Reviewerly/exHarmony huggingface dataset:
What drives success in models predicting heart failure outcome?

Abstract Introduction The development of acute heart failure (AHF) is a critical decision-point in the natural history of heart failure and carries a dismal prognosis. The lack of appropriate risk-stratification tools for AHF patients limits physician ability to precisely tailor patient-specific therapy regimen at this important juncture. Machine learning (ML) based strategies may enhance risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In this study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored ML-based prediction model for real-time decision support. Methods We used a cohort of all AHF patients admitted during a 12 years period including 10,868 patients. A total of 372 covariates were collected from admission to the end of the hospitalization (demographics, lab-tests, medical therapies, echocardiographic and administrative data). Data preprocessing included features cleaning, train-test split, imputation and normalization. We assessed model performance across two axes (1) type of prediction method and (2) type and number of covariates. The primary outcome was one-year survival from hospital discharge. For the model-type axis we experimented with seven different methods: Logistic Regression (LR), Random Forest (RF), Cox model (Cox), XGBoost, a deep neural-network (NeuralNet) and an ensembled model. Results Data pre-processing methodology combined with multiple-covariates achieved an out-ofsample AUROC prediction accuracy of more than 80% with almost all prediction models: L1/L2-LR (80.4%/80.3%); Cox (80.1%); XGBoost (80.7%); NeuralNet (80.5%). The number of covariates was a significant modifier of prediction success (p&amp;lt;0.001), the use of multiple-covariates (372) performed better (AUROC 80.4% for L1-LR) compared with using only a set of known clinical covariates (AUROC 77.8%). Conclusions The choice of the predictive modeling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored, risk prediction in AHF. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Yad Hanadiv
Time efficiency and mistake rates for online learning algorithms : A comparison between Online Gradient Descent and Second Order Perceptron algorithm and their performance on two different data sets

This dissertation investigates the differences between two different online learning algorithms: Online Gradient Descent (OGD) and Second-Order Perceptron (SOP) algorithm, and how well they perform ...
Convolutional Neural Network Based Classification of Benign and Malignant Tumors from Breast Ultrasound Images

The widely used method for diagnosing the breast cancer is a Breast ultrasound (BUS) imaging, but the interpretationwill be vary based upon the experience of radiologist. Now a days CAD systems are available to provide the informationregarding BUS image classification. However, most of the CAD systems was based upon handcrafted features. Whichare designed for classifying the tumors. Therefore, the capability of these features will decide the CAD system accuracywhich is used for classifying the tumors as benign and malignant. With the use of Convolutional Neural Network(CNN) technology, we can improve the classification of BUS images. Because it provides a new approach for classificationand generalizable image representations thereby we can get best accuracy as a result. But, the database of BUSimage having small size so it might be restricted due to that CNNs cannot trained from scratch. To overwhelmed thisdrawback, we examine the use transfer learning approach, for enabling the CNN approach to achieve best accuracyregarding BUS image classification. The final results of VGG16_TL methodology beats the AlexNet_TL. And the finalresults indicate VGG16_TL with accuracy, sensitivity, specificity, precision and F1 values of 88.23%, 88.89%, 88.89,90% and 88.2% respectively. Therefore, we can say that possibility of pre-trained CNN models achieves good accuracyin BUS image classification.
Automatic Speech Recognition System for Somali in the interest of reducing Maternal Morbidity and Mortality.

Developing an Automatic Speech Recognition (ASR) system for the Somali language, though not novel, is not actively explored; hence there has been no success in a model for conversational speech. Ne ...
The Design of an Unmanned Aerial Vehicle

This paper revolves around the game-plan, appearing, usage and testing of a programmed automated airborne vehicle. The regulator depends on a got together with mechanical assembly to switch control between the RC control and the autopilot control (that is the multiplexer/shield, for the most part called MUX).This control course (following GPS waypoints) and height by controlling the rudder and choke. The structure utilizes flight adjustment structure (co-pilot), a sensor pack, Global Positioning System(GPS) and a RF handset to screen and report tremendous boundaries, for example, rise, speed ,pitch, roll, and position. A shown programming assessment has been made to pull in the aeronautical vehicle achieve the necessary self-affiliation and keep up commendable flight activity.
A tool for automatic reasoning over decision documentation using gUFO

