Reviewerly/exHarmony huggingface dataset:
Tedarik Zincirinde Blok Zincir (Blockchain) Teknolojisi

The technological changes in recent years have played a major role in changing the rules in business life. After technological innovations such as autonomous tools, the Internet of Things, and industry 4.0, the new issue that has emerged with crypto money today is Blockchain technology. A blockchain technology is a peer-to-peer digital ledger of transactions that may be publicly or privately distributed to all users and therefore is said to be decentralized and distributed. The most important feature that distinguishes block-chain-based technologies from other technologies is that it has a special algorithm without being connected to any center. Therefore, this technology makes a significant difference in supplying fast, secure and accurate information flow among supply chain members. Reducing the costs and time that arise in the supply chain, it provides significant advantages to enterprises such as the transparency of the operations performed. The purpose of this study is; to explain the block chain technology and to contribute to supply chain processes and stakeholders. In this context, the study is considered as literature review. The study is important in terms of contributing to the relevant literature in basic level.
Identification of a Duffing oscillator using particle Gibbs with ancestor sampling

Abstract The Duffing oscillator remains a key benchmark in nonlinear systems analysis and poses interesting challenges in nonlinear structural identification. The use of particle methods or sequential Monte Carlo (SMC) is becoming a more common approach for tackling these nonlinear dynamical systems, within structural dynamics and beyond. This paper demonstrates the use of a tailored SMC algorithm within a Markov Chain Monte Carlo (MCMC) scheme to allow inference over the latent states and parameters of the Duffing oscillator in a Bayesian manner. This approach to system identification offers a statistically more rigorous treatment of the problem than the common state-augmentation methods where the parameters of the model are included as additional latent states. It is shown how recent advances in particle MCMC methods, namely the particle Gibbs with ancestor sampling (PG-AS) algorithm is capable of performing efficient Bayesian inference, even in cases where little is known about the system parameters a priori . The advantage of this Bayesian approach is the quantification of uncertainty, not only in the system parameters but also in the states of the model (displacement and velocity) even in the presence of measurement noise.
Inverting Supervised Representations with Autoregressive Neural Density Models

We present a method for feature interpretation that makes use of recent advances in autoregressive density estimation models to invert model representations. We train generative inversion models to express a distribution over input features conditioned on intermediate model representations. Insights into the invariances learned by supervised models can be gained by viewing samples from these inversion models. In addition, we can use these inversion models to estimate the mutual information between a model's inputs and its intermediate representations, thus quantifying the amount of information preserved by the network at different stages. Using this method we examine the types of information preserved at different layers of convolutional neural networks, and explore the invariances induced by different architectural choices. Finally we show that the mutual information between inputs and network layers initially increases and then decreases over the course of training, supporting recent work by Shwartz-Ziv and Tishby (2017) on the information bottleneck theory of deep learning.
A Blind Color Image Watermarking Using BRISK Features and Contourlet Transform

A digital blind-watermarking scheme on color images is designed using Contourlet Transform (CT) and QR-decomposition to provide authentication and Intellectual Property Rights protection. Firstly, cover color image is transformed to Luminance, Chroma blue and red components (YCbCr). Secondly, the luminance component of cover image is transformed by CT and divide the low-level sub-band to $$4\times 4$$ blocks (non-overlapping). Thirdly, the blocks are decomposed using QR decomposition. Finally, watermark encrypted using Arnold transform is embedded into cover image with adaptively calculated embedding factor values using Binary Robust Invariant Scalable Key-points features. In blind-watermarking extraction, the watermark is extracted without cover image from distorted watermarked images. Simulation results demonstrate that the designed digital blind-watermarking method has high invisibility and strong robustness from different attacks (image and signal processing). The proposed scheme has good results in comparison with related methods.
On posterior consistency of tail index for Bayesian kernel mixture models

Asymptotic theory of tail index estimation has been studied extensively in the frequentist literature on extreme values, but rarely in the Bayesian context.We investigate whether popular Bayesian kernel mixture models are able to support heavy tailed distributions and consistently estimate the tail index.We show that posterior inconsistency in tail index is surprisingly common for both parametric and nonparametric mixture models.We then present a set of sufficient conditions under which posterior consistency in tail index can be achieved, and verify these conditions for Pareto mixture models under general mixing priors.
Bayesian mode and maximum estimation and accelerated rates of contraction

