Dictionary Items Research Articles

Îndrumări pentru folosirea dicționarului

The article stems from the conclusion that in the first fascicle of the second edition of the Romanian Language Dictionary published in 2021, there is no chapter regarding any Guidelines for using dictionary, as it was the case in the first edition, as well as in other dictionaries published under the aegis of the Romanian Academy. To point out the importance and need of publishing such a chapter, there is a short history about the manner in which the indications and explanations referring to the structure of the dictionary items were worded and designated, as well as about the significance of conventional and special signs used in the four drafts of the Romanian Language Dictionary as well as in other dictionaries published by the Institute of Linguistics of Bucharest. Further on, the article presents in detail what the chapter about Guidelines for using dictionary from the new edition of the Romanian Language Dictionary should include, a chapter that must be published to make up for its absence.

Komponent kulturowy w nauczaniu słownictwa na zajęciach z języka obcego na poziomie akademickim na przykładzie języka włoskiego –watykańskie kulinaryzmy jako kulturemy i elementy socjolektalne

Meals are commonly known to be a crucial element of life, culture and interpersonal relationships in the Italian Peninsula. In Italian dictionaries of phraseology, idiomatic expressions representing names of culinary items are the vast majority of dictionary items. This is a sign of the importance of meals, also considered as feasting together and the Italian food centrism. As the cultural component is an integral element of language education at an academic level, Italian culinary items as components of idiomatic and fixed expressions in Vatican culinary items related to culturally rooted habits and secular Catholic rituals will be analyzed. The study presented in the paper aims at: 1. clarifying the sociolect specifics of the Vatican’s clergy and kitchen staff members; 2. determining the importance of food in the socio-cultural reality in the Vatican as a type of a culture code and an element of the regional/national culture; 3. emphasizing the essence of semantic and lexical problems related to food in academic intercultural didactics, e.g. a specialization proseminar in linguistics or translation studies or a university instructor’s own thematic seminar on culinary matters in the Italian Peninsula.

"THE NECESSITY OF RELEVANT DICTIONARY CONTEXTUALIZATIONS FOR THE TRANSLATOR OF NAVAL ARCHITECTURE TEXTS "

Translating naval architecture texts involves, besides knowledge and practice in the field, using specific terminology. The ability of handling specific terminology is supposed to be increased by relevant dictionary contextualizations illustrating the meaning and usage of naval architecture terminology. The technical dictionaries available, either on paper or as a wide web resource, provide the translation of terms, i.e. the ‚equivalent(s)’ in the target language, and, in some cases, the explanation of meaning. However, these dictionaries lack the contextualizations that can help both students and translators. The paper focuses on some existent dictionaries and their relevance for the naval architecture translator. It also provides a possible model for dictionary items accompanied by an explanation and contextualization, which prove to be very useful for students and translators. Special attention will be paid to the lexical characteristics of the Romanian and English naval architecture terminology and relevant contextualizations will be given to illustrate the meaning.

How Can a Clinical Data Modelling Tool Be Used to Represent Data Items of Relevance to Paediatric Clinical Trials? Learning from the Conect4children (c4c) Consortium

Data dictionaries for clinical trials are often created manually, with data structures and controlled vocabularies specific for a trial or family of trials within a sponsor’s portfolio. Microsoft Excel is commonly used to capture the representation of data dictionary items but has limited functionality for this purpose. The conect4children (c4c) network is piloting the Direcht clinical data modelling tool to model their Cross Cutting Paediatric Data Dictionary (CCPDD) in a more formalised way. The first pilot had the key objective of testing whether a clinical data modelling tool could be used to represent data items from the CCPDD. The key objective of the second pilot is to establish whether a small team with little or no experience of clinical data modelling can use Direcht to expand the CCPDD. Clinical modelling is the process of structuring clinical data so it can be understood by computer systems and humans. The model contains all of the elements that are needed to define the data item. Results from the pilots show that Direcht creates a structured environment to build data items into models that fit into the larger CCPDD. Models can be represented as an HTML document, mind map, or exported in various formats for import into a computer system. Challenges identified over the course of both pilots are being addressed with c4c partners and external stakeholders.