This paper proposes a method for Decision Making Documentation that is founded on a formal ontology. To do this, we first proposes an extension to DMO, a decision making ontology founded on a foundational ontology named UFO, to include concepts on on Value Proposition and Economic Preference. [Guizzardi et al. 2020] Finally, we propose a template whose slots represent concepts in the Extended DMO and should be filled with sentences in natural language representing instances of these concepts. The goal of the template is to represent the rationale in the selection alternative phase of the Decision Making. Once the slots are filled, they can be converted to an OWL ontology using gUFO, a lightweight implementation of the Unified Foundational Ontology (UFO), suitable for Semantic Web OWL 2 DL applications, to enable automatic reasoning over the document.
Open science and translation

Cybergeo has been mobilized since its creation in 1996 in the worldwide Open Science movement, which did not yet bear its name. The journal has worked continuously for internationalization, through its expert committees, the publication in all European languages and the choice of an interoperable technology that facilitates dissemination in the world's scientific search engines. It also uses open-access applications that it has developed in partnership with OpenEdition, with Lodel for the pub...
Identifying Fraudulent Data and Fake Account Profiles Analysis in Examination for Providing Security in Online Dating Site

In this research, we are focused on a new yet mysterious method of using malicious apps. You want to be software for socialization. Their main goal is to allow users to buy premium / VIP packages and Start talking to (probably false) other app accounts. False dating apps are called such systems. A comprehensive analysis to explain the whole scam dating apps ecosystem. In specific, we suggested a three-stage approach to defining the current account profiles and the ensuing analysis to evaluate their roles. Our research shows that most accounts are administered chat bots based on predefined talk models rather than real people. They further examine the business model for these applications to prove that a number of parties actually participate in the process; including developers who create software, producers selling lucrative apps and the distribution system that distributes applications to end users. Ultimately, we assess their effect on consumers (i.e. victims) and quantify total income. The results show the need of a remedy to secure consumers and this is the first systematic study on fake citation applications. Keywords: Malicious, Fraudulent Dating Apps, Google Analytics, chatbot, online dating site Cite this Article: Nalli Vinaya Kumari, K. Kavya, PV. Srikar, B. Gowtham Kumar Reddy. Identifying Fraudulent Data and Fake Account Profiles Analysis in Examination for Providing Security in Online Dating Site. Network for Iranians. Recent Trends in Parallel Computing. 2020; 7 (1): 12–17p.
Designing for Serendipity: Research Data Curation in Topic Spaces

Author(s): Lafia, Sara Katherine | Advisor(s): Kuhn, Werner | Abstract: Researchers seeking relevant data across disciplines confront the challenge of navigating technical descriptions. How can curation support the serendipitous discovery of related research data? Everyday spaces like bookshelves are designed to support browsing and exploration by placing similar resources closer together. Space and time are foundational ordering relations for knowledge organization. I ask how this ordering, which is well-established in the geographic context, can be translated to locate and organize research data in abstract topic spaces. This dissertation develops methods for making the latent topics of research metadata explicit. These methods produce spatial configurations where related research topics are co-located in neighborhoods. This has the potential to support serendipitous discovery by offering researchers ways to discover related data. I test this notion in three studies that develop topic spaces for research data curation. The first part of this dissertation in Chapter 2 focuses on supporting research data discovery with a common terminology. I develop a crosscutting base vocabulary of geospatial topics to help users discover related government data in a ubiquitous open civic data platform. Semantic annotation expands search terms by mapping users’ vernacular onto the language of metadata. In the second part of this dissertation, I shift away from addressing terminological search to supporting spatial curation by developing topic spaces. In Chapter 3, I develop two kinds of topic spaces for curating research theses and dissertations: landscapes and networks. I use topic modeling to determine the latent semantic similarity of research metadata and then produce topic spaces from these using spatialization techniques. In Chapter 4, I spatialize an institute’s multidisciplinary body of research, producing topic maps at two distinct levels of detail. Emerging spatial patterns, like centrality and proximity, support high-level narratives about cross-disciplinary research activities that complement the quantitative metrics currently cited in reviews of institutional research. Together, these three studies demonstrate strategies for developing topic spaces in which diverse, yet related, multidisciplinary research data are curated. Future research will extend these methods by tracing the impact of specific curatorial actions contributing to research data discovery and reuse.
Research Guides: Welcome to Using Primo: Searching the library with Primo

A resource guide for students and faculty about the transition to the Primo VE discovery service.
Photonic Weight Banks based on Multimode Fiber for Neuromorphic Networks

We demonstrate a photonic weight bank for neuromorphic computing by using multimode fibers. Weights control for multiple inputs of a photonic neuron are achieved by tuning the coupling power to the modes in multimode fibers.
Sentiment Analysis Provider By.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method

