Reviewerly/exHarmony huggingface dataset:
Agora: A Unified Asset Ecosystem Going Beyond Marketplaces and Cloud Services

Data, algorithms, and compute/storage infrastructure are key assets that drive data science and artificial intelligence applications. As providing all these assets requires a huge investment, data science and artificial intelligence technologies are currently dominated by a small number of providers who can afford these investments. This leads to lock-in effects and hinders features that require a flexible exchange of assets among users. In this vision paper, we present Agora, a unified asset ecosystem. The Agora system provides the technical infrastructure that allows for offering and using data and algorithms, as well as physical infrastructure components. Agora is designed as an open ecosystem of asset marketplaces and provides to a broad audience not only data but the entire data value chain (including computational resources and human expertise). Agora (i) leverages a fine-grained exchange of assets, (ii) allows for combining assets to novel applications, and (iii) flexibly executes such applications on available resources. As a result, Agora overcomes lock-in effects and removes entry barriers for new asset providers. In contrast to existing data management systems, Agora operates in a heavily decentralized and dynamic environment: Data, algorithms, and even compute resources are dynamically created, modified, and removed by different stakeholders. Agora presents novel research directions for the data management community as a whole: It requires to combine our traditional expertise in scalable data processing and management with infrastructure provisioning as well as economic and application aspects of data, algorithms, and infrastructure.
Survey on Q-learning algorithm for credit card fraud detection.

Financial fraud is an ever growing threat with way consequences within the economic business. Now-a-days the uses of electronic transactions are increasing because they are easy and time saving for customers. Individual cards have its unique identification information which give services to the user and will be paid according to their use. German Credit Data is one of the most famous datasets in the realm of fraud detection and it is available in two formats that is categorical attribute and numerical attribute. Increased in number of credit card transactions opens the door for thieves to steal credit card details and commit fraud. Thieves steal the account of users which includes the card account number or other information that would be necessarily available to a merchant during legitimate transactions. To overcome this situation we are using Q- learning algorithm which is an  off policy reinforcement learning algorithm. It’s considered off-policy because the Q- learning function learns from actions that are outside the current policy like taking random actions and therefore a policy isn’t needed. The ‘Q’ in Q-learning algorithm stands for the quality that is applied on the raw and pre- processed knowledge of data. This paper describes the existing application related to credit card fraud detection.
EFEK BEHAVIOR-DRIVEN DEVELOPMENT, HEXAGONAL ARCHITECTURE DAN EVENT SOURCING TERHADAP PENGEMBANGAN PERANGKAT LUNAK BISNIS

Software usage in business world are growing. But as time passes, many problems occurs as both software users and software features that wished to be developed increases, such as performance problems and maintainibility problems. Theres some techniques that has been developed to solve these kinds of problem (such as MVC framework, Test-Driven Development, Domain-Driven Design) but sometimes these techniques criticized as adding more problems instead. In this journal we will present our findings from our research which utilizes two techniques that is Behavior-Driven Development and Event Sourcing and observes its effects in our“Assignment Management System” we’ve developed.
Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting

Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress. However, most of the current arbitrary-shape scene text spotters use region proposal networks (RPN) to produce proposals. RPN relies heavily on manually designed anchors and its proposals are represented with axis-aligned rectangles. The former presents difficulties in handling text instances of extreme aspect ratios or irregular shapes, and the latter often includes multiple neighboring instances into a single proposal, in cases of densely oriented text. To tackle these problems, we propose Mask TextSpotter v3, an end-to-end trainable scene text spotter that adopts a Segmentation Proposal Network (SPN) instead of an RPN. Our SPN is anchor-free and gives accurate representations of arbitrary-shape proposals. It is therefore superior to RPN in detecting text instances of extreme aspect ratios or irregular shapes. Furthermore, the accurate proposals produced by SPN allow masked RoI features to be used for decoupling neighboring text instances. As a result, our Mask TextSpotter v3 can handle text instances of extreme aspect ratios or irregular shapes, and its recognition accuracy won't be affected by nearby text or background noise. Specifically, we outperform state-of-the-art methods by 21.9 percent on the Rotated ICDAR 2013 dataset (rotation robustness), 5.9 percent on the Total-Text dataset (shape robustness), and achieve state-of-the-art performance on the MSRA-TD500 dataset (aspect ratio robustness). Code is available at: https://github.com/MhLiao/MaskTextSpotterV3
Doubly Distributed Supervised Learning and Inference with High-Dimensional Correlated Outcomes

