Dictionary Selection Research Articles

Способы дипломатических отношений: когнитивнокоммуникативный подход (на материале современного английского языка)

e linguistic study of diplomatic relations is a complex problem, which is considered by representatives of different scientific schools, with their own research perspectives. The databases of scientific literature contain materials on communicative linguistics, language theory, cognitive linguistics and other disciplines that form the basis for modern research in this field. The purpose of the work is to consider the methods of diplomatic relations from the standpoint of a cognitive-communicative approach in modern English. The stated approach involves studying the structures of knowledge about diplomatic relations in correlation with the means of their representation in communication processes. Based on an extensive selection of scientific literature and dictionaries, a conceptual and definitional analysis has been carried out to determine the structure of the DIPLOMACY concept. There have been singled out both central (SKILL, RELATIONS, CONDUCT) and non-central characteristics (NEGOTIATION, MANAGE, TACT, etc.) The classification criteria have been identified with the sources of factual material reflecting 30 ways of diplomatic relations. The data on the types of diplomacy have been presented in accordance with the highlighted conceptual characteristics. In the future, the results of the study will be used to describe the interpretation of diplomatic relations in the context of various conceptual areas. It is also promising to study the cognitive models underlying the structures of knowledge about the methods of diplomatic relations, as well as to determine the cognitive and linguistic mechanisms of secondary interpretation of diplomatic relations in English. The applied aspect is the systematization of linguistic means of representation of diplomatic relations in communication.

Kualitas Isi dan Kalimat Efektif pada Buku Antologi Cerpen Berjudul Dibalik Jendela Kamar Ibnu Abbas Terbitan Jejak Publisher sebagai Sumber Bacaan Siswa Kelas 8 SMP

The use of effective sentences has a strong influence on the quality of the content of a work. In cerpen writing, it is necessary to use effective language so that both the written and the implied meanings of cerpen can be well conveyed. The study aims to describe the forms of error in the use of sentences effectively in the formation of content quality on cerpen, as well as to present a comprehensive analysis of the linguistic aspects in the cerpen anthology "Back the Window of Ibnu Abbas" as a source of reading for students of 8th grade high school. The study uses qualitative descriptive methods through a language analysis approach by evaluating the quality of the content and effectiveness of the sentences in the book. This research data is a sentence quotation in a cerpen in which there are written errors in the use of effective sentences. Data collection techniques are done with reading and recording techniques. The results of this study found: (1) repetition of verbs, (2) inappropriate dictionary selection, (3) passing sentences, and (4) ambiguous sentences in a cerpen. Such errors make sentences less effective and affect the quality of the cerpen content. The findings of this study provide an in-depth understanding of how the structure of sentences, the meaning of words, the context of language usage, and the form of words influence the understanding and experience of readers of a literary work.

Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach

Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due to the nonlinear chaos of trajectories, the unconstrained airspace maps, and the randomization of driving patterns. In this work, a deep learning model based on data-driven modern koopman operator theory and dynamical system identification is proposed. The model does not require the manual selection of dictionaries and can automatically generate augmentation functions to achieve nonlinear trajectory space mapping. The model combines stacked neural networks to create a scalable depth approximator for approximating the finite-dimensional Koopman operator. In addition, the model uses finite-dimensional operator evolution to achieve end-to-end adaptive prediction. In particular, the model can gain some physical interpretability through operator visualization and generative dictionary functions, which can be used for downstream pattern recognition and anomaly detection tasks. Experiments show that the model performs well, particularly on flight training trajectory datasets.

Improving linear orthogonal mapping based cross-lingual representation using ridge regression and graph centrality

Orthogonal linear mapping is a commonly used approach for generating cross-lingual embedding between two monolingual corpora that uses a word frequency-based seed dictionary alignment approach. While this approach is found to be effective for isomorphic language pairs, they do not perform well for distant language pairs with different sentence structures and morphological properties. For a distance language pair, the existing frequency-aligned orthogonal mapping methods suffer from two problems - (i)the frequency of source and target word are not comparable, and (ii)different word pairs in the seed dictionary may have different contribution. Motivated by the above two concerns, this paper proposes a novel centrality-aligned ridge regression-based orthogonal mapping. The proposed method uses centrality-based alignment for seed dictionary selection and ridge regression framework for incorporating influential weights of different word pairs in the seed dictionary. From various experimental observations over five language pairs (both isomorphic and distant languages), it is evident that the proposed method outperforms baseline methods in the Bilingual Dictionary Induction(BDI) task, Sentence Retrieval Task(SRT), and Machine Translation. Further, several analyses are also included to support the proposed method.