We study the problem of estimating the mode and maximum of an unknown regression function in the presence of noise.We adopt the Bayesian approach by using tensor-product B-splines and endowing the coefficients with Gaussian priors.In the usual fixed-in-advanced sampling plan, we establish posterior contraction rates for mode and maximum and show that they coincide with the minimax rates for this problem.To quantify estimation uncertainty, we construct credible sets for these two quantities that have high coverage probabilities with optimal sizes.If one is allowed to collect data sequentially, we further propose a Bayesian two-stage estimation procedure, where a second stage posterior is built based on samples collected within a credible set constructed from a first stage posterior.Under appropriate conditions on the radius of this credible set, we can accelerate optimal contraction rates from the fixed-in-advanced setting to the minimax sequential rates.A simulation experiment shows that our Bayesian two-stage procedure outperforms single-stage procedure and also slightly improves upon a non-Bayesian two-stage procedure.
Deep Generative Quantile-Copula Models for Probabilistic Forecasting

We introduce a new category of multivariate conditional generative models and demonstrate its performance and versatility in probabilistic time series forecasting and simulation. Specifically, the output of quantile regression networks is expanded from a set of fixed quantiles to the whole Quantile Function by a univariate mapping from a latent uniform distribution to the target distribution. Then the multivariate case is solved by learning such quantile functions for each dimension's marginal distribution, followed by estimating a conditional Copula to associate these latent uniform random variables. The quantile functions and copula, together defining the joint predictive distribution, can be parameterized by a single implicit generative Deep Neural Network.
FMHash: Deep Hashing of In-Air-Handwriting for User Identification

Many mobile systems and wearable devices, such as Virtual Reality (VR) or Augmented Reality (AR) headsets, lack a keyboard or touchscreen to type an ID and password for signing into a virtual website. However, they are usually equipped with gesture capture interfaces to allow the user to interact with the system directly with hand gestures. Although gesture-based authentication has been well-studied, less attention is paid to the gesture-based user identification problem, which is essentially an input method of account ID and an efficient searching and indexing method of a database of gesture signals. In this paper, we propose FMHash (i.e., Finger Motion Hash), a user identification framework that can generate a compact binary hash code from a piece of in-air-handwriting of an ID string. This hash code enables indexing and fast search of a large account database using the in-air-handwriting by a hash table. To demonstrate the effectiveness of the framework, we implemented a prototype and achieved ≥99.5% precision and ≥92.6% recall with exact hash code match on a dataset of 200 accounts collected by us. The ability of hashing in-air-handwriting pattern to binary code can be used to achieve convenient sign-in and sign-up with in-air-handwriting gesture ID on future mobile and wearable systems connected to the Internet.
OntoAgile: an ontology for agile software development processes

Currently, there is a broad portfolio of agile approaches to software development, however, in many cases their implementation is done informally and without a proper institutionalization of the agile values and principles. Although there are some proposals related to the adoption and assessment of agile approaches, efforts have been made without having a common terminology which has led to confusion and terminological conflict affecting the implementation of these approaches in organizations. This article proposes the ontology called OntoAgile, which aims to suggest a common and consistent terminology that allows sharing the knowledge generated around the implementation of the agile approaches in the software processes in a generic and formal way. Similarly, OntoAgile facilitates the assessment of the agility of the software processes from the identification of the relationships between the elements of the software processes and the agile principles and values. OntoAgile was assessed satisfactorily by three cases of application.
Student Performance Assessment Using Clustering Techniques

The application of informatics in the university system management allows managers to count with a great amount of data which, rationally treated, can offer significant help for the student programming monitoring. This research proposes the use of clustering techniques as a useful tool of management strategy to evaluate the progression of the students’ behavior by dividing the population into homogeneous groups according to their characteristics and skills. These applications can help both the teacher and the student to improve the quality of education. The selected method is the data grouping analysis by means of fuzzy logic using the Fuzzy C-means algorithm to achieve a standard indicator called Grade, through an expert system to enable segmentation.
A sketched finite element method for elliptic models

