Reviewerly/exHarmony huggingface dataset:
Demand Response of a Solar Photovoltaic Dominated Microgrid with Fluctuating Power Generation

Solar power generation systems are highly economic, renewable, and environmentally friendly. Still the primary concern over a solar photovoltaic power generation system (SPVGS) is the unreliable and highly fluctuating power generation due to irregular solar irradiation. The demand response under highly fluctuating power generation is a complex task. The developed model is a demand response model which uses machine learning technologies to perform power scheduling and load scheduling during fluctuating demand/power generation. A multi-layer algorithm model is developed which uses historical values as well as instantaneous values for the prediction of the demand and generation schedule. The proposed model uses basically two algorithms in different layers which include autoregressive acceleration model (ARAM) and support Vector regression (SVR) model. A hardware model is developed to test and validate the results. Successive re-approximation is performed in an adaptive manner which will update the model on each iteration, and this model is repeated until optimum results are obtained.
Implementation of Viola-Jones for Detection of Facial Factors of Human for Prospect of Image Recognition

This is an application for tracking and detecting human faces and eyes from video or images, which can be used for different purposes and applications.The face is one of the distinguishing factors which can easily be used for the identification of an individual.The primary goal of this paper is to study the Viola-jones (VJ) algorithm, its four stage processes (Haar like Features, Integral Images, Adaboost and Cascading) and its implementation using OpenCV for face, eye and smile detection.It's an adroit algorithm basically targets the frontal faces for better detection accuracy.
Development of augmented physics animation (APA) with the integration of crosscutting concepts about the COVID-19 as a supplement to the introductory physics course

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 Yudi Guntara, Asep Saefullah, Firmanul Catur Wibowo, Lukman Nulhakim, Dina Rahmi Darman, Ilham Akbar Darmawan, Irwanto, Sigit Setiawan, Tubagus Umar Syarif Hadi Wibowo; Development of augmented physics animation (APA) with the integration of crosscutting concepts about the COVID-19 as a supplement to the introductory physics course. AIP Conf. Proc. 2 March 2021; 2320 (1): 020025. https://doi.org/10.1063/5.0037459 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
Efficiency and Effect on the Competitive Advantage of Management Information Systems (MIS) in Classified Hotels in the City of Petra; Type of Management as Moderator

Purpose: The study aims to examine the efficiency and influence of management information systems (MIS) in classified hotels in the city of Petra on the competitive advantage; management category as moderator. Methodology/Approach: We used the SPSS version 20 to test 22 elements of the Likert scale for the outcome in factor analysis, before conducting axial component analysis, the suitability of the data for factor analysis was evaluated. Multiple hierarchical regressions was used to test the hypotheses. Findings: There is a positive statistically significant relationship between the efficiency of management information systems and the technical dimension at the level of 0.01. As for product differentiation, it reached (=0.16, p<.05), meaning that the second hypothesis has been accepted as there is a positive statistically significant relationship between the efficiency of management information systems and the differentiation of products. Originality/Value of paper: The originality of paper narrows the difference between the model of the Hotel Organization (H/O) and the competitive advantage efficacy (ESA) in terms of competitive advantage and efficiency. Specifically, this analysis introduces productivity and results on competitive advantage variables in classified hotels in the city of Petra that impact Management Information Systems (MIS). Second, empirically, by proposing a modern research paradigm and new research establishment at the level of productivity on the comparative advantage (ECA) in the hotel industry, this research aims to reduce the difference in scientific research. This study is supposed to be useful for policy decision-making, especially for managers who want to develop Management Information Systems (MIS) in Hospitality industry city of Petra.
Person identification using fusion of deep net facial features

