Set Of Dictionaries Research Articles

Stochastic configuration networks with improved supervisory mechanism

The stochastic configuration networks (SCNs) have been shown to have great potential for developing fast learning models, making them especially suitable for industrial devices. To achieve low consumption during the SCNs training process, this paper proposes an improved version of SCNs (ISCN-III). First, guided by the function dictionary set and the objective function space, a dynamic hyperparameter is proposed to replace the nonnegative sequence of the supervisory mechanism. Second, based on the dynamic hyperparameters and function dictionary set, an improved supervisory mechanism is proposed to further reduce computational consumption. Finally, the improved supervisory mechanism with the dynamic hyperparameter proves the universal approximation property of ISCN-III. ISCN-III is compared with SCNs and its improved versions using six benchmark datasets and subsequently applied to a grinding process and a continuous stirred tank reactor. The experimental results prove that ISCN-III has the advantages of low computational cost and fast convergence speed.

Dictionary-Based Multi-View Learning With Privileged Information

Multi-view learning can improve classification performance by combining information between different views. Due to the similarity in different views of the dataset, sometimes the features obtained are highly limited and redundant. At the same time, different views accumulate a large amount of noisy information, which will affect the classification performance of the model. To solve these problems, we embed privileged information in the model and introduce dictionary learning, and proposed a new dictionary-based multi-view learning method with privileged information (MVDL-PI). First, two sets of dictionaries (synthetic dictionary and analysis dictionary) and sparse representation matrices of different information domains are obtained for each view information and privilege information through dictionary learning. Then, we obtain consistency information from the regularization terms of the two different sets of synthetic dictionaries and construct a LUPI (Learning using privileged information) classifier by the sparse representation. In addition, we use alternating convex optimization and Lagrange multiplier methods to optimize the model and prove its convergence. In the experiment, we did a number of experiments comparing this method with similar recent methods. The experimental results show that the MVDL-PI method is superior to other methods in terms of stability and classification accuracy.

Information Resources of Ukrainian Terminography: Kharkiv Terminological

The relevance of the research. The study of terminological processes that take place at a certain stage of the development of society is of permanent scientific importance both in the linguistic aspect and in any other aspect of the development of technology or culture. The issue of national term creation and term usage in priority positions is conditioned by the processes of consolidation of the scientific community’s forces to consider science, technology, and culture as an integral national phenomenon.
 The purpose of the research is to study the role of the Kharkiv scientific community in the development of Ukrainian terminology during 1940–2020.
 The methodology. The statistical method and the method of generalization provide empirical material regarding the facts of the publication of terminological dictionaries in Kharkiv. The use of analytical-synthetic, logical, comparative methods allows to describe the results of the research, to establish the role of the Kharkiv scientific community in the formation, normalization and codification of the modern Ukrainian scientific language. The visualization method allows to graphically present the results of the analysis of terminographic activity.
 The results. The terminographic activity of scientific and educational institutions operating in Kharkiv unfolds in line with the nationwide processes of formation of terminological lexicography. The sectoral spectrum of terminographic works testifies to the breadth of scientific and technical, socio-economic, educational, and cultural priorities of Kharkiv region. Ukrainian terminology is represented by a wide variety of lexicographic works. Kharkiv institutions of higher education are the leading centers of terminographic work.
 The scientific novelty. Modern terminology requires studying of the achievements of the leading scientific centers of Ukraine. The study of the state of publishing activity lies in the context of evaluating the general processes of formation of scientific schools in Ukrainian terminology. Scientific and educational institutions, professional centers, creative teams, Kharkiv scientists made a significant contribution to the development of the national terminological school. The modern level of normalization and codification of special vocabulary is formed by the consolidated efforts of all scientific centers of Ukraine.
 The practical significance. The research popularizes the terminographic achievements of Kharkiv scientific schools, introduces into scientific circulation a set of dictionaries and encyclopedias describing modern scientific language.
 The conclusion. The study of the temporality of terminographic processes, the sectoral composition of terminographic works testifies to the formation of powerful scientific schools in the city universities, the innovative positions of scientific teams, the interest of university management, as well as the civic attitude, professionalism, language training, and enthusiasm of the authors.