We consider a sketched implementation of the finite element method for elliptic partial differential equations on high-dimensional models. Motivated by applications in real-time simulation and prediction we propose an algorithm that involves projecting the finite element solution onto a low-dimensional subspace and sketching the reduced equations using randomised sampling. We show that a sampling distribution based on the leverage scores of a tall matrix associated with the discrete Laplacian operator, can achieve nearly optimal performance and a significant speedup. We derive an expression of the complexity of the algorithm in terms of the number of samples that are necessary to meet an error tolerance specification with high probability, and an upper bound for the distance between the sketched and the high-dimensional solutions. Our analysis shows that the projection not only reduces the dimension of the problem but also regularises the reduced system against sketching error. Our numerical simulations suggest speed improvements of two orders of magnitude in exchange for a small loss in the accuracy of the prediction.
DropAttention: A Regularization Method for Fully-Connected Self-Attention Networks

Variants dropout methods have been designed for the fully-connected layer, convolutional layer and recurrent layer in neural networks, and shown to be effective to avoid overfitting. As an appealing alternative to recurrent and convolutional layers, the fully-connected self-attention layer surprisingly lacks a specific dropout method. This paper explores the possibility of regularizing the attention weights in Transformers to prevent different contextualized feature vectors from co-adaption. Experiments on a wide range of tasks show that DropAttention can improve performance and reduce overfitting.
Multiple Decompositions-Based Blind Watermarking Scheme for Color Images

A blind extraction watermarking method on color images is designed to protect © & identify owner of multimedia. Watermark in color is exploited to deliver universality and boundless applicability to proposed watermarking method. The host data is decomposed by applying four decomposition techniques like Discrete Wavelet Transform (DWT), QR, SVD & Schur decomposition. Arnold transformed watermark is utilized for better security. This scheme embed’s color watermark into gray-scale cover image which will be out of attacker’s expectation. A probabilistic neural network (PNN) is utilized in the watermark extraction. Robustness and quality of scheme are evaluated with quality measurement functions like Peak Signal to Noise Ratio (PSNR) and Normalized Correlation Coefficient (NCC).
The Importance of Being Clustered: Uncluttering the Trends of Statistics from 1970 to 2015

In this paper, we retrace the recent history of statistics by analyzing all the papers published in five prestigious statistical journals since 1970, namely: The Annals of Statistics, Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Series B and Statistical Science. The aim is to construct a kind of “taxonomy” of the statistical papers by organizing and clustering them in main themes. In this sense being identified in a cluster means being important enough to be uncluttered in the vast and interconnected world of the statistical research. Since the main statistical research topics naturally born, evolve or die during time, we will also develop a dynamic clustering strategy, where a group in a time period is allowed to migrate or to merge into different groups in the following one. Results show that statistics is a very dynamic and evolving science, stimulated by the rise of new research questions and types of data.
Identification of MultiObject Dynamical Systems: Consistency and Fisher Information

Learning the model parameters of a multiobject dynamical system from partial and perturbed observations is a challenging task.Despite recent numerical advancements in learning these parameters, theoretical guarantees are extremely scarce.In this article we aim to help fill this gap and study the identifiability of the model parameters and the consistency of the corresponding maximum likelihood estimate (MLE) under assumptions on the different components of the underlying multiobject system.In order to understand the impact of the various sources of observation noise on the ability to learn the model parameters, we study the asymptotic variance of the MLE through the associated Fisher information matrix.For example, we show that specific aspects of the multitarget tracking (MTT) problem such as detection failures and unknown data association lead to a loss of information which is quantified in special cases of interest.To the best of the authors' knowledge, these are new theoretically backed insights on the subtleties of MTT parameter learning.
Initialization of the Nguyen-widrow and Kohonen Algorithm on the Backpropagation Method in the Classifying Process of Temperature Data in Medan

Abstract In this paper, we propose the initialization of the Nguyen-widrow and Kohonen algorithm on the Backpropagation Neural Network in the classification of temperature in Medan. Initialization of Nguyen-widrow and Kohonen weight in Backpropagation could accelerate the training process of temperature data compared to Backpropagation with random weight. The experiment reaches target error 0.007 at 30 epoch. The result of testing show that the initialization of Nguyen-widrow and Kohonen weight in Backpropagation could recognize the test data reaches 96.52% accuracy.
Understanding and Controlling Memory in Recurrent Neural Networks

