Reviewerly/exHarmony huggingface dataset:
Early Detection of Sepsis Using Ensemblers

This paper describes a methodology to detect sepsis ahead of time by analyzing hourly patient records.The Physionet 2019 challenge consists of medical records of over 40,000 patients.Using imputation and weak ensembler technique to analyze these medical records and 3-fold validation, a model is created and validated internally.On a hidden test data set maintained by the organizers, the model obtained a utility score of 0.192.The utility score as defined by the organizers takes into account true positives, negatives and false alarms.Our team was Team Tesseract and our overall ranking was 49 out of 79 officially ranked entries.
Chapter 12: Business Math

PreviousNext No AccessComplete Math Review for the Pharmacy Technician, 5th EditionChapter 12: Business Mathhttps://doi.org/10.21019/9781582123141.ch12 SectionsAboutFull Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail PreviousNext Session Activity Recently Viewed Chapter 12: Business Math William A. Hopkins Recently Searched No search history FiguresReferencesRelatedDetails Published: 9 May 2019eISBN: 1-58212-314-4 Copyright & Permissions© 2019 by the American Pharmacists Association. All rights reserved.© 2019 by the American Pharmacists Association. All rights reserved. Publisher American Pharmacists Association HistoryPublished: 9 May 2019 Citation Information (2019), "Chapter 12: Business Math," Complete Math Review for the Pharmacy Technician, 5th Edition https://doi.org/10.21019/9781582123141.ch12 Loading ...
Improving Performance of Elastic K-Means Clustering using similarity Measures

Clustering assign a proper membership to the data. Clustering has numerous applications inmarket research, pattern recognition, data analysis and image processing. Standard K-means clustering uses crisp membership to assign the data to a single cluster only. In real world data, some noise or ambiguity is present, so k-means clustering is not able to handle those data and it generates wrong membership for the clusters. In the proposed system, Elastic k-means clustering is used for creating flexible membership. For that purpose elastic k-means clustering uses vectorization and similarity measures for improving the membership and also handles the problem of the noisy data. Unstructured documents are used for experiment and the experimental resultsshows that, the proposed system gives better accuracy than existing one.
Photon Noise

In the last chapters we have shown how to describe the coherent and incoherent dynamics of optically driven few-level systems using a master equation approach of Lindblad form. We next approach the problem typically encountered in experiment, namely acquiring information about the dynamics of this few-level system. The situation we have in mind consists of an optically driven few-level system, where the excited state suffers losses due to dephasing and photon scatterings. In the latter process a photon is emitted and the system is promoted to its groundstate. Importantly, in experiment one observes precisely this emitted photon which thus provides us with the information about the system’s dynamics. Describing the emission and detection of the photon is the topic of this chapter.
Co-Organizers: International Conference of Science Educators and Teachers (ISET) 2018

First Page
Christmas jumpers competition

It's already February and Christmas might seem like a distant memory, but here at Physics World we've been rekindling that festive feeling by sorting through the entries to our physics-themed Christmas jumper competition featured in the December 2018 issue.
A Fast-Efficient Multi Class Pattern Recognition Method

This work presents a novel method for multi class pattern recognition. The feature space is classified with minimum hardware complexity and maximum speed using straight lines, circles, parabolas etc. RK algorithm-based devices (RKD) and mathematical functional blocks classify the feature space very rapidly after learning pattern classification with a fewer numbers of training sets compared to other statistical and artificial neural network (ANN) methods. RKDs are self-learning and fast responding devices and which manipulate a single variable at a time. The RK algorithm is used for learning the range of a variable. A set of sample variable and their corresponding responds are given for learning. The mathematical functional blocks manipulate one or more variables or attributes to perform mathematical functions and the outputs of these blocks are fed to RKDs. Finally, the RKDs perform the classification functions. The classification using straight lines or curves depends upon the mathematical functional block.
Subspace Inference in SDE-Net for Bayesian Deep Neural Networks

Bayesian inference was once a gold standard for uncertainty estimation with neural networks. However, Bayesian inference is inefficient in high-dimensional parameter spaces of Deep Neural Networks (DNNs). A novel Non-Bayesian Neural Stochastic Differential Equation (SDE-Net) model quantifies epistemic uncertainty of DNNs from the perspective of dynamical systems. In this paper, we propose a subspace inference procedure in Non-Bayesian SDE-Net method for uncertainty estimation from Bayesian perspectives. This procedure contains two steps, first of all, the low-dimensional parameter subspaces of SDE-Net model are constructed to generate the first principal components from Stochastic Gradient Descent (SGD) trajectories, which involve various high-performance models. Secondly, Variational Inference (VI) and Elliptical Slice Sampling (ESS) methods are implemented in the constructed subspace to explore in the full parameter spaces for uncertainty estimation. The experimental results of Bayesian average on the derived posterior in the generated principal subspaces of model parameters show that accurate and well-calibrated results can be obtained for regression and classification tasks.
Transmitting Copyright-Protected Images over Low-Bandwidth Channels through FFT-Based Watermarking in E-Learning