This paper presents a unified framework for supervised learning and inference procedures using the divide-and-conquer approach for high-dimensional correlated outcomes. We propose a general class of estimators that can be implemented in a fully distributed and parallelized computational scheme. Modelling, computational and theoretical challenges related to high-dimensional correlated outcomes are overcome by dividing data at both outcome and subject levels, estimating the parameter of interest from blocks of data using a broad class of supervised learning procedures, and combining block estimators in a closed-form meta-estimator asymptotically equivalent to estimates obtained by Hansen (1982)'s generalized method of moments (GMM) that does not require the entire data to be reloaded on a common server. We provide rigorous theoretical justifications for the use of distributed estimators with correlated outcomes by studying the asymptotic behaviour of the combined estimator with fixed and diverging number of data divisions. Simulations illustrate the finite sample performance of the proposed method, and we provide an R package for ease of implementation.
Evaluating Side-Channel Resistance Using Low Order Rational Points Against Curve25519 and an Associated Quadratic Twist

IoT devices contribute to improving the mechanism of a system as edge devices for data sharing and automation of industrials. However, such devices are often being a target of an attacker due to their simple architecture and the lack of resources so as to protect data confidentiality using cryptosystems. In addition, although Curve25519 has been used in various security protocols and known to work even on IoT devices efficiently, the curve inherits the low order points hidden inside of the Edward curves. In this paper, the authors demonstrate side-channel attacks against Curve25519 by focusing on the points of order 4 and 8. We choose the order 4 point which does not exist on Curve25519, that exists on the twisted curve of Curve25519. More precisely, the rational point used in this paper is given by (x,y)=(-1,0) in affine coordinates. In addition, the order 8 point appears to be a high order rational point. The results reveal that the rational points might be a threat to key extraction and it demands us to find further countermeasures.
La experiencia cooperativa como momento editorial en el campo cultural

This article shows the main results of a study on the views of members of the cooperative La Furia editors, when they reflect on the experiences of maintaining their collective action in the Chilean cultural field.At the conceptual level, Becker's art world categories and Bourdieu's cultural field categories allowed us to explore the conventions that arise from the relations of technical assistance, associated work, reciprocity, diffusion and government of the cooperative.The research was carried out between 2017 and 2019, it used the qualitative methodology taking procedures from the grounded theory and the case study, it used the semi-structured interview and the observation of book fairs and the digital supports of the editorials of the cooperative La Furia; and we worked with content and social network analysis.Among the results is a description of the stages of the cooperative, the shared knowledge about the cooperative work and two dimensions that are manifested in a similar way among the publishers: working community and knowing how to edit.
Regression scores to identify risky drivers from braking pulses

Driving data record information on style and patterns of vehicles that are inmotion. These data are analysed to obtain risk scores that can later beimplemented in insurance pricing schemes. Scores may also be used in on-board sensors to create risk alerts that help drivers to keep up with safetymargins. Regression methods are proposed and a prototype real sample of 253drivers is analysed. Conclusions are drawn on the mean number of brakepulses per day as measured within 30 seconds time-intervals. Linear andlogistic regressions serve to construct a label that classifies drivers. A novelfactor based on the driving range that is defined from geo-localizationimproves the results considerably. Driving range is expressed as measures thediagonal of a rectangle that contains the furthest North-South versus East-West weekly vehicle trajectory. This factor shows that frequent braking activityis negatively related to the square of driving range.
Implementasi Sistem Informasi Muselok Menggunakan Augmented Reality Berbasis Android

&#x0D; &#x0D; &#x0D; &#x0D; Multimedia development methods with several stages such as, concept, design, material collecting, assembly, testing and distribution, with these stages can produce multimedia products. Therefore, researchers developed a Muselok application based on the method of Luther-Sutopo combine with qualitative approach. This application build to show the collection of NTB Museum. Negeri Nusa Tenggara Barat / NTB as a technology Augmented Reality / AR. From the results of making the Muselok application by using the method of Luther-Sutopo, it was found that, during the implementation, several flow changes were made during the Muselok application making process, assembly process and testing process can be changed repeatedly until it was difficult to find the problem points of the application. From the Muselok application users there is constructive and positive feedback even though there are still some shortcomings in terms of the Muselok application interface.However, it must be realized that technical aspects was not the main problem. In fact, the process between the museum manager, the design team and the views of experts were dificult to combining their’s perspective. In the future, a transdisciplinary approach is needed to be able to produce the right technology, according to needs and provide added value even in technology research especially in the related with tourism as a science.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Keywords: Augment Reality, Muselok Application, Luther -Sutopo, Museum, Tourism&#x0D; &#x0D; &#x0D; &#x0D;
Hyper-parameterized Dialectic Search for Non-linear Box-Constrained Optimization with Heterogenous Variable Types

