Reviewerly/exHarmony huggingface dataset:
Theory III: The Stepper Motor and Its Control

The stepper motor is a very popular low-cost electric motor whose drive shaft rotates in discrete angular steps, usually a basic standard 1.8 degrees/step, that is, 200 steps/rev. It derives its torque from electromagnetic energy. Depending on the coil driving sequence, it is possible to increase the step resolution in integer multiples of its standard 200 steps/rev resolution, for example, 400, 800, up to 25,600 steps/rev, which is called “microstepping.” Stepper motors give resolute and accurate position, velocity, and acceleration control of, for example, mechanical links, levers, cams, and linear slideways. It is an open-loop actuator which has certain advantages over a closed-loop servomechanism but the advantages have certain conditions and limitations which must be followed for its effective operation. Such conditions and limitation relate to its “pull-in speed,” “pull-out speed,” and the lack of knowledge of the position of the output shaft. Such disadvantages relate to it not being a closed-loop servomechanism device and that it can “lose lock” such that its output angle is unknown. This chapter describes the motor and two types of electrical power circuitry, the unipolar and bipolar drivers, which energize the motor to provide the mechanical power to propel the mechanism, which, in turn, hits or throws the ball to its target. The electrical circuitry that drives the stepper motor to a given position, or at a given velocity and acceleration is the responsibility of power H-bridges that are sequenced by a microcomputer. The chapter describes the software algorithms that accomplish this sequencing.
Bounded, Multidimensional, Integrated Memetic Evolution for Character Recognition Based on Predictive Elimination Theory and Optimization Techniques

This article describes how inspired by the natural process of evolution in genetic algorithms, memetic algorithms (MAs) are a category of cultural evolution phenomenon. The very concept of MA has been discussed in the last few years and is adding newer dimensions to MA and computational skills of algorithms. There are many optimization algorithms which fully exploit the problem under consideration. This article presents a heuristic approach for an improvised algorithm which takes into consideration various optimization parameters in isolation and tries to integrate the self-learning technique of MA. A general structure of MA according to this article should be perfectly in-line with brain activities which are neurotically tested and given maximum emphasis on local search and context-based predictive approaches rather than mathematically computing every event and taking or picking solutions based on results of certain formula. This article goes one step beyond the conventional set of the variety of problem domains, ranging from discrete optimization, continuous optimization, constrained optimization and multi objective optimization in which MAs have been successfully implemented. These optimization techniques must be processed using outcomes of predictive optimization and using a method of elimination to make the search set smaller and smaller as we progress deeper into the search. There is a scarcity of literature and also lack of availability of comprehensive reviews on MAs. The proposed technique is a better approach for solving combinatorial optimization problems. This article gives an overview of various domains and problem types in which MA can be used. Apart from this, the problem of character recognition using predictive optimization and implementation of elimination theory MA is discussed.
LibGuides: Electronic Resources: Troubleshooting: Article Access Issues

describes some workarounds and tips for common access problems for online full text resources
Probability and relative frequency

This chapter defines probability and then gives the few mathematical rules necessary to work with it. While probability is a mathematical concept, we deal with real data. The properties of observed data are explained, and a method of obtaining the distribution of relative frequencies of observed values is given. Then the relationship of relative frequency distributions to probability distributions is explained and illustrated.
Positive Personal Qualities and Depression

The article describes the results of studying the relationship between positive personal qualities and depression, as an antagonist of positive parameters.The following positive characteristics were studied: subjective well-being, happiness, hope, subjective success of activity, the ability to determine life goals, emotional maturity, the ability to comprehend the present, satisfaction with life, desire and initiative for personal growth, inclination to curiosity and research, motivation to seek and understand meaning life, gratitude, satisfaction with marriage. The level of depression was also measured. Positive characteristics and contrasted depression show multiple positive (with depression - negative) correlations among themselves, which indicates a general factor of positivity. A positive person is positive in all spheres of life and at the same time does not show any depressive manifestations. In general, depressivity is antagonistic to a person's positivity.
Optimization of Biochemical Systems Production Using Combination of Newton Method and Particle Swarm Optimization