To be effective in sequential data processing, Recurrent Neural Networks (RNNs) are required to keep track of past events by creating memories. While the relation between memories and the network's hidden state dynamics was established over the last decade, previous works in this direction were of a predominantly descriptive nature focusing mainly on locating the dynamical objects of interest. In particular, it remained unclear how dynamical observables affect the performance, how they form and whether they can be manipulated. Here, we utilize different training protocols, datasets and architectures to obtain a range of networks solving a delayed classification task with similar performance, alongside substantial differences in their ability to extrapolate for longer delays. We analyze the dynamics of the network's hidden state, and uncover the reasons for this difference. Each memory is found to be associated with a nearly steady state of the dynamics which we refer to as a 'slow point'. Slow point speeds predict extrapolation performance across all datasets, protocols and architectures tested. Furthermore, by tracking the formation of the slow points we are able to understand the origin of differences between training protocols. Finally, we propose a novel regularization technique that is based on the relation between hidden state speeds and memory longevity. Our technique manipulates these speeds, thereby leading to a dramatic improvement in memory robustness over time, and could pave the way for a new class of regularization methods.
Population subset selection for the use of a validation dataset for overfitting control in genetic programming

Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a validation dataset is a common alternative to prevent overfitting in many Machine Learning (ML) techniques, including GP. But, there is one key point which differentiates GP and other ML techniques: instead of training a single model, GP evolves a population of models. Therefore, the use of the validation dataset has several possibilities because any of those evolved models could be evaluated. This work explores the possibility of using the validation dataset not only on the training-best individual but also in a subset with the training-best individuals of the population. The study has been conducted with 5 well-known databases performing regression or classification tasks. In most of the cases, the results of the study point out to an improvement when the validation dataset is used on a subset of the population instead of only on the training-best individual, which also induces a reduction on the number of nodes and, consequently, a lower complexity on the expressions.
Generative Adversarial Network for Handwritten Text

Generative adversarial networks (GANs) have proven hugely successful in variety of applications of image processing. However, generative adversarial networks for handwriting is relatively rare somehow because of difficulty of handling sequential handwriting data by Convolutional Neural Network (CNN). In this paper, we propose a handwriting generative adversarial network framework (HWGANs) for synthesizing handwritten stroke data. The main features of the new framework include: (i) A discriminator consists of an integrated CNN-Long-Short-Term- Memory (LSTM) based feature extraction with Path Signature Features (PSF) as input and a Feedforward Neural Network (FNN) based binary classifier; (ii) A recurrent latent variable model as generator for synthesizing sequential handwritten data. The numerical experiments show the effectivity of the new model. Moreover, comparing with sole handwriting generator, the HWGANs synthesize more natural and realistic handwritten text.
Efficient computation of counterfactual explanations of LVQ models

The increasing use of machine learning in practice and legal regulations like EU's GDPR cause the necessity to be able to explain the prediction and behavior of machine learning models. A prominent example of particularly intuitive explanations of AI models in the context of decision making are counterfactual explanations. Yet, it is still an open research problem how to efficiently compute counterfactual explanations for many models. We investigate how to efficiently compute counterfactual explanations for an important class of models, prototype-based classifiers such as learning vector quantization models. In particular, we derive specific convex and non-convex programs depending on the used metric.
Weakly Supervised Multi-task Learning for Semantic Parsing

Semantic parsing is a challenging and important task which aims to convert a natural language sentence to a logical form. Existing neural semantic parsing methods mainly use &lt;question, logical form&gt; (Q-L) pairs to train a sequence-to-sequence model. However, the amount of existing Q-L labeled data is limited and hard to obtain. We propose an effective method which substantially utilizes labeling information from other tasks to enhance the training of a semantic parser. We design a multi-task learning model to train question type classification, entity mention detection together with question semantic parsing using a shared encoder. We propose a weakly supervised learning method to enhance our multi-task learning model with paraphrase data, based on the idea that the paraphrased questions should have the same logical form and question type information. Finally, we integrate the weakly supervised multi-task learning method to an encoder-decoder framework. Experiments on a newly constructed dataset and ComplexWebQuestions show that our proposed method outperforms state-of-the-art methods which demonstrates the effectiveness and robustness of our method.