abilities, social background characteristics, and personality characteristics.The preparation of textbooks in the future should pay attention to intellectual, sociological, and psychological characteristics of students.
Learning Multimodal Word Representations by Explicitly Embedding Syntactic and Phonetic Information

Word embedding (i.e., word representation) transforms words into computable mathematical expressions (usually vectors) according to semantics. Compared with human semantic representation, these purely text-based models are severely deficient because they lack perceptual information attached to the physical world. This observation promotes the development of multimodal word representation models. Multimodal models have been proven to outperform text-based models on learning semantic word representations, and almost all previous multimodal models only focus on introducing perceptual information. However, it is obvious that syntactic information can effectively improve the performance of multimodal models on downstream tasks. Therefore, this article proposes an effective multimodal word representation model that uses two gate mechanisms to explicitly embed syntactic and phonetic information into multimodal representations and uses supervised learning to train the model. We select Chinese and English as examples and evaluate the model using several downstream tasks. The results show that our approach outperforms the existing models. We have made the source code of the model available to encourage reproducible research.