Miracle-dz/UniFAR-FTUs huggingface dataset:
High Accuracy Power Quality Evaluation under a Colored Noisy Condition by Filter Bank ESPRIT

Due to the highly increasing integration of renewable energy sources with the power grid and their fluctuations, besides the recent growth of new power electronics equipment, the noise in power systems has become colored. The colored noise affects the methodologies for power quality parameters’ estimation, such as harmonic and interharmonic components. Estimation of signal parameters via rotational invariance techniques (ESPRIT) as a parametric technique with high resolution has proven its efficiency in the estimation of power signal components’ frequencies, amplitudes, and phases for quality analysis, under the assumption of white Gaussian noise. Since ESPRIT suffers from high computational effort, filter bank ESPRIT (FB-ESPRIT) was suggested for mitigation of the complexity. This manuscript suggests FB-ESPRIT as well for accurate and robust estimation of power signal components’ parameters in the presence of the colored noise. Even though the parametric techniques depend on the Gaussianity of contaminating noise to perform properly, FB-ESPRIT performs well in colored noise. The FB-ESPRIT superiority compared with the conventional ESPRIT and MUSIC techniques was demonstrated through many simulations runs on synthetic power signals with multiple harmonics, interharmonics, and subharmonic components in the presence of noises of different colors and different SNR levels. FB-ESPRIT had a significant efficiency superiority in power quality analysis with a wide gap distance from the other estimators, especially under the high level of colored noise.
Task Scheduling for cloud computing Based on Firefly Algorithm

In this paper a new method proposed, to solve the problem of scheduling resources in cloud computing, it is using a parallel scheduling model which can enhance parallel tasks scheduling while the relationship among serial tasks stay constant. The important feature of dynamic tasks is provided by users to sub-tasks in serial sequences and arranged as scheduling queue, these tasks have different and equal priorities according to running order. In this paper firefly algorithm (FA) have been introduced for scheduling the subtasks in cloud computer to prove a progress in short delay time. The most important features that can be considered are fairness and efficiency characteristics. Delay time Shortest and Fairness firefly algorithm (DSFFA) applied to solve task scheduling problem that realized the shortest delay time, which improving the user satisfaction. Experimental results show that DSFFA algorithm is better than the task scheduling –Firefly algorithm (TS-FA) algorithm in fairness, efficiency and task delay time, therefor it can be realized for the optimal scheduling in cloud computing.
Language Support for Navigating Architecture Design in Closed Form

As computer architecture continues to expand beyond software-agnostic microarchitecture to specialized and heterogeneous logic or even radically different emerging computing models (e.g., quantum cores, DNA storage units), detailed cycle-level simulation is no longer presupposed. Exploring designs under such complex interacting relationships (e.g., performance, energy, thermal, frequency) calls for a more integrative but higher-level approach. We propose Charm, a modeling language supporting closed-form high-level architecture modeling. Charm enables mathematical representations of mutually dependent architectural relationships to be specified, composed, checked, evaluated, reused, and shared. The language is interpreted through a combination of automatic symbolic evaluation, scalable graph transformation, and efficient compiler techniques, generating executable DAGs and optimized analysis procedures. Charm also exploits the advancements in satisfiability modulo theory solvers to automatically search the design space to help architects explore multiple design knobs simultaneously (e.g., different CNN tiling configurations). Through two case studies, we demonstrate that Charm allows one to define high-level architecture models in a clean and concise format, maximize reusability and shareability, capture unreasonable assumptions, and significantly ease design space exploration at a high level.
Relating Conceptual Structure With Flexible Concept Use

Our mental words are populated with concepts — rich representations of knowledge about things in the world (e.g., diamonds, pumpkins). In language, words are used to refer to these concepts (e.g., “diamond”, “pumpkin”) and to communicate with others. This is quite impressive given that a word does not activate the same information each time it is used: conceptual information is flexibly activated based on the context. For example, the phrases “raw diamond”, “baseball diamond”, and “diamond eyes” evoke different kinds of diamond information. This flexible concept use is not only exemplified in creative language, but in creative thought and natural language more generally. The goal of this thesis was to leverage methods in cognitive neuroscience, network science, and computational modeling to explore the kinds of conceptual structure that can support this flexible concept use. In the first study (Chapter 2) I capture the global structure of concepts in novel feature-based networks, and show that aspects of this network structure relate to text-based and empirical measures of flexible concept use. I subsequently narrow in on the local representations of conceptual features that relate to flexible concept use by observing what happens when concepts combine. In one fMRI study (Chapter 3) I show that feature uncertainty predicts the extent to which features (e.g., green, salty) are flexibly modulated in the brain during comprehension of adjective-noun combinations (e.g., “green pumpkin”, “salty cookie”). In follow-up studies (Chapter 4) I further reveal the relationship between feature uncertainty and flexible feature activations in combined concepts. In combinations that modify conceptual brightness (e.g., “dark diamond”, “light night”), an explicit behavioral measure of conceptual feature modulation is predicted by feature uncertainty as well as by a related predictive combinatorial Bayesian model. An associated fMRI study reveals that flexible feature modulation and feature uncertainty relate to responses in left inferior frontal gyrus (LIFG) and left anterior temporal lobe (LATL), suggesting roles for these regions in flexible concept activation. Taken together, this work reveals relationships between conceptual structure and flexible concept use in behavior and in the brain.
Dealing with Interference on Experimentation Platforms