Cultural specificities of online dictionaries for English learners: Evidence from a user survey and a multimodal discourse analysis

Abstract This study examines three popular e-dictionary platforms using a quantitative user survey coupled with a qualitative multimodal discourse analysis (MDA): Chinese Youdao, Cambridge English-Mandarin Chinese Dictionary (Cambridge), and the Free Dictionary (FD). The survey (N = 478) revealed that, despite its language flaws, Youdao was preferred by most Chinese EFL (English as a Foreign Language) learners mainly due to its search functionality and personalization. In contrast, English majors and postgraduates preferred Cambridge for its reliability and multimodal resources. FD ranked the lowest in popularity mainly for perceived inconvenience of searching and less accessibility of personalized functions. Multinomial logistic regressions and structural equation modelling showed that dictionary selection by users was contingent upon their perception of its features and their use strategies. Furthermore, MDA revealed how each dictionary contributed to the construction of a cultural identity. Youdao seemed to be a toolbox for personal use, with raw texts of varying quality, but with a clear and practical vision, in addition to customization options. Cambridge portrayed itself as a welcoming language community through appealing imagery, interactive options, and diverse navigational styles. FD was comparable to a dynamic and cluttered library, archiving large blocks of hyperlinked texts and word lists accompanied by musical animations. In brief, multimodal and cultural factors partially explained Chinese EFL learners’ preferences for a specific dictionary, highlighting the importance of adaptation to the linguistic and cultural background of the user, including customization. A general cultural and semiotic framework is proposed to examine the representation of cultural identity in e-dictionaries.

Multiview feature co‐factorization based dictionary selection for video summarization

AbstractRecently, video summarization (VS) has emerged as one of the most effective tools for rapidly understanding video big data. Dictionary selection based on self‐representation and sparse regularization is consistent with the requirement of VS, which aims to represent the original video with little reconstruction error by a small number of video frames. However, one crucial issue is that the existing methods mainly use a single view feature, which is not sufficient enough for acquiring the full pictorial details and affects the quality of the produced video summary. Although a few methods use more than one features, they only directly concatenate the features, which does not take advantage of the relationship of different features. Considering the complementarity of shallow and deep features, multiview feature co‐factorization based dictionary selection for VS is proposed in this paper to use the common information of both view features for VS. Specifically, two view features are used to fully exploit the full pictorial information of video frames, then the common information of two different views is extracted through coupled matrix factorization to conduct the dictionary selection for VS. Experiments have been carried out on two benchmark datasets, and results have demonstrated the effectiveness and superiority of the proposed method.

Two-Stage Sentiment Analysis on Indonesian Online News Using Lexicon-Based

The image of a supplier company is often associated with the well-known brand it supplies, and its reputation can be influenced by online news circulation. To maintain a positive image, it is crucial for the company to monitor and manage online news to rectify any false information. Failure to maintain a good company image can lead to customer order loss and even company shutdown. This paper aims to conduct a two-stage sentiment analysis on Indonesian news articles regarding unilateral layoffs by company XYZ. The first stage will analyze sentiment in the circulating news about the layoffs, while the second stage will assess sentiment after the company releases a press release to provide accurate information. The VADER lexicon-based method, utilizing the InSet and SentiStrength_ID Indonesian dictionaries, will be employed to analyze sentiment before and after the press release. This will enable us to compare sentiment and evaluate the effectiveness of the press release and the Indonesian dictionaries in analyzing sentiment in the news. The research findings indicate that the company's press release, aimed at correcting false information, had a positive impact by reducing negative sentiment and generating a more positive sentiment in the second stage. Moreover, the selection of the sentiment analysis dictionary also plays a critical role in determining the sentiment analysis results.

