Querying multi-source heterogeneous fuzzy spatiotemporal data

2021 ◽  
pp. 1-12
Author(s):  
Luyi Bai ◽  
Nan Li ◽  
Lishuang Liu ◽  
Xuesong Hao
Keyword(s):  
Integration Method ◽  
State Of The Art ◽  
Rapid Development ◽  
Structural Heterogeneity ◽  
Xml Schema ◽  
Data Sources ◽  
Spatiotemporal Data ◽  
Relational Data ◽  
Semantic Model ◽  
Semantic Models

With the rapid development of the environmental, meteorological and marine data management, fuzzy spatiotemporal data has received considerable attention. Even though some achievements in querying aspect have been made, there are still some unsolved problems. Semantic and structural heterogeneity may exist among different data sources, which will lead to incomplete results. In addition, there are ambiguous query intentions and conditions when the user queries the data. This paper proposes a fuzzy spatiotemporal data semantic model. Based on this model, the RDF local semantic models are converted into a RDF global semantic model after mapping relational data and XML data to RDF local semantic models. The existing methods mainly convert relational data to RDF Schema directly. But our approach converts relational data to XML Schema and then converts it to RDF, which utilizes the semi-structured feature of XML schema to solve the structural heterogeneity between different data sources. The integration process enables us to perform global queries against different data sources. In the proposed query algorithms, the query conditions inputted are converted into exact queries before the results are returned. Finally, this paper has carried out extensive experiments, calculated the recall, precision and F-Score of the experimental results, and compared with other state-of-the-art query methods. It shows the importance of the data integration method and the effectiveness of the query method proposed in this paper.

scholarly journals Deriving Boolean structures from distributional vectors

2015 ◽  
Vol 3 ◽  
pp. 375-388 ◽  
Author(s):  
German Kruszewski ◽  
Denis Paperno ◽  
Marco Baroni
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Scale Up ◽  
Semantic Relatedness ◽  
Training Data ◽  
Semantic Model ◽  
Qualitative Properties ◽  
Semantic Models ◽  
Distributional Semantic Models

Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.

scholarly journals Creating Relational Data from Unstructured and Ungrammatical Data Sources

2008 ◽  
Vol 31 ◽  
pp. 543-590 ◽  
Author(s):  
M. Michelson ◽  
C. A. Knoblock
Keyword(s):  
Information Extraction ◽  
State Of The Art ◽  
Unstructured Data ◽  
Data Sources ◽  
Relational Data ◽  
Second Step ◽  
Data Sets ◽  
Reference Set ◽  
Textual Data ◽  
Standard Values

In order for agents to act on behalf of users, they will have to retrieve and integrate vast amounts of textual data on the World Wide Web. However, much of the useful data on the Web is neither grammatical nor formally structured, making querying difficult. Examples of these types of data sources are online classifieds like Craigslist and auction item listings like eBay. We call this unstructured, ungrammatical data "posts." The unstructured nature of posts makes query and integration difficult because the attributes are embedded within the text. Also, these attributes do not conform to standardized values, which prevents queries based on a common attribute value. The schema is unknown and the values may vary dramatically making accurate search difficult. Creating relational data for easy querying requires that we define a schema for the embedded attributes and extract values from the posts while standardizing these values. Traditional information extraction (IE) is inadequate to perform this task because it relies on clues from the data, such as structure or natural language, neither of which are found in posts. Furthermore, traditional information extraction does not incorporate data cleaning, which is necessary to accurately query and integrate the source. The two-step approach described in this paper creates relational data sets from unstructured and ungrammatical text by addressing both issues. To do this, we require a set of known entities called a "reference set." The first step aligns each post to each member of each reference set. This allows our algorithm to define a schema over the post and include standard values for the attributes defined by this schema. The second step performs information extraction for the attributes, including attributes not easily represented by reference sets, such as a price. In this manner we create a relational structure over previously unstructured data, supporting deep and accurate queries over the data as well as standard values for integration. Our experimental results show that our technique matches the posts to the reference set accurately and efficiently and outperforms state-of-the-art extraction systems on the extraction task from posts.

Modelling of Semantic Processes and Evolutional Semantic Model

2019 ◽  
Vol 26 (27) ◽  
pp. 197-210 ◽  
Author(s):  
Anastasia Fedorova
Keyword(s):  
Key Words ◽  
Dynamic Models ◽  
Scientific Literature ◽  
Semantic Model ◽  
Vast Array ◽  
Special Procedure ◽  
Semantic Models ◽  
Semantic Evolution ◽  
Semantic Processes ◽  
The Way

In Linguistics the terms model and modelling have a vast array of meanings, which depends on the purpose and the object, and the type of the scientific research. The article is dedicated to the investigation of a special procedure of semantic processes modelling, deducing and substantiating the notion “evolutional semantic model”, the content and operational opportunities of which differ drastically from the essence and purpose of the known from the scientific literature phenomenon of the same name. In the proposed research this variety of modelling is oriented towards the description of the dynamics of the legal terms content loading, the estimation of possible vectors of the semantic evolution on the way of its terminalization/determinalization. The evolutional model of semantics has here as its basis the succession of sememes or series of sememes, the order of which is determined with accounting of a number of parameters. The typical schemes of the meaning development, illustrated by the succession of sememes, are considered to be the models of semantic laws (evolutional semantic models = EMS). Their function is the explanation of the mechanism and the order of the stages of the semantic evolution of the system of the words which sprung from one root on the way of its legal specialization, and, therefore, the proposed in the paper experience of semantic laws modelling differs from the expertise of the “catalogue of semantic derivations”, proposed by H. A. Zaliznjak, which doesn’t have as its purpose the explanation of meaning displacements, and from the notion of semantic derivation, models of derivation, dynamic models, worked out by O. V. Paducheva, which also only state such a displacement, without proving its reality. Key words: evolutional semantic model (EMS), modelling, semantic law, sememe, pre(law).