Face base identification is the method of recognising individuals through face images having application domains like information security and surveillance system. Deep networks have proved to be successful for facial identification in which entire images are passed as input to deep-net, and the network does feature extraction as well as classification. However, such a process requires millions of images to work with and implementing the same sometimes becomes complicated and time-consuming. This paper utilises pre-trained deep networks Alexnet and VGG-16 as feature extractors in which contribution to more significant level layers are utilised as feature vectors. The feature vectors are then diminished using a combination of PCA and LDA. After the reduction in the dimensionality of highlight vectors, they are intertwined and characterised using support vector machines. The proposed framework is assessed on VIDTIMIT database, highlighting the performance as far as precision, accuracy, and recall.
Logic-induced Bisimulations

We define a new logic-induced notion of bisimulation (called $\rho$-bisimulation) for coalgebraic modal logics given by a logical connection, and investigate its properties. We show that it is structural in the sense that it is defined only in terms of the coalgebra structure and the one-step modal semantics and, moreover, can be characterised by a form of relation lifting. Furthermore we compare $\rho$-bisimulations to several well-known equivalence notions, and we prove that the collection of bisimulations between two models often forms a complete lattice. The main technical result is a Hennessy-Milner type theorem which states that, under certain conditions, logical equivalence implies $\rho$-bisimilarity. In particular, the latter does \emph{not} rely on a duality between functors $\mathsf{T}$ (the type of the coalgebras) and $\mathsf{L}$ (which gives the logic), nor on properties of the logical connection $\rho$.
Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection

As a key component of automating the entire process of applying machine learning to solve real-world problems, automated machine learning model selection is in great need. Many automated methods have been proposed for machine learning model selection, but their inefficiency poses a major problem for handling large data sets. To expedite automated machine learning model selection and lower its resource requirements, we developed a progressive sampling-based Bayesian optimization (PSBO) method to efficiently automate the selection of machine learning algorithms and hyper-parameter values. Our PSBO method showed good performance in our previous tests and has 20 parameters. Each parameter has its own default value and impacts our PSBO method’s performance. It is unclear for each of these parameters, how much room for improvement there is over its default value, how sensitive our PSBO method’s performance is to it, and what its safe range is. In this paper, we perform a sensitivity analysis of these 20 parameters to answer these questions. Our results show that these parameters’ default values work well. There is not much room for improvement over them. Also, each of these parameters has a reasonably large safe range, within which our PSBO method’s performance is insensitive to parameter value changes.
Towards Improvements on Multi-tenant RDBMS Migration in the Cloud Environment

In a multi-tenant environment, the companies referred to as tenants share a common application and Relational Database Management System (RDBMS) instances to store their data. However, with the rapid adoption of multi-tenant databases, the cloud provider faces two challenges: Tenants have irregular workload patterns. Also, tenants require a strict guarantee of their rental services’ quality and performance, known as a Service Level Agreement (SLA). In this research, a Multi-Tenant Database Management System (MT DBMS) is presented. A multi-tenant migration algorithm called MT-M is presented, which migrates the violated tenants on an elastic cluster of machines to mitigate the SLA Violations. Experiment results show that the proposed MT-M algorithm is ideal for the migration of the violated multi-tenant databases, reducing SLA violations’ total number compared to the previous migration algorithms.
Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion

Purpose Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ‐specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. Methods The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC‐visually lossless, is applied to compress the image. We demonstrate the proposed method on three‐dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak‐signal‐to‐noise ratio ( ), structural similarity ( ), and compression ratio ( ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. Results The results show that the method can significantly improve of most well‐known compression methods. DLAD combined with HEVC‐visually lossless achieves the highest average of 6.45, which is 36% higher than that of the original HEVC and outperforms other state‐of‐the‐art lossless medical image compression methods. The means of and are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. Conclusions We thus conclude that the method has a high potential to be applied in teleintervention applications.
SOCIOECONOMIC INDICATORS OF SELECTED WEST AFRICAN COUNTRIES AND PROSPECT FOR 2030 DEVELOPMENT AGENDA

