Reviewerly/exHarmony huggingface dataset:
Similarity Noise Training for Image Denoising

Deep learning has attracted a lot of attention lately, thanks.Thanks to its high modeling performance, technological advancement, and big data for training, deep learning has achieved a remarkable improvement in both high and low-level vision tasks.One crucial aspect of the success of a deep learning-based model is an adequate large data set for fueling the training stage.But in many cases, well-labeled large data is hard to acquire.Recent works have shown that it is possible to optimize denoising models by minimizing the difference between different noise instances of the same image.Yet, it is not a common practice to collect data with different noise instances of the same sample.Addressing this issue, we propose a training method that enables training deep convolutional neural network models for Gaussian denoising to be trained in cases of no ground truth data.More specifically, we propose to train a deep learning-based denoising model using only a single noise instance.With that in mind we develop a non-local self-similarity noise training method that uses only one noise instance.
Predictive Multiplicity in Classification

Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicting predictions. In this paper, we define predictive multiplicity as the ability of a prediction problem to admit competing models with conflicting predictions. We introduce formal measures to evaluate the severity of predictive multiplicity and develop integer programming tools to compute them exactly for linear classification problems. We apply our tools to measure predictive multiplicity in recidivism prediction problems. Our results show that real-world datasets may admit competing models that assign wildly conflicting predictions, and motivate the need to measure and report predictive multiplicity in model development.
TensorFlow.js: Machine Learning for the Web and Beyond

TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.
Amazonas – Stochastic Method of Modeling Geological Systems with Arbitrary Distribution of Properties, Including Statistically Unsteady Ones, Based on Non-Parametric Statistics

Summary The paper considers the method Amazonas developed by the author, which is based on the use of non-parametric (rank) statistics in a sliding window of a given form. The use of nonparametric (rank) statistics, which makes it possible to model the spatial distributions of both discrete and continuous properties with arbitrary distributions of values, including multimodal, reproduce sharp transitions of properties without the need to build a facies model, provides resistance to clustering, incompleteness and the presence of individual error values in the source data. The ability to set an offset allows to reproduce both smooth and sharp transitions between the values, with a varying degree of variability to extrapolate the possible behavior of the system outside the studied area. The applied mathematical formalism makes it possible to obtain a realistic stochastic model of the simulated environment, directly determined by the initial data, taking into account the heterogeneity existing in it, which can be associated with lithology and other factors, without the need for preliminary expert identification of these laws. The method is suitable for modeling both stationary and non-stationary distributed spatial variables, and allows to take into account the instrumental error and the natural variation of the simulated parameters.
Homomorphic Computation in Reed-Muller Codes

With the ongoing developments in artificial intelligence (AI), big data, and cloud services, fully homomorphic encryption (FHE) is being considered as a solution for preserving the privacy and security in machine learning systems. Currently, the existing FHE schemes are constructed using lattice-based cryptography. In state-of-the-art algorithms, a huge amount of computational resources are required for homomorphic multiplications and the corresponding bootstrapping that is necessary to refresh the ciphertext for a larger number of operations. Therefore, it is necessary to discover a new innovative approach for FHE that can reduce the computational complexity for practical applications. In this paper, we propose a code-based homomorphic operation scheme. Linear codes are closed under the addition, however, achieving multiplicative homomorphic operations with linear codes has been impossible until now. We strive to solve this problem by proposing a fully homomorphic code scheme that can support both addition and multiplication simultaneously using the Reed-Muller (RM) codes. This can be considered as a preceding step for constructing code-based FHE schemes. As the order of RM codes increases after multiplication, a bootstrapping technique is required to reduce the order of intermediate RM codes to accomplish a large number of operations. We propose a bootstrapping technique to preserve the order of RM codes after the addition or multiplication by proposing three consecutive transformations that create a one-to-one relationship between computations on messages and that on the corresponding codewords in RM codes.
Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions

We address the task of automatically grading the language proficiency of spontaneous speech based on textual features from automatic speech recognition transcripts.Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling, language modeling, and native language identification (L1).We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a featurerich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates.Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task.We discuss areas for improvement and potential applications for text-only speech scoring.
Author detection: Analyzing tweets by using a Naïve Bayes classifier