In an e-learning environment, security plays an important role while transmitting information in form of image or text through an open access network like Internet. In a lowbandwidth channel, it becomes difficult to download large image files and also the checking for authentication becomes cumbersome. In our proposed methodology, the sender can successfully send large image files to the receiver through a lowbandwidth channel and also we have emphasized on the authentication of the image for originality.
A Rule-Based Parsing System for Encoding Kennicott’s Collation of the Hebrew Bible

Qohelet, son of David king in Jerusalem
Community networks: a civil society perspective

A community-based approach to solving connectivity issues is on the agenda of both researchers and developers, with useful and replicable solutions. A comprehensive review of such initiatives may serve both scholars and practitioners. The Global Information Society Watch (GISW) - a yearly thematic report series on the state of the information society from a civil society perspective - has launched its 2018 edition on a timely and interesting topic: community networks (Finlay, 2018). Authored by the Association for Progressive Communications' members and experts, the report reveals global economic, social, cultural and technical issues concerning connectivity, and replicable solutions. Community networks fill the gap left by governments and large telecommunication operators, enabling affordable access for the disconnected. The 2018 GISW includes eight thematic studies and 43 country reports. We propose an overview of these reports' key issues by selecting four regionally and thematically diverse country cases: Brazil, Kenya, India, and Portugal. Despite regional differences, a common feature of community-based connectivity solutions is the participatory approach: sharing costs and risks helps underserved communities.
PERKEMBANGAN BALITA USIA 6-60 BULAN BERDASARKAN KEJADIAN ANEMIA DAN PEMBERIAN STIMULASI MELALUI PENDIDIKAN ANAK USIA DINI

Background: Children development is measured by four aspects; motoric skills, language, behavior, and mental development. Early childhood development was influenced by maternal nutrition during pregnancy, infant nutrition, stimulation, and parents’ knowledge.&#x0D; Objective: This study aimed to describe the development of children aged 6-60 months subject to anemia and stimulation through early childhood education.&#x0D; Method: The study was quantitative descriptive with 158 toddlers involved as respondents. Tools and materials used in this study were digital haemometer and DDST sheets.&#x0D; Result: Toddlers with normal development and suspect were majority experienced mild anemia (34.81%, 14,56%, respectively). While toddlers who followed early childhood education, majority had normal development (45.57%). Non-participating early childhood education tended to have developmental suspect (23.4%). Three toddlers with severe anemia (Hb 7.2 to 7.9 g%) had normal development, presumably due to early childhood education and one toddler with severe anemia (Hb 6 gr%) and not attending early education had suspected development.&#x0D; Conclusion: Toddlers with or without anemia had equal development, while toddlers who were given stimulation through early childhood education tended to have normal development.&#x0D; Key Word: Early childhood development, anemia, stimulation of early childhood education
Joint Word Segmentation and Stemming with Neural Sequence Labeling for Myanmar Language

Word segmentation is widely-studies sequence labeling problem using mach ine learning method like conditional rando m fields.In word segmentation, deep learn ing approaches have achieved state -of-theart performance.Normally, seg mentation is considered as a separate process fro m stemming.Our approach proposes a joint model that has stronger capabilities for Myan mar word segmentation and stemming.As far as we know, this is the first work on joint Myanmar word segmentation and stemming.In this paper, we evaluate the performance o f neural network arch itecture that relies on t wo sources of information about syllable-and character-level representation, by using LSTM, CNN, GRU and CRF.For the co mparison and analysis process, we examine the importance of different network designs and different factors such as the last layer of the network and different optimizers.
Aspect Based Mobile Recommendation System

