Reviewerly/exHarmony huggingface dataset:
Time-Padded Random Forest Ensemble to Capture Changes in Physiology Leading to Sepsis Development

Background: Sepsis is the leading cause of in-hospital deaths, and it is one of the costliest complications to treat.Detection of sepsis is complicated and not yet efficient.Each hour of delay in treatment for a septic patient results in a 4-8% increase in chance of mortality.Method: The dataset provided consists of files that contain hourly parameter measurements for over 40,000 unique patients.Due to the complex nature of this challenge problem, a model of similar complexity was necessary.A boosted random forest ensemble was chosen and developed in MATLAB in hopes of producing the best results for this challenge.The provided data was time padded for 8 additional hours' worth of data, 10-fold cross-validated, and imputed with previous data.Many ensemble methods were tested with Random Under-Sampling Boosting performing the best.For this model, the hyper-parameters were optimized via a grid search to find an optimal model.Results: Using the optimized hyper-parameters along with the correct pre-processing techniques, a 10-fold average utility score of 0.421 was achieved on the training sets A and B combined.We participated in Physionet Challenge under the name SOS: Searching of Sepsis and the utility score on full test set is 0.314.Our official rank is #14.
Artificial Intelligence – A Revolutionizing Factor in E-Commerce

The future of industries is currently more dependent upon its presence on online platforms. This online presence not only visualizes, promotes, or advertises your brand but also helps in gaining huge customers. Also, customers are now gradually turning their interests towards online shopping which are easier, time-saving, and more personalized compared to the conventional practice of visiting physical stores. So one of the most popularizing and crucial tools used by e-commerce brands to attract people is through their artificial intelligence services. AI is constantly changing and updating the world of e-commerce in terms of its customer service and experience. Effective utilization of AI can aid in identifying concealed insights, trend forecasting, and beneficial financial decision making. AI has influenced the traditional way of replenishment and merchandising by simply using data analytics to indicate which product has to be replenished and which has to be discounted. According to a recent report of "Business Insider" predicts that about 85% of the customer services will be handled by AI-powered bots which can immediately respond to calls, chats, and emails with almost no human intervention. This paper encompasses the various AI tools empowered by the e-retail brands to attract their customers, the various ways by which AI influences both the retailer and the customer, and successful e-retail brands that employed AI for their advancement. In addition to this, the paper discusses how AI is going to dominate the e-commerce venture in the near future.
A Blockchain and Internet of Things Based Architecture Design for Energy Transaction

Recently, the paradigm of the electric power industry has been digitized, focusing on renewable energy.While the transition to energyinformation and communications technologies (ICT) convergence is accelerating, there will be many changes in energy policy in the future.In particular, in terms of energy demand management, self-sustaining decentralization will be centered around the region, and the virtual power plant (in the form of a micro-grid) will be constructed on a regional basis according to the activation of distributed energy resources (DERs), including renewable energy.For this, business models such as the electricity trading brokerage market should also be considered.In this paper, we design an energy blockchain transaction structure that combines blockchain and the energy Internet of Things (IoT) in a virtual power plant environment.The system presented in this paper can use web-based software to Fog-enabled transactions with an integrated control system that requires intermediaries and DERs.In addition, it is possible to facilitate the activation of energy brokerage transactions by providing an environment for efficient transactions by collecting vast amounts of advanced metering infrastructure information on a fog server.
High Dimensional Quantum Key Distribution with Biphoton Frequency Combs through Energy-Time Entanglement

Biphoton frequency combs offer a photon-efficient way to achieve a very high-rate quantum key distribution by utilizing both time and frequency-bin encoding, while offering theoretically proven unconditional security.
A Probability based Classification of Named Entities for Malayalam Language combining Word, Part of Speech and Lexicalized features

Named Entity Recognition is the process wherein named entities which are designators of a sentence are identified. Designators of a sentence are domain specific. The proposed system identifies named entities in Malayalam language belonging to tourism domain which generally includes names of persons, places, organizations, dates etc. The system uses word, part of speech and lexicalized features to find the probability of a word belonging to a named entity category and to do the appropriate classification. Probability is calculated based on supervised machine learning using word and part of speech features present in a tagged training corpus and using certain rules applied based on lexicalized features.
Experimental Regulations for AI: Sandboxes for Morals and Mores