scholarly journals Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application

2021 ◽  
Vol 11 (9) ◽  
pp. 4248
Author(s):  
Hong Hai Hoang ◽  
Bao Long Tran
Keyword(s):  
Object Segmentation ◽  
State Of The Art ◽  
Rapid Development ◽  
Spatial Relationship ◽  
Learning Technologies ◽  
Average Precision ◽  
Novel Approach ◽  
3D Camera ◽  
Robot Grasping ◽  
Instance Segmentation

With the rapid development of cameras and deep learning technologies, computer vision tasks such as object detection, object segmentation and object tracking are being widely applied in many fields of life. For robot grasping tasks, object segmentation aims to classify and localize objects, which helps robots to be able to pick objects accurately. The state-of-the-art instance segmentation network framework, Mask Region-Convolution Neural Network (Mask R-CNN), does not always perform an excellent accurate segmentation at the edge or border of objects. The approach using 3D camera, however, is able to extract the entire (foreground) objects easily but can be difficult or require a large amount of computation effort to classify it. We propose a novel approach, in which we combine Mask R-CNN with 3D algorithms by adding a 3D process branch for instance segmentation. Both outcomes of two branches are contemporaneously used to classify the pixels at the edge objects by dealing with the spatial relationship between edge region and mask region. We analyze the effectiveness of the method by testing with harsh cases of object positions, for example, objects are closed, overlapped or obscured by each other to focus on edge and border segmentation. Our proposed method is about 4 to 7% higher and more stable in IoU (intersection of union). This leads to a reach of 46% of mAP (mean Average Precision), which is a higher accuracy than its counterpart. The feasibility experiment shows that our method could be a remarkable promoting for the research of the grasping robot.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

scholarly journals Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Asmaa El Hannani ◽  
Rahhal Errattahi ◽  
Fatima Zahra Salmam ◽  
Thomas Hain ◽  
Hassan Ouahmane
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Error Detection ◽  
State Of The Art ◽  
Rapid Development ◽  
Unified Framework ◽  
Human Machine Interaction ◽  
Detection Analysis ◽  
Extensive Evaluation ◽  
Effectiveness And Efficiency

AbstractSpeech based human-machine interaction and natural language understanding applications have seen a rapid development and wide adoption over the last few decades. This has led to a proliferation of studies that investigate Error detection and classification in Automatic Speech Recognition (ASR) systems. However, different data sets and evaluation protocols are used, making direct comparisons of the proposed approaches (e.g. features and models) difficult. In this paper we perform an extensive evaluation of the effectiveness and efficiency of state-of-the-art approaches in a unified framework for both errors detection and errors type classification. We make three primary contributions throughout this paper: (1) we have compared our Variant Recurrent Neural Network (V-RNN) model with three other state-of-the-art neural based models, and have shown that the V-RNN model is the most effective classifier for ASR error detection in term of accuracy and speed, (2) we have compared four features’ settings, corresponding to different categories of predictor features and have shown that the generic features are particularly suitable for real-time ASR error detection applications, and (3) we have looked at the post generalization ability of our error detection framework and performed a detailed post detection analysis in order to perceive the recognition errors that are difficult to detect.

Melanocytic Lesions of the Conjunctiva

2010 ◽  
Vol 134 (12) ◽  
pp. 1785-1792 ◽  
Author(s):  
Artur Zembowicz ◽  
Rajni V. Mandal ◽  
Pitipol Choopong
Keyword(s):  
Personal Experience ◽  
State Of The Art ◽  
Data Sources ◽  
Clinical Aspects ◽  
Review Of The Literature ◽  
Melanocytic Lesions ◽  
Pathologic Features

Abstract Context—Melanocytic proliferations are among the most common neoplasms of the conjunctiva. They often represent challenging lesions for pathologists unfamiliar with unique histologic features of melanocytic proliferations in this location and with nomenclature used by ophthalmologists. Objective—To comprehensively review clinical aspects, pathologic features, and management of melanocytic proliferations of the conjunctiva. Data Sources—Review of the literature and personal experience of the authors. Conclusions—Classification, state of the art, and practical aspects of pathology of melanocytic proliferations of the conjunctiva are discussed.

scholarly journals The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

2021 ◽  
Vol 2021 (1) ◽  
pp. 209-228
Author(s):  
Yuantian Miao ◽  
Minhui Xue ◽  
Chao Chen ◽  
Lei Pan ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Rapid Development ◽  
Black Box ◽  
Problem Space ◽  
Specific Data ◽  
Learning Techniques ◽  
Audio Data ◽  
Iot Devices ◽  
Asr System

AbstractWith the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy.

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