Now-a-days, there is a trend of changing mobile phone models in very short duration. To achieve benefit of choosing a mobile phone satisfying our requirements, mobile phone recommendation system is of great importance. As we know, there are many web sites that provide number of reviews and ratings for each and every mobile phone model available in market. From this, we can understand the consumer opinions and reviews about any mobile product. Existing systems were based on complete review of product as good or bad. But, there is need to have a way with which we can review the product with view of each aspect such as camera, battery, look etc. For this, we have developed a system which provides a recommendation of mobile phone model by considering aspects with user’s choice. For this, we have used reviews, ratings provided by group of users on social website, Amazon. The data is collected with the use of “Octoparse”, a web scraping software and the text data collected is analyzed using Stanford's CoreNLP for sentiment analysis. Our approach provides recommendation, considering user provided aspects (i.e. camera, battery, look etc.) with the use of apache mahout and hybrid recommendation. Our approach showed outstanding performance for mobile phone recommendation.
ParaESSU Game using Performant Internet-of-Things (IoT)

Eastern Samar State University Salcedo Campus envisions to be an academic paradise in this side of the country. Like any organization it encounters environmental problems like improper garbage disposal. It aimed to design and develop and test a ParaESSU game. A waterfall model of software development was utilized on the creation of the game. Planning, conceptualization and data gathering were the activities in designing, interface design and coding followed to insure the system’s functionality. The game was evaluated using the ISO 9126-1 Quality Model and System Usability Scale (SUS) to show that the game was technically and operationally effective. Every after testing the game was updated to conform to the suggestions made by the evaluators. Results showed that the game can now be utilized and ready for deployment. SUS showed high scale which means that first time user can play the game without any demonstration and guidance. Further studies must be directed to add details not specified in the game and to further enhance its structure.
Overview

Dachman-Soled et al. (TCC '15) proposed a notion called locally decodable and updatable non-malleable codes, which provide the security guarantees of a non-malleable code while allowing for efficient random access. They also considered such codes that are leakage-resilient, allowing for adversaries who continually leak information in addition to tampering. The locality of their construction was Ω(log⁡n).We prove that super-constant locality is inherent by showing tight upper and lower bounds. We show that a locally decodable and updatable non-malleable code with block size χ∈poly(λ) requires locality δ(n)∈ω(1), where n=poly(λ) is the message length and λ is security parameter. Furthermore, we present a construction of a locally decodable and updatable non-malleable code with block size χ∈Ω(λ1/μ) (for constant 0<μ<1) with locality δ(n), for any δ(n)∈ω(1), and n=poly(λ).
A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information

© 2015 IEEE. Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective feature selection approaches are proposed by integrating MOABC with three filter fitness evaluation criteria, which are mutual information, fuzzy mutual information and the proposed fuzzy mutual information. The proposed multi-objective feature selection approaches are examined by comparing them with three single-objective ABC-based feature selection approaches on six commonly used datasets. The results show that the proposed approaches are able to achieve better performance than the original feature set in terms of the classification accuracy and the number of features. By using the same evaluation criterion, the proposed multi-objective algorithms generally perform better than the single-objective methods, especially in terms of reducing the number of features. Furthermore, the proposed fuzzy mutual information criterion outperforms mutual information and the original fuzzy mutual information in both single-objective and multi-objective manners. This work is the first study on multi-objective ABC for filter feature selection in classification, which shows that multi-objective ABC can be effectively used to address feature selection problems.&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;div&gt;&lt;table&gt;&lt;tr&gt;&lt;td&gt;© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.&lt;/td&gt;&lt;/tr&gt;&lt;/table&gt;&lt;/div&gt;&lt;/div&gt;
Heterogeneous Ensemble Structure based Universal Spam Profile Detection System for Social Media Networks

The exponential rise in internet technology and online social media networks have revitalized human-being to connect and socialize globally irrespective of geographical and any demographic boundaries. Additionally, it has revitalized business communities to reach target audiences through social media networks. However, as parallel adverse up-surge the ever-increasing presence of malicious users or spam has altered predominant intend of such social media network by propagating biased contents, malicious contents and fraud acts. Avoiding and neutralizing such malefic users on social media network has remained a critical challenge due to gigantically large size and user’s diversity such as Facebook, Twitter, and LinkedIn etc. Though exploiting certain user’s behavior and content types can help identifying malicious users, majority of the existing methods are limited due to confined parametric assessment, and inferior classification approaches. With intend to provide spam profile detection system in this paper a novel heterogeneous ensemble-based method is developed. The proposed model exploits user profile features, user’s activity features, location features and content features to perform spam user profile detection. To ensure optimality of computational significances, we applied multi-phased feature selection method employing Wilcoxon Rank Sum test, Significant Predictor test, and Pearson Correlation test, which assured retaining optimal feature sets for further classification. Subsequently, applying an array of machine learning methods, including Logistic regression, decision tree, Support Vector Machine variants with Linear, Polynomial and RBF kernels, Least Square SVM with linear, polynomial and RBF kernels, ANN with different kernels, etc we constituted a robust ensemble model for spam user profile classification. Simulations revealed that the proposed ensemble classification model achieves accuracy and F-score higher than 98%, which is the highest amongst major works done so far. It affirms suitability and robustness of the proposed model for real time spam profile detection and classification on social media platforms.
Improving Student Learning and Engagement in Cybersecurity Through Designing and Building Secure Internet of Things (IoT) Systems