Self-Balancing Dictionary Learning for Relaxed Collaborative Representation of Hyperspectral Image Classification

Supervised dictionary learning and representation learning framework has demonstrated its superiority for hyperspectral image classification. Relaxed collaborative representation (RCR) has also been acknowledged as an effective method in balancing the similarity and difference between features. In this paper, a new dictionary learning method is introduced to balance discrimination and reconstruction of training samples. In the dictionary learning stage, two new indicators are designed to measure the discriminability of items and can be improved by optimizing coding coefficients. The imposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm between independent item and the mean of class-specific samples constrains the similarity, and the calculated weights measure the difference. In label determination stage, considering that the residuals of RCR are adversely affected by the significant difference of features, a new classification approach is introduced. Class labels are assigned without calculating the reconstruction errors but calculating the levels of comprehensive contribution from all training samples instead. Since the effectiveness will be degraded when dealing with more complex circumstances, a region-based version is further introduced. It can further improve the discrimination of dictionary items due to the reduced categories in each sub-image and reduce the computation cost. The experimental results on several hyperspectral datasets demonstrate that our methods can effectively improve the classification performance.

Transfer Model Collaborating Metric Learning and Dictionary Learning for Cross-Domain Facial Expression Recognition

Facial expression recognition has drawn increasing attention because of its great potential in human behavior analysis. Traditional recognition models usually assume that the training set is sufficient, and the training and testing data sets have the same distribution. However, these two factors are not satisfied in some cases. In this study, a transfer model collaborating metric learning and dictionary learning called TMMLDL is proposed to address the transfer facial expression recognition problem. To reduce the impact of cross-domain distribution variation, the information of global structure and pairwise constraints are utilized among training images in different domains. In particular, a discriminative metric space is learned into dictionary learning procedure such that the dictionary items can well present the discriminative information of different facial expression classes in the metric subspace. The proposed model tunes dictionary and metric space in an alternative optimization algorithm, which is guaranteed to obtain the optimal model parameters simultaneously. The experimental results on nine cross-domain facial expression classification tasks show that the proposed model achieves the satisfactory recognition performance.

Mechanical dissimilarity of defects in welded joints via Grassmann manifold and machine learning

Assessing the harmfulness of defects based on images is becoming more and more common in industry. At present, these defects can be inserted in digital twins that aim to replicate in a mechanical model what is observed on a component so that an image-based diagnosis can be further conducted. However, the variety of defects, the complexity of their shape, and the computational complexity of finite element models related to their digital twin make this kind of diagnosis too slow for any practical application. We show that a classification of observed defects enables the definition of a dictionary of digital twins. These digital twins prove to be representative of model-reduction purposes while preserving an acceptable accuracy for stress prediction. Nonsupervised machine learning is used for both the classification issue and the construction of reduced digital twins. The dictionary items are medoids found by a k-medoids clustering algorithm. Medoids are assumed to be well distributed in the training dataset according to a metric or a dissimilarity measurement. In this paper, we propose a new dissimilarity measurement between defects. It is theoretically founded according to approximation errors in hyper-reduced predictions. In doing so, defect classes are defined according to their mechanical effect and not directly according to their morphology. In practice, each defect in the training dataset is encoded as a point on a Grassmann manifold. This methodology is evaluated through a test set of observed defects totally different from the training dataset of defects used to compute the dictionary of digital twins. The most appropriate item in the dictionary for model reduction is selected according to an error indicator related to the hyper-reduced prediction of stresses. No plasticity effect is considered here (merely isotropic elastic materials), which is a strong assumption but which is not critical for the purpose of this work. In spite of the large variety of defects, we provide accurate predictions of stresses for most of defects in the test set.

