Reviewerly/exHarmony huggingface dataset:
Towards Automatic Modelling of Thematic Domains of a National Literature: Technical Issues in the Case of Russian

A significant part of modern technologies associated with the development of artificial intelligence systems and digital analytics of diverse data relies on methods of computer text processing (NLP, speech technologies). However, NLP methods are applied primarily to specialized texts, such as scientific literature, technical documentation, news, etc., or social media discourse. Fiction texts are usually left out of the focus of NLP practitioners as the fictional world seems to be of less significance or less “information value” from a practical point of view. Moreover, due to the poetic and metaphorical nature of literary texts, the use of some NLP methods (e.g., topic modelling) for fiction analysis turned out to be more complicated. At the same time, the influence of literature both on the consciousness of individuals and on the formation of social values can hardly be overestimated. Besides, making computers “understand” fiction in a similar way as humans do would be a real challenge for artificial intelligence. The article puts forward the idea of modelling thematic areas of literature on a national scale, which should reveal the main thematic domains of national literature as a whole. It will allow a better understanding of the cultural traits of the national consciousness in a given historical period and contribute to either literary studies or practical tasks. Methodological approaches to determining and modelling themes of literary works are considered, technical difficulties arising in the process are described, and the ways to solve them are suggested. The proposed methodology has been implemented in the design of the corpus of Russian short stories of 1900-1930s and can be applied in the development of artificial intelligence systems that process large volumes of literary texts in any language.
Simulated Analysis of Using Artificial Neural Network to Identify Fake Images Based on Metadata

Abstract Many phony pictures are spreading through advanced media these days. Identification of such phony pictures is unavoidable for the revealing of the picture based cybercrimes. Fashioning pictures what’s more, recognizing such pictures are promising examination territories in this advanced time. The altered pictures are an identified utilizing neural organize which additionally perceives the locales of the picture that have been controlled and uncovers the sections of the first picture. It tends to be actualized on Android stage and subsequently made accessible to normal clients. The pressure proportion of the remote content in a phony picture is not the same as that of the first picture and is recognized utilizing Error Level Analysis. Another include utilized alongside pressure proportion is picture metadata. Despite the fact that it is conceivable to change metadata content creation it inconsistent all alone, here it is utilized as a supporting parameter for mistake level examination choice.
Radar Emitter Identification Based on Fully Connected Spiking Neural Network

Abstract In the face of the increasing complex electromagnetic environment and new radar system, it is difficult to extract radar emitter characteristics based on manual mode to meet requirements of modern cognitive electronic warfare. In order to improve the intelligence level of radar emitter identification, a new method based on Spiking Neuron Network (SNN) for radar emitter identification is proposed in this paper. Firstly, five kinds of common radar signals are converted into two-dimensional gray scale images by using time-frequency analysis method. Then, the images are converted into spikes by Poisson coder, which are put into a fully connected spiking neural network for training and emitter identification. Finally, the simulation results prove the validity of this method by comparing with the traditional neural network.
Spline-Approximation and Spline-Extrapolation Methods in Telecommunication Problems

One of the main tasks that need to be solved in telecommunications are the problems of analysis and synthesis of telecommunication systems. These include the tasks of restoring random signals and self-similar traffic, managing network objects and the network as a whole, operating procedures for telecommunication objects and networks. The problems of forecasting are also considered, in particular, predicting the characteristics of network traffic, maintaining the quality characteristics of QoS during its service, and forming the requirements for network buffer devices. When solving such problems, the spline-approximation method was used and a spline-extrapolation method was developed based on various types of spline-functions, namely: linear, quadratic, quadratic B-splines, cubic, cubic B-splines. As the selection criterion of a particular type of spline while using spline-approximation method and spline-extrapolation method, the error of reconstruction or prediction is selected, the accuracy of which can be increased by using one or another type of spline, depending on the restored or predicted object. In some cases, the expediency of using wavelet-approximation and wavelet-extrapolation is shown, depending on the problems being solved. The results obtained allow to provide the required characteristics and scalability of the solutions obtained.
Useful chatbot experience provides technological satisfaction: An emerging market perspective

