Reviewerly/exHarmony huggingface dataset:
Adjoint image warping using multivariate splines with application to four‐dimensional computed tomography

Purpose Adjoint image warping is an important tool to solve image reconstruction problems that warp the unknown image in the forward model. This includes four‐dimensional computed tomography (4D‐CT) models in which images are compared against recorded projection images of various time frames using image warping as a model of the motion. The inversion of these models requires the adjoint of image warping, which up to now has been substituted by approximations. We introduce an efficient implementation of the exact adjoints of multivariate spline based image warping, and compare it against previously used alternatives. Methods Using symbolic computer algebra, we computed a list of 64 polynomials that allow us to compute a matrix representation of trivariate cubic image warping. By combining an on‐the‐fly computation of this matrix with a parallelized implementation of columnwise matrix multiplication, we obtained an efficient, low memory implementation of the adjoint action of 3D cubic image warping. We used this operator in the solution of a previously proposed 4D‐CT reconstruction model in which the image of a single subscan was compared against projection data of multiple subscans by warping and then projecting the image. We compared the properties of our exact adjoint with those of approximate adjoints by warping along inverted motion. Results Our method requires halve the memory to store motion between subscans, compared to methods that need to compute and store an approximate inverse of the motion. It also avoids the computation time to invert the motion and the tunable parameter of the number of iterations used to perform this inversion. Yet, a similar and often better reconstruction quality was obtained in comparison with these more expensive methods, especially when the motion is large. When compared against a simpler method that is similar to ours in computational demands, our method achieves a higher reconstruction quality in general. Conclusions Our implementation of the exact adjoint of cubic image warping improves efficiency and provides accurate reconstructions.
Inexact and Stochastic Generalized Conditional Gradient with Augmented Lagrangian and Proximal Step

In this paper we propose and analyze inexact and stochastic versions of the CGALP algorithm developed in [25], which we denote ICGALP , that allow for errors in the computation of several important quantities. In particular this allows one to compute some gradients, proximal terms, and/or linear minimization oracles in an inexact fashion that facilitates the practical application of the algorithm to computationally intensive settings, e.g., in high (or possibly infinite) dimensional Hilbert spaces commonly found in machine learning problems. The algorithm is able to solve composite minimization problems involving the sum of three convex proper lower-semicontinuous functions subject to an affine constraint of the form Ax = b for some bounded linear operator A. Only one of the functions in the objective is assumed to be differentiable, the other two are assumed to have an accessible proximal operator and a linear minimization oracle. As main results, we show convergence of the Lagrangian values (so-called convergence in the Bregman sense) and asymptotic feasibility of the affine constraint as well as strong convergence of the sequence of dual variables to a solution of the dual problem, in an almost sure sense. Almost sure convergence rates are given for the Lagrangian values and the feasibility gap for the ergodic primal variables. Rates in expectation are given for the Lagrangian values and the feasibility gap subsequentially in the pointwise sense. Numerical experiments verifying the predicted rates of convergence are shown as well.
Using Word Embeddings for Italian Crime News Categorization

Several studies have shown that the use of embeddings improves outcomes in many Natural Language Processing (NLP) activities, including text categorization.This paper focuses on how word embeddings can be used on newspaper articles related to crimes.The scope is the categorization of the news articles based on the type of crime they report.We compare different Word2Vec models and methods to obtain word embeddings.Then, we exploit both supervised and unsupervised Machine Learning categorization algorithms.Experiments were conducted on an Italian dataset of 15,361 crime news articles showing very promising results.
Stochastic Bigger Subspace Algorithms for Nonconvex Stochastic Optimization

It is well known that the stochastic optimization problem can be regarded as one of the most hard problems since, in most of the cases, the values of f and its gradient are often not easily to be solved, or the F(∙, ξ) is normally not given clearly and (or) the distribution function P is equivocal. Then an effective optimization algorithm is successfully designed and used to solve this problem that is an interesting work. This paper designs stochastic bigger subspace algorithms for solving nonconvex stochastic optimization problems. A general framework for such algorithm is presented for convergence analysis, where the so-called the sufficient descent property, the trust region feature, and the global convergence of the stationary points are proved under the suitable conditions. In the worst-case, we will turn out that the complexity is competitive under a given accuracy parameter. We will proved that the SFO-calls complexity of the presented algorithm with diminishing steplength is O(ϵ-1/1-β) and the SFO-calls complexity of the given algorithm with random constant steplength is O(ϵ-2) respectively, where β ∈ (0.5,1) and ϵ is accuracy and the needed conditions are weaker than the quasi-Newton methods and the normal conjugate gradient algorithms. The detail algorithm framework with variance reduction is also proposed for experiments and the nonconvex binary classification problem is done to demonstrate the performance of the given algorithm.
Methods for Detoxification of Texts for the Russian Language