Multi-scale deep feature fusion based sparse dictionary selection for video summarization

The explosive growth of video data constitutes a series of new challenges in computer vision, and the function of video summarization (VS) is becoming more and more prominent. Recent works have shown the effectiveness of sparse dictionary selection (SDS) based VS, which selects a representative frame set to sufficiently reconstruct a given video. Existing SDS based VS methods use conventional handcrafted features or single-scale deep features, which could diminish their summarization performance due to the underutilization of frame feature representation. Deep learning techniques based on convolutional neural networks (CNNs) exhibit powerful capabilities among various vision tasks, as the CNN provides excellent feature representation. Therefore, in this paper, a multi-scale deep feature fusion based sparse dictionary selection (MSDFF-SDS) is proposed for VS. Specifically, multi-scale features include the directly extracted features from the last fully connected layer and the global average pooling (GAP) processed features from intermediate layers, then VS is formulated as a problem of minimizing the reconstruction error using the multi-scale deep feature fusion. In our formulation, the contribution of each scale of features can be adjusted by a balance parameter, and the row-sparsity consistency of the simultaneous reconstruction coefficient is used to select as few keyframes as possible. The resulting MSDFF-SDS model is solved by using an efficient greedy pursuit algorithm. Experimental results on two benchmark datasets demonstrate that the proposed MSDFF-SDS improves the F-score of keyframe based summarization more than 3% compared with the existing SDS methods, and performs better than most deep-learning methods for skimming based summarization.

Terminological System of Hydraulic Engineering: Diffuseness of Terminological Fields and Polycentricity

The presents a study of the lexical units of the language of the specialty “Hydrotechnikal Сonstruction” based on a selection of dictionaries, textbooks, articles, regulations and scientific and journalistic texts. There is analysis of concepts of term, terminological field, terminological system, core and their interpretation in the scientific literature. The purpose of the study is to propose a thematic and derivational classification of hydrotechical terms to build a model of a terminological system. The multidimensionality of the hydrotechnical profile, including construction, hydrology and ecology, as well as related to biology, hydropower generation, navigation, agriculture and other branches of scientific and practical activity, is noted. In this regard, a horizontal structure of the terminological fields of the hydrotechnical system is proposed; the set of lexical units of the language of the specialty “hydraulic engineering” is considered as an ordered terminological system with a multi-core structure. The polycentricity of the system of hydrotechnical terms and the difusseness of its constituent microfields has been proved using a quantitative method. The novelty of the study is caused by the specifics of the material, which has been little studied so far, as well as the approach to its systematization. Observation of the hydrotechnical vocabulary made it possible to put forward a hypothesis about the relatively small number of borrowings in its corpus, which in turn determines the ambivalent nature of many hydrotechnical terms and the diffuseness of the microfields that form the terminological system. The study of hydrotechnical terms showed that the process of formation of terminological units can take place in a different sequence: from word to term or from term to word. Moreover, along with the term, its etymon can continue to be used in the general literary language. Derivation is active; however, the borrowing of terms is not the main source of replenishment of hydrotechnical terminology. The conclusions are drawn about the special structure of the terminological system, which reflects the multidimensional nature of. The Hydrotechnikal Сonstruction”, polycentric nature of the hydrotechnical terminological system, which includes intersecting term fields, is explained by the history of the industry and inter-industry relations. The results are necessary for the preparation of a manual for foreigners studying this discipline in Russian, which represents the relevance and practical significance of the study.

Adaptive local sparse representation for compressive hyperspectral imaging

Coded aperture snapshot spectral imaging (CASSI) is an effective way for hyperspectral imaging. In CASSI, the key issue is to accurately and efficiently reconstruct the 3D hyperspectral image from its corresponding coded 2D image. Due to the ill-posed nature, reconstruction errors are inevitable, a feasible solution is to add an RGB camera for complementary sampling to reduce the reconstruction error. In this paper, we investigate the structural changes of local image patches in different bands and their correlation with RGB observation, propose a reconstruction method for dual-camera CASSI system. Specifically, we learn an adaptive dictionary with RGB observation, then use RGB observation to guide the selection of the adaptive dictionary for each local image patch of the reconstruction target, and finally reconstruct the original hyperspectral image through an iterative numerical algorithm. This method fuses the spatial and spectral information obtained from RGB observations into the reconstruction process, experimental results show that the proposed method can greatly improve the reconstruction quality, especially the reconstruction of the details, and reduce more time compared with past dictionary-based reconstruction methods.