In the context of digital social media, where users have multiple ways to obtain information, it is important to have tools to detect the authorship within a corpus supposedly created by a single author. With the tremendous amount of information coming from social networks there is a lot of research concerning author profiling, but there is a lack of research about the authorship identification. In order to detect the author of a group of tweets, a Naïve Bayes classifier is proposed which is an automatic algorithm based on Bayes’ theorem. The main objective is to determine if a particular tweet was made by a specific user or not, based on its content. The data used correspond to a simple data set, obtained with the Twitter API, composed of four political accounts accompanied by their username and tweet identifier as it is mixed with multiple user tweets. To describe the performance of the classification model and interpret the obtained results, a confusion matrix is used as it contains values like accuracy, sensitivity, specificity, Kappa measure, the positive predictive and negative predictive value. These results show that the prediction model, after several cases of use, have acceptable values against the observed probabilities.
The Optimization of a Page Rank Based Key Classes Classifier using Simulated Annealing with ROC-AUC and Recall Metrics

Nowadays, software projects from different industrial sectors tend to grow from tens of classes towards sizes of hundreds or even thousands of classes. Key classes or hotspots are the most important classes in a project. They represent the starting point for any maintenance operation. In this context key classes detection is an important software engineering task, especially in projects where documentation is poor or missing totally. In the state of the art there are several key classes classifiers based on different representations and algorithms. We focus on the empirical parameters of a classifier based on weighted graph representation of the source code combined with a Page Rank algorithm which give the best results compared to previous works results available online. The empirical parameters represent weights assigned to several relations between classes like: inheritance between two classes, interface implementation between a class and an interface etc. Initially the parameters were manually set, having empirical values. It is not known if other sets of values for the parameters will not give better diagnostic abilities to the classifier. To test the entire parameters state space is virtually impossible for 12 Java software projects and 15 parameters with values varying in the integer range. Using the Simulated Annealing optimization algorithm we start with the manually set values for the parameters and we optimize the objective functions based on ROC-AUC and Recall metrics.
ANALISIS STRATEGI PEMASARAN DAN PELAYANAN DALAM UPAYA PENINGKATAN KUALITAS DAYA SAING BIRO PERJALANAN HAJI DAN UMROH PROSPEKTIF EKONOMI SYARI’AH (Studi Pada PT. Makkah Multazam Safir dan Al Madinah)

Hajj becomes part of the fifth pillar of faith after the creed, prayer, fasting, and alms. Hajj and Umrah are required for those who are powerful (capable), each man or woman once in a lifetime. In general, the Indonesian Muslim community performs the Hajj and Umrah to the Grand Mosque in Makkah through Hajj and Umrah travel. The number of devotees of Umrah pilgrims in Indonesia is so much the main attraction for entrepreneurs in the field of travel. This study uses a qualitative approach and is a type of field research (Field Research) and compares the pilgrimage and umrah services of PT. Makkah Multazam Safir and Al-Madinah Tour &amp; Travel in Bandar Lampung which together are business entities that provide services or providers of Hajj and Umrah pilgrimage services in Baitullah. Two different data presentations will provide us with valid information regarding a program and services offered to prospective pilgrims in need. From the analysis that will be done will minimize errors in the future.
Privacy versus Location Accuracy in Opportunistic Wearable Networks

Future wearable devices are expected to increasingly exchange their positioning information with various Location-Based Services (LBSs). Wearable applications can include activity-based health and fitness recommendations, location-based social networking, location-based gamification, among many others. With the growing opportunities for LBSs, it is expected that location privacy concerns will also increase significantly. Particularly, in opportunistic wireless networks based on device-to-device (D2D) connectivity, a user can request a higher level of control over own location privacy, which may result in more flexible permissions granted to wearable devices. This translates into the ability to perform location obfuscation to the desired degree when interacting with other wearables or service providers across the network. In this paper, we argue that specific errors in the disclosed location information feature two components: a measurement error inherent to the localization algorithm used by a wearable device and an intentional (or obfuscation) error that may be based on a trade-off between a particular LBS and a desired location privacy level. This work aims to study the trade-off between positioning accuracy and location information privacy in densely crowded scenarios by introducing two privacy-centric metrics.
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period

