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BAHASA INDONESIA DALAM HUBUNGANNYA DENGAN PENGEMBANGAN KEBUDAYAAN NASIONAL
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-1, JANUARI (No. 1) |
| Halaman | : | 15-31 |
| Abstrak | : | - |
Learning in Nonstationary Environments: A Survey
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 12-25 |
| Abstrak | : | pplications that generate data from nonstationary environments, where the underlying phenomena change over time, are becoming increasingly prevalent. Examples of these applications include making inferences or predictions based on financial data, energy demand and climate data analysis, web usage or sensor network monitoring, and malware/spam detection, among many others. In nonstationary environments, particularly those that generate streaming or multi-domain data, the probability density function of the data-generating process may change (drift) over time. Therefore, the fundamental and rather naïve assumption made by most computational intelligence approaches - that the training and testing data are sampied from the same fixed, albeit unknown, probability distribution - is simply not true. Learning in nonstationary environments requires adaptive or evolving approaches that can monitor and track the underlying changes, and adapt a model to accommodate those changes accordingly. In this effort, we provide a comprehensive survey and tutorial of established as well as state-of-the-art approaches, while highlighting two primary perspectives, active and passive, for learning in nonstationary environments. Finally, we also provide an inventory of existing real and synthetic datasets, as well as tools and software for getting started, evaluating and comparing different approaches. |
ILMU PENGETAHUAN BAGI PEMBANGUNAN KESEHATAN NASIONAL
| Pengarang | : | Daoed Joesoef |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | X-1, JANUARI (No. 1) |
| Halaman | : | 5-14 |
| Abstrak | : | - |
Computational Intelligence for Changing Environments
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 10-11 |
| Abstrak | : | - |
CIS Publication Spotlight
| Pengarang | : | Derong Liu,Chin-Teng Lin,Kay Chen Tan,Kendall, Graham,Angelo Cangelosi |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 4) |
| Halaman | : | 7-9 |
| Abstrak | : | - |
A Polynomial with a Root Mod p for Every p has a Real Root
| Pengarang | : | Rodrigo Angelo & Max Wenqiang Xu |
| Nama Majalah/Jurnal | : | The American Mathematical Monthly |
| Volume / Edisi | : | 132 (No. 9) |
| Halaman | : | 918-925 |
| Abstrak | : | We prove that if a polynomial with rational coefficients has a root mod p for every large prime p, then it has a real root. We show with examples how to use Chebotarev’s density theorem to study roots of polynomials mod p, leading up to our proof. As an application, we show that the primes can’t be covered by finitely many positive definite binary quadratic forms. |
On the Set of Points Represented by Harmonic Subseries
| Pengarang | : | Vjekoslav Kovac |
| Nama Majalah/Jurnal | : | The American Mathematical Monthly |
| Volume / Edisi | : | 132 (No. 9) |
| Halaman | : | 895-911 |
| Abstrak | : | We help Alice play a certain “convergence game” against Bob and win the prize, which is a constructive solution to a problem by Paul Erd?s and Ronald Graham, posed in their 1980 book on open questions in combinatorial number theory. Namely, after several reductions using peculiar arithmetic identities, the game outcome shows that the set of points(∑????∈????1????,∑????∈????1????+1,∑????∈????1????+2),obtained as A ranges over infinite sets of positive integers, has a non-empty interior. This generalizes a two-dimensional result by Erd?s and Ernst Straus. |
PELARIAN DARI IRIAN MASALAH TANGGUNG JAWAB BERSAMA
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-9, SEPTEMBER (No. 9) |
| Halaman | : | 881-892 |
| Abstrak | : | - |
Predictive Cloud Computing with Big Data: Professional Golf and Tennis Forecasting
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | IEEE Computational intelligence |
| Volume / Edisi | : | 10 (No. 3) |
| Halaman | : | 62-76 |
| Abstrak | : | Major Golf and Grand Slam Tennis tournaments such as Australian Open, The Masters, Roland Garros, United States Golf Association (USGA), Wimbledon, and United States Tennis Association (USTA) United States (US) Open provide real-time and historical sporting information to immerse a global fan base in the action. Each tournament provides realtime content, including streaming video, game statistics, scores, images, schedule of play, and text. Due to the game popularities, some of the web servers are heavily visited and some are not, therefore, we need a method to autonomously provision servers to provide a smooth user experience. Predictive Cloud Computing (PCC) has been developed to provide a smart allocation/deallocation of servers by combining ensembles of forecasts and predictive modeling to determine the future origin demand for web site content. PCC distributes processing through analytical pipelines that correlate streaming data, such as scores, media schedules, and player brackets with a future-simulated tournament state to measure predicted demand spikes for content. Social data streamed from Twitter provides social sentiment and popularity features used within predictive modeling. Data at rest, such as machine logs and web content, provide additional features for forecasting. While the duration of each tournament varies, the number of origin website requests range from 29,000 to 110,000 hits per minute. The PCC technology was developed and deployed to all Grand Slam tennis events and several major golf tournaments that took place in 2013 and to the present, which has decreased wasted computing consumption by over 50%. We propose a novel forecasting ensemble that includes residual, vector, historical, partial, adjusted, cubic and quadratic forecasters. In addition, we present several predictive models based on Multiple Regression as inputs into several of these forecasters. We conclude by empirically demonstrating that the predictive cloud technology is able to forecast the computing load on origin web servers for professional golf and tennis tournaments. |
HUBUNGAN PERDAGANGAN JEPANG-ASEAN: BEBERAPA MASALAH DAN PROSPEK
| Pengarang | : | J. Panglaykim. |
| Nama Majalah/Jurnal | : | Analisa |
| Volume / Edisi | : | IX-9, SEPTEMBER (No. 9) |
| Halaman | : | 824-880 |
| Abstrak | : | - |