ACD-EDMD: Analytical Construction for Dictionaries of Lifting Functions in Koopman Operator-Based Nonlinear Robotic Systems

Koopman operator theory has been gaining momentum for model extraction, planning, and control of data-driven robotic systems. The Koopman operator’s ability to extract dynamics from data depends heavily on the selection of an appropriate dictionary of lifting functions. In this letter, we propose ACD-EDMD, a new method for Analytical Construction of Dictionaries of appropriate lifting functions for a range of data-driven Koopman operator based nonlinear robotic systems. The key insight of this work is that information about fundamental topological spaces of the nonlinear system (such as its configuration space and workspace) can be exploited to steer the construction of Hermite polynomial-based lifting functions. We show that the proposed method leads to dictionaries that are simple to implement while enjoying provable completeness and convergence guarantees when observables are weighted bounded. We evaluate ACD-EDMD using a range of diverse nonlinear robotic systems in both simulated and physical hardware experimentation (a wheeled mobile robot, a two-revolute-joint robotic arm, and a soft robotic leg). Results reveal that our method leads to dictionaries that enable high-accuracy prediction and that can generalize to diverse validation sets. The associated GitHub repository of our algorithm can be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UCR-Robotics/ACD-EDMD</uri> .

A Study on Dictionary Selection in Compressive Sensing for ECG Signals Compression and Classification.

The paper proposes a comparative analysis of the projection matrices and dictionaries used for compressive sensing (CS) of electrocardiographic signals (ECG), highlighting the compromises between the complexity of preprocessing and the accuracy of reconstruction. Starting from the basic notions of CS theory, this paper proposes the construction of dictionaries (constructed directly by cardiac patterns with R-waves, centered or not-centered) specific to the application and the results of their testing. Several types of projection matrices are also analyzed and discussed. The reconstructed signals are analyzed quantitatively and qualitatively by standard distortion measures and by the classification of the reconstructed signals. We used a k-nearest neighbors (KNN) classifier to evaluate the reconstructed models. The KNN module was trained with the models from the mega-dictionary used in the classification block and tested with the models reconstructed with class-specific dictionaries. In addition to the KNN classifier, a neural network was used to test the reconstructed signals. The neural network was a multilayer perceptron (MLP). Moreover, the results are compared with those obtained with other compression methods, and ours proved to be superior.

Structure Parameter Optimized Kernel Based Online Prediction With a Generalized Optimization Strategy for Nonstationary Time Series

In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction algorithms as usual consist of the selection of kernel structure parameters and the kernel weight vector updating. For structure parameters, the kernel dictionary is selected by some sparsification techniques with online selective modeling criteria, and moreover the kernel covariance matrix is intermittently optimized in the light of the covariance matrix adaptation evolution strategy (CMA-ES). Optimizing the real symmetric covariance matrix can not only improve the kernel structure's flexibility by the cross relatedness of the input variables, but also partly alleviate the prediction uncertainty caused by the kernel dictionary selection for nonstationary time series. In order to sufficiently capture the underlying dynamic characteristics in prediction-error time series, a generalized optimization strategy is designed to construct the kernel dictionary sequentially in multiple kernel connection modes. The generalized optimization strategy provides a more self-contained way to construct the entire kernel connections, which enhances the ability to adaptively track the changing dynamic characteristics. Numerical simulations have demonstrated that the proposed approach has superior prediction performance for nonstationary time series.

Identifying and Translating Subjective Content Descriptions Among Texts

An agent pursuing a task may work with a corpus of documents as a reference library. Subjective content descriptions (SCDs) provide additional data that add value in the context of the agent’s task. In the pursuit of documents to add to the corpus, an agent may come across new documents where content text and SCDs from another agent are interleaved and no distinction can be made unless the agent knows the content from somewhere else. Therefore, this paper presents a hidden Markov model-based approach to identify SCDs in a new document where SCDs occur inline among content text. Additionally, we present a dictionary selection approach to identify suitable translations for content text and SCDs based on [Formula: see text]-grams. We end with a case study evaluating both approaches based on simulated and real-world data.