<p class='IJASEITAbtract'><span lang='EN-US'>In the presented paper, an improved method that combines the Newton method with Particle Swarm Optimization (PSO) algorithm to optimize the production of biochemical systems was discussed and presented in detail. The optimization of the biochemical system's production became difficult and complicated when it involves a large size of biochemical systems that have many components and interaction between chemical. Also, two objectives and several constraints make the optimization process difficult. To overcome these situations, the proposed method was proposed by treating the biochemical systems as a nonlinear equations system and then optimizes using PSO. The proposed method was proposed to improve the biochemical system's production and at the same time reduce the total of chemical concentration involves. In the proposed method, the Newton method was used to deal with nonlinear equations system, while the PSO algorithm was utilized to fine-tune the variables in nonlinear equations system. The main reason for using the Newton method is its simplicity in solving the nonlinear equations system. The justification of choosing PSO algorithm is its direct implementation and effectiveness in the optimization process. In order to evaluate the proposed method, two biochemical systems were used, which were E.coli pathway and S. cerevisiae pathway. The experimental results showed that the proposed method was able to achieve the best result as compared to other works.</span>
GE Lumination™ LED 2x2 Recessed Troffer with Emergency Backup, 120-277 Volt, 3300 Lumen, 26 Watt, 4000K, White, LBT22

The GE BT series fixtures fit perfectly into existing drop ceilings for high-tech retrofits that bring a fresh perspective to any space.
Application of clustering technique in writing analytical exposition text

The objectives of the research were to find out: (1) whether or not the application of clustering technique enhances students’ ability in writing analytical exposition text in the eleventh grade of SMA Negeri 1 Pancarijang and (2) whether or not the application of clustering technique in writing analytical exposition text is interesting for the eleventh grade students of SMA Negeri 1 Pancarijang. This research employed a Quasi-experimental design that applied experimental and control group. The population of the research was the eleventh grade (XI) students of SMA Negeri 1 Pancarijang in academic years 2013-2014. A total number of population was 294 students,and two classes of them were taken as a sample by using random sampling technique, class XI IPA 1 as the experimental group and class XI IPA 3 as a control group. The result of data analysis showed that there wasa significant difference between achievement of the students who applied clustering technique and those who did not apply clustering technique (conventional technique) in writing analytical exposition text. It was proved by the mean score of the experimental group was higher than the control group in post-test (77.0 > 62.2). Furthermore, the result of the t-test value (5.382) was greater than t-table (α = 0.05; df = 64; t-table = 2.000) which means that H 1 was accepted. The data analysis of questionnaire showed that the students had high interest in the application of clustering technique in writing analytical exposition. It was supported by 22 students (66.7%) who were strongly interested and 11 students (33.3%) who were interested. The mean score of students’ answers in the questionnaire (87.0) was classified as a strongly interested category
Identification of Information Security Management Factors

Considering the important role of information in organizations, an appropriate management is required for maintaining the security of the information. The information security management system is part of a general management system in an organization and based on the business risk approach aims to establish, implement, use, monitor, revise, maintain, and improve security that leads to the protection of information and minimizes unauthorized access. The main objective of this chapter is to identify factors and indicators of information security based on the information management system. For this purpose, after reviewing the literature and having opinions of 30 experts, a conceptual framework for public organizations is proposed that includes information security management factors such as financial, technical, operation and communication, human resources, data and information classification, environmental and physical, and managerial.
An Extended Occlusion Detection Approach for Video Processing

Occlusions become conspicuous as failure regions in video processing when unified over time because the contraventions of the restriction of brightness have accumulated and evolved in occluded regions. The accuracy at the boundaries of the moving objects is one of the challenging areas that required further exploration and research. This paper presents the work in process approach that can detect occlusion regions by using pixel-wise coherence, segment-wise confidence and interpolation technique. Our method can get the same result as usual methods by solving only one Partial Differential Equations (PDE) problem; it is superior to existing methods because it is faster and provides better coverage rates for occlusion regions than variation techniques when tested against a varied number of benchmark datasets. With these improved results, we can apply and extend our approach to a wider range of applications in computer vision, such as background subtraction, tracking, 3D reconstruction, video surveillance, video compression.
Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines

An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem of causal inference in a bivariate case, where the joint distribution of two variables is observed. We consider, in particular, data on discrete domains. The state-of-the-art causal inference methods for continuous data suffer from high computational complexity. Some modern approaches are not suitable for categorical data, and others need to estimate and fix multiple hyper-parameters. In this contribution, we introduce a novel method of causal inference which is based on the widely used assumption that if X causes Y, then P(X) and P(Y|X) are independent. We propose to explore a semi-supervised approach where P(Y|X) and P(X) are estimated from labeled and unlabeled data respectively, whereas the marginal probability is estimated potentially from much more (cheap unlabeled) data than the conditional distribution. We validate the proposed method on the standard cause-effect pairs. We illustrate by experiments on several benchmarks of biological network reconstruction that the proposed approach is very competitive in terms of computational time and accuracy compared to the state-of-the-art methods. Finally, we apply the proposed method to an original medical task where we study whether drugs confound human metagenome.
NSH and Segment Routing Integration for Service Function Chaining (SFC)

This document describes two application scenarios where NetworkService Header (NSH) and Segment Routing (SR) techniques can bedeployed together to support Service Function Chaining (SFC) in anefficient manner while maintaining separation of the service andtransport planes as originally intended by the SFC architecture. Inthe first scenario, an NSH-based SFC is created using SR as thetransport between SFFs. SR in this case is just one of manyencapsulations that could be used to maintain the transport-independent nature of NSH-based service chains. In the secondscenario, SR is used to represent each service hop of the NSH-basedSFC as a segment within the segment-list. SR and NSH in this case areintegrated. In both scenarios SR is responsible for steering packetsbetween SFFs along a given SFP while NSH is responsible formaintaining the integrity of the service plane, the SFC instancecontext, and any associated metadata. These application scenariosdemonstrate that NSH and SR can work jointly and complement each otherleaving the network operator with the flexibility to use whichevertransport technology makes sense in specific areas of their networkinfrastructure, and still maintain an end-to-end service plane usingNSH.
RANCANG BANGUN SISTEM INVENTORI KONTROL DALAM MENUNJANG PELAYAN UNTUK MENINGKATKAN KEPUASAN PELANGGAN

Companies that are engaged in general trading, suppliers and distributors for the procurement of goods or equipment and services to assist the operations and management of companies, especially improving company services in the field of drinking water are still using manual systems, where data is reported in written form, making it difficult , ineffective and efficient in inputting data and searching for goods. Therefore, this study proposes making a control inventory system to create a faster, more precise and accurate system. The analysis and modeling systems for programs use unified modeling language (UML) as a tool to help in object-oriented programming languages which are then implemented with the php programming language and use MySQL as a database. From this study, an inventory system can be produced that can monitor stock of goods in the warehouse properly, effectively and efficiently so that it can support services and improve customer satisfaction.
Nonconformity, recall and crisis management

Technical and Quality Management must have the capability to operate both proactively and reactively. This chapter focuses on the processes of nonconformance decision making, the steps that can be taken to address issues, and the progression of corrective/preventive actions. It also reviews the practices and considerations involved in situations of product recall and crisis management, providing detail on preparations that can be made in advance to make the difference between a ‘business disaster’ and a ‘well managed, controlled incident’.
The Statistical Dictionary-Based String Matching Problem

In the Dictionary-based String Matching (DSM) problem, a retrieval system has access to a source sequence and stores the position of a certain number of strings in a posting table. When a user inquires the position of a string, the retrieval system, instead of searching in the source sequence directly, relies on the the posting table to answer the query more efficiently. In this paper, the Statistical DSM problem is a proposed as a statistical and information-theoretic formulation of the classic DSM problem in which both the source and the query have a statistical description while the strings stored in the posting sequence are described as a code. Through this formulation, we are able to define the efficiency of the retrieval system as the average cost in answering a users' query in the limit of sufficiently long source sequence. This formulation is used to study the retrieval performance for the case in which (i) all the strings of a given length, referred to as k-grams , and (ii) prefix-free codes.
The effect of using a semi-automated image processing mobile application on undergrad students' perception in a complex science experiment

Certain scientific education experiments require calculation and analysis, which take multiple steps. It would be more effective in science lab learning if we could apply a mobile device technology to help students deal with complicated steps in the scientific analysis. SDS-PAGE is a basic protein analysis for several applications. In the traditional approach, users have to calculate the protein molecular weight which takes 30 minutes on average. This paper implemented and evaluated a novel method for calculating the protein molecular weight on SDS-PAGE using a mobile app and applied it to a science lab experiment to support learning from anywhere at any time. The experimental results showed that the average protein molecular weight calculation time of the mobile app approach was significantly lower than the traditional approach. In addition, based on the perception questionnaire, it was found that the protein learning time was reduced and user satisfaction was increased.
Decentralized Evaluation of Quadratic Polynomials on Encrypted Data

