Reviewerly/exHarmony huggingface dataset:
Algorithms for multi-frame image super-resolution under applicative noise based on deep neural networks

The article describes algorithms for multi-frame image super-resolution, which recover high-resolution images from a sequence of low-resolution images of the same scene under applicative noise. Applicative noise generates local regions of outlying observations in each image and reduces the image resolution. So far, little attention has been paid to this problem. At the same time, the use of deep neural networks is considered to be a promising method of image processing, including multi-frame image super-resolution. The article considers the existing solutions to the problem and suggests a new approach based on using several pre-trained convolutional neural networks and directed acyclic graph neural networks trained by the authors. The developed approach and the algorithms based on this approach involve iterative processing of the input sequence of low-resolution images using different neural networks at different processing stages. The stages include registration of low-resolution images, their segmentation performed in order to determine regions damaged by applicative noise, and transformation performed in order to increase the resolution. The approach combines the strengths of the existing solutions while lacking their drawbacks resulting from the use of approximate mathematical data models required for the synthesis of the image processing algorithms within the statistical theory of solutions. The experimental studies demonstrated that the suggested algorithm is fully functional and allows more accurate recovery of high-resolution images than the existing analogues.
Action Prediction of AI Bot in FightingICE by using Deep Learning Model

Video games have been popular and can be divided into many categories, such as fighting games, real-time strategy, role-playing, etc. For the last decade, game AI has been applied to non-player characters (NPCs) to achieve desired behavior goals. In this study, a model based on Deep Learning was created to predict AI game action. The research question is, "Is it possible to predict the opponent&#x2019;s action on time? The prediction will enable us to choose a game action to win the opponent. FightingICE platform was chosen to be the testbed. FightingICE is a fighting game platform with API developed for research purposes at Ritsumeikan University that allows custom AI to be inserted. The deep learning model was created using game data from default AI bots in FightingICE speed-running competitions. The result is that the model can predict AI selecting action with 98% accuracy.
CK-Encoder: Enhanced Language Representation for Sentence Similarity

In recent years, neural networks have been widely used in natural language processing, especially in sentence similarity modeling. Most of the previous studies focused on the current sentence, ignoring the commonsense knowledge related to the current sentence in the task of sentence similarity modeling. Commonsense knowledge can be remarkably useful for understanding the semantics of sentences. CK-Encoder, which can effectively acquire commonsense knowledge to improve the performance of sentence similarity modeling, is proposed in this paper. Specifically, the model first generates a commonsense knowledge graph of the input sentence and calculates this graph by using the graph convolution network. In addition, CKER, a framework combining CK-Encoder and sentence encoder, is introduced. Experiments on two sentence similarity tasks have demonstrated that CK-Encoder can effectively acquire commonsense knowledge to improve the capability of a model to understand sentences.
Face Recognition based on Convoluted Neural Networks: Technical Review

Human beings recognize and classify objects with biological senses and brain that processes the input into meaningful information. Other than that humans have come to recognize each other in multiple ways one of which is visual recognition of faces. As a biological trait human faces are certainly a biometric such they are universal, distinctive, mostly permanent and collectable. With that a computerized face recognition system can constructed relying on visual information present on each face uniquely. Generally a face recognition system consists of two main phases, face detection phase where presence of a human face is verified on visual input and face recognition phase where detected face is processed for identification. One of the most sought after methods in field image processing for face recognition is CNN (Convoluted Neural Networks). CNNs have proved its effectiveness and accuracy in many CNN based face detection and face recognition systems. As such in this paper the architecture of CNN is presented. Then different techniques for face detection and face recognition based on CNNs are reviewed. In reviewed papers CNNs have repeatedly demonstrated effectiveness and accuracy on multiple benchmarks for face recognition application.
Lesions Segmentation of Medical Ultrasound Images with Boundary Enhancement Strategy

Segmentation plays an important role for ultrasound image analysis. However, it is still a challenging problem for automatic target segmentation on ultrasound image owing to the low resolution and low contrast with surrounding tissues or organs on ultrasound image. Previous work demonstrated that shape of the segmented object can provide priors knowledge to help estimate missing boundaries. In this work, we encode the boundary prior information into the loss function. Experiments are conducted with some networks such as CPFNet, U-Net, Fast-SCNN on the hemangioma ultrasound dataset and the breast cancer ultrasound dataset The best results are achieved by our proposed method on CPFNet with dice indices of 87.89% and 71.73%, respectively. The experimental results demonstrate that our boundary enhancement strategy can effectively improve the segmentation performance on ultrasound images without increasing the parameters of model.
Robust steganography of large capacity lossless images based on visual cryptography and ptychography

