Reviewerly/exHarmony huggingface dataset:
THE IMPLEMENTATION OF DIDACTIC PRINCIPLE OF SYSTEMATIZATION AND CONTINUITY IN ELECTRONIC TEXTBOOKS

The article deals with a relevant issue of studying the didactic potential of electronic textbooks, which has become particularly timely in the context of significant challenges to the education system caused by the pandemic. Contemporary scholars note that current educational practice is well ahead of the development of scientifically sound patterns and conditions for the formation and functioning of the components of the didactic system of e-learning. The purpose of the article is to analyze the positive and negative aspects of the implementation of the didactic principle of systematization and continuity in electronic textbooks. According to the research aim advantages and disadvantages of using electronic textbooks, in particular in of realization didactic principle of systematization and continuity are analyzed in the paper. It was found, that the expanded capacity of electronic textbooks will not only allow to implement that principle on the qualitatively new level but at the same time increase the didactic risks of their realization. The requirements for electronic textbooks, which will allow to minimize didactic risks and prevent potential negative impact on the educational process are formulated. While designing an e-textbook it is necessary to follow the sequence of presentation of educational material in a systematic and structured form and take into account the purpose, tasks, competencies to be formed and specific of a particular subject. Authors should take into account the retrospective and prospective relations of the educational material in order to ensure continuity in the acquisition of knowledge and skills. It is recommended to adhere to the general conceptual framework, use unified terminology, avoid duplication of educational material, methodically unsound hyperlinks, because the abuse both external and internal hyperlinks distracts from the main course of material presentation.
Reconstruction of the Face Image from Speech Recording: a Neural Networks Approach

Human speech can contain information about the personality, gender, emotional state of the speaker. In this article, we will explore the possibility of reconstructing an image of a human face from a recording of his voice. To achieve this goal, we design an autoencoder neural network and train it using data from AVSpeech, a dataset that contains videos of people talking, collected from the popular video hosting YouTube. To train the neural network, we select short pieces of audio containing speech and save a frame with the face of the corresponding person. The audio data is converted into a spectrogram, and images with human faces are cropped and normalized from the saved frames. Based on the data obtained, we train an autoencoder network using a self-supervised learning approach.
Study on the influence of different number of neuron layers and activation functions on neural networks

Nowadays, the gradual development of neural network has reached a certain level, and the constantly updated and improved architecture has been applied in many interdisciplinary fields. It is well known that the performance of a neural network often depends on the depth of the network and the activation function used. In order to further explore the influence of neural network depth and activation function on network performance, the author constructs a small neural network to test the performance of a neural network with different depths and different activation functions. This neural network is used to solve the problem of multi-classification of iris. It adopts the dataset provided by TensorFlow to judge the categories of iris according to four features. The author observed the accuracy of iris classification by changing the depth and activation function of the network, and finally analyzed the possible influence of the network depth and activation function on the network through the accuracy. After ten tests respectively (the network adopts sigmoid as activation function), the average accuracy of single-layer neural network is 0.965, the average accuracy of two-layer neural network is 0.961, and the average accuracy of three-layer neural network is 0.958. The average accuracy of two-layer neural network with the hyperbolic tangent as its activation function is 0.960. Through analyzing these results and some reasoning, the author is able to draw a conclusion on the corresponding influence exerted on the network, which would simplify the esoteric process of the construction of a neural network.
Framework for automatic segmentation of breast cancer using lightweight convolutional neural networks

Given a public database of medical images, testing and comparing different neural network algorithms using that database without a region of interest is a challenging task. This work aims to use the CBIS-DDSM (Curated Breast Imaging Subset of the DDSM - Database for Screening Mammography) and InBreast database, to test a lightweight model of convolutional neural networks (CNN) to segment the masses of tumors. The proposed model is a reduced version of the “U-Net” architecture to be lighter and faster to segment images in computers of routine use of health professionals. The proposed method, available in Google Colaboratory format for easy replication and modifications, can achieve competitive results comparing to the default “U-Net”. This work is a reproducible tool and does not achieve state of art results that uses other methods, but can be customized to enhance accuracy. Results showed that the model can predict tumors masks of both Medial Lateral Oblique (MLO) and Craniocaudal (CC) cases. We define a premise using region of interest to define what is a true positive and with that premise the model achieved a mean dice coefficient of 0.60 and a mean accuracy of 0.40. With test CPU hardware the model can predict 32 images per second, with dedicated GPU the model can predict 237 images per second.
Intrinsic Cramér–Rao bounds for distributed Bayesian estimator