Segmentation of Gastric Computerized Tomography Images under Intelligent Algorithms in Evaluation of Efficacy of Decitabine Combined with Paclitaxel in Treatment of Gastric Cancer.

To analyze the evaluation of artificial intelligence algorithm combined with gastric computed tomography (CT) image in clinical chemotherapy for advanced gastric cancer, 112 patients with advanced gastric cancer were selected as the research object. Among which, 56 patients in the experimental group received paclitaxel (PTX) combined with decitabine sequential decitabine maintenance therapy. Fifty-six patients in the control group received first-line treatment with decitabine combined with cisplatin. The image segmentation algorithm based on fast interactive dictionary selection was used to process gastric CT images. Complete response (CR), partial response (PR), stable disease (SD), progressive disease (PD), response rate (RR), disease control rate (DCR), and overall survival (OS) after treatment were recorded. The true-positive rate (TPR) and coincidence ratio (CR) of the proposed algorithm for image segmentation were significantly higher than those of the mean shift algorithm and the iCoseg algorithm. The mean edge distance (MED) and edge distance variance (EDV) were significantly lower than the mean shift algorithm and the iCoseg algorithm, and the differences were considerable (P < 0.05). The number of CR (5 cases), PR (13 cases), RR (18 cases), and DCR (44 cases) in the experimental group was significantly higher than that in the control group, while the number of PD (12 cases) was significantly lower than that in the control group (P < 0.05). The number of patients complicated with hematological toxicity, leucopenia, thrombocytopenia, and digestive tract reaction in the experimental group was less than that in the control group (P < 0.05). From the comparison of long-term efficacy, the survival rate of patients in both groups showed a decreasing trend within 24 months, but the decreasing trend of survival rate of patients in the experimental group was better than that in the control group. In short, the proposed algorithm had better segmentation performance than traditional algorithms. Compared with first-line treatment with decitabine and cisplatin, PTX in combination with decitabine sequential citabine maintenance regimens had better disease control rates, lower toxicity, and more effective improvements in patient quality of life and longer survival in patients with advanced gastric cancer.

La consommation de chocolat et ses topoï dans la fiction des Lumières

This article investigates chocolate in Ancien Régime society through a selection of treatises, dictionaries, and novels from the Enlightenment. These texts provide valuable information on its benefits, preparation, and consumption – revealing new dietary as well as social rituals, closely linked to the libertine imagination. In addition, the novels inform the evolution of descriptive practices. The analysis of short excerpts enables us to propose a few topoi, such as “to take one’s chocolate,” “to invite to take chocolate,” “to feel pleasure with chocolate” or “(to attempt) to administer poison or narcotic in chocolate.”

Keyframe Extraction From Laparoscopic Videos via Diverse and Weighted Dictionary Selection

Laparoscopic videos have been increasingly acquired for various purposes including surgical training and quality assurance, due to the wide adoption of laparoscopy in minimally invasive surgeries. However, it is very time consuming to view a large amount of laparoscopic videos, which prevents the values of laparoscopic video archives from being well exploited. In this paper, a dictionary selection based video summarization method is proposed to effectively extract keyframes for fast access of laparoscopic videos. Firstly, unlike the low-level feature used in most existing summarization methods, deep features are extracted from a convolutional neural network to effectively represent video frames. Secondly, based on such a deep representation, laparoscopic video summarization is formulated as a diverse and weighted dictionary selection model, in which image quality is taken into account to select high quality keyframes, and a diversity regularization term is added to reduce redundancy among the selected keyframes. Finally, an iterative algorithm with a rapid convergence rate is designed for model optimization, and the convergence of the proposed method is also analyzed. Experimental results on a recently released laparoscopic dataset demonstrate the clear superiority of the proposed methods. The proposed method can facilitate the access of key information in surgeries, training of junior clinicians, explanations to patients, and archive of case files.