MLK-SVD, the new approach in deep dictionary learning

The aim of this study is to improve the classification efficiency of advanced methods using a multilayered dictionary learning framework. This paper presents the new idea of “multilayered K-singular value decomposition (MLK-SVD)” dictionary learning as a multilayer method of classification. This method starts by building a sparse representation at the patch level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. In this research using class labels of training data, the label information is associated with each dictionary item (columns of the dictionary matrix) to enforce discrimination in sparse codes during the dictionary learning process. Also, this algorithm instead of learning one shallow dictionary learned multiple levels of dictionaries. The proposed formulation of deep dictionary learning provides the basis to develop more efficient dictionary learning algorithms. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. The performance of the proposed method is evaluated on MNIST and CIFAR-10 datasets, and results show that this method can help in advancing the state of the art.

Entropy based dictionary learning for image classification

In this paper, a new discriminative dictionary learning algorithm is introduced. An entropy based criterion is embedded into the objective function to enforce a proper structure for the dictionary items when decomposing signals of different classes. The proposed criterion influences the dictionary items to participate in the decomposition of a smaller number of classes as possible. Unlike the other methods, columns of the dictionary are not restricted to have pre-assigned labels and they are free to be representative of any class or to share features of several classes. The number of shared and discriminative items along with the number of dictionary items for each specific class is learned dynamically during the optimization process, depending on the complexity of the classification task and the distribution of different classes. The experimental results demonstrate that the proposed entropy based dictionary learning (EDL) algorithm outperforms other discriminative dictionary learning methods using several real-world image datasets.

T2-FDL: A robust sparse representation method using adaptive type-2 fuzzy dictionary learning for medical image classification

In this paper, a robust sparse representation for medical image classification is proposed based on the adaptive type-2 fuzzy learning (T2-FDL) system. In the proposed method, sparse coding and dictionary learning processes are executed iteratively until a near-optimal dictionary is obtained. The sparse coding step aiming at finding a combination of dictionary atoms to represent the input data efficiently, and the dictionary learning step rigorously adjusts a minimum set of dictionary items. The two-step operation helps create an adaptive sparse representation algorithm by involving the type-2 fuzzy sets in the design process of image classification. Since the existing image measurements are not made under the same conditions and with the same accuracy, the performance of medical diagnosis is always affected by noise and uncertainty. By introducing an adaptive type-2 fuzzy learning method, a better approximation in an environment with higher degrees of uncertainty and noise is achieved. The experiments are executed over two open-access brain tumor magnetic resonance image databases, REMBRANDT and TCGA-LGG, from The Cancer Imaging Archive (TCIA). The experimental results of a brain tumor classification task show that the proposed T2-FDL method can adequately minimize the negative effects of uncertainty in the input images. The results demonstrate the outperformance of T2-FDL compared to other important classification methods in the literature, in terms of accuracy, specificity, and sensitivity.

CLEAN: Frequent pattern-based trajectory compression and computation on road networks

The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.

AIRBORNE LIDAR POINT CLOUD CLASSIFICATION BASED ON MULTILEVEL POINT CLUSTER FEATURES

Abstract. Airborne Light Detection And Ranging (LiDAR) has become an important means for efficient and high-precision acquisition of 3D spatial data of large scenes. It has important application value in digital cities and location-based services. The classification and identification of point cloud is the basis of its application, and it is also a hot and difficult problem in the field of geographic information science.The difficulty of LiDAR point cloud classification in large-scale urban scenes is: On the one hand, the urban scene LiDAR point cloud contains rich and complex features, many types of features, different shapes, complex structures, and mutual occlusion, resulting in large data loss; On the other hand, the LiDAR scanner is far away from the urban features, and is like a car, a pedestrian, etc., which is in motion during the scanning process, which causes a certain degree of data noise of the point cloud and uneven density of the point cloud.Aiming at the characteristics of LiDAR point cloud in urban scene.The main work of this paper implements a method based on the saliency dictionary and Latent Dirichlet Allocation (LDA) model for LiDAR point cloud classification. The method uses the tag information of the training data and the tag source of each dictionary item to construct a significant dictionary learning model in sparse coding to expresses the feature of the point set more accurately.And it also uses the multi-path AdaBoost classifier to perform the features of the multi-level point set. The classification of point clouds is realized based on the supervised method. The experimental results show that the feature set extracted by the method combined with the multi-path classifier can significantly improve the cloud classification accuracy of complex city market attractions.