GEMMI and Servalcat restrain REFMAC5.

Macromolecular refinement uses experimental data together with prior chemical knowledge (usually digested into geometrical restraints) to optimally fit an atomic structural model into experimental data, while ensuring that the model is chemically plausible. In the CCP4 suite this chemical knowledge is stored in a Monomer Library, which comprises a set of restraint dictionaries. To use restraints in refinement, the model is analysed and template restraints from the dictionary are used to infer (i) restraints between concrete atoms and (ii) the positions of riding hydrogen atoms. Recently, this mundane process has been overhauled. This was also an opportunity to enhance the Monomer Library with new features, resulting in a small improvement in REFMAC5 refinement. Importantly, the overhaul of this part of CCP4 has increased flexibility and eased experimentation, opening up new possibilities.

Structure-Aware Denoising for Real-world Noisy Point Clouds with Complex Structures

Point cloud denoising is a crucial and fundamental step in geometry processing, which has achieved significant progress in the last two decades. Denoising real-world noisy point clouds is a very challenging problem since it is hard to describe the complex real-world noise by simple distributions such as Gaussian distribution. Furthermore, existing methods may suffer from performance degradation when dealing with real-world noisy point clouds with complex structures, which contain not only sharp features (sharp edges, sharp corners, etc.) but also smooth features, fine features, etc. To solve the above-mentioned problems, we propose a novel structure-aware denoising approach by exploiting the prior information in both external clean point clouds and the given noisy point cloud. We first group nonlocal self-similarity (NSS) patches from a set of external clean point clouds. Then, we employ the Gaussian Mixture Model (GMM) learning algorithm to learn external NSS priors over patch groups. Next, the internal priors are learned from the given noisy point cloud in the same way to refine the prior model. We integrate both the learned external and internal priors into a set of orthogonal dictionaries to efficiently estimate point normals. Finally, we propose a feature-aware point updating method through adaptive neighborhood selection to reposition points to match the estimated normals. Extensive experiments show that our approach achieves favorable comprehensive performance compared with many popular or state-of-the-art methods in terms of both objective and visual perception. The source code can be found at https://zhiyongsu.github.io.

Real-Time Camera Localization during Robot-Assisted Telecystoscopy for Bladder Cancer Surveillance

Telecystoscopy can lower the barrier to access critical urologic diagnostics for patients around the world. A major challenge for robotic control of flexible cystoscopes and intuitive teleoperation is the pose estimation of the scope tip. We propose a novel real-time camera localization method using video recordings from a prior cystoscopy and 3D bladder reconstruction to estimate cystoscope pose within the bladder during follow-up telecystoscopy. We map prior video frames into a low-dimensional space as a dictionary so that a new image can be likewise mapped to efficiently retrieve its nearest neighbor among the dictionary images. The cystoscope pose is then estimated by the correspondence among the new image, its nearest dictionary image, and the prior model from 3D reconstruction. We demonstrate performance of our methods using bladder phantoms with varying fidelity and a servo-controlled cystoscope to simulate the use case of bladder surveillance through telecystoscopy. The servo-controlled cystoscope with 3 degrees of freedom (angulation, roll, and insertion axes) was developed for collecting cystoscope videos from bladder phantoms. Cystoscope videos were acquired in a 2.5D bladder phantom (bladder-shape cross-section plus height) with a panorama of a urothelium attached to the inner surface. Scans of the 2.5D phantom were performed in separate arc trajectories each of which is generated by actuation on the angulation with a fixed roll and insertion length. We further included variance in moving speed, imaging distance and existence of bladder tumors. Cystoscope videos were also acquired in a water-filled 3D silicone bladder phantom with hand-painted vasculature. Scans of the 3D phantom were performed in separate circle trajectories each of which is generated by actuation on the roll axis under a fixed angulation and insertion length. These videos were used to create 3D reconstructions, dictionary sets, and test data sets for evaluating the computational efficiency and accuracy of our proposed method in comparison with a method based on global Scale-Invariant Feature Transform (SIFT) features, named SIFT-only. Our method can retrieve the nearest dictionary image for 94–100% of test frames in under 55[Formula: see text]ms per image, whereas the SIFT-only method can only find the image match for 56–100% of test frames in 6000–40000[Formula: see text]ms per image depending on size of the dictionary set and richness of SIFT features in the images. Our method, with a speed of around 20 Hz for the retrieval stage, is a promising tool for real-time image-based scope localization in robotic cystoscopy when prior cystoscopy images are available.