This paper addresses the intrinsic Cramér–Rao bounds (CRBs) for a distributed Bayesian estimator whose states and measurements are on Riemannian manifolds. As Euclidean-based CRBs for recursive Bayesian estimator are no longer applicable to general Riemannian manifolds, the bounds need redesigning to accommodate the non-zero Riemannian curvature. To derive the intrinsic CRBs, we append a coordination step to the recursive Bayesian procedure, where the proposed sequential steps are prediction, measurement and coordination updates. In the coordination step, the estimator minimises the Kullback–Liebler divergence to obtain the consensus of multiple probability density functions (PDFs). Employing the PDFs from those steps together with the affine connection on manifolds the Fisher Information Matrix (FIM) and the curvature terms of the corresponding intrinsic bounds are derived. Subsequently, the design of a distributed estimator for Riemannian information manifold with Gaussian distribution – referred to as distributed Riemannian Kalman filter – is also presented to exemplify the application of the proposed intrinsic bounds. Finally, simulations utilising the designed filter for a distributed quaternionic estimation problem verifies that the covariance matrices of the filter are never below the formulated intrinsic CRBs. • Distributed Bayesian estimator requires coordination after the measurement process. • Kullback–Liebler averaging is utilised in the distributed Bayesian estimator. • Posterior intrinsic CRB depends on the prior intrinsic CRB. • Post-coordination CRB depends on the posterior intrinsic CRBs from nearby agents. • The curvature of a Riemannian manifold determines the collective intrinsic CRBs.
Cheating Detectable Secret Sharing Scheme from Multiplicative Homomorphic Authentication Function

Cheating Detectable Secret Sharing (CDSS for short) is a secret sharing scheme that adds the property of detecting the presence of cheaters submitting invalid shares when reconstructing a secret. A major topic of research in CDSS has been the attempt to improve efficiency with suitable authentication functions. In this paper, we construct CDSS based on an authentication function with multiplicative homomorphism, which has not been employed so far. It possesses some interesting properties and possesses the advantage that security can be proven simply by showing possession of such properties. Furthermore, we construct a secure CDSS with multiplicative homomorphic under some parameter constraints and show that this scheme is sufficient for practical use. The scheme achieves a bit length that is one bit longer than the theoretical lower bound.
Ontology-aware dynamically adaptable free-form natural language agent interface for querying databases

Studying the literature, one can see a large number of systems that provide natural language interfaces to databases. Despite their importance, these interfaces address only one part of the problem: transforming natural language queries to SQL queries and perhaps executing them against the underlying database. To truly handle the problem, it should be possible to develop dynamically adaptable database interfaces that can (1) adjust their functional behavior on the fly using only domain knowledge and (2) dynamically bind themselves to arbitrary databases and interface with them with no (or very little) human intervention. In particular, the interfaces should have a fixed process (algorithms and codes) and rely only on domain knowledge for adapting their functionality to any arbitrary database. This paper addresses this problem by offering a free-form natural language interface agent that, given domain ontologies, can bind itself to a target database and provide a natural language interface to it. The agent system uses its ontologies to establish mappings on-the-fly between a specific domain ontology and the underlying database’s metadata, and then to transform free-form natural language queries to formal SQL queries that can execute against the underlying database. The preliminary simulations using our proof-of-concept prototype showed that our system successfully attached itself to databases and achieved high recall and precision in transforming natural language queries to formal ones.
DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation

Recent advances in optical flow prediction have been made possible by using feature pyramids and iterative refining. Though downsampling in feature pyramids may cause foreground items to merge with the background, the iterative processing could be incorrect in optical flow experiments. Particularly the outcomes of the movement of narrow and tiny objects can be more invisible in the flow scene. We introduce a novel method called DeepPyNet for optical flow estimation that includes feature extractor, multi-channel cost volume, and flow decoder. In this method, we propose a deep recurrent feature pyramid-based network for the end-to-end optical flow estimation. The feature extraction from each pixel of the feature map keeps essential information without modifying the feature receptive field. Then, a multi-scale 4D correlation volume is built from the visual similarity of each pair of pixels. Finally, we utilize the multi-scale correlation volumes to continuously update the flow field through an iterative recurrent method. Experimental results demonstrate that DeepPyNet significantly eliminates flow errors and provides state-of-the-art performance in various datasets. Moreover, DeepPyNet is less complex and uses only 6.1M parameters 81% and 35% smaller than the popular FlowNet and PWC-Net+, respectively.
Application of convolutional neural networks in optical text recognition to junk data filtering

