Reviewerly/exHarmony huggingface dataset:
A comparative investigation of approaches for web search results clustering

The huge diversity of topics covered by the online textual documents is expected as these documents are originated from various resources and cover various topics. Looking to all of these aspects, how to search and find specific documents relative to a specific topic in the user mind and how to facilitate the browsing process? And how to reflect properly the user intention to the information retrieval system to perform the searching and delivering task in precise and fast process? This paper investigates various techniques used for clustering the web search results in order to meet that user's information needs. The goal is not to facilitate finding specific documents only but also to make it easier to preview the general structure and distribution of the topics and to reveal hidden topics in the corpus. The aim of this paper is to provide the reader with the relevant background in much more detail.
Administrasi keuangan sekolah

School financial administration is a process of recording and controlling school finances that are carried out responsibly, honestly, openly, in an orderly manner, meticulously, effectively, efficiently so that it is directed to the optimal achievement of school goals. This financial administration process includes the preparation of RPS, preparation of RKAS, use, and accountability.
River's edge

"River's Edge" is the title of a series of collage artworks created from images obtained via the Internet through the medium of generative programming. In this series of images, the artist used a keyword associated with his childhood memory "River's Edge"to conduct an in-depth search for and gather associated images, which he then assembled into vivid and visually appealing collages. In the "River's Edge" artwork, blue, gray, and green sections of the collected images were associated with water, stone, and sky. Pieces from hundreds of images were extracted from the Internet in data form, processed, and emplaced in the images. The creative process was based on an algorithm that examined the collected images, extracted appealing sections, subjected them to a limited set of modifications, and then emplaced them into the artwork. Although the algorithm's functionalities are limited to magnification, rotation, and choosing the areas to extract from the collected imagery, the process made it possible to create a wide variety of collages. In the numerous trials that were conducted to develop this art form, several new expressions were identified, and many beautiful patterns were created.
Outgoing webhooks

Now that we have extensively discussed incoming webhooks, it’s time to turn the tables around and talk about outgoing webhooks. Outgoing webhooks work the same as incoming webhooks, just the other way around. This time, Teams is going to call our API when an event occurs, so let’s see how we set up that API.
Introduction

International Journal of Neural SystemsVol. 31, No. 10, 2103008 (2021) Free AccessIntroductionLazaros IliadisLazaros IliadisDemocritus University of Thrace, School of Engineering, Head of the Department of Civil Engineering, Sector of Mathematics and Programming, Lab of Mathematics and Informatics ISCE, University Campus, 67100, Kimmeria, Xanthi, Greecehttps://doi.org/10.1142/S0129065721030088Cited by:0 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in neural networks today! FiguresReferencesRelatedDetails Recommended Vol. 31, No. 10 Metrics History Published: 23 August 2021 PDF download
Autoencoder Artificial Neural Network Public Key Cryptography in Unsecure Public channel Communication

As we all known that cryptography is a procedure to hide data so that it can’t be access or modified by any unauthorized entity. At the present digital world security is a main concern. To maintain this security there are many cryptographic algorithm exist. But the world technology grew each and every day so we have to find some new algorithms to maintain the security at higher level. In the proposed and implemented work used artificial neural network to increase the security during data communication in digital world. Autoencoder Neural Network is a new approach in the era of digital world so that used here in cryptographic algorithm to increase the strength of the security. There are three basic aims of cryptography availability, privacy and integrity easily achieved by this new approach. This work examine that the attacker can’t get access the data however he/she exist in the same network or not. Neural Network’s uncertainty property make this possible. This approach also examined on different data size and key size. Proposed work used the autoencoder for encryption and decryption. The final experimental result show our purposed algorithm efficient and accurate and also show how this approach perform better. Proposed and implemented algorithm can be easily used for secure data communication with more efficiently.
Generating Images of Face Poses for Pose Varying Face Recognition