Poverty, health inequity, high cost of food prices, high electricity tariffs among other socioeconomic challenges are common futures of West African economies. This paper examined socioeconomic indicators of the West African economy between 1995 to 2019 using the profile and trend analytic approaches. The socioeconomic indicators examined are poverty head count ratio, under-5 mortality rate, government education expenditure as a percentage of gross domestic product (GDP), government health expenditure as a percentage of GDP, carbon dioxide(CO2) emission, electricity consumption (Kwh per capita), food production as a proxy for food security and real GDP. The selected West African countries include Mali, Benin, Cote d’ Ivoire, Ghana and Nigeria following availability of data. Findings from the analysis revealed that the 15 West African economies are diverse across these dimensions of development. It further showed that with the exception of Nigeria (although, by far the largest economy in the region), the focused countries have performed averagely in relation to the socioeconomic indicators. Moreover, tepid economic growth (Real GDP) was observed for these countries as a result of lower commodity prices and impact of pandemics (Ebola and COVID-19), while income per capita ranged from $452 to $3,678) within the region. However, much gap is still existing towards achieving the related Sustainable Development Goals(SDGs) and Targets. The policy implication of the findings inter alia is that sound socioeconomic policy framework at the regional level based on pro-development strategies is needed to improve the socioeconomic status of the West African countries for Agenda 2030 in line with the evidence from the paper. This agenda should not be divorced from structural transformation and enhanced regionalism and integration.
Perancangan Aplikasi Prediksi Jumlah Pendaftar Siswa Baru Dengan Metode Regresi Linier (Studi Kasus: SMA RK Deli Murni Bandar Baru)

The problem of the SMA RK Deli Murni Bandar Baru school is to predict how many facilities that need to be provided for new students such as chairs, tables and others. This study discusses the prediction of the number of new student registrants at SMA RK Deli Murni Bandar Baru based on the amount of tuition fees using a simple linear regression method. From a commercial point of view, the use of data mining can be used to handle the explosion of data volumes, using computational techniques can be used to produce information needed which is an asset that can increase the competitiveness of an institution. Prediction is almost the same as classification and estimation, except that in the prediction the value of the results will be in the future. This system can be used to predict the number of applicants in the following year to help the school. The advantage is that this simple linear regression method is very simple so that it is easy to calculate and use. Saves the time needed to solve problems, especially those that are very complex.
Design of Individualized Recommended Algorithm in College Online Platform

In order to improve the quality of education, modern colleges and universities generally begin to use information technology to carry out online education, which pays more attention to students’ personalized needs and self-learning awareness. Therefore, online education needs to independently recommend courses according to students’ needs. In order to achieve this function, this paper will carry out relevant analysis, first of all, build the basic framework of online education platform in Colleges and universities, and then integrate personalized recommendation course algorithm to design the online education platform with this function.
Solving some ordinary differential equations numerically using differential evolution algorithm with a simple adaptive mutation scheme

Differential equations have been used to describe phenomena in many fields. However, not all of differential equations can be solved in analytic ways so there is some approach being made to solve them numerically. One of the approaches is using Fourier series expansion to approximate solutions of linear and nonlinear ordinary differential equations (ODEs). The coefficients of Fourier series expansion are estimated by an optimization method using Differential Evolution (DE). In this case, differential evolution will be used to minimize the residual function of the Fourier series that are implemented into the ordinary differential equations. A modification from the original DE is made by putting a simple adaptive scheme into the mutation part of the DE algorithm. The results show good performance of DE in solving various ODEs.
Accelerated implementation for testing IID assumption of NIST SP 800-90B using GPU