Provider By.U is a relatively new and attractive telecommunications service with claims to be the first digital provider in Indonesia. All services are done digitally with the By.U application that offers convenience. Even so not all users are satisfied with the service, there are criticisms and suggestions, one of which is delivered through the By.U app review feature on the Google Play Store. Sentiment analysis is performed to extract information related to provider by.U. The steps taken are scrapping review data, positive and negative labeling, preprocessing data including data cleaning, data normalization, stopword removal and negation handling, sentiment classification using Support Vector Machine (SVM) and TF-IDF as feature extraction. TF-IDF+SVM with 5-Fold Validation produces pretty good accuracy with an average accuracy of 84.7%, precision of 84.9%, recall of 84.7%, and f-measure of 84.8%. The highest accuracy results in fold 2, 86.1%. The effect of TF-IDF on the measurement of model performance is not so great, but it is better.
Enterprise strategic shift of technology: cloud-based systems verses traditional distributed system

Cloud computing can be perceived as an amalgamation of technology, facilitator, source and as an application that raised the curtain for a shift in the global data management system. There is a need for more research and promotion to encourage the laggards towards this revolution. The present research is such a trial to explain laggards about the road map, framework and possible benefits of the strategic shift from traditional or distributed systems to cloud-based systems. The research work is mainly carried out to facilitate optimal information technology (IT) services using cloud-based technology for a medium-sized multi-national company. The study tool, a questionnaire, is shared with 250 respondents of different cadres in the organisation based on 28 countries worldwide. To explain the necessity of this shift, the study collected the version of the employees regarding their favouritism for cloud technology adoption (CA) as well as the need for user training (UT) for practicing cloud adoption.
An agenda for the future

As we have seen in this book, the theory of belief functions is a modelling language for representing and combining elementary items of evidence, which do not necessarily come in the form of sharp statements, with the goal of maintaining a mathematical representation of an agent’s beliefs about those aspects of the world which the agent is unable to predict with reasonable certainty.
Visual-geometric Representation of Theorem Proofs in Nomotex DLS

The paper examines the features of visual thinking, the authors note the importance of visual examples in modern engineering education. The article describes a methodological approach to presenting the course, based on visual theorem proofs. An example is considered the course “Theory of multivariable function” Visual examples were created at the Department of Computational Mathematics and Mathematical Physics of the Bauman Moscow State Technical University. The proposed methodological approach to the development of the course “Theory of multivariable function” allows you to use the elements of the created course in various variations for disciplines using the concept of functions of many variables, depending on the direction of training (specialty).
On the Construction of Distance Education Resources and Platform Based on Cloud Computing

With the emergence of cloud computing, a new network technology, the Internet has entered the “cloud” era of resource-based to service-oriented transformation. As an important part of China’s education system structure, modern distance education relies on computer network as the basis, so it will also meet new challenges. Cloud computing brings new ideas to platform construction and resource integration in distance education, and provides greater help for distance education with service as the core concept.
Practice-oriented technologies as a means of forming communicative competences of students

This research is devoted to the consideration and study of the system of practice-oriented technologies as the main tools for the formation of communicative competencies of modern university students. The essence and specificity of both practice-oriented technologies and communicative competencies are considered, which allows to carry out the most complete and objective analysis of this topic in future. The object of the research is precisely in the specifics of the formation of such competencies, which is complicated by the current problems in the field of professional training of students. The role of individual elements of practice-oriented technologies in the system of forming of the professional culture of modern specialists is comprehended and actualized. In the course of the study, the authors identified the key method that laid the foundation for this work, namely the questionnaire method. The topic of the survey was to identify the role of practice-oriented technologies as key means of forming the communicative competencies of modern students. The respondents were the teaching staff of several leading universities of the Republic of Crimea. In our opinion, the results of such a questionnaire and of this study as a whole can serve as an important foundation for further research on this topic, since they are fully reflected in the form of a comprehensive conclusion, which is based primarily on the analytical and reflective activities of modern teachers. The results of the study can be found in the final part of it.
Unsupervised Learning and Recommendation Algorithms

This chapter will explore popular techniques used in the industry to handle unlabeled data. You will discover how you can implement them using PySpark. You will also be introduced to the basics of segmentation and recommendation algorithms and the data preparation involved therein. By end of this chapter, you will have an appreciation for unsupervised techniques and their use in day-to-day data science activities.
Understanding and Predicting Academic Performance Through Cloud Computing Adoption