PHMG-KH560 as antifungal and reinforcement media for paper materials

Improving the strength decrease caused by fungal metabolites is still an arduous task in the field of paper materials protection. A set of damaged Kangxi Dictionary from Qing Dynasty was chosen as samples in this work. The main goal of this work is to develop high-efficiency and eco-friendly media with the characteristics of antifungal and reinforcement, to preserve paper materials and continue the historical and scientific research value of cultural heritage. PHMG as an efficient antifungal was used in this work. For better behavior, the epoxy group of KH560 can be induced by the amino group in PHMG by ring opening reaction to form PHMG-KH560 colloidal complex. Spraying PHMG-KH560 on paper could establish Si-O-C bonding and increased the hydrogen bond between polymeric material and paper fibers, which simultaneously inhibited fungi and improved mechanical strength of the original paper, even after the artificial aging. PHMG-KH560 had certain antifungal activity against five fungi, such as Aspergillus versicolor, and Aspergillus Niger , and had better inhibitory effect on Cladosporium herbarum, Trichoderma viride , and Penicillium expansum . The ancient writing ink on the paper sprayed with PHMG-KH560 did not affect its visual beauty, which also indicated that the complex reagent was expected to be applied to the antifungal and reinforcement of paper materials.

Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids

A general crossword grid generation is considered an NP-complete problem and theoretically it could be a good candidate to be used by cryptography algorithms. In this article, we propose a new algorithm for generating perfect crosswords grids (with no black boxes) that relies on using tries data structures, which are very important for reducing the time for finding the solutions, and offers good opportunity for parallelisation, too. The algorithm uses a special tries representation and it is very efficient, but through parallelisation the performance is improved to a level that allows the solution to be obtained extremely fast. The experiments were conducted using a dictionary of almost 700,000 words, and the solutions were obtained using the parallelised version with an execution time in the order of minutes. We demonstrate here that finding a perfect crossword grid could be solved faster than has been estimated before, if we use tries as supporting data structures together with parallelisation. Still, if the size of the dictionary is increased by a lot (e.g., considering a set of dictionaries for different languages—not only for one), or through a generalisation to a 3D space or multidimensional spaces, then the problem still could be investigated for a possible usage in cryptography.

FORMATION PECULARITIES OF THE TERMINOLOGICAL FIELD CULTURE (BASED ON THE MATERIAL OF THE BRITISH FORUM "INTELLIGENCE SQUARED")

The article deals with the peculiarities of terminological fields and their formation. The specificity of the terminological field CULTURE has been analysed. Main linguistic tendencies concerning such notions as “a term”, “a terminological system”, and “terminology” have been outlined. The data posted on the forum Intelligence Squared and a set of dictionaries have been taken into consideration. The main classifications of terms have been considered. The terminological field CULTURE has been admitted relevant to the peculiar context. The context has been acknowledged to provide terms’ monosemanticity whereas terms lacking context cause ambiguity. The interconnection between terminology and cultural identity has been highlighted. The terminological field CULTURE has been characterised. The analysis of definitions has been applied in order to explore its nuclear unit. Taking into account the studies in the sphere of peripheral and nuclear semantics the structure of the terminological field CULTURE has been introduced. The cases of usage have been illustrated. The main thematic groups within the terminological field CULTURE have been represented. The notion of the lexeme “culture” has been worked out to consider its versatility and evolution through the ages. The list of its constituents functioning on the forum Intelligence Squared has been shaped. 207 terminological units have been singled out and their usage has been specified. Six elements have been proved to be keynotes. The terminological field CULTURE has been defined as a structure where a semantic component prevails. Not only terms but terminological combinations have been admitted to function as well. It has been illustrated that the terminological field CULTURE comprises terms representing different parts of speech (nouns, adjectives, verbs). The prevalence of nouns has been accentuated.