In cryptosystems and cryptographic modules, insufficient entropy of the noise sources that serve as the input into random number generator (RNG) may cause serious damage, such as compromising private keys. Therefore, it is necessary to estimate the entropy of the noise source as precisely as possible. The National Institute of Standards and Technology (NIST) published a standard document known as Special Publication (SP) 800-90B, which describes the method for estimating the entropy of the noise source that is the input into an RNG. The NIST offers two programs for running the entropy estimation process of SP 800-90B, which are written in Python and C++. The running time for estimating the entropy is more than one hour for each noise source. An RNG tends to use several noise sources in each operating system supported, and the noise sources are affected by the environment. Therefore, the NIST program should be run several times to analyze the security of RNG. The NIST estimation runtimes are a burden for developers as well as evaluators working for the Cryptographic Module Validation Program. In this study, we propose a GPU-based parallel implementation of the most time-consuming part of the entropy estimation, namely the independent and identically distributed (IID) assumption testing process. To achieve maximal GPU performance, we propose a scalable method that adjusts the optimal size of the global memory allocations depending on GPU capability and balances the workload between streaming multiprocessors. Our GPU-based implementation excluded one statistical test, which is not suitable for GPU implementation. We propose a hybrid CPU/GPU implementation that consists of our GPU-based program and the excluded statistical test that runs using OpenMP. The experimental results demonstrate that our method is about 3 to 25 times faster than that of the NIST package.
An Improved Bubble Sort Method - Marking Bubble Sort

The advent of the era of big data requires high efficiency of data algorithm execution. In the data structure, the traditional bubble sort execution efficiency is low, by studying an improved bubble sort-mark bubble sort method, the number of executions can be reduced from 8 to 3, which greatly improves the execution efficiency of the algorithm.
Tertiary Non-English Majors’ Attitudes Towards Autonomous Technology-Based Language Learning

Technology has emerged as an indispensable part of general education and, in particular, English language education.Accordingly, the utilization of technology for autonomous language learning (ALL) should be imperative.Therefore, this research aims to scrutinize non-English majors' attitudes towards autonomous technology-based language learning (ATLL) at the context of Da Lat University.The research participants were 425 non-English majors conveniently sampled answering the closed-ended questionnaires, and 25 of whom were invited for semi-structured interviews.The quantitative data from questionnaires were processed by SPSS, while the qualitative data from interviews were analyzed employing the content analysis approach.The results unraveled that the research participants had positive attitudes towards ATLL.Among three components of attitudes, it was found out that non-English majors were highly conscious of the important roles of technology in ALL (cognitive attitudes), they had positive feelings towards the use of technology in ALL (affective attitudes), and they were unsure of activities for using technology for ALL (behavioral attitudes).Such preliminary results are expected to contribute to understanding the enhancement of learner autonomy by integrating technology into autonomous English language teaching and learning.
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images

Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. Recently CNN based approaches have surpassed classical methods in a number of perception tasks. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in autonomous driving. In this paper, we propose a novel training and evaluation pipeline for fisheye images. We make use of SuperPoint as our baseline which is a self-supervised keypoint detector and descriptor that has achieved state-of-the-art results on homography estimation. We introduce a fisheye adaptation pipeline to enable training on undistorted fisheye images. We evaluate the performance on the HPatches benchmark, and, by introducing a fisheye based evaluation methods for detection repeatability and descriptor matching correctness on the Oxford RobotCar datasets.
The six-dos transposition cipher based on the rubik s cube

Internet-based communication has become an important part of our daily lives [1].It expands its usefulness into businesses and commercials such as banking transactions, patients monitoring, and military operations.Data security is at the heart of this commission that supports secure and safe passage of data traversal.Particularly, the storing and transmitting of data has been blessed with the emergence of technology known as encryption or cryptography [2][3][4] that offers an acceptable level of security.As the sophistication of attacks increase, researches towards improving existing algorithms, or even constructing a new breed of algorithms further takes place to produce more robust algorithms that can withstand all known attacks [5][6][7][8][9][10][11].On what concern us, we proposed in this paper is a diffusion method inspired by the Hungarian cube.
Introduction to the Special Issue on Privacy and Security in Evolving Internet of Multimedia Things

introduction Introduction to the Special Issue on Privacy and Security in Evolving Internet of Multimedia Things Share on Editors: Suraj Sharma View Profile , Xuyun Zhang View Profile , Hesham El-Sayed View Profile , Zhiyuan Tan View Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 16Issue 3sOctober 2020 Article No.: 93pp 1–3https://doi.org/10.1145/3423955Online:17 December 2020Publication History 0citation77DownloadsMetricsTotal Citations0Total Downloads77Last 12 Months38Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access