Reviewerly/exHarmony huggingface dataset:
Statistical Optimal Transport posed as Learning Kernel Embedding

The objective in statistical Optimal Transport (OT) is to consistently estimate the optimal transport plan/map solely using samples from the given source and target marginal distributions. This work takes the novel approach of posing statistical OT as that of learning the transport plan's kernel mean embedding from sample based estimates of marginal embeddings. The proposed estimator controls overfitting by employing maximum mean discrepancy based regularization, which is complementary to $ϕ$-divergence (entropy) based regularization popularly employed in existing estimators. A key result is that, under very mild conditions, $ε$-optimal recovery of the transport plan as well as the Barycentric-projection based transport map is possible with a sample complexity that is completely dimension-free. Moreover, the implicit smoothing in the kernel mean embeddings enables out-of-sample estimation. An appropriate representer theorem is proved leading to a kernelized convex formulation for the estimator, which can then be potentially used to perform OT even in non-standard domains. Empirical results illustrate the efficacy of the proposed approach.
Building a More Diverse and Inclusive Cataloging Cooperative

The Program for Cooperative Cataloging’s Strategic Directions document for 2018-2021 mark the first explicit mention of diversity and inclusion in PCC strategic planning, but the organization has a significant record of supporting diversity, inclusion and collaboration within the library cataloging community throughout its 25 year history. This paper will outline what the PCC has done to be more diverse and inclusive in its work and collaborations, and offer recommendations on where PCC should go next to even further support diversity, equity, and inclusion with collaboration in the future.
Security & Privacy in IoT Using Machine Learning & Blockchain: Threats & Countermeasures.

Security and privacy of the users have become significant concerns due to the involvement of the Internet of things (IoT) devices in numerous applications. Cyber threats are growing at an explosive pace making the existing security and privacy measures inadequate. Hence, everyone on the Internet is a product for hackers. Consequently, Machine Learning (ML) algorithms are used to produce accurate outputs from large complex databases, where the generated outputs can be used to predict and detect vulnerabilities in IoT-based systems. Furthermore, Blockchain (BC) techniques are becoming popular in modern IoT applications to solve security and privacy issues. Several studies have been conducted on either ML algorithms or BC techniques. However, these studies target either security or privacy issues using ML algorithms or BC techniques, thus posing a need for a combined survey on efforts made in recent years addressing both security and privacy issues using ML algorithms and BC techniques. In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain. First, we discuss and categorize various security and privacy threats reported in the past twelve years in the IoT domain. Then, we classify the literature on security and privacy efforts based on ML algorithms and BC techniques in the IoT domain. Finally, we identify and illuminate several challenges and future research directions in using ML algorithms and BC techniques to address security and privacy issues in the IoT domain.
CHAIN: Concept-harmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks

With the great success of networks, it witnesses the increasing demand for the interpretation of the internal network mechanism, especially for the net decision-making logic. To tackle the challenge, the Concept-harmonized HierArchical INference (CHAIN) is proposed to interpret the net decision-making process. For net-decisions being interpreted, the proposed method presents the CHAIN interpretation in which the net decision can be hierarchically deduced into visual concepts from high to low semantic levels. To achieve it, we propose three models sequentially, i.e., the concept harmonizing model, the hierarchical inference model, and the concept-harmonized hierarchical inference model. Firstly, in the concept harmonizing model, visual concepts from high to low semantic-levels are aligned with net-units from deep to shallow layers. Secondly, in the hierarchical inference model, the concept in a deep layer is disassembled into units in shallow layers. Finally, in the concept-harmonized hierarchical inference model, a deep-layer concept is inferred from its shallow-layer concepts. After several rounds, the concept-harmonized hierarchical inference is conducted backward from the highest semantic level to the lowest semantic level. Finally, net decision-making is explained as a form of concept-harmonized hierarchical inference, which is comparable to human decision-making. Meanwhile, the net layer structure for feature learning can be explained based on the hierarchical visual concepts. In quantitative and qualitative experiments, we demonstrate the effectiveness of CHAIN at the instance and class levels.
Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems

Findings in recent years on the sensitivity of convolutional neural networks to additive noise, light conditions and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. Convolutional neural networks are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state of the art bottom-up object recognition models, e.g., deep convolutional neural networks. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state of the art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model.
New deterministic initialization method for soft computing global optimization algorithms

&lt;span&gt;The initialization stage12 of a Soft Computing (SC) algorithm is vital as it affects the success rate of algorithms in solving multi-peak global optimization problems. The individuals in an initial population, which are known as search agents, are often generated randomly using pseudo-random number generator (PRNG) due to unavailability of prior information on the location of global peak (GP). The random nature of the generated search agents causes uneven distribution of the initial population over the search space (SS), which may lead the search towards unpromising regions from the very beginning. This paper proposes a new deterministic initialization method (DIM) for SC algorithms where search agents are evenly fixed in the SS by using a simple deterministic formulation. The performance of the proposed DIM is then compared to the conventional PRNG and more recent quasi-random number generator (QRNG). An optimization case study is carried out using two popular SC algorithms which are the Particle Swarm Algorithm (PSO) and the Evolutionary Programming (EP), and three relatively new SC algorithms which are the Whale Optimization Algorithm (WOA), the Elephant Herding Optimization (EHO), and the Butterfly Optimization Algorithm (BOA). The optimization is done on various one-dimensional (1D) benchmark functions, as well as practical problems such as partial shading condition (PSC). Simulation results show that the proposed DIM successfully improved the performance of each SC algorithm under study in solving almost all tested functions with 99% success rate compared to 88.7% and 80.2% for the QRNG and PRNG approaches respectively. Furthermore, the WOA is the most reliable and robust among the five SC techniques under study with a success &lt;/span&gt;
An Elo-based rating system for TopCoder SRM

We present an Elo-based rating system for programming contests. We justify a definition of performance using the logarithm of a player's rank. We apply the definition to rating TopCoder SRM. We improve the accuracy, guided by experimental results. We compare results with SRM ratings.
Daniel, Louis S - Biographical entry - Index to the AMA archive

Daniel, Louis S - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Weighted Measurement Fusion Cubature Kalman Filter for Nonlinear System

Based on the weighted measurement fusion (WMF) algorithm and the well-known Cubature Kalman Filter (CKF), a weighted measurement fusion Cubature Kalman Filter (WMF-CKF) is presented. As the Taylor series expansion term increases, WMF- CKF will gradually converge to the optimal centralized fusion (CF-CKF) algorithm. It is proved that the WMF-CKF has asymptotic optimality. The weighted measurement fusion algorithm effectively performs data compression, which greatly reduces the amount of calculation. It will improve the real-time performance by compressing redundant information from multiple sensors. The examples are given to show the effectiveness of the proposed algorithm.
Intelligent Receivers for Electronic Warfare Applications

In this paper, we propose an algorithm to perform modulation classification on a 5-class problem consisting of AM, 2-PSK, 4-PSK, 8-PSK and 16-QAM modulation schemes using a combination of features based on the first order cyclostationarity, second- and higher-order moments, and then extend the idea of classification to an intelligent receiver which classifies and demodulates the signal without prior information regarding the transmitted signal.
Comparative Study Between Bahasa Indonesia Textbook for X Grade Published by The Ministry of Education and Culture and Tiga Serangkai Private Publisher

This study focuses on analyzing the quality of Indonesian textbooks for the 2013 Curriculum in class X that published by the government and private publishers (Tiga Serangkai). The analysis will be based on content quality, language quality, presentation quality, and graphic quality. This research is comparative descriptive research, which means this research will describe and compare the two Indonesian textbooks. The method of data collection that used in this study are observation, documentary, and recording. After all data has been collected, it is then analyzed using comparative descriptive analysis based on the Badan Nasional Standar Pendidikan (BNSP). Based on the results of data analysis, it was concluded that almost all Indonesian language books published by the government and private publishers were in accordance with BNSP provisions which are (1) quality of content, (2) quality of language, (3) quality of presentation, and (4) quality of graphics.
A Study of a Vocational Education Curriculum on Pneumatic Control