The purpose of this study is to investigate the impact of cloud computing adaptation on academic performance and how cloud computing technology directly and indirectly impacting or facilitating the learning environment for students. The research supports the TAM (Technology Acceptance Model) Theory. Data was collected by using an electronic web-based questionnaire completed by 500 online respondents. For data collection, we have to use Partial Least Square (PLS), and Statistical Package examined data for Social Science (SPSS). In which three tests that are Reliability Test, Factor Analysis, and Regression Analyses method were used to analyze the priorities of explanation. A quantitative approach questionnaire has been used to collect the data from online respondents of Karachi. The research shows that cloud computing technology plays a crucial role in the e-learning field. Not only it increases efficiency in academic activities, but it also helps to work effectively. Within no time, students can share, store, and transfer their data information through various electronic devices. This study is initiated to investigate the cloud computing effects on academic activities or e-learning environment. The limitations of this research are sample size, which we have received is 500, which is limited for our study. In the present study, it was unable to approach to all the students of the university as the recent research is conducted only in Karachi city. The survey was undertaken solely by concentrating on how cloud computing affects academic performance. Still, the analysis can also be performed on how cloud computing technology can change the business sector or the corporate sector. We can focus on other variables as well in future researches.
A Click-Through Rate Prediction Algorithm Based on Real-Time Advertising Data Logs

Advertisement is one of the ways merchants attract consumers and enhance their influence. In recent years, under the big picture of smart city and smart life, the advertising business has shown a trend of specialization. It has always been a tough problem for advertisers of how to show potential consumers in real time the advertisements in a precise way. This passage reveals an experiment that alters the logistic regression function model and fits the data logs that users produce in order to forecast the possibility that the user clicks the real-time advertisement designed for him/her. The results of the experiment show that compared to traditional advertising, such altering improves the effectiveness of precise advertising, and has the edge of shorter operating time against other models based on deep learning. An aspect to improve is the comparatively lower accuracy of forecast. Such results indicate an alternative for certain small and medium enterprises to lower their advertising costs.
Using Big Data for Clinical Decision Making

Abstract The current workforce is ill prepared for the rise in Americans 65 and older from 46.3 million in 2010 to 98.2 million by 2050, a national increase of 112.2 % accompanied by increasing chronic conditions. The increase in older Americans, the prevalence of those with dementia, accompanied by behavioral symptoms of dementia (BSD) is increasing. Innovative technology may alert health providers to early signs of decline in frail older adults with multiple chronic conditions. Remote monitoring in the home and community living spaces can address complex care needs for older adults. Monitoring may identify and predict deviations in a person’s daily routine that herald a change in a chronic condition. We present two examples that can potentially assist in clinical decision making. The first exemplar used 24/7 sensor data to identify changes, potentially clinically significant, such that early intervention may prevent hospitalizations; the second exemplar presents the use of pattern recognition software (THEME TM) for temporal pattern analysis, to identify and quantify behavior patterns with regard to intensity, frequency and complexity, such that interventions may be individually tailored and timed. Clinical researchers and technology developers need to collaborate early in the process to consider the sources and frequency of clinical measures for meaningful predictions. One major challenge lies in the interpretation of the vast amounts of within individual data. Our insights strive to improve future interdisciplinary development of monitoring systems to support aging in place and support clinical decisions for timely and effective care for frail older adults.
MateBot: The Design of a Human-Like, Context-Sensitive Virtual Bot for Harmonious Human-Computer Interaction

The virtual bot is one of the hot topics in artificial intelligence, where most of the current studies focus on chatbots. Nevertheless, the context-sensitive virtual bot, especially with rich human-like interactions (e.g., appearance change, context-aware narration/recommendation) regarding the ambient changes (e.g., location, focused scene) through various built-in sensors, would have broader application. Towards this direction, we propose MateBot, a human-like, context-sensitive virtual bot, which supports harmonious human-computer interaction on smartphones. The design of MateBot consists of three parts. First, a context sensing network is used to recognize the input background information and face information, and modify the appearance of the virtual bot through the conversion of the encoding network. Second, a human-like bot appearance generation network can generate a virtual bot image with a human-like appearance through the GAN network and modify the appearance of the virtual bot with context-sensitive information. Third, a personalized conversation network is devised to communicate with human users. Furthermore, we apply MateBot to the intelligent travel scenario to justify its practicality, and the experiment results show that the bot can better increase the user’s sense of substitution and improve the communication efficiency between human users and virtual bots.