We introduce the first study of the automatic detoxification of Russian texts to combat offensive language. This kind of textual style transfer can be used for processing toxic content on social media or for eliminating toxicity in automatically generated texts. While much work has been done for the English language in this field, there are no works on detoxification for the Russian language. We suggest two types of models—an approach based on BERT architecture that performs local corrections and a supervised approach based on a pretrained GPT-2 language model. We compare these methods with several baselines. In addition, we provide the training datasets and describe the evaluation setup and metrics for automatic and manual evaluation. The results show that the tested approaches can be successfully used for detoxification, although there is room for improvement.
Making the Most of Volcanic Eruption Responses

Last year, a new collaborative initiative conducted a hypothetical volcano response exercise. A month later, they put the knowledge gained to use during an actual eruption.
A tutorial on applications of power watershed optimization to image processing

This tutorial review paper consolidates the existing applications of the power watershed (PW) optimization framework in the context of image processing. In the literature, it is known that PW framework when applied to some well-known graph-based image segmentation and filtering algorithms such as random walker, isoperimetric partitioning, ratio-cut clustering, multi-cut and shortest path filters yield faster yet consistent solutions. In this paper, the intuition behind the working of PW framework, i.e. exploitation of contrast invariance on image data is explained. The intuitions are illustrated with toy images and experiments on simulated astronomical images. This article is primarily aimed at researchers working on image segmentation and filtering problems in application areas such as astronomy where images typically have huge number of pixels. Classic graph-based cost minimization methods provide good results on images with small number of pixels but do not scale well for images with large number of pixels. The ideas from the article can be adapted to a large class of graph-based cost minimization methods to obtain scalable segmentation and filtering algorithms.
Features of Critical Thinking of Individual Entrepreneurs With Different Levels of Self-Regulation

In the crisis conditions of entrepreneurs business functioning in Russian, it is important to develop critical thinking in conjunction with the skills of self-regulation of behavior. It is assumed that entrepreneurs with different styles of self-regulation differ in the level of critical thinking, and there is also a relationship between the styles of self-regulation and the level of critical thinking. The theoretical basis of the study is the idea of the relationship between the degree of formation of individual self-regulation and the productivity of performing various types of professional activities. The sample of the study consisted of 140 individual entrepreneurs engaged in entrepreneurial activity for more than two years, aged from 30 to 50 years. We used techniques to analyze the style of self-regulation of behavior, volitional self-regulation, critical thinking and mental abilities. The study allowed us to expand the understanding of the relationship between critical thinking and self-regulation, to reveal the content aspects of critical thinking of entrepreneurs with different levels of self-regulation of behavior. We found out that in the group of entrepreneurs with a high level of self-regulation of behavior, there is a high level of volitional self-regulation, perseverance, self-control, information processing skills and critical thinking skills. We came to conclusion that there are differences at the content and semantic level among entrepreneurs, differentiated by the type of self-regulation. For entrepreneurs with a low level of self-regulation, it is planned to conduct a program for the development of critical thinking.
Consideration for Affects of an XOR in a Random Number Generator Using Ring Oscillators

A cloud service to offer entropy has been paid much attention to. As one of the entropy sources, a physical random number generator is used as a true random number generator, relying on its irreproducibility. This paper focuses on a physical random number generator using a field-programmable gate array as an entropy source by employing ring oscillator circuits as a representative true random number generator. This paper investigates the effects of an XOR gate in the oscillation circuit by observing the output signal period. It aims to reveal the relationship between inputs and the output through the XOR gate in the target generator. The authors conduct two experiments to consider the relevance. It is confirmed that combining two ring oscillators with an XOR gate increases the complexity of the output cycle. In addition, verification using state transitions showed that the probability of the state transitions was evenly distributed by increasing the number of ring oscillator circuits.
Digital Yuan – currency or policy tool?