Background: Chatbots have revolutionised marketing and specifically customer services by delivering 24/7 customer care. This technology saves resources and time, whilst enhancing customer experience that has become a critical differentiating factor in an increasing competitive and hyperconnected landscape. Objectives: This research examined how perceived ease of use (PEOU), perceived playfulness (PP) and perceived usefulness (PU) of chatbots influence customer experience and how these experiences influence users’ satisfaction in an emerging market context. Method: A self-administered questionnaire was distributed to 333 South African millennials and subjected to exploratory factor analysis and multiple regression analysis to test the proposed hypotheses. Results: The results indicate that all the hypotheses suggested have been positive and significant. Whilst experience significantly predicted satisfaction, it is worth noting that PU was the strongest predictor of experience. Conclusion: Marketers should devote time, money and recourses in developing and incorporating self-service technologies, such as chatbots, that are useful for their marketing strategies that will enhance customer experience.
A Comparative Study Between PID and Fuzzy Controller

Students earlier were reluctant to adopt smart techniques for controlling for various reasons like stabilizing properties and characteristics of performance could not be verified, analytical comparison of its advantages/disadvantages over other conventional methods were not known and moreover the approaches were not widely used by working engineers. Whereas newer intelligent control technologies like fuzzy, neural and hybrid controls are being adopted now-a-days. Fuzzy logic is an effective controller tool where variable relationships can be represented in logical form. Fuzzy logic quantifies the linguistic variable terms so that they can be described as continuous values and allows mathematical equations to represent the system characteristics and responses. In process or manufacturing industries like PI or PID techniques of control, are widely adopted due to their simple method, their easy operation, and its tuning techniques are easy to understand.
On Some Geometric Aspects of the Class of hv-Convex Switching Components

In the usual aim of discrete tomography, the reconstruction of an unknown discrete set is considered, by means of projection data collected along a set U of discrete directions. Possible ambiguous reconstructions can arise if and only if switching components occur, namely, if and only if non-empty images exist having null projections along all the directions in U. In order to lower the number of allowed reconstructions, one tries to incorporate possible extra geometric constraints in the tomographic problem, such as the request for connectedness, or some reconstruction satisfying special convexity constraints. In particular, the class \(\mathbb {P}\) of horizontally and vertically convex connected sets (briefly, hv-convex polyominoes) has been largely considered.
A Proposal to Use Elliptical Curves to Secure the Block in E-voting System Based on Blockchain Mechanism

E-voting based blockchain technology has potential use in strengthening cybersecurity and securing votes. However, one of Blockchain's fundamental mechanisms, Proof-of-Work, is not possible to be implemented in the E-voting based blockchain since it requires a very high resource. As a result, there is still a possibility to alter the data within transaction block and recalculating its hash will be easy. In addition, without the Proof-of-Work, the authority of the block could not be validated. Therefore, the digital signature could be the answer to solve these problems. This research will provide the implementation result of elliptical curve cryptography for digital signature using two different signature algorithms, namely, ECDSA (curve secp256k1) and Ed25519, in terms of evaluating its performance during key-pair generation, block signing and signature verification on E-voting system based on Blockchain.
Concise CNN model for face expression recognition

Face Expression Recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.
Repetition effects in task-driven eye movement analyses after longer time-spans

Visualizations are an important tool in risk management to support decision-making of recipients of risk reports. Many trainings aim at helping managers to better understand how to read such visualizations. In this paper we present first results of an ongoing large study on the effect of repeated presentation of risk visualizations from annual reports. This is of importance to find out if such repetitions have an effect on accuracy and the behavior of readers. Contrary to other studies we had longer time-spans of months and weeks between two trials. We found that fixation durations are different after second presentation and that the number of fixations generally are lower. We also analyzed scan paths, indicating that regions that are more semantically meaningful are more often in the center of attention. We call for more studies with longer time spans between two trials as we found some interesting patterns.
Study of the quality of virtual university services at the National University of the Peruvian Amazon, during the COVID-19 pandemic

This paper presents a descriptive study on the first implementation of virtual education services at the National University of the Peruvian Amazon (UNAP), in the context of the COVID-19 pandemic. The city of Iquitos has a weak telecommunications infrastructure, although four companies with national coverage offer their Internet services (Movistar, Bitel, Claro, and Entel). The surveys conducted were based on the UNE 66181:2008 standard, considering indicators such as levels of information, employability, ease of assimilation, and accessibility. The results indicate that this first virtual education experience has not been satisfactory for a large number of participating students, due to the poor quality of the internet service, the instability of the UNAP service, and functional characteristics of the software used (Moodle, Zoom, etc.). Finally, it's important to mention that the student's perception of the UNAP virtual classes is negative.
Stacked Framework for Ensemble of Heterogeneous Classification Algorithms