Abstract Cybersecurity education aims to bring the awareness of the importance of security and privacy issues to students. This will help students change how they think when they develop and implement computer applications to consider security problems while they design and test their products. As our life these days depends heavily on Internet and the usage of computer and mobile applications, the need for cybersecurity professional and experts will continue to expand. Therefore, graduating students who have proper cybersecurity instruction becomes a necessity. This can be achieved by incorporating modern security analysis tools and engaging students in building secure systems throughout the undergraduate curriculum. The primary goals are: 1) to have more systems and products with fewer exploits and vulnerabilities, and 2) to increase the number of professional individuals who are interested in cybersecurity careers and have the proper cybersecurity knowledge and training. One key challenge in implementing and designing cybersecurity exercises in classrooms is having the proper infrastructure that allows conducting experiments in an isolated environment without jeopardizing security. To overcome this challenge, this paper presents “Secure-It-Yourself” kit. The kit utilizes Raspberry Pi unit as a development environment for hands-on activities and exercises. The advantage of using Raspberry Pi units is creating a configurable isolated sandbox for security hands-on exercises without affecting the current lab infrastructure. The corner stone in cybersecurity education is offering a set of engaging projects and exercises by which the students get hands-on experience to reinforce concepts covered in the classroom. This paper focuses on assembling a “Secure-It-Yourself” kit for students that can be utilized to bring the awareness of secure systems while enjoying building “Do-It-Yourself” projects. The kit includes all needed components and instructions to build small Internet of Things (IoT) systems. The “Secure-It-Yourself” kit gives instructors the needed flexibility to design activities that explain how security concepts and secure design principles should be used to build and implement secure systems. Also, this kit helps students to acquire the recommended National Initiative Cybersecurity Education (NICE) security Knowledge Skills and Abilities (KSAs) relevant to the scope of this paper. The kit can be used in undergraduate Security courses and in summer camps curriculums designed for high school students. The following components are included in the kit: Raspberry Pi unit, motion detection sensor, camera, breadboard, bush button switches, LEDs, jumper wires, and buzzers. The instructor’s kit includes additional components such as: RFID readers and electric locks that support fail-safe and fail-secure features. The paper discusses how the kit is used in two security courses to develop projects and exercises that enhance students’ security knowledge and skills.
Benchmarking management techniques for massive IIoT time series in a fog architecture

Within the Industrial Internet of Things (IIoT) scenario, the online availability of a growing number of assets in factories enables the collection of vast amounts of data. Each asset produces time-series collections that must be handled with proper techniques while providing effective ingestion and retrieval performance in complex architectures, maintaining compliance with company and infrastructure boundaries. In this paper, we describe an experience in the management of massive time-series, conducted in a plant of Avio Aero. Firstly, we propose a fog-based architecture to ease the collection and analysis of these massive datasets. Then, we present the results of an empirical comparison of four DBMSs (PostgreSQL, Cassandra, MongoDB, and InfluxDB) in the ingestion and retrieval of gigabytes of real IIoT data. In particular, we tested different DBMS features under different types of queries. Results show that InfluxDB provides very good performance, but PostgreSQL can still be an interesting alternative.
Mediating Effects of Organizational Culture on the Relationships between Employee Happiness and Job Satisfaction in UAE Public Organizations

People that are pleased with their lives usually experience greater satisfaction in their jobs. In addition, happiness is correlated with evidence of success in the workplace and can increase an employee’s effectiveness, performance and job satisfaction levels at work. It is therefore, important for organizations to identify the factors which influence employee happiness to enhance its cultural value-offering for employees and, in turn, increase their levels of job satisfaction. Although UAE government staffs has shown an increase in employee happiness and job satisfaction, there is still significant room for continuous improvement, especially by including the organizational culture. Hence the importance and need for conducting research into employee happiness research in UAE public government offices is paramount. Quantitative methodology was used and SPSS and AMOS software were used to analyze the data. The main analysis that provides inferential result to help answer the research questions was conducted using the covariance-based structural equation modelling approach. The results were reported in tables and figures accompanied by a description of each piece of information contained in the tables and figures.