Since the seminal paper on Fully Homomorphic Encryption (FHE) by Gentry in 2009, a lot of work and improvements have been proposed, with an amazing number of possible applications. It allows outsourcing any kind of computations on encrypted data, and thus without leaking any information to the provider who performs the computations. This is quite useful for many sensitive data (finance, medical, etc.). Unfortunately, FHE fails at providing some computation on private inputs to a third party, in cleartext: the user that can decrypt the result is able to decrypt the inputs. A classical approach to allow limited decryption power is distributed decryption. But none of the actual FHE schemes allows distributed decryption, at least with an efficient protocol. In this paper, we revisit the Boneh-Goh-Nissim (BGN) cryptosystem, and the Freeman’s variant, that allow evaluation of quadratic polynomials, or any 2-DNF formula. Whereas the BGN scheme relies on integer factoring for the trapdoor in the composite-order group, and thus possesses one public/secret key only, the Freeman’s scheme can handle multiple users with one general setup that just needs to define a pairing-based algebraic structure. We show that it can be efficiently decentralized, with an efficient distributed key generation algorithm, without any trusted dealer, but also efficient distributed decryption and distributed re-encryption, in a threshold setting. We then provide some applications of computations on encrypted data, without central authority.
Are All Languages Equally Hard to Language-Model?

For general modeling methods applied to diverse languages, a natural question is: how well should we expect our models to work on languages with differing typological profiles? In this work, we develop an evaluation framework for fair cross-linguistic comparison of language models, using translated text so that all models are asked to predict approximately the same information. We then conduct a study on 21 languages, demonstrating that in some languages, the textual expression of the information is harder to predict with both n-gram and LSTM language models. We show complex inflectional morphology to be a cause of performance differences among languages.
The Influence of Information Sharing Linkages on Business Performance: Evidence from Micro and Small Enterprises in Sarawak

This paper investigates the relationship between information sharing linkages (i.e. customer linkage, supplier linkage and internal linkage) and micro and small business performance in Sarawak, Malaysia. The empirical results reveal that customer linkage and supplier linkage have positively influenced business performance meanwhile internal linkage has negatively influenced business performance. Due to the challenge of obtaining the responses from business owners and managers of medium-sized enterprises, the responses are recorded only from micro and small businesses. This leads us to conclude that the findings can help small and medium enterprises, researchers and academicians to better understand on the importance of information sharing linkages that affect business performance. Additionally, the findings provide evidence on the ability of the enterprises to utilize the information sharing linkage as a resource to improve business performance.
Daily market recap for 31.05.2018

Bitcoin really wants to push above the 7563.09 line for a move higher, but it is finding a lot of resistance and uncertainty in it's determination. The Crypto has opened at 7291, made a slight low at 7280.60 and made a high at 7562.10. Price settled at 7
Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel Gaussian pada Kekuatan Sinyal Seluler

Cellular signal strength may be affected by its location, so researches concerning signal strength need information about location and analysis method that observe spatial aspect. Spatial Regression analysis evaluates location in modeling relation between explanatory variables and response variable. One of the spatial regression analyses is Geographically Weighted Regression (GWR). This method utilizes location to create weight matrix using certain weighting function. GWR analysis with Gaussian kernel weighting function creates better model than Ordinary Least Square model. The model created using GWR is local model which parameter estimation differs in each observation point. Clustering of observation point is performed to summarize the result of GWR. The number of optimum clusters in clustering based on coefficient is five clusters while the number of optimum clusters in clustering based on p value of t test is four clusters.
CARs-RP: Lasso Based Class Association Rules Pruning

Classification based on association rules gets more and more interest in research and practice. In many contexts, rules are often mined from sparse data in high-dimensional spaces, which leads to large number of rules with considerable containment and overlap. Pruning is often used in search for an optimal subset of rules. This paper introduces a method for class association rules (CARs) pruning. It learns weights for a set of CARs by maximising the likelihood function subject to the sum of the absolute values of the weights. The pruning strength is controlled by a shrinkage parameter ⋋. The suggested method allows the user to choose the appropriate subset of CARs. This is achieved based on a trade-off between the accuracy and complexity of the resulting classifier which is controlled by changing ⋋. Experimental analysis shows that the introduced method allows to build more concise classifiers with comparable accuracy to other methods.