Translation Trainees’ Uses of and Attitudes towards Different Types of Dictionaries: A Case Study

The focus of most studies on dictionary users has been on foreign and second language learners, with seldom attention paid to translation trainees. Given that the dictionary is an indispensable tool used by translators and translators in the making alike, research in this regard is crucial to uncover the uses and attitudes of this group of dictionary users’ towards different forms of dictionaries. Revealing translation trainees’ practices facilitates the correction of wrong ones and helps in guiding students towards the efficient use of dictionaries. The ultimate goal is, thus, informing translation pedagogy. Hence, the present study aims at investigating translation trainees’ uses of dictionaries and their attitudes towards different types of dictionaries. The focus is on uncovering the uses and attitudes of Saudi undergraduate female translation trainees towards the different forms of dictionaries available for them today. The article in hand specifically reports on the quantitative phase of a two-phase, mixed-methods case study. A group of 95 undergraduate students answered a multi-item, online questionnaire, with the aim of revealing their dictionary uses and preferences. The results confirmed the participants’ preference for and heavy reliance on electronic dictionaries, due to their convenience, comprehensiveness, portability and being up-to-date. The results further indicated the participants’ preference for bilingual over monolingual dictionaries. Surprisingly, the results also showed that a high number of the respondents reported using Google Translate frequently. Despite these findings, the survey results revealed that the participants have good knowledge of and do in fact use a wide selection of bilingual and monolingual dictionaries, including paper and electronic ones.

Church Slavonic Words имарменя, фатунъ, фортунa in Maximus the Greek’s Works

Within the linguistic research on the works of Maximus the Greek, the article raises the question of the peculiarities of an individual intellectual dictionary in his creative work. The object of this study is the authors use of three terms conveying the concept of "fate", in particular the lexical borrowing from the Greek είμαρμένη and from the Latin words fatum and fortuna, rarely used in Church Slavonic literature up to the 16 th cent. The use of significant terms is described through lexicographical analysis in the first two volumes of the modern edition of Maximus the Greeks works. Special attention is paid to the comparison of the meanings of individual words and their functioning with the data taken from the historical section of the National Corpus of the Russian language, and as well as from a selection of dictionaries of Old Church Slavonic and Church Slavonic languages, in order to identify the characteristic features of lexical preferences of Maximus the Greek. Thorough contextual analysis of the texts allows us to show how, conveying the concept of necessity caused by the stars or mysterious destiny, the author shows himself as an innovator, enriching Church Slavonic vocabulary with new borrowings. The article verifies the hypothesis about the reasons for lexical preferences of Maximus the Greek and makes assumptions about the interpretation of synonymy of the words denoting "fate" that appears in the studied texts. Linguistic goals, formulated on the basis of the results obtained, can be achieved only taking into consideration the general trends in the development of religious and philosophical thought in Europe in the Early Modern Era.

Терминография языка саха: формирование и становление

The paper, following the modern theory of terminography, considers the formation and composition of the terminology of the Sakha language selection and quality of vocabulary dictionaries, and their composition (inventory and interpretation) parameters. Yakut terminography has dictionaries for all branches of knowledge: nine - in natural sciences, six - in social (humanitarian) sciences, one - in technical sciences, with there being also four macro-compositional (multi-field) dictionaries. The authors provide a classification analysis of terminological dictionaries for typologies and compositional parameters. According to compositional parameters, there are multi-field and single-field dictionaries in Sakha terminology. An inventory of terms is a collection and description of all words-terms belonging to the selected field of knowledge or its thematic fragment. Inventory dictionaries are Russian-Sakha, once again confirming the leading role of the Russian terminological school. New concepts with their designations come to us through the Russian language. The defining type includes dictionaries with the head vocabulary provided with definitions (brief interpretations). The definition should be accurate, concise, but sufficient and should not contain redundant information. The definition is an excellent technique for highlighting the terminological meaning of a polysemantic word and a word with an undifferentiated meaning. With terminological dictionaries intersecting in some way, the faceted principle of dividing them was subsequently adopted. Faceted typology is convenient for sorting varieties of dictionaries by their parallel bases. Sakha terminography, comprising multi-field and mixed dictionaries, needs a faceted typology. Faceted typology involves the identification of specific lexicographic parameters serving as the basis for the classification of dictionaries.