Projective Double Reconstructions Based Dictionary Learning Algorithm for Cross-Domain Recognition.

Dictionary learning plays a significant role in the field of machine learning. Existing works mainly focus on learning dictionary from a single domain. In this paper, we propose a novel projective double reconstructions (PDR) based dictionary learning algorithm for cross-domain recognition. Owing the distribution discrepancy between different domains, the label information is hard utilized for improving discriminability of dictionary fully. Thus, we propose a more flexible label consistent term and associate it with each dictionary item, which makes the reconstruction coefficients have more discriminability as much as possible. Due to the intrinsic correlation between cross-domain data, the data should be reconstructed with each other. Based on this consideration, we further propose a projective double reconstructions scheme to guarantee that the learned dictionary has the abilities of data itself reconstruction and data crossreconstruction. This also guarantees that the data from different domains can be boosted mutually for obtaining a good data alignment, making the learned dictionary have more transferability. We integrate the double reconstructions, label consistency constraint and classifier learning into a unified objective and its solution can be obtained by proposed optimization algorithm that is more efficient than the conventional l1 optimization based dictionary learning methods. The experiments show that the proposed PDR not only greatly reduces the time complexity for both training and testing, but also outperforms over the stateof- the-art methods.

Layered convolutional dictionary learning for sparse coding itemsets

Dictionary learning for sparse coding has been successfully used in different domains, however, has never been employed for the interesting itemset mining. In this paper, we formulate an optimization problem for extracting a sparse representation of itemsets and show that the discrete nature of itemsets makes it NP-hard. An efficient approximation algorithm is presented which greedily solves maximum set cover to reduce overall compression loss. Furthermore, we incorporate our sparse representation algorithm into a layered convolutional model to learn nonredundant dictionary items. Following the intuition of deep learning, our convolutional dictionary learning approach convolves learned dictionary items and discovers statistically dependent patterns using chi-square in a hierarchical fashion; each layer having more abstract and compressed dictionary than the previous. An extensive empirical validation is performed on thirteen datasets, showing better interpretability and semantic coherence of our approach than two existing state-of-the-art methods.

A Sparse Representation Model Using the Complete Marginal Fisher Analysis Framework and Its Applications to Visual Recognition

This paper presents an innovative sparse representation model using the complete marginal Fisher analysis (CMFA) framework for different challenging visual recognition tasks. First, a complete marginal Fisher analysis method is presented by extracting the discriminatory features in both the column space of the local samples based within the class scatter matrix and the null space of its transformed matrix. The rationale of extracting features in both spaces is to enhance the discriminatory power by further utilizing the null space, which is not accounted for in the marginal Fisher analysis method. Second, a discriminative sparse representation model is proposed by integrating a representation criterion such as the sparse representation and a discriminative criterion for improving the classification capability. In this model, the largest step size for learning the sparse representation is derived to address the convergence issues in optimization, and a dictionary screening rule is presented to purge the dictionary items with null coefficients for improving the computational efficiency. Experiments on some challenging visual recognition tasks using representative datasets, such as the Painting-91 dataset, the 15 scene categories dataset, the MIT-67 indoor scenes dataset, the Caltech 101 dataset, the Caltech 256 object categories dataset, the AR face dataset, and the extended Yale B dataset, show the feasibility of the proposed method.

Discriminative-Dictionary-Learning-Based Multilevel Point-Cluster Features for ALS Point-Cloud Classification

Efficient presentation and recognition of on-ground objects from airborne laser scanning (ALS) point clouds are a challenging task. In this paper, we propose an approach that combines a discriminative-dictionary-learning-based sparse coding and latent Dirichlet allocation (LDA) to generate multilevel point-cluster features for ALS point-cloud classification. Our method takes advantage of the labels of training data and each dictionary item to enforce discriminability in sparse coding during the dictionary learning process and more accurately further represent point-cluster features. The multipath AdaBoost classifiers with the hierarchical point-cluster features are trained, and we apply them to the classification of unknown points by the heritance of the recognition results under different paths. Experiments are performed on different ALS point clouds; the experimental results have shown that the extracted point-cluster features combined with the multipath classifiers can significantly enhance the classification accuracy, and they have demonstrated the superior performance of our method over other techniques in point-cloud classification.

