Reviewerly/exHarmony huggingface dataset:
Pre-treating GNSS time series using a recurrent neural network to improve the automated detection of jump discontinuities

&amp;lt;p&amp;gt;Ground motion monitoring is one of the main goals in the geoscientist community and at the time it is mainly performed by analyzing time series of data. Our capability of describing the most significant features characterizing the time evolution of a point-position is affected by the presence of undetected discontinuities in the time series. One of the most critical aspects in the automated time series analysis, which is quite necessary since the amount of data is increasing more and more, is still the detection of discontinuities and in particular the definition of their epoch. A number of algorithms have already been developed and proposed to the community in the last years, following different statistical approaches and different hypotheses on the coordinates behavior. In this work, we have chosen to analyze GNSS time series and to use an already published algorithm (STARS) for jump detection as a benchmark to test our approach, consisting of pre-treating the time series to be analyzed using a neural network. In particular, we chose a Long Short Term Memory (LSTM) neural network belonging to the class of the Recurrent Neural Networks (RNNs), ad hoc modified for the GNSS time series analysis. We focused both on the training algorithm and the testing one. The latter has been the object of a parametric test to find out the number of predicted data that mostly emphasize our capability of detecting jump discontinuities. Results will be presented considering several GNSS time series of daily positions. Finally, a discussion on the possible integration of machine learning approaches and classical deterministic approaches will be done.&amp;lt;/p&amp;gt;
From Specialists to Generalists: Issues and Perspectives on Cross-Training Catalogers

This workshop gave a detailed account of the steps taken by the Technical Services Departments of two university libraries that were able to change their professional and paraprofessional staffs from a single task to a multiple task orientation using cross-training, development of consistent documentation, shared knowledge, core competencies and by increasing skills. These changes, although complex and sometimes difficult to implement, increased productivity and produced positive results at Louisiana State University and the University of Tennessee, Knoxville. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
Multiclass Data Imbalance Oversampling Techniques (Mudiot) and Random Selection of Features

Class imbalance is a serious issue in classification problem. If a class is unevenly distributed the classification algorithm unable to classify the response variable, which will result in inaccuracy. The technique Multiclass Data Imbalance Oversampling Techniques (MuDIOT) is to find out the factors which have a hidden negative impact on classification. To alleviate the negative impact the technique MuDIOT concentrates on balancing the data and the result minimizes the problems raised due to uneven distribution of classes. The dataset chosen has a multiclass distribution problem and it is handled to produce better results of classification.
Storyboardgraphy

Throughout the history of filmmaking, storyboarding has been used to pre-visualise films and help with production.Here I propose a new film visualisation tool to complement storyboarding, which I call 'Storyboardgraphy' and define as a 'film pre-and post-visualization tool showing shot sizes and lengths along a timeline'.A storyboardgraphic consists of two axes, with the X-axis representing the scene timeline, the Y-axis the size of the shots, and the characters depicted as recognisable curves.Storyboardgraphy is an excellent way of visualising a) shot sizes/lengths in a simple graphic; b) shot pacing (changes in the lengths of shots over the film timeline); and c) the dynamic of shot sizes (the order of shots along the scene timeline), which are not easily perceived on a conventional storyboard.Therefore storyboardgraphy is of huge interest, not only for pre-visualisation and production, but also for post-visualisation when editing, teaching film-making and studying film.
Quantum Algorithms

Abstract We have come a long way from Chap. 10.1007/978-3-030-61601-4_1 To recap on what we have learnt, we have understood important quantum mechanical phenomena such as superposition and measurement (through the Stern-Gerlach and Mach-Zehnder experiments). We have also learnt that while quantum computers can in principle break classical encryption protocols, they can also be used to make new secure channels of communication. Furthermore, we have applied quantum logic gates to qubits to perform quantum computations. With entanglement, we teleported the information in an unknown qubit to another qubit. This is quite a substantial achievement.
The Development Of Multimedia-Based Digital Simulation E-Book For Vocational Schools