The theory of causal inference, as formalized by the potential outcomes framework, relies on an assumption that the experimental units are independent. When independence is not tenable, we say there is interference, and the core results of causal inference can no longer be guaranteed. Recent research efforts have focused on extending the theory to a setting where interference is present. The many advantages of experimentation platforms over more traditional settings of causal inference—no issue of non-compliance, large number of experimental units, ease of collecting outcomes over the course of an experiment—make them an ideal setting for studying causality with interference. With this setting in mind, we explore how multi-level designs, Experiment-of-Experiments, can allow us to detect and mitigate the effects of interference on experimentation platforms. In particular, we develop a design-based statistical test for the no-interference assumption. We further design an empirical procedure for comparing the effectiveness of cluster-based randomized designs. Finally, we show that randomized saturation designs can be optimized to improve the bias and variance of standard estimators, and extend these results to a new category of randomized designs: optimized saturation designs.
Determinants of Child Height in Uganda: A Consideration of the Selection Bias Caused by Child Mortality

This paper reports a methodology for analysis and presents the determinants of child height in Uganda. A two-stage estimation method that evaluated the effects of covariates on child height for age after controlling for the selection bias caused by child mortality was necessary. Important determinants of child health in Uganda are the child's and some maternal characteristics. Some environmental factors (at the levels of both community aggregate and household) have significance. The effects of mothers’ characteristics were relatively more sensitive to correction of the selection bias. In particular, mother's secondary education almost doubled its effect and became significant in determining the height of children. Overall results suggest that Uganda is facing a phase of health transition in which the effect of socio-economic variables (at both individual and community levels) are beginning to show up significantly. Although an all-round developmental effort is essential, selective interventions aiming to improve female education and, where that is difficult, extension of appropriate information through radio are likely to improve the survival and health of children.
Analysis of High-Dimensional Time Series with Applications on Brain Signals

Author(s): Wang, Yuxiao | Advisor(s): Ombao, Hernando | Abstract: Neuronal populations behave in a coordinated manner both during resting-state and while executing tasks such as learning and memory retention. One of the major challenges to analyzing brain signals such as electroencephalograms (EEGs) and functional magnetic resonance imaging (fMRI)is high dimensionality. There can be hundreds of channels in a typical EEG recording, and the number of voxels in a fMRI recording can be hundreds of thousands. We developed computationally efficient and theoretically justified tools for analyzing high dimensional brain signals. Our approach is to extract the optimal lower dimensional representations for each brain region and then characterize and estimate connectivity between regions through these factors. This approach is motivated by the fact that electroencephalograms (EEGs) from many channels within each region exhibit a high degree of multicollinearity and synchrony thereby suggesting that it would be sensible to extract summary factors for each region. We focus on two types of linear filters. These methods were compared through simulations under different conditions and the results provide insights on advantages and limitations of each. We also performed exploratory analysis of resting state EEG data and fMRI data. The spectral properties of the factors were estimated and connectivity between regions via the factors using coherence measures were computed. We implemented these methods in a Matlab toolbox XHiDiTS. The toolbox was utilized to investigate consistency of these factors across all epochs during the entire resting-state period. In order to quantify the effective connectivity among sources in a densely voxelated (high-dimensional) cortical surface, we developed the source-space factor vector autoregressive (VAR) model. The first step in our procedure is to estimate cortical activity from multichannel electroencephalograms (EEG) using anatomically constrained brain imaging methods. Following parcellation of the cortical surface into disjoint regions of interest (ROIs), latent factors within each ROI are computed using principal component analysis. These factors are ROI specific low-rank approximations (or representations) which allow for efficient estimation of connectivity in the high-dimensional cortical source space. The second step is to model effective connectivity between ROIs by fitting a VAR model jointly on all the latent processes. The different cortical sources within a ROI may share common factors as each source is a mixture of these VAR factors. From this commonality we derive the connectivity between the sources. Measures of cortical connectivity, in particular partial directed coherence (PDC), are formulated using the VAR parameters. We illustrate the proposed model to investigate connectivity and interactions between cortical ROIs during resting state.
Incentivizing Commuters to Carpool: A Large Field Experiment with Waze