Deep learning has attracted several researchers in the field of computer vision due to its ability to perform face and object recognition tasks with high accuracy than the traditional shallow learning systems. The convolutional layers present in the deep learning systems help to successfully capture the distinctive features of the face. For biometric authentication, face recognition (FR) has been preferred due to its passive nature. Processing face images are accompanied by a series of complexities, like variation of pose, light, face expression, and make up. Although all aspects are important, the one that impacts the most face-related computer vision applications is pose. In face recognition, it has been long desired to have a method capable of bringing faces to the same pose, usually a frontal view, in order to ease recognition. Synthesizing different views of a face is still a great challenge, mostly because in non-frontal face images there are loss of information when one side of the face occludes the other. Most solutions for FR fail to perform well in cases involving extreme pose variations as in such scenarios, the convolutional layers of the deep models are unable to find discriminative parts of the face for extracting information. Most of the architectures proposed earlier deal with the scenarios where the face images used for training as well as testing the deep learning models are frontal and nearfrontal. On the contrary, here a limited number of face images at different poses is used to train the model, where a number of separate generator models learn to map a single face image at any arbitrary pose to specific poses and the discriminator performs the task of face recognition along with discriminating a synthetic face from a realworld sample. To this end, this paper proposes a representation learning by rotating the face. Here an encoder-decoder structure of the generator enables to learn a representation that is both generative and discriminative, which can be used for face image synthesis and pose-invariant face recognition. This representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator.
Writing Functions

Writing your own functions in R enables you to combine a set of R commands into a function that is easy to call and can be generalized. Functions are foundational to R. To become a more advanced user or developer of R, a good understanding of what functions are and how to write them is crucial. Broadly speaking, a function takes one or more inputs and processes them to produce and return output.
ADM MUHAMA RIDO

Students are a very important element in educational and teaching activities in schools. Educational institutions are established for the benefit of students. Therefore it needs to get enough attention from the implementation of education in order to achieve the objectives of national education as a whole. There fore, as a prospective educator later, in order to be able to administer for students so that the learning process runs well it is necessary to understand about the administration of students / students. Therefore, the author tries to make a paper that discusses the administration of students.
Abstract 1632: Deep learning functional associations using histopathology images

Abstract Histopathological images have critical value for understanding cancer subtype, local structure, and malignancy. Manual interpretation of such images can be time-consuming and error-prone, while also failing to make use of the power of image aggregation for coordinated data mining. Large-scale patient cohorts such as TCGA provide high-resolution H&amp;E images that complement other -omics data, with the important advantage that images provide spatial information about heterogeneity and stromal cell interactions. We implemented a convolutional neural network (CNN) to classify H&amp;E slides according to tissue type, cancer subtype, and genomic mutations. We modified the CNN architecture of Inception v3 according to our problem and trained it with tiled whole slide images from 31 solid cancers in TCGA, and evaluated our algorithm on holdout data. We first classified tumor/normal status from the images and determined AUC values for each cancer type. We then applied our method for tissue classification in a multilabel classification scenario. Furthermore, in few cancers (e.g. gastric cancer) we determined cancer subtypes using supervised learning. Finally we evaluated our model for classifying mutational status of cancer-related genes. To improve classification accuracy, we modified Inception architecture for multi-task classification by replacing the final fully-connected layer by a shared layer which provides the input to individual task layers corresponding to each gene mutation. Furthermore, we adopted image segmentation approaches (e.g.. auto-thresholding) to improve sample background removal, and used several approaches (e.g. resampling, cross entropy weighting) to rebalance the data. In some cases we achieved high classification results (e.g. AUC=0.7 for TP53 in lung adenocarcinoma). Our study suggests that histological images can be used to classify tumors according to their mutational status, and can provide biomarkers for molecular subtyping. Citation Format: Javad Noorbakhsh, Saman Farahmand, Mohammad Soltanieh Ha, Sandeep Namburi, Kourosh Zarringhalam, Jeff Chuang. Deep learning functional associations using histopathology images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1632.
administrasi PTK

This article aimed to describe Administration of educators and education personnel. It is the overall process of the activities of educators to achieve educational goals effectively and efficiently. This article the author uses the literature study method, by collecting material sourced from books, journals and others related to the administrative process of educators and education personnel.
Guinea – Alpha Condé Sworn In

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 57, Issue 12 p. 22977B-22977C Internal Developments Guinea – Alpha Condé Sworn In First published: 27 January 2021 https://doi.org/10.1111/j.1467-825X.2021.09793.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume57, Issue12January 2021Pages 22977B-22977C RelatedInformation
Predicting Onset of Dementia Using Clinical Notes and Machine Learning: Case-Control Study (Preprint)