Central bank digital currencies (CBDCs) are Bitcoin-inspired currencies that combine aspects of virtual currencies with FIAT money. Many central banks and countries around the world are thinking about or planning to introduce some form of CBDC. Among them, China is leading the way, being the first country to issue their CBDC (Digital RMB) into circulation, even if it is yet a pilot program only. This article focuses not only on the Digital RMB and its legal implementation but also on the political, historical, and economic circumstances of its birth, which, when talking about anything related to China, is often crucial to understanding the phenomenon more than the legal provisions themselves. The aim is to deepen the understanding of legal policies in China and open a discussion about the Digital RMB.
Real Time Multi Face Blurring on Uncontrolled Environment based on Color Space algorithm

Faces blurring is one of the important complex processes that is considered one of the advanced computer vision fields. The face blurring processes generally have two main steps to be done. The first step has detected the faces that appear in the frames while the second step is tracking the detected faces which based on the information extracted during the detection step. In the proposed method, an image is captured by the camera in real time, then the Viola Jones algorithm used for the purpose of detecting multiple faces in the captured image and for the purpose of reducing the time consumed to handle the entire captured image, the image background is removed and only the motion areas are processed. After detecting the faces, the Color-Space algorithm is used to tracks the detected faces depending on the color of the face and to check the differences between the faces the Template Matching algorithm was used to reduce the processes time. Finally, thedetected faces as well as the faces that were tracked based on their color were obscured by the use of the Gaussian filter. The achieved accuracy for a single face and dynamic background are about 82.8% and 76.3% respectively.
What’s to bullying a bot?

Abstract In human-chatbot interaction, users casually and regularly offend and abuse the chatbot they are interacting with. The current paper explores the relationship between chatbot humanlikeness on the one hand and sexual advances and verbal aggression by the user on the other hand. 283 conversations between the Cleverbot chatbot and its users were harvested and analysed. Our results showed higher counts of user verbal aggression and sexual comments towards Cleverbot when Cleverbot appeared more humanlike in its behaviour. Caution is warranted with the interpretation of the results however as no experimental manipulation was conducted and causality can thus not be inferred. Nonetheless, the findings are relevant for both the research on the abuse of conversational agents, and the development of efficient approaches to discourage or prevent verbal aggression by chatbot users.
GeoEPIC: Innovating a Solution to Implement Virtual Field Experiences for Education in the Time of COVID-19 and the Post-Pandemic Era

The spring of 2020 marked the start of many countries closing their borders to travel due to the COVID- 19 pandemic. These closures disrupted travel and learning opportunities for K- 16 education. International field trips and study abroad programs were some of the first canceled opportunities with domestic field trips becoming more complicated to implement safely. With termination of these experiences, students lost cooperative and collaborative opportunities for place-based, field-based education. To address these losses of situated learning opportunities for students, a digital learning platform was developed to increase access to educators and students to free virtual field-based experiences. The platform is enhanced to allow teachers easy adoption of resources with supporting workshops to help educators in creating and delivering this rich education content through authentic, virtual landscapes.
Prototype Cataloging Interface Based on the IFLA Library Reference Model (LRM). Part 1: Conceptual Design

The aim of this paper is to present a prototype cataloging interface, which provides easier data entry, follows the cataloger’s thought process and is based on the advantages of the IFLA LRM model. The paper summarizes all stages of the conceptual design and shows how the LRM was implemented. The main purpose of the cataloging interface design is to show how a LRM-based cataloging module might look like and how it could improve the existing cataloging process.
Cutset-type Possibilistic C-means Clustering Algorithms Based on Semi-supervised Information

Cutset-type Possibilistic C-Means clustering (C-PCM) algorithm can significantly reduce the coincident clustering phenomenon of the Possibilistic C-Means clustering (PCM) algorithm by introducing the cut-set concept into the PCM. The C-PCM also has strong robustness to noise and outliers. However, the C-PCM still suffers from the center migration problem for datasets with small targets. In order to solve this problem, a Semi-Supervised Cutset-type Possibility C-Means (SS-C-PCM) clustering algorithm is proposed by introducing the semi-supervised learning mechanism into the objective function of the C-PCM and utilizing some prior information to guide the clustering process. Meanwhile, in order to improve the segmentation efficiency and accuracy of color images, a differential evolutionary superpixel-based Semi-Supervised Cutset-type Possibilistic C-Means (desSS-C-PCM) clustering algorithm is proposed. In the desSS-C-PCM, the Differential Evolutionary Superpixel(DES) algorithm is used to obtain the spatial neighborhood information of an image, which is integrated into the objective function of the semi-supervised C-PCM to improve the segmentation quality. Simultaneously, the color histogram is used to reconstruct the new objective function to reduce the computational complexity of the algorithm. Several experiments of artificial data clustering and color image segmentation show that the proposed algorithm can effectively improve the clustering effect of datasets with small targets and the execution efficiency compared with several related algorithms.
A Novel Nonlinear Control for Uncertain Polynomial Type-2 Fuzzy Systems (Case Study: Cart-Pole System)