In recent years, as the application of pneumatic control technology has expanded, it is an urgent matter to train production engineers engaged in pneumatic control systems. However, as educational curriculum and teaching materials are not well developed, vocational education on pneumatic control is not sufficiently done. Therefore, it is indispensable to study the curriculum and teaching material development aiming at fostering pneumatic control technicians who will become immediate fight at the production site, and to develop those results to vocational education. In this paper, we examined the educational curriculum for pneumatic control engineers by organizing necessary technical elements. Then, we prepared a textbook on the basics of pneumatic control and developed it to education for vocational training instructors. Finally, we measured the degree of mastery to confirm the effect. As a result, we confirmed that the mastery degree was improved. We have also developed teaching materials with the pneumatic positioning system necessary to effectively implement the proposed vocational education curriculum.
Momentum Improves Normalized SGD

We provide an improved analysis of normalized SGD showing that adding momentum provably removes the need for large batch sizes on non-convex objectives. Then, we consider the case of objectives with bounded second derivative and show that in this case a small tweak to the momentum formula allows normalized SGD with momentum to find an $\epsilon$-critical point in $O(1/\epsilon^{3.5})$ iterations, matching the best-known rates without accruing any logarithmic factors or dependence on dimension. We also provide an adaptive method that automatically improves convergence rates when the variance in the gradients is small. Finally, we show that our method is effective when employed on popular large scale tasks such as ResNet-50 and BERT pretraining, matching the performance of the disparate methods used to get state-of-the-art results on both tasks.
I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.
Exploring the Possibility of Virtual Reality Exergaming as a Cognitive Screening System

Regular screening is important for early detection of cognitive impairment.Standard cognitive screening tests can be time consuming and tedious.Individuals at risk of cognitive decline should be doing regular exercise, and Virtual Reality (VR) exergames are one way to motivate them to do so.If these exergames can include non-obtrusive cognitive tests, then users can be regularly screened without it being such a tedious process.However, exergaming and VR might threaten the validity of these tests.This paper investigates whether this is likely to be the case and discusses some practical concerns around this approach of screening for cognitive decline.The results of this study suggest that standard cognitive tests are not rendered invalid in VR exergaming.However, more work is needed to investigate the effects of integrating tests into gameplay elements.
Inferential Induction: Joint Bayesian Estimation of MDPs and Value Functions.

Bayesian reinforcement learning (BRL) offers a decision-theoretic solution to the problem of reinforcement learning. However, typical model-based BRL algorithms have focused either on ma intaining a posterior distribution on models or value functions and combining this with approx imate dynamic programming or tree search. This paper describes a novel backwards induction pri nciple for performing joint Bayesian estimation of models and value functions, from which many new BRL algorithms can be obtained. We demonstrate this idea with algorithms and experiments in discrete state spaces.
One-Dimensional Pollutant Transport Modelling of Cadmium (Cd), Chromium (Cr) and Lead (Pb) in Saguling Reservoir

The existing conditions of the Saguling Reservoir are reported to have suffered severe heavy metal pollution due to the presence of wastewater inputs from various types of industries flowing into Citarum River and then accumulating in the Saguling Reservoir. From the results of calibration tests of heavy metal models on water using the Root Mean Square Error (RMSE) analysis and Relative Error (RE) analysis, obtained dispersion coefficients on Cadmium, Chromium, and Lead metals sequentially 1 m 2 / second (with RMSE 0,00515 and 34% relative error); 1 m 2 / second (with RMSE 0.00595 and relative error 26%); and 2.5 m 2 / second (with RMSE 0.028205 and relative error 41.25%) which shows that the model has good capability to simulate the concentration of heavy metals approaching the actual data both in the dry and wet seasons. From the results of the verification test models of concentration of cadmium, lead and chromium in sediments using the Root Mean Square Error (RMSE) analysis and Relative Error (RE) analysis, obtained sequentially 18.53 and 77%; 10.43 and 47.15%; 2.789 and 33%. Error values in sediment concentrations are quite large because of the difficulty of making assumptions that are close to natural conditions.
Compact radial shearing interferometer with a randomly encoded cosinusoidal zone plate for wavefront measurements