Problem definition: Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. Academic/practical relevance: In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. Methodology: Our field experiment involves more than half a million users across four U.S. states between June 10 and July 3, 2019. We identify users who can save a significant commute time by carpooling through the use of a high-occupancy vehicle (HOV) lane, users who can still use an HOV lane but have a low time saving, and users who do not have access to an HOV lane on their commute. We send them in-app notifications with different framings: mentioning the HOV lane, highlighting the time saving, emphasizing the monetary welcome bonus (for users who do not have access to an HOV lane), and a generic carpool invitation. Results: We find a strong relationship between the affinity to carpool and the potential time saving through an HOV lane. Managerial implications: Specifically, we estimate that mentioning the HOV lane increases the click-through rate (i.e., proportion of users who clicked on the button inviting them to try the carpool service) and the onboarding rate (i.e., proportion of users who signed up and created an account with the carpool service) by 133%–185% and 64%–141%, respectively, relative to a generic invitation. We conclude by discussing the implications of our findings for carpool platforms and public policy.
“I don't understand it so it can't be good”: Users' acg domain expertise and perceived quality of video tags

Tag quality is an important factor to the success of social tagging systems and platforms. Users' domain expertise may influence they perceive tag quality. This study aims to explore how users of different domain experience (frequent user, occasional user, and non‐user) perceive the quality of the same tags. We examined an online video community, Bilibili, which specializes in Anime, Comic and Games (ACG) subculture. We asked 60 users to watch 15 videos and rate the 95 tags of these videos, and found that: 1) Users with more domain expertise give higher ratings for tags' relevance to the videos and their retrieval value; 2) Occasional users have the lowest understandability rating, followed by non‐users, and frequent users; 3) users think high‐frequency tags are less suitable for retrieval. These results may provide insights to high quality tag selection for personalized recommendation and retrieval.
Ivabradine in the management of coronary artery disease with or without left ventricular dysfunction or heart failure

Heart rate reduction is an integral part of antianginal therapy. In this article, we examine currently available data related to the use of ivabradine in patients with angina pectoris with and without left ventricular dysfunction (LVD) or heart failure (HF). We explore results from large morbidity–mortality trials of ivabradine in stable coronary artery disease (CAD) without HF (SIGNIfY), stable CAD with LVD (BEAUTIfUL), and systolic HF (SHIfT). While ivabradine did not improve outcomes in the main population in SIGNIfY, analyses of the antianginal effect of ivabradine in a pre-specified angina subgroup ( n = 12 049) were in line with knowledge of the antianginal properties of ivabradine, with improvements in angina class vs. placebo ( P = 0.01) and a trend towards less elective coronary revascularization ( P = 0.058). A post hoc analysis in a subgroup of 1507 BEAUTIfUL patients with limiting angina at baseline showed that risk reduction in favour of ivabradine, which was nominally statistically significant for hospitalization for myocardial infarction; the results were even better in patients with heart rate ≥70 b.p.m. Similarly, a post hoc analysis in 2220 SHIfT patients with angina at baseline indicated that the beneficial prognostic effects of ivabradine were maintained. The main differences between the three studies are LVD and ivabradine dosage (which was higher in SIGNIfY than in the other two trials). We conclude that heart rate reduction has a different role according to LVD. Provided that it is used at the recommended dose, ivabradine has been proved to be an effective, useful antianginal agent in patients with and without LVD.
Answering Skyline Queries Over Incomplete Data With Crowdsourcing

Due to the pervasiveness of incomplete data, incomplete data queries are vital in a large number of real-life scenarios. Current models and approaches for incomplete data queries mainly rely on the machine power. In this paper, we study the problem of <italic>skyline queries over incomplete data with crowdsourcing</italic>. We propose a novel query framework, termed as <inline-formula><tex-math notation="LaTeX">${\sf BayesCrowd}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">BayesCrowd</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq1-2946798.gif"/></alternatives></inline-formula>, which takes into account the data correlation using the Bayesian network. We leverage the typical <italic>c-table</italic> model on incomplete data to represent objects. Considering budget and latency constraints, we present a suite of effective task selection strategies. Moreover, we introduce a <italic>marginal utility</italic> function to measure the benefit of crowdsourcing one task. In particular, the probability computation of each object being an answer object is at least as hard as #SAT problem. To this end, we propose an <italic>adaptive</italic> DPLL (i.e., Davis-Putnam-Logemann- Loveland) algorithm to speed up the computation. Extensive experiments using both real and synthetic data sets confirm the superiority of <inline-formula><tex-math notation="LaTeX">${\sf BayesCrowd}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">BayesCrowd</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq2-2946798.gif"/></alternatives></inline-formula> to the state-of-the-art method, in terms of execution time, monetary cost, and latency minimization.
A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems

Recently, wireless technologies have been growing actively all around the world. In the context of wireless technology, fifth-generation (5G) technology has become a most challenging and interesting topic in wireless research. This article provides an overview of the Internet of Things (IoT) in 5G wireless systems. IoT in the 5G system will be a game changer in the future generation. It will open a door for new wireless architecture and smart services. Recent cellular network LTE (4G) will not be sufficient and efficient to meet the demands of multiple device connectivity and high data rate, more bandwidth, low-latency quality of service (QoS), and low interference. To address these challenges, we consider 5G as the most promising technology. We provide a detailed overview of challenges and vision of various communication industries in 5G IoT systems. The different layers in 5G IoT systems are discussed in detail. This article provides a comprehensive review on emerging and enabling technologies related to the 5G system that enables IoT. We consider the technology drivers for 5G wireless technology, such as 5G new radio (NR), multiple-input–multiple-output antenna with the beamformation technology, mm-wave commutation technology, heterogeneous networks (HetNets), the role of augmented reality (AR) in IoT, which are discussed in detail. We also provide a review on low-power wide-area networks (LPWANs), security challenges, and its control measure in the 5G IoT scenario. This article introduces the role of AR in the 5G IoT scenario. This article also discusses the research gaps and future directions. The focus is also on application areas of IoT in 5G systems. We, therefore, outline some of the important research directions in 5G IoT.
Bedah Total Server : referensi lengkap teknologi server, data center, virtualization, cloud computing & enterprise system

Dalam buku ini dibahas tentang definisi server dan enterprise, memahami fungsi server, karakter server, kecanggihan sebuah server, faktor pendukung server, cara kerja komputer secara umum, platform processor intel, regulasi mengenai data center di Indonesia, definisi komputasi awan, implementasi komputasi awan, fungsi storage server secara umum, dan sekilas mengenai seagate
Impact evaluation using Difference-in-Differences

Purpose This paper aims to present the Difference-in-Differences (DiD) method in an accessible language to a broad research audience from a variety of management-related fields. Design/methodology/approach The paper describes the DiD method, starting with an intuitive explanation, goes through the main assumptions and the regression specification and covers the use of several robustness methods. Recurrent examples from the literature are used to illustrate the different concepts. Findings By providing an overview of the method, the authors cover the main issues involved when conducting DiD studies, including the fundamentals as well as some recent developments. Originality/value The paper can hopefully be of value to a broad range of management scholars interested in applying impact evaluation methods.
Intuitive Numerical Information Processes in Consumer Judgment

Numerical information is ubiquitous in modern life. The prevalence of numerical information in the marketplace necessitates understanding how consumers handle and interpret that information, for both theoretical and practical reasons. Past research has largely focused on consumers’ encoding of numbers, calculative limitations, and usage of heuristics. This dissertation will contribute to this burgeoning literature in several ways. First, I identify a general tendency in how consumers calculate ratios based on an intuitive model of division. Specifically, consumers tend to divide larger numbers by smaller numbers. The intuitive model of division has marketing implications for both consumers’ evaluations of quantity offers and sensitivities to promotions. Next, I examine how consumers draw inferences from distributional information. In contrast to the assumption that consumers utilize means to assess central tendency, I demonstrate that consumers use the modal response to judge what is typical, with implications for consumers’ inferences about product ratings and other social distributions. Intuitive Numerical Information Processes in Consumer Judgment Daniel Joseph Bodin Villanova GENERAL AUDIENCE ABSTRACT Numerical information is ubiquitous in modern life. The prevalence of numerical information in the marketplace necessitates understanding how consumers handle and interpret that information, for both theoretical and practical reasons. Past research has largely focused on how consumers’ mentally perceive numbers, how difficult it is to engage in calculation, and usage of mental shortcuts. This dissertation will contribute to this burgeoning literature in several ways. First, I identify a general tendency in how consumers calculate ratios based on an intuitive model of division. Specifically, consumers tend to divide larger numbers by smaller numbers. The intuitive model of division has marketing implications for both consumers’ evaluations of quantity offers and sensitivities to promotions. Next, I examine how consumers draw inferences from distributional information. In contrast to the assumption that consumers utilize means to assess central tendency, I demonstrate that consumers use the modal response to judge what is typical, with implications for consumers’ inferences about product ratings and other social distributions.