Ensemble methods fabricate a sequence of classifiers for classifying fresh instances by procuring a weighted vote of their individual predictions. Toning down the error and increasing accuracy is an avant-garde problem in ensemble classification. This paper presents a novel generic object-oriented voting and weighting adapted stacking framework for utilizing an ensemble of classifiers for prediction. This universal framework operates based on the weighted average of the probabilities of any suite of base learners and the final prediction is the aggregate of their respective votes. For illustrative purposes, three familiar heterogeneous classifiers, such as the Support Vector Machine, [Formula: see text]-Nearest Neighbor and Naïve Bayes, are utilized as candidates for ensemble classification using the proposed stacked framework. Further, the ensemble classifier built upon the framework is compared with others and evaluated using various cross-validation levels and percentage splits on a range of benchmark datasets. The outcome distinguishes the framework from the competition. The proposed framework is used to predict the crime propensity of prisoners most accurately, with 99.9901% accuracy.
Online path planning for unmanned aerial vehicles to maximize instantaneous information

In this article, an online path planning algorithm for multiple unmanned aerial vehicles (UAVs) has been proposed. The aim is to gather information from target areas (desired regions) while avoiding forbidden regions in a fixed time window starting from the present time. Vehicles should not violate forbidden zones during a mission. Additionally, the significance and reliability of the information collected about a target are assumed to decrease with time. The proposed solution finds each vehicle’s path by solving an optimization problem over a planning horizon while obeying specific rules. The basic structure in our solution is the centralized task assignment problem, and it produces near-optimal solutions. The solution can handle moving, pop-up targets, and UAV loss. It is a complicated optimization problem, and its solution is to be produced in a very short time. To simplify the optimization problem and obtain the solution in nearly real time, we have developed some rules. Among these rules, there is one that involves the kinematic constraints in the construction of paths. There is another which tackles the real-time decision-making problem using heuristics imitating human-like intelligence. Simulations are realized in MATLAB environment. The planning algorithm has been tested on various scenarios, and the results are presented.
Interactive Explanations: Diagnosis and Repair of Reinforcement Learning Based Agent Behaviors

Reinforcement learning techniques successfully generate convincing agent behaviors, but it is still difficult to tailor the behavior to align with a user's specific preferences. What is missing is a communication method for the system to explain the behavior and for the user to repair it. In this paper, we present a novel interaction method that uses interactive explanations using templates of natural language as a communication method. The main advantage of this interaction method is that it enables a two-way communication channel between users and the agent; the bot can explain its thinking procedure to the users, and the users can communicate their behavior preferences to the bot using the same interactive explanations. In this manner, the thinking procedure of the bot is transparent, and users can provide corrections to the bot that include a suggested action to take, a goal to achieve, and the reasons behind these decisions. We tested our proposed method in a clone of the video game named Super Mario Bros., and the results demonstrate that our interactive explanation approach is effective at diagnosing and repairing bot behaviors.
A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering

Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering.However, most GCN-based models have inferior embedding propagation mechanism, leading to low information extraction efficiency.Besides, the existing methods suffer from high computational complexity for large user-item interaction graphs.In order to solve the above problems, we propose LII-GCCF that integrates Linear transformation, Initial residual and Identity mapping into the Graph Convolutional Collaborative Filtering model.First, initial residual and identity mapping are applied to optimize the information propagation of graph convolution, which privide abundant interaction and alleviate information loss problem.Second, LII-GCCF removes the unnecessary nonlinear transformation based on the characteristics of collaborative filtering to simplify the graph convolution process.Comprehensive experiments are conducted on two public datasets, and the results demonstrate that LII-GCCF has a significant improvement over other state-of-the-art methods.
IX-ResNet: fragmented multi-scale feature fusion for image classification

With the continuous in-depth study of convolutional neural network in computer vision, how to improve the performance of network structure has been the focus of current research. Recent works have shown that multi-scale feature concatenation, shortcut connection and grouping convolution can effectively train deeper networks and improve the accuracy and effectiveness of the network. In this paper, we present a novel feature transformation strategy of fragmented multi-scale feature fusion. Moreover, an efficient modularized image classification network, IX-ResNet, is proposed based on this new strategy. IX-ResNet consists of many large isomorphic modules stacked in the form of residual network while Each large module can be composed of many small heterogeneous modules. The performance of IX-ResNet is verified on cifar-10, cifar-100 and ImageNet-1 K datasets, which indicates that IX-ResNet model using fragmented multi-scale feature fusion strategy can further improve accuracy compare to the original grouping convolution network ResNeXt with the same or even lower parameters.
3D Topology-Preserving Segmentation with Compound Multi-Slice Representation

