Intelligent Data Reduction (IDARE)

A description of the Intelligent Data Reduction (IDARE) expert system and an IDARE user's manual are given. IDARE is a data reduction system with the addition of a user profile infrastructure. The system was tested on a nickel-cadmium battery testbed. Information is given on installing, loading, maintaining the IDARE system

Optimizing Data Collection for Machine Learning

Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting data incurs unnecessary present costs, while under-collecting may incur future costs and delay workflows. We propose a new paradigm for modeling the data collection workflow as a formal optimal data collection problem that allows designers to specify performance targets, collection costs, a time horizon, and penalties for failing to meet the targets. Additionally, this formulation generalizes to tasks requiring multiple data sources, such as labeled and unlabeled data used in semi-supervised learning. To solve our problem, we develop Learn-Optimize-Collect (LOC), which minimizes expected future collection costs. Finally, we numerically compare our framework to the conventional baseline of estimating data requirements by extrapolating from neural scaling laws. We significantly reduce the risks of failing to meet desired performance targets on several classification, segmentation, and detection tasks, while maintaining low total collection costs.Comment: Accepted to NeurIPS 202

Emerson E & S Division\u27s Management Technique: An Exploratory Survey Into the Benefits of an Integrated Business Environment

Over the past few years, integrated business environments have become popular in the defense industry . An integrated business environment is a method used to unify all management and engineering activities required for a company to perform on a contract. The follow i ng research provides an introduction to the concepts of managing technological business through streamlining the efforts of all human and non-human resources involved, and secondarily , an exploratory survey into some of the important benefits and open issues that are being explored and debated among users, developers and maintainers of test program sets

A heuristic relaxed extrapolated algorithm for accelerating PageRank

The PageRank algorithm for determining the importance of Web pages has become a central technique in Web search. This algorithm uses the Power method to compute successive iterates that converge to the principal eigenvector of the Markov chain representing the Web link graph. In this work we present an effective heuristic Relaxed and Extrapolated algorithm based on the Power method that accelerates its convergence. A hybrid parallel implementation of this algorithm has been designed by combining various OpenMP threads for each MPI process and several strategies of data distribution among nodes have been analyzed. The results show that the proposed algorithm can significantly speed up the convergence time with respect to the parallel Power algorithm.This research was partially supported by the Spanish Ministry of Science and Innovation under Grant Number TIN2011-26254 and Grant Number TIN2015-66972-C5-4-R, and by the European Union FEDER (CAPAP-H5 network TIN2014-53522-REDT)

Reasoned A(I)administration: explanation requirements in EU law and the automation of public administration

Mechanisms to control public power have been developed and shaped around human beings as decision-makers at the centre of the public administration. However, technology is radically changing how public administration is organised and reliance on Artificial Intelligence is on the rise across all sectors. While carrying the promise of an increasingly efficient administration, automating (parts of) administrative decision-making processes also poses a challenge to our human-centred systems of control of public power. This article focuses on one of these control mechanisms: the duty to give reasons under EU law, a pillar of administrative law designed to enable individuals to challenge decisions and courts to exercise their powers of review. First, it analyses whether the duty to give reasons can be meaningfully applied when EU bodies rely on AI systems to inform their decisionmaking. Secondly, it examines the added value of secondary law, in particular the data protection rules applicable to EU institutions and the draft EU Artificial Intelligence Act, in complementing and adapting the duty to give reasons to better fulfil its purpose in a (partially) automated administration. This article concludes that the duty to give reasons provides a useful starting point but leaves a number of aspects unclear. While providing important safeguards, neither EU data protection law nor the draft EU Artificial Intelligence Act currently fill these gaps.The progression of EU law: Accommodating change and upholding value

Greyscale to binary image conversion

V první části bakalářské práce jsou popsány základní pojmy v oblasti získávání a popisu digitálního obrazu. Další část teoreticky popisuje možnosti zpracovávání obrazu, které jsou nutným základem pro správné oddělení textu od pozadí a tím pádem správný převod šedotónového snímku na binární. Dále byl proveden rozbor úlohy s přípravou dat pro zpracovávání. Následně byly aplikovány metody pro převod šedotónových snímků na binární a sestavena uživatelská aplikace. Posledním krokem je zhodnocení praktické realizace převodu, jeho subjektivní testování na dotazovaných respondentech a objektivní testování pomocí OCR softwaru.In the first part of the bachelor’s thesis are described the basic terms in obtaining and description of a digital image. The next part describes the theory about possibilities of image processing, which are essential for the correct separation of the text from the background, and thus for the correct conversion grayscale image to binary image. Then was performed the analysis of the task with the preparation of data for image processing. Then were applied different methods for converting grayscale to binary images and it was compiled user application. The final step is to evaluate the practical realization of the conversion, the subjective testing of respondents and objektive testing with the OCR software.

Space station automation of common module power management and distribution, volume 2

The new Space Station Module Power Management and Distribution System (SSM/PMAD) testbed automation system is described. The subjects discussed include testbed 120 volt dc star bus configuration and operation, SSM/PMAD automation system architecture, fault recovery and management expert system (FRAMES) rules english representation, the SSM/PMAD user interface, and the SSM/PMAD future direction. Several appendices are presented and include the following: SSM/PMAD interface user manual version 1.0, SSM/PMAD lowest level processor (LLP) reference, SSM/PMAD technical reference version 1.0, SSM/PMAD LLP visual control logic representation's (VCLR's), SSM/PMAD LLP/FRAMES interface control document (ICD) , and SSM/PMAD LLP switchgear interface controller (SIC) ICD

Contribution to Financial Modeling and Financial Forecasting

This thesis consists of three chapters. Each chapter is independent research that is conducted during my study. This research is concentrated on financial time series modeling and forecasting. On first chapter, the research aims to prove that any abnormal behavior in debt level is a signal of future unexpected return for firms that is listed in indexes in this study, hence it is a signal to buy. In order to prove this theory multiple indexes from around the world were taken into consideration. This behavior is consistent in most of indexes around the word. The second chapter investigate the effect of United State president speech on value of United State Currency in Foreign Exchange Rate market. In this analysis it is shown that during the time the president is delivering a speech there is distinctive changes in USD value and volatility in global markets. This chapter implies that this effect cannot be captured by linear models, and the impact of the presidential speech is short term. Finally, the third chapter which is the major research of this thesis, suggest two new methods that potentially enhance the financial time series forecasting. Firstly, the new ARMA-RNN model is presented. The suggested model is inheriting the process of Autoregressive Moving Average model which is extensively studied, and train a recurrent neural network based on it to benefit from unique ability of ARMA model as well as strength and nonlinearity of artificial neural network. Secondly the research investigates the use of different frequency of data for input layer to predict the same data on output layer. In other words, artificial neural networks are trained on higher frequency data to predict lower frequency. Finally, both stated method is combined to achieve more superior predictive model