A radial shearing interferometer (RSI) using a randomly encoded cosinusoidal zone plate (RECZP) to measure the wavefront is proposed. The RECZP has two foci, i.e., a virtual focus and a real focus, so its Fresnel diffractions contain only two beams. These two beams can be regarded as the extended beam and the contracted beam in the RSI, respectively. This RSI is composed of a RECZP and a charge-coupled device (CCD). The radial shearing rate is continuously adjustable by changing the distance between the CCD and RECZP, which is good for measurement sensitivity and dynamic range for different situation requirements. In the simulation experiment, we analyzed the influence of beam tilt error, distance error of zone plate and CCD, CCD camera nonlinearity, and noise on wavefront reconstruction results. We also analyzed the effects of different fabrication errors (randomly encoded principle error, sidewall angle error, depth error, and alignment error of amplitude zone plate and phase zone plate) on the diffraction intensity distributions, which determine the fabrication tolerance of the RECZP. Experimentally compared with a ZYGO interferometer, the RECZP-RSI optical system can get good results.
Inertial Sensors Integrated with Clothing to Localize People Inside Buildings

This article presents a wearable system that localizes people in the indoor environment, using data from inertial sensors. The sensors measure the parameters of human motion, tracking the movements of the torso and foot. For this purpose, they were integrated with shirt and the shoe insole. The values of acceleration measured by the sensors are sent via Bluetooth to a smartphone. The localization algorithm implemented on the smartphone, presented here, merges data from the shirt and the shoe to track the steps made by the user and filter out the artefacts caused by movements the shirt and torso. The experimental verification of the algorithm is also presented.
Breyer, Bruno - Biographical entry - Index to the AMA archive

Breyer, Bruno - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Fast and Reliable System for Managing Customer Information of a Local Electric Power Generator

Web applications have become a vital requirement for organizations in managing information, because running any organization through the classic way makes the management process tough. The purpose of this paper is to propose a fast and reliable way to solve the problems that supervisors of an electric power generator face in managing customer information. This will be done through a web-based system. The system will be developed with ASP.NET web framework and MySQL database management system. Ajax and jQuery library will be utilised for smart searching facility and printing dynamic receipts.  It will be easy to use and supports all smart devices because of the help of bootstrap framework. IIS web server and Microsoft Azure cloud virtual machine are used for hosting and running the system. To investigate the workability and efficiency of the system a questionnaire and an interview have been conducted and analysed. The results confirmed that, by using the system, the supervisors can manage customers’ information in an efficient way and it will save a lot of time and money and they will be able to back-up data easily. The findings of the paper can be applied for managing big-data by public or private sectors in Kurdistan.
Adhocracy as a Modern Form of Organization of Russian Universities Activities

Currently, state institutions of higher education are under business pressure, public and public authorities, that leads to the creation of conditions for dynamic changes in the external and internal environment of universities. In this regard, this paper is devoted to the issues of the Russian universities transformation from a professional bureaucracy into an adhocratic structure, which allows you to flexibly respond to the challenges of the external environment, to solve new challenges facing universities. The methodological base of the study includes theories of organization management in relation to higher education. The authors consider in sufficient detail the theoretical aspects of the differences between professional and innovative organizations, their structure, design parameters, coordination mechanisms, and situational factors. As a result of the study, important conclusions were drawn regarding the mission of the modern university; features of the transition from a professional type of organization to an adhocratic one; the main distinguishing features of adhocracy from other management systems are formulated. The practical significance of the article lies in the fact that the higher education system has been proposed a new structure characteristic of the innovative form of organization. The results of the study may be of interest to government bodies that shape and implement national educational policies.