<sec> <title>BACKGROUND</title> Clinical trials need efficient tools to assist in recruiting patients at risk of Alzheimer disease and related dementias (ADRD). Early detection can also assist patients with financial planning for long-term care. Clinical notes are an important, underutilized source of information in machine learning models because of the cost of collection and complexity of analysis. </sec> <sec> <title>OBJECTIVE</title> This study aimed to investigate the use of deidentified clinical notes from multiple hospital systems collected over 10 years to augment retrospective machine learning models of the risk of developing ADRD. </sec> <sec> <title>METHODS</title> We used 2 years of data to predict the future outcome of ADRD onset. Clinical notes are provided in a deidentified format with specific terms and sentiments. Terms in clinical notes are embedded into a 100-dimensional vector space to identify clusters of related terms and abbreviations that differ across hospital systems and individual clinicians. </sec> <sec> <title>RESULTS</title> When using clinical notes, the area under the curve (AUC) improved from 0.85 to 0.94, and positive predictive value (PPV) increased from 45.07% (25,245/56,018) to 68.32% (14,153/20,717) in the model at disease onset. Models with clinical notes improved in both AUC and PPV in years 3-6 when notes’ volume was largest; results are mixed in years 7 and 8 with the smallest cohorts. </sec> <sec> <title>CONCLUSIONS</title> Although clinical notes helped in the short term, the presence of ADRD symptomatic terms years earlier than onset adds evidence to other studies that clinicians undercode diagnoses of ADRD. De-identified clinical notes increase the accuracy of risk models. Clinical notes collected across multiple hospital systems via natural language processing can be merged using postprocessing techniques to aid model accuracy. </sec>
Understanding users and their goals

This chapter focuses on broad categories about user populations and user behaviors. It presents information on what we know about users in general, about users' expectations with websites and digital products, and about generational and gender differences in users. This information provides a foundation for creating personas and writing scenarios for user subgroups. Sample personas are included. Distinctions between use cases and user stories are explained.
Ensemble Classification

This chapter is concerned with ensemble classification, i.e. using a set of classifiers to classify unseen data rather than just a single one. The classifiers in the ensemble all predict the correct classification of each unseen instance and their predictions are then combined using some form of voting system. The idea of a random forest of classifiers is introduced and issues relating to the selection of a different training set and/or a different set of attributes from a given dataset when constructing each of the classifiers are discussed. A number of alternative ways of combining the classifications produced by an ensemble of classifiers are considered. The chapter concludes with a brief discussion of a distributed processing approach to dealing with the large amount of computation often required to generate an ensemble.
The Application of Interactive Task Based Learning For EFL Students of UNISMUH Makassar.

Speaking plays an important role in the production of a variety of languages, it can be in form of discussions, speeches, debates, argumentation, conversation, story telling and retelling. However this study aims to develop a learning strategy based on Task Based Learning that can improve speaking ability. The Research questions are (1) what learning strategy is appropriate to increase the students speaking skills? (2) How is the student achievement under the implementation of task-based learning (3) is the application of task-based learning effective in improving students ability to speak? The study consists of (1) need analysis, (2) design strategy, (3) implementation, and (4) evaluation. The study will be implemented over three steps using the design development research. The principles and procedures are using qualitative and quantitative. The participant of the research was a speaking class subject of UNISMUH. The study concludes that although the participants have show their ability to speak using task based learning but improvement is needed to increase fluency, acuracy, flexibility, comprehensibility appropriately and effectively to make thier speaking better. Recomendation to English lecturur also given as pedagogical implication of this study.
Repair and rebuild on an offshore rig

An offshore platform needed one of its water injection pumps back in service after it had been left unused and unpreserved for seven years. A call to the OEM, Sulzer, gave the unused pump a rapid response and a new lease of life.
Xylem launches third annual “Make Your Mark 30-Day Challenge”

Xylem employees and stakeholders are coming together from World Water Day (22 March 2019) to Earth Day (22 April 2019) through the “Make Your Mark 30-Day Challenge” to help solve water challenges through activities around the world, such as educational events for children focused on the water crisis and hosting walks for water to raise awareness of water issues.
Audit Sistem Informasi Rs Abdul Moeloek

Audit Sistem Informasi Rs Abdul Moeloek
Generic ANN

In Chapter 9, a milestone was reached by building a generic neural network that works independently to the number of inputs, the number of hidden layers, and the number of hidden neurons within such layers. There were 2 main limitations which are as follows: The output layer can have only a single neuron. As a result, each input sample must have only a single output. A single training sample is used. This chapter overcomes the limitations described in Chapter 9 in addition to adding new features. There are 4 milestones in this chapter which are as follows: Work with any number of outputs and not only limited to a single output. Use more than 1 training sample. Introduce the bias in both forward and backward passes. Support the batch gradient descent.
Fall Detection for Elderly Person using Neuro-fuzzy System and Wavelet Transformation