In this paper, a new nonlinear adaptive control method for controlling the polynomial type-2 fuzzy systems with uncertain parameters is presented. In this paper, it is assumed that the vector state of the system is not always available, and so it is necessary to first use the observer and, while stabilizing the system, control it with minimal error. The control gain is calculated by using least squares in such a way that the vector of the polynomial type-2 fuzzy system state vector follows the state of a stable reference system. In the simulation, the cart-pole system is considered. This nonlinear complex system first converts to a polynomial type-2 fuzzy model and then is well controlled by the proposed method. Also, changes in parameters and the effect of disturbance have also been studied. The results show that the proposed method is effective.
Dog Breed Prediction using Convolutional Neural Network

Deep learning gives the strength on the way to train algorithms model that can handle the difficulties of info classification also prediction grounded on totally on arising information as of raw information. Convolutional Neural Networks (CNNs) gives single often used method for image classification and detection. In this exertion, we define a CNNbased approach for spotting dogs in per chance complex images and due to this fact reflect inconsideration on the identification of the one of kinds of dog breed. The experimental outcome analysis supported the standard metrics and thus the graphical representation confirms that the algorithm (CNN) gives good analysis accuracy for all the tested datasets.
Doctrines, modalities and comonads

Abstract Doctrines are categorical structures very apt to study logics of different nature within a unified environment: the 2-category Dtn of doctrines. Modal interior operators are characterised as particular adjoints in the 2-category Dtn . We show that they can be constructed from comonads in Dtn as well as from adjunctions in it, and we compare the two constructions. Finally we show the amount of information lost in the passage from a comonad, or from an adjunction, to the modal interior operator. The basis for the present work is provided by some seminal work of John Power.
An Architecture for Digital Processes in Manufacturing with Blockchain, Docker and Cloud Storage

The Blockchain is one of the last decade emerging technologies in software architectures. Its nature of a distributed ledger database allowing verifiable and tamper-proof transactions between untrusted parties makes it suitable for a vast class of domains concerning business processes, including Cloud Manufacturing, a new paradigm for the manufacturing industry based on cloud technologies, for which decentralization and security are key factors. However, existing solutions are still weak in terms of collaboration in providing and consuming heterogeneous services in the Cloud, therefore a standard framework is necessary to overcome this limit. In this paper, we suggest an architecture for consuming digital processes in a manufacturing environment, based on Blockchain and Smart Contracts. Our primary contribution is the integration of Blockchain with two other popular technologies: Docker, a highly portable and scalable container-based platform to run applications, and Cloud Storage. In this system, the logic of a single process is defined by the owner in a self-contained Docker image, whose digest is safely stored in the chain, while input and output files can be stored in a traditional cloud storage service. On each new consumer request, the best runner node is selected through the solution of a simple task assignment problem with a deep learning approach.
Learning Self-Similarity in Space and Time as a Generalized Motion for Action Recognition

Spatio-temporal convolution often fails to learn motion dynamics in videos and thus an effective motion representation is required for video understanding in the wild. In this paper, we propose a rich and robust motion representation method based on spatio-temporal self-similarity (STSS). Given a sequence of frames, STSS represents each local region as similarities to its neighbors in space and time. By converting appearance features into relational values, it enables the learner to better recognize structural patterns in space and time. We leverage the whole volume of STSS and let our model learn to extract an effective motion representation from it. The proposed method is implemented as a neural block, dubbed SELFY, that can be easily inserted into neural architectures and learned end-to-end without additional supervision. With a sufficient volume of the neighborhood in space and time, it effectively captures long-term interaction and fast motion in the video, leading to robust action recognition. Our experimental analysis demonstrates its superiority over previous methods for motion modeling as well as its complementarity to spatio-temporal features from direct convolution. On the standard action recognition benchmarks, Something-Something-V1 & V2, Diving-48, and FineGym, the proposed method achieves the state-of-the-art results.