This research is development research aimed to develop a learning product in the form of a Multimedia-based E-book on Digital Simulation Subjects which are feasible or valid and easy to use or practical. The test subjects in this research and development consisted of content experts, media experts and subject teachers of digital simulation and grade 10th students of SMK Negeri 4 Gowa. The ADDIE development model used in this study consisted of five stages, namely: (1) analysis stage, (2) design stage, (3) development stage, (4) implementation stage (5) evaluation stage. The development of multimedia-based digital simulation e-book through the material expert validation stage with the score of 4.73 and media validation 4.60 shows that the material and design or media evaluation in the e-book is feasible or valid. Then, the teacher's response obtained a score of 4.71, the average score of individual student trials was 92.14, and the average score of the student group trial was 90, which means the e-book is easy to use by teachers and students. there were no students who acquired a low score from the test results after learning using the e-book which means it is effectively used.
HUBUNGAN ANTARA PENGETAHUAN GIZI DAN MOTIVASI BELAJAR DENGAN KREATIVITAS SISWA DALAM MENGOLAH MAKANAN

The objective of the research is to study correlation between knowledge of nutrition and learning motivation with student creativity in a food process. The research carried out at Public Senior Vocational School (SMKN) in Jakarta, with 90 respondents selected by using cluster random sampling. The research concludes that there are positive correlation between knowledge of nutrition with student creativity in a food process, with ry1 = .728 and 1 499.4316.91ˆ XY  , and there is a positive correlation between learning motivation with student creativity in a food process, with ry2 =.863 and Y ˆ =25.021+ 1.516X2. Furthermore, there is a positive correlation between those two independent variables with student creativity in a food process, with Ry.12 = .971 and =5.354+1.559X1+ 1.543X2.
An efficient cryptanalysis scheme for secure data storage system in cloud using optimal dual encryption algorithm

Cloud computing gives adaptable infrastructure and high storage capacity for Big Data applications. Many of the cloud clients have been migrating or building their business into the cloud. However, various potential clients are as yet reluctant to take advantage of the cloud due to security and privacy concerns. Mainly, the proposed system consists of three modules such as: i) cryptographic security system (CSS) using dual encryption algorithm; ii) security blocking system using optimal double attack; and iii) generating optimal security system. At first, we design the security system using hybridisation of blowfish and data encryption standard (DES). From this, we evaluate how much amount of original information retrieved. Then, finally, to increase the security system, we design an optimal dual encryption based security system. At last, the quality of the work compared to other encryption techniques with minimum encryption time.
Executing time and cost-aware task scheduling in hybrid cloud using a modified DE algorithm

Task scheduling is one of the basic problems in cloud computing. In hybrid cloud, tasks scheduling faces new challenges. In this paper, we propose a GaDE algorithm, based on differential evolution algorithm, to improve single objective scheduling performance of a hybrid cloud. In order to better deal with the multi-objective task scheduling optimisation in hybrid clouds, on the basis of the GaDE and Pareto optimum of quick sorting method, we present a multi-objective algorithm, named NSjDE. This algorithm also makes considerations to reduce the frequency of evaluation. Comparing with experiments of Min-Min algorithm, GaDE algorithm and NSjDE algorithm, results show that for the single object task scheduling, GaDE and NsjDE algorithms perform better in getting the approximate optimal solution. The optimisation speed of multi-objective NSjDE algorithm is faster than the single-objective jDE algorithm, and NSjDE can produce more than one non-dominated solution meeting the requirements, in order to provide more options to the user.
Beyond Borders

Abstract The paper argues that museums have focused on the separation of the senses, with galleries dedicated primarily to visual learning modalities, while performance spaces are more likely to preference listening as a dedicated task for the stationary listener. The author/artist argues that gallery architecture and auditorium acoustics can be so well designed that they exclude possibility. Secondarily, she argues that the designs may as likely ignore the auditory to the degree that experience in one space can overwhelm the purpose of adjacent space. This reflection on history, acoustics, and atmosphere are manipulated in three installations to illustrate how creating art in public spaces and museum settings can transform experience. The artist highlights how to manipulate the characteristics of sound, how sound travels, echoes and reverberates, in ways that can extend perception.
Dbscan Assisted by Hybrid Genetic K Means Algorithm

The data mining algorithms functioning is main concern, when the data becomes to a greater extent. Clustering analysis is a active and dispute research direction in the region of data mining for complex data samples. DBSCAN is a density-based clustering algorithm with several advantages in numerous applications. However, DBSCAN has quadratic time complexity i.e. making it complicated for realistic applications particularly with huge complex data samples. Therefore, this paper recommended a hybrid approach to reduce the time complexity by exploring the core properties of the DBSCAN in the initial stage using genetic based K-means partition algorithm. The technological experiments showed that the proposed hybrid approach obtains competitive results when compared with the usual approach and drastically improves the computational time.
Depth Map Denoising and Inpainting using Object Shape Priors