Česko-slovenské vzťahy na poli knižnej kultúry (1918–1939)

The paper offers a probe into a specific topic that has not yet been much discussed in the context of relations between Czechs and Slovaks, namely their cooperation in the field of book culture during and immediately after the end of the First Czechoslovak Republic. Using available archival and library sources, it provides information on the three most important Czech publishing projects in terms of their influence on the development of Slovak literature. On the one hand these projects took advantage of the new political context and the Slovak book market, which was undergoing a transformation, on the other hand, they also created the conditions for original artistic creation and translation and even successfully promoted Slovak authors abroad. The paper selected publishers who published Slovak literature in its original versions and omits a set of Slovak textbooks, dictionaries, anthologies and other printed materials that were intended to help in the organisation of the new state and that dominated the period discussed in terms of volume. It examines and evaluates the Slovak production of three Prague publishing houses: L. Mazáč, Melantrich and Družstevní práce.

Translation system from Tunisian Dialect to Modern Standard Arabic

SummaryNowadays, Tunisian Dialect (TD) is widely used in social media such as Facebook and Twitter to communicate and express opinions. However, the absence of a TD translator prevents comprehension and communication for non‐Tunisian users. Thus, Tunisian translator becomes an important task to overcome these problems. In this article, our main objective is to create a translator from TD to Modern Standard Arabic (MSA). In this context, we are carrying out an in‐depth linguistic study in order to better choose the translation rules which are applicable on the TD sentences. This study guarantees the best quality to obtain MSA sentences. Indeed, our proposed method is based on a linguistic approach. In addition, the method consists of the elaboration of a set of dictionaries and the construction of inflectional, morphological, and syntactic grammars using finite‐state transducers. Then, our method is implemented and tested with new technologies provided by the NooJ linguistic platform. To evaluate our constructed tool, we apply it to two different test corpora containing more than 15,000 words. The obtained results are promising and do highlight our proposed method.

Research on Extraction of Useful Tourism Online Reviews Based on Multimodal Feature Fusion

To effectively identify the influencing factors of the perceived usefulness of multimodal data in online reviews of tourism products, this article explores the optimization method of online tourism products based on user-generated content and conducts feature fusion of multimodal data in online reviews of tourism products from the perspective of data fusion analysis. Therefore, based on the word vector model, this article proposes a method to select the seed word set of emotion dictionary. In this method, emotional words are represented in vector form and the distance between word vectors is calculated to form the selection criteria and classification basis of seed word set, and then the sentiment dictionary of online review is formed by category judgment. This article takes the real online review data of tourism products as the research object, carries out descriptive statistical analysis, uses machine learning and deep learning methods, carries out text vector embedding and image content recognition, integrates image and text feature vector, constructs multimodal online review usefulness classification model, and conducts model test. The experimental results show that, compared with the single-mode reviews containing only text or pictures, the multimodal reviews combined with text and pictures can better predict the usefulness of online reviews, improve the quality of online reviews, give full play to the potential value of user-generated content, provide optimization ideas for product providers, and provide decision support for product consumers.

Hyperspectral target detection method based on spatial-spectral joint weighted dictionary learning with online updating mechanism

In recent years, many target detection methods for hyperspectral images based on sparse representation (SR) theory have been proposed and achieved good results. However, these methods still have some deficiencies. Specifically, these methods usually give the same weight to the pixels in the neighborhood when they use the spatial information, and obtain the spectral average features to replace the spectral features of the central pixel. However, the influence of each pixel in the neighborhood of the central pixel may be different. In addition, when the dictionary learning method is used for target detection, its performance depends on the constructed dictionary. To solve these problems, an innovative target detection method based on spatial-spectral joint weighted dictionary learning with an online updating mechanism is proposed. First of all, different from traditional SR method, this method integrates the spatial adaptive neighborhood information into the sparse coding stage to realize the spatial-spectral weighted joint SR. Second, the online dictionary updating mechanism and a two-step optimization method are used together to update the dictionary and sparse coding coefficients alternately, which can learn the optimal dictionary set in real time. The experimental results show that the proposed method has better performance when compared with four state-of-the-art dictionary learning-based methods for hyperspectral target detection.