We propose a robust steganography scheme for large-capacity lossless images based on visual cryptography and ptychography. The introduction of the quick response code as a container for information hiding can realize the lossless extraction of information and performs well in robustness when subjected to compression, noise, and tailoring attacks. The security of the proposed system is provided by the Arnold scrambling algorithm, the visual cryptographic scheme, and the structural parameters of ptychography. The proposed method is verified with optical experiments. In the experiments, a spatial light modulator is used to complete specific experiments through nonmechanical moving ptychography, which greatly reduces the error of traditional ptychography experiments and improves the quality of reconstruction. The results demonstrate that the method can achieve satisfactory robustness performance with high repeatability and excellent information fidelity.
Sentiment analysis of natural language processing based on deep learning model

The word vector representation technology based on neural language model can automatically learn the effective feature representation of words from large-scale unlabeled text data sets, and has made important progress in many natural language processing tasks and research. While achieving the above classification performance improvement, the training time of EMCNN relative to MCNN is reduced by 36% in subjective and objective classification, and in emotion 7 classification A reduction of 33.82%.
Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network

The forgery involved in region duplication is a common type of video tampering, where the traditional techniques used to detect video tampering are ineffective and inefficient for the forged videos under complex backgrounds. To overcome this issue, a novel video forgery detection model is introduced in this research paper. Initially, the input video sequences are collected from Surrey University Library for Forensic Analysis (SULFA) and Sondos datasets. Further, spatiotemporal averaging method is carried out on the collected video sequences to obtain background information with a pale of moving objects for an effective video forgery detection. Next, feature extraction is performed using the GoogLeNet model for extracting the feature vectors. Then, the Unsupervised Feature Selection with Multi-Subspace Randomization and Collaboration (UFS-MSRC) approach is used to choose the discriminative feature vectors that superiorly reduce the training time and improve the detection accuracy. Finally, long short-term memory (LSTM) network is applied for forgery detection in the different video sequences. The experimental evaluation illustrated that the UFS-MSRC with LSTM model attained 98.13% and 97.38% of accuracy on SULFA and Sondos datasets, where the obtained results are better when compared to the existing models in video forgery detection.
Library and Information Education in the Republic of Kazakhstan in the Second Half of the 20th — Early 21st Century

The article presents the main stages of the development of library and information education in the Republic of Kazakhstan in the second half of the 20th — beginning of the 21st century. The sources for this study were collections of archival documents, monographs, publications in periodicals and serials, conference materials and Internet resources, official documents, including regulatory documents. The author considers evolution of library education taking into account the principle of linking its development and theory and practice of librarianship with the socio-political and economic life of the country. The paper identifies socio-economic conditions and state policy in the field of library construction as the main factors determining the directions and specifics of transformation of the library education system. Much attention is paid to the work of higher educational institutions and their role in the formation of personnel potential of the library sector of the Republic of Kazakhstan. The author characterizes the system of advanced training of librarians, developed in the republic in the second half of the 20th century, and describes its goals, objectives, structure and the main ways and forms of advanced training. The paper presents the historical facts reflecting the achievements and problems in the field of staffing of libraries of the republic. The modern stage is distinguished by the rapid pace of development of science and technology, the increase in the volume of information produced and consumed by people, and raised requirements for the quality of qualification of library and information specialists. Currently, the number of state educational grants has increased in the country; libraries maintain link between science, education and practical activities; the decision has been made to develop the Concept for the development of librarianship in the Republic of Kazakhstan. The author concludes that changes in the society are the main stimulus for optimizing library and information education; and the development of education, the training of highly qualified library specialists contribute to the effective implementation of socio-cultural policy, the solution of urgent socio-economic problems and facilitate the increase in the intellectual and cultural level of society.
Pharmaceutical Reagent Inventory Strategy Based on Contract Shelf Life and Patient Demand