Abstract In this paper, the problem of constructing a model for detecting and filtering unwanted spam messages is solved. A fully connected convolutional neural network (FCNN) was chosen as the model of the classifier of unwanted emails in email. It allows you to divide emails into two categories: spam and not spam . The main result of the research is a software application in the C++ language, which has a micro-service architecture and solves the problem of image classification. The app can handle more than 10 6 requests per minute in real-time.
Broad Learning System with Particle Swarm Optimization and Singular Value Decomposition

Broad Learning System (BLS), a newly-developing alternative approach of learning for deep neural network, has attracted much attentions from researchers all over the world due to its straightforward network structure and powerful performance to deal with classification and regression problems. The number of feature nodes and enhancement nodes in classical BLS is determined by grid search method which leads to heavy training burden, while the weights between input data and feature nodes are randomly initialized and fine-tuned taking advantages of sparse autoencoder. Different from that, a new BLS with Particle Swarm optimization (PSO) and Singular Value Decomposition (SVD) is raised in this paper. PSO algorithm is introduced to acquire the optimal number of feature nodes and enhancement nodes, which greatly reduces the search time. In addition, the weights between input data and feature nodes are initialized by SVD method, which avoids using iteration method to optimize them and also reduces computational cost. The experimental results on several regression datasets demonstrate that BLS with PSO and SVD can not only find optimal number of system nodes much faster than classical BLS but also achieve considerable satisfactory accuracy.
METHODS FOR DETERMINATION OF DEFORMATIONS WITH THE USE OF DIGITAL IMAGE CORRELATION TECHNOLOGIES

In order to obtain reliable information about the stress-strain state of the structure, subjected to loading, it is necessary to determine deformations` distribution. In some cases, it is almost impossible to assess stress-strain state with the traditional approaches. However, the DIC methods provide reliable information about the fields of displacement and deformation almost without limitations. Such approaches are rather effective for determination of the stress-strain state on smooth surfaces and in zones with stress concentrators. The DIC method is based on the comparison of the intensity of speckle pictures` distribution of optically rough surfaces. The combination of the intensities of correlation peaks with the corresponding calculation algorithms at the subpixel level makes it possible to obtain high measurement accuracy with simpler hardware compared to electronic interferometry technologies. The main purpose of this work is the detailed analysis of techniques and methods for determination of deformations with the use of digital image correlation. The article includes detailed review of existing studies of this topic and description of main principles for analytical computation of the optical data.
Systematic evaluation of abnormal detection methods on gas well sensor data

Natural gas, as a kind of clean energy, has attracted significant attention in the global market. However, how to ensure the safety and high efficiency in natural gas production becomes a hot research problem in the gas industry. The real-time abnormal status detection of the natural gas well empowers the decision-maker to prevent potentially catastrophic damage and correct unexpected situations. In this paper, we systematically evaluate the 9 state-of-the-art machine learning methods to detect such anomalous status on large sensor data collected from 4 natural gas wells. In addition, we have identified the most important features that can improve anomaly detection performance. The challenges and potential research directions have been discussed. This is the first work to investigate different types of anomaly detection methods on natural gas well sensor data. Our research results provide valuable insights for developing specific anomaly detection systems in the natural gas industry.
Privacy-Preserving Federated Generative Adversarial Network for IoT

Federated Learning (FL) shows its great vitality for terminal devices of Internet of Things (IoT) in privacy protection. However, the amount of data on each device in IoT are imbalanced which makes the global training model rarely effective. Although Generative Adversarial Network (GAN) can generate data to alleviate the above problem, it has the characteristic of training instability and still carries the risk of privacy breach. In this paper, we propose a novel privacy-preserving federated GAN framework, named P-FedGAN, to train a federated generative model with privacy protection function. Each device uses the trained model to generate enough synthetic data to replace sensitive data to train desired model that can complete typical data analysis tasks. We carry out several sets of experiments to test the validity of proposed framework. The results of the experiments indicate that proposed framework meets differential privacy constraints and produces high-quality model utility compared to federated average algorithm simultaneously.
Data Trimming Methods to Improve Gesture Classification

This paper introduces a data processing method to enhance the performance of a gesture classification model. When tested on the UTD-MHAD dataset, the HMM model initially rendered a poor performance due to seemingly resembling gestures. To tackle this problem, data has been altered via normalization and selection of significant joints that determine the gesture. Refining data prior to classifying generates a better performance in both HMM and LSTM models, highlighting the significance of data processing across different types of classification models.
Lightweight Image Super-Resolution via Dual Feature Aggregation Network