Recent EU legislative and policy initiatives aim to offer flexible, innovation-friendly, and future-proof regulatory frameworks. Key examples are the EU Coordinated Plan on AI and the recently published EU AI Regulation Proposal which refer to the importance of experimenting with regulatory sandboxes so as to balance innovation in AI against its potential risks. Originally developed in the Fintech sector, regulatory sandboxes create a test bed for a selected number of innovative projects, by waiving otherwise applicable rules, guiding compliance, or customizing enforcement. Despite the burgeoning literature on regulatory sandboxes and the regulation of AI, the legal, methodological, and ethical challenges of these anticipatory or, at times, adaptive regulatory frameworks have remained understudied. This exploratory article delves into the some of the benefits and intricacies of allowing for experimental instruments in the context of the regulation of AI. This article’s contribution is twofold: first, it contextualizes the adoption of regulatory sandboxes in the broader discussion on experimental approaches to regulation; second, it offers a reflection on the steps ahead for the design and implementation of AI regulatory sandboxes.
Sepsis Onset Prediction Applying a Stacked Combination of a Recurrent Neural Network and a Gradient Boosted Machine

Early detection and treatment of sepsis is of utmost importance concerning sepsis outcome and costs.However, revealing patterns in vital signs and laboratory measurements which facilitate reliable prediction of sepsis onset remains challenging.Especially exploiting the time series characteristic of those measurements is expected to play a major role concerning successful sepsis prediction.Within this work, we propose a stacked combination of a recurrent neuronal network (RNN) and a light gradient boosted machine (LGBM) to target the objective of sepsis onset prediction.Here, 8 vital signs, 26 laboratory measurements and 3 demographic parameters are included as input to our classification model.Our last running model achieved a utility score on full test set of 0.114 (TU Dresden -IBMT).
Landmark Points Detection in Case of Human Facial Tracking and Detection

This paper describes the human facial landmark points detection is very important in the field of image processing as face detect, face identifies, face re-construct, face corners alignment, different head pose and facial expression analysis. Facial landmark is an essential point for applying face processing operation ranging from biometric recognition to mental states. In this paper, Haar cascading face detection technique is used to face detection and tracking. Histogram of Oriented Gradients (hog) has been used for 68 landmark points detection in case of human tracking and detection and support vector machine (svm) classifier are used for 68 landmark points detection for right-left eyebrow, left-right eye, nose, lips, chin, and jaw. The existing methods work effectively but many issues occur in detection as of different head poses, facial expressions, facial occlusion, illumination, colour, shadowing and self-shadowing etc. The performance of experimental results shows the advantages of our purposed method is highly accurate in terms of facial 68 landmark points tracking and detection and less error detection rate with the Multi-PIE database.
Data Security Model using Artificial Neural Networks and Database Fragmentation in Cloud Environment

Data security is a one of the challenging issue in present scenario. In that one of the on demand service is cloud computing and it provides so many services for end users and also it provides a dynamic environment for end user to provide quality of services on data it leads to improve in the confidentiality of the data. The proposed work presents a new cloud data security model with the help of Artificial Neural Network. It improves the confidentiality and security in cloud environment. This proposed algorithm is implemented using dynamic hashing fragmented component. It is implemented for storing fragmented sensitive secret data. The neural network cryptographic proposed algorithm is used for data to deal with encryption process for secret and improve the confidentiality. This algorithm applied for various number of cloud databases and it shows high confidentiality on data security.
Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer (BC) Dataset

Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming to economically developing country like India. Government of India made a lot of effort to make aware the women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but their performance varies with the kind of data available. In this study we, apply four different Kernels such as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel (RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with respect to accuracy, runtime, specificity and precision. The investigations outcome displays that RBFK provides greater accuracy with minimal errors
A Chaotic Image Encryption Algorithm Robust against Phase Space Reconstruction Attacks