We consider the dialectic search paradigm for box-constrained, non-linear optimization with heterogeneous variable types. In particular, we devise an implementation that can handle any computable objective function, including non-linear, non-convex, non-differentiable, non-continuous, non-separable and multi-modal functions. The variable types we consider are bounded continuous and integer, as well as categorical variables with explicitly enumerated domains. Extensive experimental results show the effectiveness of the new local search solver for these types of problems.
Solar Radiation as Alternative Power Supply for Electricity Generation in Nigeria

In this study, the Angstrom-Prescott regression equation which has been used to estimate the monthly average daily solar radiation on a horizontal surface was used.The values of the regression coefficients a and b as 0.34 and 0.42 were determine.The statistical error estimations such as RMSE and MBE used to test the correlation between the measured global solar radiation and the calculated global solar radiation.The RMSE was found to be 0.05 as low as expected and the MBE was also found to be 0.015.Global radiations obtained in this study are higher with mean value of 23.60 MJm -2 and the measured value 23.48 MJm -2 which shows the presence of high global solar energy potential in Maiduguri, Nigeria.The global solar radiation intensity predicted in this study can also be utilized in design, analysis and performance estimation of solar energy systems, which is gaining significant attention in Nigeria and the world at large.
RECOGNIZING SENTIMENT PREDICTION ON TWITTER DATA

Escorted by the wide spread of Internet today, people have found a new way of expressing their opinions.It is a platform with a variety of information where an individual can also view the opinions of others.This is continuously growing and becoming an important factor in decision making for various organisations, businesses and even for Politics.In this paper we have chosen the most popular social media platform i.e.Twitter for our Sentiment Analysis.Eventually, Acknowledging the opinions beyond the tweets is of great concern.The fundamental aim of Sentiment Analysis is to reason feelings and ideas of individuals.We have made data analysis with tweets related to a topic and thereby classified their polarity using different machine learning algorithms.
An AudioWorklet-based signal engine for a live coding language ecosystem

This paper reports on early advances in the design of an ecosystem for creating new live coding languages, optimal for audio synthesis, machine learning and machine listening. We present the design rationale and challenges when applying the Web Audio API, and in particular, an AudioWorklet-based solution to refactoring our digital signal processing library Maximilian.js for our high-performance signal synthesis engine. Furthermore, we contribute with a new system implementation, engineered for modern web applications, and for the live coding community to design their own idiosyncratic languages and interfaces applying our signal engine. The evaluation shows that the system runs with high reliability, efficiency and low latency.
Analyzing the Readability Level of the English Textbooks Used in Jordan

Because of the significance of the textbooks in the teaching and learning processes, it is essential to revise them permanently. This study investigated the readability level of the 12th- grade English textbooks and also examined if the reading texts used in Action Pack 12 were suitable linguistically for the 12th -grade students. This study adopted the qualitative descriptive design by using the Fry Graph to calculate the readability level. The researchers chose randomly 20 texts out of 36 texts as a sample of the study. The results revealed that 80% out of the 20 texts were under the level of the 12th-grade students in Jordan. Also, the results indicated that 15 % of the texts were invalid and only 5% of the texts were within the 12th-grade students’ level. The findings also indicated that Action Pack 12 was suitable linguistically for 12th-grade students. Several recommendations are put forth.
A Reliability Verification of Screening Time Prediction Reporting of ‘Cine-Hangeul’

Cine-Hangeul is a program that can predict the running time of a movie based on the screenplay before production.This paper seeks to verify the prediction reporting function of Cine-Hangeul, which is the standard Korean screenplay format.Moreover, this paper presents a method to increase the accuracy of the Cine-Hangeul reporting function.The objective of this paper is to offer a correction method based on scientific evidence because the current Cine-Hangeul reporting function has many errors.The verification process for five scenarios and movies confirmed that the default setting value of Cine-Hangeul's screening time prediction reporting was many errors.Cine-Hangeul analyzes the amount of textual information to predict the time of the scene and the time of the dialogue and helps predict the total time of the movie.Therefore, if a certain amount of text information is not available, the accuracy is unreliable.The current Cine-Hangeul prediction report confirms that the efficiency is high when the scenario volume is about 90 to 100 pages.As a result, prediction of screening time by Cine-Hangeul, a Korean scenario standard format program, confirmed the verification that it could secure the same level of reliability as the actual screening time by correcting the reporting settings.This verification also affirms that when applying about 50 percent of the basic set of screening time reporting, it is almost identical to the screening time.
Improved Single Feistel Circuit Supporter by A Chaotic Genetic Operator