Distributed parallel stochastic gradient descent algorithms are workhorses for large scale machine learning tasks. Among them, local stochastic gradient descent (Local SGD) has attracted significant attention due to its low communication complexity. Previous studies prove that the communication complexity of Local SGD with a fixed or an adaptive communication period is in the order of O (N3/2 T1/2) and O (N3/4 T3/4) when the data distributions on clients are identical (IID) or otherwise (Non-IID), where N is the number of clients and T is the number of iterations. In this paper, to accelerate the convergence by reducing the communication complexity, we propose STagewise Local SGD (STL-SGD), which increases the communication period gradually along with decreasing learning rate. We prove that STL-SGD can keep the same convergence rate and linear speedup as mini-batch SGD. In addition, as the benefit of increasing the communication period, when the objective is strongly convex or satisfies the Polyak-Lojasiewicz condition, the communication complexity of STL-SGD is O (N log T ) and O (N1/2 T1/2) for the IID case and the Non-IID case respectively, achieving significant improvements over Local SGD. Experiments on both convex and non-convex problems demonstrate the superior performance of STL-SGD.
The Use of Youtube Vlog to Improve The Students’ Listening Skill of MTs Samarinda

The background of research is students have less motivation in learning English and some students feel difficult in understand vocabularies that were pronounced. As a result, they did not know the word and its meaning. It happens because they couldn’t hear the articulation of the word clearly. The objective of this research is to find out is YouTube vlog can improve students’ listening skill of eighth grade at MTs DDI Tani Aman Loa Janan. The design of this research was Classroom Action Research (CAR) which researcher following Kemmis and McTaggart action research procedures. The subject of this research was students of VIIIB at MTs DDI Tani Aman were about 29 students. The instrument of this research is observation checklist, test, and field note. The result of this research indicates that there was improvement of the students’ listening skill. Most of the students gradually gained good scores at the second cycle. The score of Minimum Mastery Criterion (KKM) of English lesson was 75.00. In the preliminary study there were 2 students or 7.69% students who passed the KKM and mean score of preliminary study was 28.51. Next, the result of the first cycle showed that there were 9 students or 31.03% students who passed the KKM considering their mean score of the test gained 145.47%. next, the result of the second cycle shows that there were 27 students or 96.42% students who passed the KKM which their mean scores derived 88.21 and gained 209.40% of improvement. So, the final result is found that more than 75% students with individual score in listening skill passed the KKM.
Segmentation of Driving Areas for Autonomous Vehicle based on Deep Learning Method Using Automotive Camera Senosor

This paper employs a deep learning method for segmenting drivable and non-drivable areas in an urban environment using the BDD (Berkeley Deep Drive) 100K image database. In particular, we propose a semantic segmentation network using an encoder module based on depth-wise separable, non-bottleneck-1D, and pyramid pooling. The BDD 100K dataset provides reference classification results of directly drivable area, alternative drivable area, and none, under different weather, time, and location conditions. The performance of the developed network is verified using a computation environment equipped with a GPU (Graphics Processing Unit) for evaluating its feasibility of real-time implementation.
Two-stage Behavior Cloning for Spoken Dialogue System in Debt Collection

With the rapid growth of internet finance and the booming of financial lending, the intelligent calling for debt collection in FinTech companies has driven increasing attention. Nowadays, the widely used intelligent calling system is based on dialogue flow, namely configuring the interaction flow with the finite-state machine. In our scenario of debt collection, the completed dialogue flow contains more than one thousand interactive paths. All the dialogue procedures are artificially specified, with extremely high maintenance costs and error-prone. To solve this problem, we propose the behavior-cloning-based collection robot framework without any dialogue flow configuration, called two-stage behavior cloning (TSBC). In the first stage, we use multi-label classification model to obtain policies that may be able to cope with the current situation according to the dialogue state; in the second stage, we score several scripts under each obtained policy to select the script with the highest score as the reply for the current state. This framework makes full use of the massive manual collection records without labeling and fully absorbs artificial wisdom and experience. We have conducted extensive experiments in both single-round and multi-round scenarios and showed the effectiveness of the proposed system. The accuracy of a single round of dialogue can be improved by 5%, and the accuracy of multiple rounds of dialogue can be increased by 3.1%.
SEBF: A Single-Chain based Extension Model of Blockchain for Fintech

The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of blockchain for fintech (SEBF). In the financial environment, we design a four-layer architecture for this model. By employing the external trusted or-acle group and a financial regulator agency, a variety types of data can be effectively stored in the blockchain, such that the data type extension based on a single-chain is realized. The experimental results indicate that the proposed model can improve the efficiency of simplified payment verifi-cation.
Stochastic Model for Generating Synthetic Hourly Global Horizontal Solar Radiation Data Sets Based on Auto Regression Characterization