We present a system that improves the quality of noisy and incomplete depth maps captured with inexpensive range sensors. We use a model-based approach that measures the discrepancy between a model hypothesis and observed depth data. We represent the model hypothesis as a 3D level-setembedding function and the observed data as a point cloud coming from a segmented region associated to the object of interest. The discrepancy between the model and the observed data defines an objective function, that is minimized to obtain pose, scale and shape. The variation in shape of the object of interest is mapped with Gaussian Process Latent Variable Models GPLVM and the object pose is estimated using Lie algebra. The integration of a synthetic depth map, obtained from the optimal model, and the observed depth map is carriedout with variational techniques. As consequence, we work in the observed space (depth space) rather than in a high dimensional space (3D points or volumetric space), achieving outstanding results in the improved depth maps.
Breast cancer data classification using deep neural network

Artificial neural networks and their variants play an important role in the analysis and classification of different biomedical data. Deep learning is an advanced machine learning approach which has been used in many applications in the last few years. Worldwide breast cancer is a major disease for women; it is one of the most challenging jobs to detect at an early stage. The authors in this work have taken an attempt to classify the breast cancer data collected from the UCI machine learning repository. Malignant and benign two different types of breast cancer tumours are classified using deep neural network (DNN). Before classification two pre-processing steps are done for improving the accuracy. The correlation and one-hot encoding of the dataset was done for getting some relevant features that can be used as the input to the DNN. Around 94% of classification accuracy is achieved by using a six-layer DNN classifier. The result is also compared with some earlier works and it is found that the proposed classifier is providing better results as compared to others.
Geospatial Resource Integration in Support of Homeland Defense and Security

The U.S. Department of Defense (DoD) has faced numerous challenges within the realm of Geospatial Information Systems and Science in fostering a Common Operational Picture suitable to homeland defense and security. This paper details the challenges and successes since September 11th, 2001 to build common ground for all federal, state, local governments, and non-government organizations that depend on geospatial data to provide for the safety and security of the Nation. An analysis of the protracted integration of commercial GIS technologies within the DoD and the speed, openness, and scale this expertise can bring is discussed as an issue for the Federal response to disasters. Finally, distinct successes of collaboration and integration of common standards and data currently in use at military commands is discussed as a robust path to improve future geospatial efforts.
Fuzzy logic system to detect Human Activity Recognition

These days health sector became a vast development in the entire world. Health care plays major role is of many scenario at present. The newest way of health care is to monitor the elder people with the smart activity recognition technique. The elder people needs someone assistance to pass on their daily life, as these modern world has no time for looking each and every one, this recognition technique helps the sons and daughters to monitor their old aged parents whether they are standing, walking, sitting or falling. This helps to monitor each and every step of elder people to be noticed where they were when we are in work. This system tracks the moments of elder people.
An Enhanced Framework for Content Based Medical Image Retrieval using Deep Neural Network

As the technology growth fuelled by low cost tech in the areas of compute, storage the need for faster retrieval and processing of data is becoming paramount for organizations. The medical domain predominantly for medical image processing with large size is critical for making life critical decisions. Healthcare community relies upon technologies for faster and accurate retrieval of images. Traditional, existing problem of efficient and similar medical image retrieval from huge image repository are reduced by Content Based Image Retrieval (CBIR) . The major challenging is an semantic gap in CBIR system among low and high level image features. This paper proposed, enhanced framework for content based medical image retrieval using DNN to overcome the semantic gap problem. It is outlines the steps which can be leveraged to search the historic medical image repository with the help of image features to retrieve closely relevant historic image for faster decision making from huge volume of database. The proposed system is assessed by inquisitive amount of images and the performance efficiency is calculated by precision and recall evaluation metrics. Experimental results obtained the retrieval accuracy is 79% based on precision and recall and this approach is preformed very effectively for image retrieval performance.
Grouping and Summarizing Data

So far, you have learned to write simple queries that include filtering and ordering. You can also work with expressions built with operators and functions. Chapters 5 and 6 taught you how to write queries with multiple tables so that the data makes sense in applications and reports. Now it’s time to learn about a special type of query, aggregate queries, used to group and summarize data. You may find that writing aggregate queries is more challenging than the other queries you have learned so far, but by taking a step-by-step approach, you will see that they are not difficult to write at all. Be sure to take the time to understand the examples and complete all the exercises before moving on to the next section.
Python