We propose a new topology-preserving method for 3D image segmentation. We treat the image as a stack of 2D images so that the topological computation can be carried only within 2D in order to achieve computational efficiency. To enforce the continuity between slices, we propose a compound multi-slice representation and a compound multi-slice topological loss that incorporates rich topological information from adjacent slices. The quantitative and qualitative results show that our proposed method outperforms various strong baselines, especially for structure-related evaluation metrics.
Security of quantum-key-distribution protocol by using the post-selection technique

The post-selection technique was proposed by Christandl, König, and Renner [Phys. Rev. Lett. 102, 020504 (2009)] to simplify the security of quantum-key-distribution schemes. This work applies this technique to derive the security bounds for the six-state Scarani-Acin-Rigbordy-Gisin 2004 quantum key distribution protocol. This protocol can extract secure keys from a source emitting multi-photons due to its robustness from photon number splitting attacks, making it a good candidate for practical implementations. We also compare the security bounds for this protocol under collective attacks to the security bounds obtained after applying the post-selection technique when using finite resources. Our results demonstrate that the bounds for optimal attack are close to the bound for collective attack for many signals. Notably, the six-state SARG04 protocol proves to be more robust to the PNS attacks when compared to the original four-state SARG04 protocol. This demonstrates the power of the post-selection technique in deriving the security bounds for the six-state protocol when finite resources are used.
A Two-Stage Network for Image Deblurring

Blind deblurring is a typical challenge in image processing, carried out to correct various complex types of distortions that occur in the real world. Although learning-based deblurring methods have substantially outperformed the traditional algorithms, they fail to make full use of image priors, leading to inconsistent data distributions between the restored images and sharp images. In this paper, we divide the deblurring process into two steps and propose a two-stage network. The first stage generates an initial deblurred image using a common convolutional network. The second stage converts the initial data distribution into a latent sharp image distribution to obtain sharp edges through a prior network. Moreover, we propose a relativistic training strategy to train the prior network, which aims to learn the latent sharp image priors. In addition, we use a coarse-to-fine multiscale framework that shares weights between the different scales. The experimental results show that compared with other state-of-the-art methods, our method achieves competitive performances on benchmark datasets.
A Novel Weakly Supervised Framework Based On Noisy-Label Learning For Medical Image Segmentation

Obtaining accurately annotated medical images for training segmentation models is expensive, time-consuming and labor-intensive. Although a variety of approaches based on weak annotations like points, scribbles and bounding boxes have been designed to address this problem, their performance is still limited. Inspired by recent studies on noisy-label learning, we propose a novel two-stage framework where a size-constrained loss is used to directly learn from the weak annotations in the first stage and a noise-robust loss is introduced to learn from pseudo labels in the second stage. The noise-robust loss function, named Edge-Dice, is based on the confidence in the network's prediction and the pseudo labels. Our approach differs from previous works by taking a natural step towards stronger supervision, in which predictions made by weak supervision methods are incorporated into another round of training using noise-robust methods. Experiments with the ACDC 2017 dataset showed that our method achieved 86.27% Dice for left ventricular segmentation with only 1% of the full annotations, and it outperformed existing methods with the same set of weak annotations.
How Students Construct Scaffolds by Using Drafts for Solving Tasks

Modeling is considered a key process in teaching and learning today. However, the transition from collective educational modeling to the use of the model as an individual tool is not investigated enough yet. The purpose of this study is to dis cover and qualitatively describe the phenomena of construction (reconstruction) of own instruments by schoolchildren in a situation where there are no external requirements for modeling. The article describes the proactive use of a draft in solving two math problems by 508 schoolchildren from twenty 3—10 grades of one school and 160 schoolchildren from three other schools using different programs. Analysis of the content of children’s drafts shows that the effective solving a problem is possible if students use the model they previously have studied and also transform and individualize the sign-symbolic form of this model (thus “translating” the model to “their own language”). The initiative in using a draft is largely determined by the way of previous learning.