A large number of non-repetitive multi-year hourly solar radiation time-series dataset are desired when applying Monte Carlo techniques for planning and design of solar energy systems. Solar radiation models which utilize the clearness index and average value decomposition methods are commonly used to generate synthetic set of solar irradiances for this purpose. In this paper, a novel stochastic solar radiation model based on probability distributions of the first-order differences of hourly global solar horizontal radiation is proposed. The first-order differences are modeled using a trend component and a stochastic component represented using the cumulative distribution functions, both extracted from historical data taken over a window of 31 days around the considered day of the year. Measured solar radiation data from four different locations with varying climate characteristics were used to evaluate the proposed model in comparison to two previously reported models. The proposed method performed consistently better in terms of the similarity of probability distributions and autocorrelation functions, for all four locations and datasets.
A trade-off between classical and quantum circuit size for an attack against CSIDH

Abstract We propose a heuristic algorithm to solve the underlying hard problem of the CSIDH cryptosystem (and other isogeny-based cryptosystems using elliptic curves with endomorphism ring isomorphic to an imaginary quadratic order 𝒪). Let Δ = Disc(𝒪) (in CSIDH, Δ = −4 p for p the security parameter). Let 0 &lt; α &lt; 1/2, our algorithm requires: A classical circuit of size <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mn>2</m:mn> <m:mrow> <m:mover> <m:mi>O</m:mi> <m:mo stretchy="false">˜</m:mo> </m:mover> <m:mfenced open="(" close=")"> <m:mrow> <m:mo>log</m:mo> <m:mo stretchy="false">(</m:mo> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mi mathvariant="normal">Δ</m:mi> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mrow> <m:msup> <m:mo stretchy="false">)</m:mo> <m:mrow> <m:mn>1</m:mn> <m:mo>−</m:mo> <m:mi>α</m:mi> </m:mrow> </m:msup> </m:mrow> </m:mrow> </m:mfenced> </m:mrow> </m:msup> <m:mo>.</m:mo> </m:math> $2^{\tilde{O}\left(\log(|\Delta|)^{1-\alpha}\right)}.$ A quantum circuit of size <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mn>2</m:mn> <m:mrow> <m:mover> <m:mi>O</m:mi> <m:mo stretchy="false">˜</m:mo> </m:mover> <m:mfenced open="(" close=")"> <m:mrow> <m:mo>log</m:mo> <m:mo stretchy="false">(</m:mo> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mi mathvariant="normal">Δ</m:mi> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mrow> <m:msup> <m:mo stretchy="false">)</m:mo> <m:mrow> <m:mi>α</m:mi> </m:mrow> </m:msup> </m:mrow> </m:mrow> </m:mfenced> </m:mrow> </m:msup> <m:mo>.</m:mo> </m:math> $2^{\tilde{O}\left(\log(|\Delta|)^{\alpha}\right)}.$ Polynomial classical and quantum memory. Essentially, we propose to reduce the size of the quantum circuit below the state-of-the-art complexity <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mn>2</m:mn> <m:mrow> <m:mover> <m:mi>O</m:mi> <m:mo stretchy="false">˜</m:mo> </m:mover> <m:mfenced open="(" close=")"> <m:mrow> <m:mo>log</m:mo> <m:mo stretchy="false">(</m:mo> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mi mathvariant="normal">Δ</m:mi> <m:mrow> <m:mo>|</m:mo> </m:mrow> <m:mrow> <m:msup> <m:mo stretchy="false">)</m:mo> <m:mrow> <m:mn>1</m:mn> <m:mrow> <m:mo>/</m:mo> </m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:mrow> </m:mrow> </m:mfenced> </m:mrow> </m:msup> </m:math> $2^{\tilde{O}\left(\log(|\Delta|)^{1/2}\right)}$ at the cost of increasing the classical circuit-size required. The required classical circuit remains subexponential, which is a superpolynomial improvement over the classical state-of-the-art exponential solutions to these problems. Our method requires polynomial memory, both classical and quantum.
What Drives Financial Development? A Meta-Regression Analysis

This paper offers a meta-regression analysis of the literature on the drivers of financial development. Our results based on 1900 estimates suggest that institutional quality is positively correlated to both private sector credit and stock market capitalization (both as share of GDP). Domestic financial openness has a positive effect on both proxies for financial development, while trade openness seems only important for stock market capitalization. Inflation has an adverse effect on financial development, which is larger for stock market capitalization. Finally, we conclude that the literature has not yet robustly established that remittances and trust matter for financial development.