This paper describes a modification of a chaotic logistic map which could be exploited in a field of image encryption.After a summary of basic image encryption methods and problems, the paper mentions properties of a modified version of the logistic map.It is shown that the proposed changes help to achieve greater robustness against phase space reconstruction attacks.The paper also describes the usage of a modified map in an image encryption algorithm.Other techniques applied in the proposed algorithm include key diffusion, ciphertext chaining or four step diffusion stage.Evaluation of properties of the proposed algorithm is done by means of commonly used techniques.The numerical results are then compared with values obtained by other published algorithms.
The Ant Colony Optimization Model to Determine the Parameters of Hydrograph Unit Snyder

The relationship of rain flow is needed to get an accurate peak discharge and volume value. This value is require so that the analysis of hydrological problems produces an approach that is closer to reality in the watershed. Calibration and validation have been carried out in the form of statistical steps. The reliability of calibration with statistics in certain sections provides less accurate results. HSS snyder calculation uses the parameters Cp and Ct. These two parameters cannot be determined with certainty. Therefore, the parameters Cp and Ct need to be optimized to get a more reliable value. Calculation of the relationship of rain flow using the Snyder synthetic hydrograph method was chosen because it was able to be used if the watershed characteristics data were limited. Hydrograph calibration is commonly used with standard deviation statistics between the two data. Ant colony optimization was performed so that the calculation results are even better so that it gives a true picture of the hydrological phenomenon in an area. Field data calibration with the optimization of the Snyder hydrograph provides better R and SD values than the HSS Snyder without optimization. The optimization result of the Snyder hydrograph is Q p = 0.44 . A / t p .
Architecture and application of intelligent heating network system based on cloud computing platform

The vigorous development of Internet-related technologies such as big data, the IoT, and cloud computing has brought intelligent central heating networks? intelligent upgrade opportunities. The article discusses the current situation of the urban central heating system and its intelligence and analyzes the new characteristics of the intelligent central heating network under the new situation. Because of the existing central heating system?s existing problems, we have designed a complete set of sys?tem architecture and heating networks? application plans.
A Robust Isolated Automatic Speech Recognition System using Machine Learning Techniques

In order to make fast communication between human and machine, speech recognition system are used. Number of speech recognition systems have been developed by various researchers. For example speech recognition, speaker verification and speaker recognition. The basic stages of speech recognition system are pre-processing, feature extraction and feature selection and classification. Numerous works have been done for improvement of all these stages to get accurate and better results. In this paper the main focus is given to addition of machine learning in speech recognition system. This paper covers architecture of ASR that helps in getting idea about basic stages of speech recognition system. Then focus is given to the use of machine learning in ASR. The work done by various researchers using Support vector machine and artificial neural network is also covered in a section of the paper. Along with this review is presented on work done using SVM, ELM, ANN, Naive Bayes and kNN classifier. The simulation results show that the best accuracy is achieved using ELM classifier. The last section of paper covers the results obtained by using proposed approaches in which SVM, ANN with Cuckoo search algorithm and ANN with back propagation classifier is used. The focus is also on the improvement of pre-processing and feature extraction processes.
Virtual Reality-Based Education (VRBE): Understanding Students’ Readiness and Expectancies

Driven by the rapid advancement in the field of Science and Technology, students regardless of areas and programmes are now exposed to a variety of immersive technologies. Therefore, it has become a challenge for educators in designing a meaningful learning experience. The use of such technologies has led to the emergence of new approaches in a learning experience, namely one that involves Virtual Reality (VR) and Augmented Reality (AR). Thus, understanding students’ readiness and expectancies is very important in leveraging the use of these technologies in teaching and learning. This study was conducted with the aim of addressing 2 objectives which are to identify the level of readiness of law students at the Faculty of Law, UKM in using VR in their learning; and, to identify the students’ expectancies when using VR in learning. Questionnaires were used to obtain data that were intended to measure the student’s readiness and expectancies. The findings showed that most of the students at the Faculty of Law have the technological readiness to use VR in their learning in law classrooms. However, some considerations need to be taken especially the availability of devices which does not ensure the existence of total willingness to accept something as new as using VR in a law class. As a conclusion, the findings of this study have brought into attention that VR technology can be used in law classrooms despite its discipline which has always been traditional (lectures and tutorials) and conventional (numerous readings, note-takings, and listening). However, the preparation of students’ affective domain is crucial especially in providing them with the strategies to learn with the help of VR technology.
Open Digital Education Space: Classification of E-Services at University