With the power of deep learning, super-resolution (SR) methods enjoy a dramatic boost of performance. However, they usually have a large model size and high computational complexity, which hinders the application in devices with limited memory and computing power. Some lightweight SR methods solve this issue by directly designing shallower architectures, but it will affect SR performance. In this paper, we propose the dual feature aggregation strategy (DFA). It enhances the feature utilization via feature reuse, which largely improves the representation ability while only introducing marginal computational cost. Thus, a smaller model could achieve better cost-effectiveness with DFA. Specifically, DFA consists of local and global feature aggregation modules (LAM and GAM). They work together to further fuse hierarchical features adaptively along the channel and spatial dimensions. Extensive experiments suggest that the proposed network performs favorably against the state-of-the-art SR methods in terms of visual quality, memory footprint, and computational complexity.
A Survey on Original Video-based Watermarking

With the quick development of communication technology, digital video has become an indispensable media content for people’s daily life and entertainment, and its copyright protection has also turned into a vital research topic. The digital video watermarking is expected to become a technology that can solve the problem of digital video network infringement. Based on various embedding positions, the digital video watermarking algorithm includes two major categories: the compressed video-based and original video-based watermarking algorithm. The latter has stronger versatility than the former, hence, in this paper, we mainly focus on the survey of the original video-based watermarking algorithm.
A Fuzzy Adaptive Strong Tracking Algorithm with Fading Factor

The maximum acceleration parameter determines the effect of the current statistical (CS) model. Thus, when tracking weak maneuvering targets or targets whose actual acceleration exceeds the given value, the tracking performance of the traditional algorithm that sets with a priori fixed value will drop sharply. To solve this problem, a fuzzy adaptive strong tracking algorithm with fading factor (IAFCS-IMM) is proposed. The algorithm adopts a two-level fuzzy logic system. Through the first-level fuzzy logic, a maneuvering factor representing the maneuverability of the target is obtained according to the estimated acceleration information of the model, and the maximum acceleration parameter is adaptively modified. The second-level fuzzy logic is adopted to adjust the model update probability of interacting multiple model (IMM) algorithm according to the maneuver factor. Besides, a fading factor is introduced in the filtering process, which can enhance the robustness of the filter to the sharp mutation of the target state. Simulation results demonstrate that IAFCS-IMM algorithm achieves good results in filtering accuracy and tracking stability of maneuvering targets.
Animal Attribute Classification via Knowledge Graph Expert System

Traditional methods are difficult to classify animals scientifically and effectively based on their multiple attributes. In this paper, we establish a multi-level knowledge graph expert system to classify animals scientifically and effectively. Specifically, we implement a hierarchical knowledge graph of direct and indirect rules to enable the scientific and effective matching of multiple attributes to animal categories. Our software implements a knowledge graph expert system and achieves significant results. Our approach can be used as a reference for intelligent attribute classification of goods and objects.
Classification of Breast Cancer Images by Transfer Learning Approach Using Different Patching Sizes

Cancer is the second leading cause of death worldwide, and breast cancer is the fifth leading cause of death. Therefore, early diagnosis is a vital stage. The diagnosis of this cancer is usually made with histopathological images. In this study, four classes of breast cancer images (normal, in-situ/regional, benign, and invasive) were detected by using a transfer learning approach. The BACH data set was used in this study. The images in the data set were divided into small patches of 256 × 256, 128 × 128, and 75 × 75 sizes from the places where the cell density is high. In addition, the bottleneck feature was used, which provides higher performance in low image numbers. In this way, the bottleneck feature performances of the models were investigated, and it was aimed to get a high accuracy rate with the processes performed in the pre-processing steps. As a result, the success of our system is 99% accuracy in 256 × 256 patch size and it varies according to the size of the image patches.
Text as tautology: an exploration in inference, transitivity, and logical compression

Abstract Rhetorical structure theory (RST) and relational propositions have been shown useful in analyzing texts as expressions in propositional logic. Because these expressions are systematically derived, they may be expected to model discursive reasoning as articulated in the text. If this is the case, it would follow that logical operations performed on the expressions would be reflected in the texts. In this paper the logic of relational propositions is used to demonstrate the applicability of transitive inference to discourse. Starting with a selection of RST analyses from the research literature, analyses of the logic of relational propositions are performed to identify their corresponding logical expressions and within each expression to identify the inference path implicit within the text. By eliminating intermediary relational propositions, transitivity is then used to progressively compress the expression. The resulting compressions are applied to the corresponding texts and their compressed RST analyses. The application of transitive inference to logical expressions results in abridged texts that are intuitively coherent and logically compatible with their originals. This indicates an underlying isomorphism between the inferential structure of logical expressions and discursive coherence, and it confirms that these expressions function as logical models of the text. Potential areas for application include knowledge representation, logic and argumentation, and RST validation.