Generalized Pooling for Robust Object Tracking.

Feature pooling in a majority of sparse coding-based tracking algorithms computes final feature vectors only by low-order statistics or extreme responses of sparse codes. The high-order statistics and the correlations between responses to different dictionary items are neglected. We present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to model the statistical distribution of sparse codes. Since immediate matching between two distributions usually requires high computational costs, we introduce the Fisher vector to derive a more compact and discriminative representation for sparse codes of the visual target. We encode target patches by local coordinate coding, utilize Gaussian mixture model to compute Fisher vectors, and finally train semi-supervised linear kernel classifiers for visual tracking. In order to handle the drifting problem during the tracking process, these classifiers are updated online with current tracking results. The experimental results on two challenging tracking benchmarks demonstrate that the proposed approach achieves a better performance than the state-of-the-art tracking algorithms.

Human action recognition based on locality-constrained linear coding

Large intra-class variations of action features lead to low classification accuracy of action recognition,on the other hand,current algorithms exist drawbacks in computational complexity and extension of recognizable action classes. A method based on locality-constrained linear coding( LLC) for action recognition from depth images was proposed. In order to reduce the intra-class variations and increase classification accuracy,joints' positions,velocities and acceleration features were concatenated to form local action features,then LLC was used to calculate sparse representations of local action features. Analytical solution of LLC ensures computational speed of our method is up to 760 frames per second. Dictionary is composed by sub-dictionaries learned by K-means from features of each class separately,so global optimization is avoided during extending recognizable action classes. Moreover,to avoid classifier to be over-fitting,a dimensionality reduction method based on labels of dictionary items was proposed. The proposed method was evaluated on MSRAction3 D dataset captured by depth cameras. The experimental results show that the proposed approach achieves classification accuracy of 85. 7%.

CSMMI: class-specific maximization of mutual information for action and gesture recognition.

In this paper, we propose a novel approach called class-specific maximization of mutual information (CSMMI) using a submodular method, which aims at learning a compact and discriminative dictionary for each class. Unlike traditional dictionary-based algorithms, which typically learn a shared dictionary for all of the classes, we unify the intraclass and interclass mutual information (MI) into an single objective function to optimize class-specific dictionary. The objective function has two aims: 1) maximizing the MI between dictionary items within a specific class (intrinsic structure) and 2) minimizing the MI between the dictionary items in a given class and those of the other classes (extrinsic structure). We significantly reduce the computational complexity of CSMMI by introducing an novel submodular method, which is one of the important contributions of this paper. This paper also contributes a state-of-the-art end-to-end system for action and gesture recognition incorporating CSMMI, with feature extraction, learning initial dictionary per each class by sparse coding, CSMMI via submodularity, and classification based on reconstruction errors. We performed extensive experiments on synthetic data and eight benchmark data sets. Our experimental results show that CSMMI outperforms shared dictionary methods and that our end-to-end system is competitive with other state-of-the-art approaches.

Representative selection based on sparse modeling

Selecting representatives for multimedia analysis applications could greatly reduce the time and memory consumption. Many representative selection methods have been proposed to select a subset from the database as representatives. However, current methods cannot guarantee that the selected subset could represent the global distribution of the entire dataset. In order to evaluate how well the subset represents the global distribution of the whole dataset, we use the distance metric: Kullback–Leibler (KL) divergence between the distribution of the fake dataset reconstructed from the subset and the distribution of the true dataset. In this work, we propose a sparse modeling based method to select representatives. The proposed method formulates the representative selection problem as a discrete dictionary learning problem. Based on the assumption that the dataset can be approximately reconstructed by linear combinations of dictionary items, we design a two-step iterative representative selection algorithm, which can minimize this KL divergence. Experiments evaluate the proposed algorithm in several multimedia analysis applications, including image and video summarization, classification using representative images and classification using representative features, and our method is shown to outperform state-of-the-art methods.