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.
E Safaah Sistem Informasi Pejabat Pengelola Informasi Dan Dokumentasi (PPID) Dinas Komunikasi Informatika Sandi Dan Statistik (Diskomsantik) Berbasis Android

Abstract&#x0D; Information and Documentation Management Officer (PPID), functions as a manager and conveyer of documents owned by public bodies in accordance with the mandate of Law 14/2008 concerning Public Information Disclosure. The development of information and communication technology in this era of globalization is growing rapidly. With the high need for information, the faster the information system will develop, especially in the use of information. As in the Tangerang PPID information system, Jakarta PPID, Subang PPID, and Semarang PPID which have been developed into an Android-based information system. The method used in the development of this final project information system is the Waterfall Method. The research was conducted at the Department of Communication, Informatics, Passwords and Statistics (DISKOMSANTIK) of Pandeglang City. Data collection and data analysis techniques were system engineering, requirement analysis, design, coding, testing, and maintenance. The results obtained are that the writer can develop an information system for Android Information Management Officers, Information system development, Android Information Management and Documentation Officers (PPID).&#x0D; Keywords: Android, Development, information system, Information Management and Documentation Officer (PPID), pandeglang.
A preliminary study on tech-based health advisory online system: The case study of UPSI computing students

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation N. S. Azman, B. Rahmatullah, K. F. Tamrin, Y. M. Qahtan; A preliminary study on tech-based health advisory online system: The case study of UPSI computing students. AIP Conf. Proc. 21 March 2024; 2750 (1): 040006. https://doi.org/10.1063/5.0148931 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Pengembangan bahan ajar modul berbasis discovery learning pada materi laju reaksi

This research aims to (1) knowing the results of a chemical study on the action based reaction materials used at school by BSNP standards,(2) knowing that the modules on discovery learning on the action-based reaction rate matter are appropriate for media experts and materials experts,(3) knowing the results of student learning after being given the discovery learning module on reaction rate materials. (4) know students' responses to the discovery learning based modules on reaction rate materials. Research results from an average of 81% of those produced from teaching materials analysis have both the strenghts and deformityof content, language worthiness, presentation and feasibility of expertise. Average pretest class experiment in 57,25%. Whereas the average score of 83,5% experimental posttest class. N-gain values a normalized chemical student experiment class based on the percentage of that is 83,82%. Student response or satisfaction percentages of deveoloped modules are acquired on an averaged of 93%, which could be concluded that the developedment of the taught materials the discovery learning based modules on a action-based reaction rare matter is higher than the minimum criteria (KKM) set. T_count more that t_table (8,747 more that 1,685) and H0 is therefore denied and Ha received. Thus it may be concluded that the there is an impact on the development of taught materials the discovery learning based modules on action on reaction materials.
Karl Popper on Deduction

Abstract We outline Karl Popper’s theory of deduction, which he developed in the 1940s. In his theory it is assumed that a consequence relation is given or otherwise constructed by postulation. Logical operations, which may be available in this consequence relation, are then characterized by means of relational definitions, and logical operators are introduced as names for these operations by means of inferential definitions. Using logically structured sentences thus introduced, the inference laws for them are immediately obtained from the inferential definitions.
Application for New Student Admission at SMKS Al-Akhyar II Jakarta with C4.5 Algorithm

Al-Akhyar II Jakarta Vocational High School (Smk) is a school that has been accredited with A superior. However, the application of new student admissions still has some difficulties in knowing the potential of prospective students who will attend school. This makes schools have to selectmore students who will be accepted at Smks Al-Akhyar IIJakarta.Therefore,a method is needed to identify prospective new students at school. Data Mining with the C4.5 Algorithm can be used to select and classify prospective new students at school by making a decision tree based on existing data and selecting prospective students. Data on prospective students whoregisterandtaketests in the 2019 academic year, 2020 and 2021 will be selected using theC4.5method whichwill latersee students who are accepted at Smks Al-Akhyar IIJakarta. The resultsthat have been tested using training data and comparing thetraining dataandtestingdatahaveanaccuracyof97%using theC4.5method.Testswere also carried out on 89 respondents who agreed that this design was carried out to overcome problems at Smks Al-Akhyar II Jakarta.