As the function and R&D level of in vitro diagnostic reagents continue to improve, the need for hospitals for in vitro diagnostic reagents in clinical diagnosis also keeps increasing. However, under the influence of management, process, technology, equipment, materials, employees, and other unexpected disturbing factors, the output of reagents often has random uncertainty, and it is difficult to provide the finished products required by orders on time, in quality and quantity. A secondary supply chain consisting of reagent manufacturers, distributors, and hospitals is constructed, and the inventory control models of in vitro diagnostic reagent supply chain under three strategies of centralized decision-making, hospital-owned inventory, and reagent distributor-managed inventory are established, respectively, and the maximum expected returns of the supply chain system under different strategies are analyzed to achieve the optimal production decision of reagent manufacturers and the optimal procurement decision of hospitals. The results show that reducing the random output probability and patient demand uncertainty has a significant effect on improving the expected return of in vitro diagnostic reagent supply chain, and as the random output probability of reagent manufacturers and patient consumption demand uncertainty increase, the strategy of managing inventory by distributors in collaboration is always better than the strategy of managing inventory by hospitals' own warehouses, which can achieve higher expected return and better inventory quantity, but when the out-of-stock cost of hospitals is too high above a certain threshold, the hospital will tend to adopt the self-inventory strategy.
Animal Classification Using CNN with VGG-16 Architecture

Proficient and solid observing of wild animals right at their habitat is fundamental. This venture fosters a calculation to distinguish the animals in untamed life. Since there are enormous number of various animals physically recognizing them can be a troublesome task. This calculation orders animals in light of their pictures so we can screen them all the more effectively. Animal recognition and grouping can assist with forestalling creature vehicle mishaps, follow animal and forestall robbery. This can be accomplished by applying powerful profound learning algorithms. It shows that the proposed procedure positively affect arrangement exactness.
Examination of the Relationship Between Digital Literacy and Lifelong Learning Tendencies of Primary School Teachers

This research aims to examine the relationship between primary school teachers' digital literacy and lifelong learning tendencies. The research sample consists of 153 primary school teachers who were determined via convenience sampling among purposive sampling methods and voluntarily participated in the study. The participants were serving in the province of Ordu, Turkey. Data collection tools used in the study were Personal Information Form, Digital Literacy Scale, and Lifelong Learning Tendency Scale (LLTS). In the analysis of the collected data, Kolmogorov Smirnov and Shapiro Wilk tests, unrelated samples t-test, one-way analysis of variance (ANOVA), Tukey and Games Howell test, Kruskal Wallis, and Pearson Product-Moment Correlation Analysis were used. Research findings demonstrated that the digital literacy of primary school teachers is at the "good" level and lifelong learning tendencies at the "strongly agree" level. Primary school teachers' digital literacy does not differ by the variables of gender and educational status; yet differs significantly by age, seniority, and in-service training. Lifelong learning tendencies differ significantly in favor of women by gender; do not differ according to age, seniority, education level, and in-service training. In addition, a moderately positive correlation was found between the digital literacy and lifelong learning tendencies of primary school teachers.
Review of Shearography for Dual-Directional Measurement

Shearography is a coherent optical technique that allows the identification of the first derivative of deformation in the shearing direction. Due to direct measuring strain information, shearography is suited for non-destructive testing and evaluation (NDT/NDE). However, if there is a small defect parallel to the shearing direction, the first derivative of deformation in the direction has no noticeable change, and the defect is not visible. Therefore, the development of a shearography system with dual-directional simultaneous measurement of the first derivatives of deformation both in x- and y-directions is highly demanded in the field of NDT/NDE. It is suited to inspect complicated defects, such as long and narrow slots, microcracks, etc. This paper presents a review of shearography for different dual-directional systems developed in the last two decades. After a brief overview of shearography, the paper will display two dual-directional shearographic techniques—temporal phase-shift (TPS) and spatial phase-shift (SPS) methods. TPS dual-shearing systems are suited for static measurements, while the SPS dual-shearing systems are useful for dynamic measurements. The basic theories, optical layouts, and comparisons are presented. The advantages and disadvantages of practical applications are discussed.
Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting

In recent years, renewable energy sources have experienced remarkable growth. However, their spatial and temporal diversity makes their large-scale integration into the current power grids difficult, as the balance between the electricity output and the consumption must be maintained at all times. Therefore, it is important to focus on the resources forecast to enhance the integration of renewable energy sources, such as solar in this study. In this article, a comparative analysis of two main machine learning methods was conducted for the prediction of the hourly photovoltaic output power. Furthermore, since various factors, such as climate variables, can impact the solar photovoltaic power and complicate the prediction process, the principal component analysis was employed to investigate the interactions between the multiple predictors and minimize the dimensionality of the datasets. The prevalent factors were then used in the predictive models as inputs. This field research is very crucial because the higher the prediction accuracy, the greater the profit for energy dealers and the lower the costs for customers.
Cross-Lingual Phrase Retrieval