The article suggests new approaches to describing digital education space. The differentiation of the concepts "digital pedagogy", "digital didactics" and "digital education" is carried out. The study of the problem of e-services typology has both practical importance for structuring a digital education space of the university and methodological, prognostic significance that allows the authors to solve the task of developing a fundamentally new type of educational e-services. In the typology of services, it is necessary to single out services of educational program management and services of the content management, for digital education space. The second group of services is classified by the authors on two bases: firstly, stages of the content acquisition and, secondly, the theory of multiple intelligence, which is interpreted in terms of "teacher intelligence", proposed by the authors
Identifying journalistically relevant social media texts using human and automatic methodologies

Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.
University Information System Security Risk Assessment using NIST 800-30

A good and fast information system is supported by good information technology. To achieve its business goals, optimal and integrated information technology will support good quality services. The XYZ University Information System (UIS) provides a variety of information needed by students, lecturers, and all staff. But the system that is running is still experiencing problems in its use that can pose various risks. To prevent that, a risk assessment is carried out on the UIS to identify various possible risks and prevent them by forming a risk management. This research will be conducted using NIST 800-30. This standard is used with the aim of anticipating risks so that the organization does not experience losses. The preparation of UIS information security risk management carried out in this study has succeeded in identifying 32 risk scenarios, prioritizing risks, providing direction in managing risks and accepting processes whether risks are acceptable or should be mitigated.
Time Series Data Prediction using Elman Recurrent Neural Network on Tourist Visits in Tanah Lot Tourism Object

The prediction of time series data is a forecast using the analysis of a relationship pattern between what will be predicted (prediction) and the time variable. The prediction process using the recurrent neural network (RNN) model could recognize and learn the data pattern of time series, but the presence of fluctuations in data makes the introduction of data patterns difficult to be learned. The data used for forecasting are tourist visits to Tanah Lot Bali tourist attraction for 10 years (2008-2017). The training process uses the RNN method on high fluctuating data, which requires a relatively long time in recognizing and studying the data patterns. Modification of the RNN method on learning rate and momentum by using dynamic values, can shorten learning time. The results showed the learning time using the RNN dynamic value, smaller than the variants of the RNN method such as the RNN Elman, Jordan RNN, Fully RNN, LSTM and the feedforward method (Backpropagation). The resulting error value is 0,05105 MSE. This value is smaller than the Fully RNN, Jordan RNN, LSTM and Feedforward methods. The elman method has the shortest training time among other models. The purpose of this research is to make a prediction design consisting of sliding windows techniques, training with neural network models and validation of results with k-fold cross-validation.
Integration of AIDC Technology in Mobile via QR Code for Enhancing the Library Services: A Case Study of Don Bosco College Central Library, Arunachal Pradesh

In this digital era, QR Code is becoming one of the most popular tools and it great help to website towards strengthening the relationship and promoting enhanced to existing library website. Q R Code is a state of art AIDC technology that is gaining popularity with Library fraternity in India. It is one such technology which can offer to the user demand of providing access to e-resources through mobile. These advanced technologies offer access very convenient, instantaneous information, across boundaries of subject and particular area. Don Bosco College Central Library is set an example in experimenting with adopting new technology developments; where it is computerization and automation or adopting other ICT to improve facilities their services. The rapid developments in ICT have given a solid foundation for revolutionary changes in the information handling capabilities of information dissemination. Don Bosco College Central Library may well reach out to the remote users who were considered unlikely to connect because of absence of a medium. Users are becoming increasingly web savvy reliant on advance technologies for sharing, accessing and determining their information. Keeping in mind, researcher integrated AIDC in Mobile via QR Code Technology in Don Bosco College Central Library Website for enhancing library services.
Deep Neural Network Classifier in Breast Cancer Prediction

The Breast cancer is the most life menacing disease among women. Early prophecy assurances the endurance of patients. In this work, first Deep neural network classifiers with different hidden layers with different nodes are used to explore the anthropometric information and blood investigation strictures and to predict the disease. Then machine learning algorithms such as SVM and Decision tree are also trained with the same data. Finally the performance of each classifier was deliberated. The pre-processed data of admitted patients with the breast cancer perception are used to train and test the classifiers. This article shack glow on the concert estimation based on right and erroneous data classification