Single image super resolution based on multi-scale structure and non-local smoothing

In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.

Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition

Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4 2 , and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.

Abnormal behavior detection using sparse representations through sequential generalization of k-means

The potential capability to automatically detect and classify human behavior as either normal or abnormal events is an important aspect in intelligent monitoring/surveillance systems. This study presents a new high-performance framework for detecting behavioral abnormalities in video streams by utilizing only the patterns for normal behaviors. In this paper, we used a hybrid descriptor, called a foreground optical flow energy (FGOFE), which makes use of two effective motion techniques in order to extract the most descriptive spatiotemporal features in video sequences. The FGOFE descriptor can effectively capture both weak and sudden incidents in a scene. The sequential generalization of k-means (SGK) algorithm was applied in this study to generate the dictionary set that can sparsely represent each signal; in addition, the orthogonal matching pursuit algorithm was utilized to recover high-dimensional sparse features when referring to a few numbers of noisy linear measurements. Using the SGK allows gaining a less complex and quicker implementation compared to other dictionary learning methods. We conducted comprehensive experiments to analyze and evaluate the ability of our framework in detecting abnormalities using several public benchmarks, which contain different abnormal samples and various contextual compositions. The experimental results show that the proposed framework achieved high detection accuracy (up to 95.33%) and low frame processing time (31 ms on average) compared to the relevant related work.

When Sentiment Is News: The Polarity Pattern Approach

We present a new approach and set of dictionaries for the analysis of sentiments in financial and business news headlines at the firm level. The proposed Polarity Pattern Approach (PPA) captures the deep structure of context and syntax and reduces measurement errors evident in previous approaches for classifying the tone of words and sentences in news headlines. We demonstrate the superior predictive validity of PPA using an out-of-sample dataset. As expected, negative news headlines are negatively related to stock returns and positively related to trading volumes, while positive news headlines are positively related to both, especially for finance news. Prior studies report a non-significant or, counter intuitively, negative relationship between positive news and stock returns, which we attribute to measurement error in previous approaches. Additionally, using market activities, we jointly estimate cross-sectional regression to compare the explanatory power of the PPA approach with commonly used methods. We show how PPA’s tone assignment significantly outperforms other dictionaries and approaches, including the commercial package (RavenPack) that uses both machine learning and deep learning. The PPA, including dictionaries, provides finance researchers with a new, more valid method of sentiment analysis for media news. Link to PPA tone assignment dataset: https://www.whensentimentisnews.com

Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps


 
 
 Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem.Methodology: The dictionaries are learned from the estimated abundance data taking advantage of the depth correlation between abundance maps and the non-local self- similarity over the spatial domain. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm.Results: Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.Conclusions: In this work, we propose a hyperspectral and multispectral image fusion method based on a non-local centralized sparse representation on abundance maps. This model allows us to include the non-local redundancy of abundance maps in the fusion problem using spectral unmixing and improve the performance of the sparsity-based fusion approaches.
 
 

Improvements on Learning Kernel Extended Dictionary for Face Recognition

Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.

DEVELOPMENT OF A MOBILE APPLICATION FOR LEARNING THE KAZAKH LANGUAGE

During the research, a mobile educational application was developed. Using a mobile app is a key tool for improving user knowledge. The app helps users to study without a teacher in their free time. The app was built using the Xcode IDE. The optional quiz includes many features such as reading, grammar, verbs, and speaking. This will be of great help for users who want to learn more Kazakh. The Kazakh language is developing rapidly. Therefore, the pace of learning the Kazakh language is growing day by day. Currently, it is beneficial to use mobile applications related to distance learning. The application helps to learn the Kazakh language anytime, anywhere. The grammar of the Kazakh language is more complex than other languages. With this in mind, each section of the appendix describes the rules of 1-2 grammars. At the end of each section there is a set of dictionaries to speed up the construction of user sentences. That is, the user who has passed all the sections speaks the Kazakh language at a high level.