Rancang Bangun Pembelajaran Online Sistem Operasi Windows 7 dengan HTML 5

Website yang ada saat ini umumnya dibangun menggunakan tag – tag dalam HTML 4.1, dimana HTML 4.1 belum mendukung tampilan audio dan video, tetapi audio dan video dapat dilihat dan didengar dengan adanya aplikasi tambahan sesuai dengan keperluan. HTML 5 dikembangkan W3C yang telah mendukung audio dan video pada website tanpa harus tergantung pada aplikasi tambahan. Pada penulisan ini, penulis mengimplementasikan pembelajaran sistem operasi windows 7 di dalam website yang akan dibangun, dan penulis menggunakan metodologi waterfall, yang dimulai dengan tahap pendefinisian kebutuhan, analisis kebutuhan, perancangan sistem, pengkodean website, dan pengujian. Pada pengkodean website, penulis menggunakan actionscript untuk merancang konten dari website yang akan dibangun. Dari hasil rancang bangun website pembelajaran ini, diharapkan dapat membantu masyarakat untuk mengenal dan mengoperasikan sistem operasi windows 7 secara mendasar dengan mudah

Small ensemble of kriging models for optimization

The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP. The important factor that controls the efficiency of EGO is the GP covariance function (or kernel) which should be chosen according to the objective function. Traditionally, a pa-rameterized family of covariance functions is considered whose parameters are learned through statistical procedures such as maximum likelihood or cross-validation. However, it may be questioned whether statistical procedures for learning covariance functions are the most efficient for optimization as they target a global agreement between the GP and the observations which is not the ultimate goal of optimization. Furthermore, statistical learning procedures are computationally expensive. The main alternative to the statistical learning of the GP is self-adaptation, where the algorithm tunes the kernel parameters based on their contribution to objective function improvement. After questioning the possibility of self-adaptation for kriging based optimizers, this paper proposes a novel approach for tuning the length-scale of the GP in EGO: At each iteration, a small ensemble of kriging models structured by their length-scales is created. All of the models contribute to an iterate in an EGO-like fashion. Then, the set of models is densified around the model whose length-scale yielded the best iterate and further points are produced. Numerical experiments are provided which motivate the use of many length-scales. The tested implementation does not perform better than the classical EGO algorithm in a sequential context but show the potential of the approach for parallel implementations

A general treatment of snow microstructure exemplified by an improved relation for thermal conductivity

Finding relevant microstructural parameters beyond density is a longstanding problem which hinders the formulation of accurate parameterizations of physical properties of snow. Towards a remedy, we address the effective thermal conductivity tensor of snow via anisotropic, second-order bounds. The bound provides an explicit expression for the thermal conductivity and predicts the relevance of a microstructural anisotropy parameter <i>Q</i>, which is given by an integral over the two-point correlation function and unambiguously defined for arbitrary snow structures. For validation we compiled a comprehensive data set of 167 snow samples. The set comprises individual samples of various snow types and entire time series of metamorphism experiments under isothermal and temperature gradient conditions. All samples were digitally reconstructed by micro-computed tomography to perform microstructure-based simulations of heat transport. The incorporation of anisotropy via <i>Q</i> considerably reduces the root mean square error over the usual density-based parameterization. The systematic quantification of anisotropy via the two-point correlation function suggests a generalizable route to incorporate microstructure into snowpack models. We indicate the inter-relation of the conductivity to other properties and outline a potential impact of <i>Q</i> on dielectric constant, permeability and adsorption rate of diffusing species in the pore space

The Impact of Shape on the Perception of Euler Diagrams

Euler diagrams are often used for visualizing data collected into sets. However, there is a significant lack of guidance regarding graphical choices for Euler diagram layout. To address this deficiency, this paper asks the question `does the shape of a closed curve affect a user's comprehension of an Euler diagram?' By empirical study, we establish that curve shape does indeed impact on understandability. Our analysis of performance data indicates that circles perform best, followed by squares, with ellipses and rectangles jointly performing worst. We conclude that, where possible, circles should be used to draw effective Euler diagrams. Further, the ability to discriminate curves from zones and the symmetry of the curve shapes is argued to be important. We utilize perceptual theory to explain these results. As a consequence of this research, improved diagram layout decisions can be made for Euler diagrams whether they are manually or automatically drawn

Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Dozens of new models on fixation prediction are published every year and compared on open benchmarks such as MIT300 and LSUN. However, progress in the field can be difficult to judge because models are compared using a variety of inconsistent metrics. Here we show that no single saliency map can perform well under all metrics. Instead, we propose a principled approach to solve the benchmarking problem by separating the notions of saliency models, maps and metrics. Inspired by Bayesian decision theory, we define a saliency model to be a probabilistic model of fixation density prediction and a saliency map to be a metric-specific prediction derived from the model density which maximizes the expected performance on that metric given the model density. We derive these optimal saliency maps for the most commonly used saliency metrics (AUC, sAUC, NSS, CC, SIM, KL-Div) and show that they can be computed analytically or approximated with high precision. We show that this leads to consistent rankings in all metrics and avoids the penalties of using one saliency map for all metrics. Our method allows researchers to have their model compete on many different metrics with state-of-the-art in those metrics: "good" models will perform well in all metrics.Comment: published at ECCV 201

A new method of RF power generation for two-beam linear colliders

In this paper we discuss a new approach to two-beam acceleration. The energy for RF production is initially stored in a long-pulse electron beam which is efficiently accelerated to about 1.2 GeV by a fully loaded, conventional, low frequency (~1 GHz) linac. The beam pulse length is twice the length of the high-gradient linac. Segments of this long pulse beam are compressed using combiner rings to create a sequence of higher peak power drive beams with gaps in between. This train of drive beams is distributed from the end of the linac against the main beam direction down a common transport line so that each drive beam can power a section of the main linac. After a 180-degree turn, each high-current, low-energy drive beam is decelerated in low-impedance decelerator structures, and the resulti ng power is used to accelerate the low-current, high-energy beam in the main linac. The method discussed here seems relatively inexpensive is very flexible and can be used to accelerate beams for lin ear colliders over the entire frequency and energy range

Interferon β-1a in relapsing multiple sclerosis: four-year extension of the European IFNβ-1a Dose-C omparison Study

Background: Multiple sclerosis (MS) is a chronic disease requiring long-term monitoring of treatment. Objective: To assess the four-year clinical efficacy of intramuscular (IM) IFNb-1a in patients with relapsing MS from the European IFNb-1a Dose-C omparison Study. Methods: Patients who completed 36 months of treatment (Part 1) of the European IFNb-1a Dose-C omparison Study were given the option to continue double-blind treatment with IFNb-1a 30 mcg or 60 mcg IM once weekly (Part 2). Analyses of 48-month data were performed on sustained disability progression, relapses, and neutralizing antibody (NA b) formation. Results: O f 608/802 subjects who completed 36 months of treatment, 493 subjects continued treatment and 446 completed 48 months of treatment and follow-up. IFNb-1a 30 mcg and 60 mcg IM once weekly were equally effective for up to 48 months. There were no significant differences between doses over 48 months on any of the clinical endpoints, including rate of disability progression, cumulative percentage of patients who progressed (48 and 43, respectively), and annual relapse rates; relapses tended to decrease over 48 months. The incidence of patients who were positive for NAbs at any time during the study was low in both treatment groups. Conclusion: C ompared with 60-mcg IM IFNb-1a once weekly, a dose of 30 mcg IM IFNb-1a once weekly maintains the same clinical efficacy over four years

Style Blink: Exploring Digital Inking of Structured Information via Handcrafted Styling as a First-Class Object

Structured note-taking forms such as sketchnoting, self-tracking journals, and bullet journaling go beyond immediate capture of information scraps. Instead, hand-drawn pride-in-craftmanship increases perceived value for sharing and display. But hand-crafting lists, tables, and calendars is tedious and repetitive. To support these practices digitally, Style Blink ("Style-Blocks+Ink") explores handcrafted styling as a first-class object. Style-blocks encapsulate digital ink, enabling people to craft, modify, and reuse embellishments and decorations for larger structures, and apply custom layouts. For example, we provide interaction instruments that style ink for personal expression, inking palettes that afford creative experimentation, fillable pens that can be "loaded"with commands and actions to replace menu selections, techniques to customize inked structures post-creation by modifying the underlying handcrafted style-blocks and to re-layout the overall structure to match users' preferred template. In effect, any ink stroke, notation, or sketch can be encapsulated as a style-object and re-purposed as a tool. Feedback from 13 users show the potential of style adaptation and re-use in individual sketching practices