This document outlines a new color image encryption technology development.After splitting the original image into 240-bit blocks and modifying the first block by an initialization vector, an improved Feistel circuit is applied, sponsored by a genetic crossover operator and then strong chaining between the encrypted block and the next clear block is attached to set up the confusion-diffusion and heighten the avalanche effect, which protects the system from any known attack.Simulations carried out on a large database of color images of different sizes and formats prove the robustness of such a system.
Sample Analysis on Series and Differential Equations of Standard Tests in Latest Three Years

Standard tests; Series; Differential equation; Calculus
A Hardware-efficient Weight Sampling Circuit for Bayesian Neural Networks

The main problems of deep learning are requiring a large amount of data for learning, and prediction with excessive confidence. A Bayesian neural network (BNN), in which a Bayesian approach is incorporated into a neural network (NN), has drawn attention as a method for solving these problems. In a BNN, the probability distribution is assumed for the weight, in contrast to a conventional NN, in which the weight is point estimated. This makes it possible to obtain the prediction as a distribution and to evaluate how uncertain the prediction is. However, a BNN has more computational complexity and a greater number of parameters than an NN. To obtain an inference result as a distribution, a BNN uses weight sampling to generate the respective weight values, and thus, a BNN accelerator requires weight sampling hardware based on a random number generator in addition to the standard components of a deep learning neural network accelerator. Therefore, the throughput of weight sampling must be sufficiently high at a low hardware resource cost. We propose a resource-efficient weight sampling method using inversion transform sampling and a lookup-table (LUT)-based function approximation for hardware implementation of a BNN. Inversion transform sampling simplifies the mechanism of generating a Gaussian random number from a uniform random number provided by a common random number generator, such as a linear feedback shift register. Employing an LUT-based low-bit precision function approximation enables inversion transform sampling to be implemented at a low hardware cost. The evaluation results indicate that this approach effectively reduces the occupied hardware resources while maintaining accuracy and prediction variance equivalent to that with a non-approximated sampling method.
Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation

Autoregressive models recently achieved comparable results versus state-of-the-art Generative Adversarial Networks (GANs) with the help of Vector Quantized Variational AutoEncoders (VQ-VAE). However, autoregressive models have several limitations such as exposure bias and their training objective does not guarantee visual fidelity. To address these limitations, we propose to use Reinforced Adversarial Learning (RAL) based on policy gradient optimization for autoregressive models. By applying RAL, we enable a similar process for training and testing to address the exposure bias issue. In addition, visual fidelity has been further optimized with adversarial loss inspired by their strong counterparts: GANs. Due to the slow sampling speed of autoregressive models, we propose to use partial generation for faster training. RAL also empowers the collaboration between different modules of the VQ-VAE framework. To our best knowledge, the proposed method is first to enable adversarial learning in autoregressive models for image generation. Experiments on synthetic and real-world datasets show improvements over the MLE trained models. The proposed method improves both negative log-likelihood (NLL) and Fr\'echet Inception Distance (FID), which indicates improvements in terms of visual quality and diversity. The proposed method achieves state-of-the-art results on Celeba for 64 $\times$ 64 image resolution, showing promise for large scale image generation.
Improving Earth hot-spot detection from MODIS data using MODVOLC algorithm

&amp;lt;p&amp;gt;Space-borne hot-spot detection on the Earth surface is key to monitoring and studying volcanic activity, wildfires and anthropogenic heat sources from space. Lower intensity thermal emission hot-spots, which often represent the onset of volcanic eruptions and large wildfires, are difficult to detect. We are improving the MODVOLC algorithm, which monitors Earth&amp;amp;#8217;s surface for hot-spots by analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) data every 48 hours, to allow lower intensity thermal emission detection. Improving the existing MODVOLC algorithm for hot-spot detection from MODIS image data is not trivial. A new approach, which we refer it to as the Maximum Radiance Algorithm for MODIS, has been explored. The new approach requires a MODIS 4 &amp;amp;#181;m and accompanying 12 &amp;amp;#181;m global radiance time-series at ~1 km grid spacing. This reference data set describes the maximum radiance that has been measured from each square km of Earth&amp;amp;#8217;s surface over a ten year period (having first excluded high natural and anthropogenic heat sources from the time-series, using the existing MODVOLC approach). For each new geolocated MODIS image data, the observed radiance for each pixel is compared with this reference, and if its radiance exceeds the historical maximum, it can be considered a potential hot-spot. A dynamic tolerance is used to then confirm if the potential hot-spot is an actual hot-spot. We show that this new approach for hot-spot detection offers significant advantage over existing techniques for lower intensity thermal emission hot-spot detection during both day and nighttime conditions.&amp;lt;/p&amp;gt;