Cross-lingual retrieval aims to retrieve relevant text across languages.Current methods typically achieve cross-lingual retrieval by learning language-agnostic text representations in word or sentence level.However, how to learn phrase representations for cross-lingual phrase retrieval is still an open problem.In this paper, we propose XPR, a cross-lingual phrase retriever that extracts phrase representations from unlabeled example sentences.Moreover, we create a large-scale cross-lingual phrase retrieval dataset, which contains 65K bilingual phrase pairs and 4.2M example sentences in 8 English-centric language pairs.Experimental results show that XPR outperforms stateof-the-art baselines which utilize word-level or sentence-level representations.XPR also shows impressive zero-shot transferability that enables the model to perform retrieval in an unseen language pair during training.Our dataset, code, and trained models are publicly available at github.com/cwszz/XPR/.
LGNet: Local and global representation learning for fast biomedical image segmentation

Medical image segmentation plays a crucial role in clinical diagnosis and therapy systems, yet still faces many challenges. Building on convolutional neural networks (CNNs), medical image segmentation has achieved tremendous progress. However, owing to the locality of convolution operations, CNNs have the inherent limitation in learning global context. To address the limitation in building global context relationship from CNNs, we propose LGNet, a semantic segmentation network aiming to learn local and global features for fast and accurate medical image segmentation in this paper. Specifically, we employ a two-branch architecture consisting of convolution layers in one branch to learn local features and transformer layers in the other branch to learn global features. LGNet has two key insights: (1) We bridge two-branch to learn local and global features in an interactive way; (2) we present a novel multi-feature fusion model (MSFFM) to leverage the global contexture information from transformer and the local representational features from convolutions. Our method achieves state-of-the-art trade-off in terms of accuracy and efficiency on several medical image segmentation benchmarks including Synapse, ACDC and MOST. Specifically, LGNet achieves the state-of-the-art performance with Dice’s indexes of 80.15% on Synapse, of 91.70% on ACDC, and of 95.56% on MOST. Meanwhile, the inference speed attains at 172 frames per second with [Formula: see text] input resolution. The extensive experiments demonstrate the effectiveness of the proposed LGNet for fast and accurate for medical image segmentation.
Parallel adaptive guidance network for image inpainting

Abstract Motivated by human behavior, dividing inpainting tasks into structure reconstruction and texture generation helps to simplify restoration process and avoid distorted structures and blurry textures. However, most of tasks are ineffective for dealing with large continuous holes. In this paper, we devise a parallel adaptive guidance network(PAGN), which repairs structures and enriches textures through parallel branches, and several intermediate-level representations in different branches guide each other via the vertical skip connection and the guidance filter, ensuring that each branch only leverages the desirable features of another and outputs high-quality contents. Considering that the larger the missing regions are, less information is available. We promote the joint-contextual attention mechanism(Joint-CAM), which explores the connection between unknown and known patches by measuring their similarity at the same scale and at different scales, to utilize the existing messages fully. Since strong feature representation is essential for generating visually realistic and semantically reasonable contents in the missing regions, we further design attention-based multiscale perceptual res2blcok(AMPR) in the bottleneck that extracts features of various sizes at granular levels and obtains relatively precise object locations. Experiments on the public datasets CelebA-HQ, Places2, and Paris show that our proposed model is superior to state-of-the-art models, especially for filling large holes.
Hierarchical and Multi-Scale Variational Autoencoder for Diverse and Natural Non-Autoregressive Text-to-Speech

This paper proposes a hierarchical and multi-scale variational autoencoder-based non-autoregressive text-to-speech model (HiMuV-TTS) to generate natural speech with diverse speaking styles. Recent advances in non-autoregressive TTS (NAR-TTS) models have significantly improved the inference speed and robustness of synthesized speech. However, the diversity of speaking styles and naturalness are needed to be improved. To solve this problem, we propose the HiMuV-TTS model that first determines the global-scale prosody and then determines the local-scale prosody via conditioning on the global-scale prosody and the learned text representation. In addition, we improve the quality of speech by adopting the adversarial training technique. Experimental results verify that the proposed HiMuV-TTS model can generate more diverse and natural speech as compared to TTS models with single-scale variational autoencoders, and can represent different prosody information in each scale.