This study proposes a new methodology to detect falls and non-falls using a Neural Network with Weighted Fuzzy Membership Functions (NEWFM). Dataset acquired from subjects was applied to NEWFM after carrying out wavelet transforms. In order to test the performance evaluation of the fall detection by the NEWFM, the dataset was separated test set and training set at 2 to 8 and 5 to 5 ratios to carry out experiments. Based on the performance evaluation of the NEWFM, the sensitivity, accuracy, and specificity were shown to be 94.67%, 91.86% and 89.41%, respectively when the test set to the training set at the ratio was 2 to 8 and 91%, 91% and 91%, respectively, when the test set to the training set at the ratio was 5 to 5. This study also compares the performance evaluation of backpropagation (BP) and that of NEWFM.
Accessing Data in the Cloud

Abstract This chapter describes the legal framework governing law enforcement access to data in a cloud environment, giving particular attention to developments in the European Union, the United States, and international law, specifically the Council of Europe Cybercrime Convention (Cybercrime Convention). The Convention addresses the investigation and prosecution of cybercrime within the domestic jurisdiction, as well as facilitating international co-operation against transborder cybercrimes. The provisions represent a certain consensus among the signatories about the appropriate exercise of law enforcement powers in cyberspace, including a cloud environment. However, the exercise of law enforcement agency (LEA) powers raises a number of jurisdictional questions. Each of these issues presents a boundary issue for LEAs, cloud service providers, and cloud users; a boundary between lawful and unlawful behaviours or regulated and unregulated activities. The chapter looks at how and when those boundaries apply and what mechanisms and procedures have been adopted, or are proposed, to address the needs of LEAs in a cloud environment.
Effectiveness of a Smartwatch App in Detecting Induced Falls: Observational Study (Preprint)

<sec> <title>BACKGROUND</title> Older adults are at an increased risk of falls with the consequent impacts on the health of the individual and health expenditure for the population. Smartwatch apps have been developed to detect a fall, but their sensitivity and specificity have not been subjected to blinded assessment nor have the factors that influence the effectiveness of fall detection been fully identified. </sec> <sec> <title>OBJECTIVE</title> This study aims to assess accuracy metrics for a novel fall detection smartwatch algorithm. </sec> <sec> <title>METHODS</title> We performed a cross-sectional study of 22 healthy adults comparing the detection of induced forward, side (left and right), and backward falls and near falls provided by a smartwatch threshold-based algorithm, with a video record of induced falls serving as the gold standard; a blinded assessor compared the two. Three different smartwatches with two different operating systems were used. There were 226 falls: 64 were backward, 51 forward, 55 left sided, and 56 right sided. </sec> <sec> <title>RESULTS</title> The overall smartwatch app sensitivity for falls was 77%, the specificity was 99%, the false-positive rate was 1.7%, and the false-negative rate was 16.4%. The positive and negative predictive values were 98% and 84%, respectively, while the accuracy was 89%. There were 249 near falls: the sensitivity was 89%, the specificity was 100%, there were no false positives, 11% were false negatives, the positive predictive value was 100%, the false-negative predictive value was 83%, and the accuracy was 93%. </sec> <sec> <title>CONCLUSIONS</title> Falls were more likely to be detected if the fall was on the same side as the wrist with the smartwatch. There was a trend toward some smartwatches and operating systems having superior sensitivity, but these did not reach statistical significance. The effectiveness data and modifying factors pertaining to this smartwatch app can serve as a reference point for other similar smartwatch apps. </sec>
Dependent Types

Type theory regards individuals as belonging to a specific type. In computer science type-checking measures ensure that a program will compute properly. We see typing in everyday speech too, when a ‘Who?’ question expects a person. To define a type, it is necessary to specify when two elements are equal. Such concerns with sorts and identity are at stake in metaphysics. A <italic>dependent type</italic> occurs where one type is parametrized by elements of another. The two related constructions of <italic>dependent sum</italic> and <italic>dependent product</italic> appear in natural language and modern mathematics. In logic, these constructions amount to the quantifiers central to Russell’s logic. Dependent types are formulated in terms of <italic>contexts</italic> constructed in an order, showing how some concepts <italic>presuppose</italic> others. There are strong resonances with the ideas of Collingwood. With the notion of dependency, we can now analyse event types properly, demonstrating their difference from object types.
The scheduler

This chapter details the capabilities offered by the task scheduler(s) in Nucleus SE and compares with those of Nucleus RTOS. Extensive details of the context save and restore process are included.