Bibliografie tematiche / Network extraction

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Letteratura scientifica selezionata sul tema "Network extraction"

Autore: Grafiati

Pubblicato: 10 dicembre 2022

Ultima modifica: 28 gennaio 2023

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Articoli di riviste sul tema "Network extraction"

Neal, Zachary P. "backbone: An R package to extract network backbones". PLOS ONE 17, n. 5 (31 maggio 2022): e0269137. http://dx.doi.org/10.1371/journal.pone.0269137.

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Networks are useful for representing phenomena in a broad range of domains. Although their ability to represent complexity can be a virtue, it is sometimes useful to focus on a simplified network that contains only the most important edges: the backbone. This paper introduces and demonstrates a substantially expanded version of the backbone package for R, which now provides methods for extracting backbones from weighted networks, weighted bipartite projections, and unweighted networks. For each type of network, fully replicable code is presented first for small toy examples, then for complete empirical examples using transportation, political, and social networks. The paper also demonstrates the implications of several issues of statistical inference that arise in backbone extraction. It concludes by briefly reviewing existing applications of backbone extraction using the backbone package, and future directions for research on network backbone extraction.

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PELLEGRINI, Lilla, Monica LEBA e Alexandru IOVANOVICI. "CHARACTERIZATION OF URBAN TRANSPORTATION NETWORKS USING NETWORK MOTIFS". Acta Electrotechnica et Informatica 20, n. 4 (21 gennaio 2020): 3–9. http://dx.doi.org/10.15546/aeei-2020-0019.

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We use tools and techniques specific to the field of complex networks analysis for the identification and extraction of key parameters which define ”good” patterns and practices for designing public transportation networks. Using network motifs we analyze a set of 18 cities using public data sets regarding the topology of network and discuss each of the identified motifs using the concepts and tools of urban planning.

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Baptista, Diego, e Caterina De Bacco. "Principled network extraction from images". Royal Society Open Science 8, n. 7 (luglio 2021): 210025. http://dx.doi.org/10.1098/rsos.210025.

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Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data. Motivated by this, we present a principled model to extract network topologies from images that is scalable and efficient. We map this goal into solving a routing optimization problem where the solution is a network that minimizes an energy function which can be interpreted in terms of an operational and infrastructural cost. Our method relies on recent results from optimal transport theory and is a principled alternative to standard image-processing techniques that are based on heuristics. We test our model on real images of the retinal vascular system, slime mould and river networks and compare with routines combining image-processing techniques. Results are tested in terms of a similarity measure related to the amount of information preserved in the extraction. We find that our model finds networks from retina vascular network images that are more similar to hand-labelled ones, while also giving high performance in extracting networks from images of rivers and slime mould for which there is no ground truth available. While there is no unique method that fits all the images the best, our approach performs consistently across datasets, its algorithmic implementation is efficient and can be fully automatized to be run on several datasets with little supervision.

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Yang, Kaili, Weihong Cui, Shu Shi, Yu Liu, Yuanjin Li e Mengyu Ge. "Semi-Automatic Method of Extracting Road Networks from High-Resolution Remote-Sensing Images". Applied Sciences 12, n. 9 (7 maggio 2022): 4705. http://dx.doi.org/10.3390/app12094705.

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Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.

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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW". International Journal of Computational Intelligence and Applications 12, n. 04 (dicembre 2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.

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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble difficult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and difficulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible rules. Furthermore, this enabled rule extraction that resulted in fewer rules than those in previously proposed methods. This paper summarizes experimental results of rule extraction using E-Re-RX by multiple standard backpropagation MLPs and provides deep discussions. The results make it possible for the output from a neural network ensemble to be in the form of rules, thus open the "black box" of trained neural networks ensembles. Finally, we provide valuable conclusions and as future work, three open questions on the E-Re-RX algorithm.

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Et.al, Mahyuddin K. M. Nasution. "Social Network Extraction Unsupervised". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n. 3 (11 aprile 2021): 4443–49. http://dx.doi.org/10.17762/turcomat.v12i3.1824.

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In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.

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Chaitanya, Aravapalli Sri, Suvarna Vani Koneru e Praveen Kumar Kollu. "Road Network Extraction using Atrous Spatial Pyramid Pooling". International Journal of Innovative Technology and Exploring Engineering 8, n. 9 (30 luglio 2019): 31–33. http://dx.doi.org/10.35940/ijitee.h7459.078919.

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Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.

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Luo, Shuai, Kai Yang, Lijuan Yang, Yong Wang, Xiaorong Gao, Tianci Jiang e Chunjiang Li. "Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network". Sensors 22, n. 3 (23 gennaio 2022): 859. http://dx.doi.org/10.3390/s22030859.

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In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.

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Milano, Nicola, e Stefano Nolfi. "Autonomous learning of features for control: Experiments with embodied and situated agents". PLOS ONE 16, n. 4 (15 aprile 2021): e0250040. http://dx.doi.org/10.1371/journal.pone.0250040.

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The efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including an additional neural network dedicated to features extraction trained through self-supervision. In this paper we introduce a method that permits to continue the training of the features extracting network during the training of the control network. We demonstrate that the parallel training of the two networks is crucial in the case of agents that operate on the basis of egocentric observations and that the extraction of features provides an advantage also in problems that do not benefit from dimensionality reduction. Finally, we compare different feature extracting methods and we show that sequence-to-sequence learning outperforms the alternative methods considered in previous studies.

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Hsu, Pai-Hui. "EVALUATING THE INITIALIZATION METHODS OF WAVELET NETWORKS FOR HYPERSPECTRAL IMAGE CLASSIFICATION". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (17 giugno 2016): 83–89. http://dx.doi.org/10.5194/isprs-archives-xli-b7-83-2016.

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The idea of using artificial neural network has been proven useful for hyperspectral image classification. However, the high dimensionality of hyperspectral images usually leads to the failure of constructing an effective neural network classifier. To improve the performance of neural network classifier, wavelet-based feature extraction algorithms can be applied to extract useful features for hyperspectral image classification. However, the extracted features with fixed position and dilation parameters of the wavelets provide insufficient characteristics of spectrum. In this study, wavelet networks which integrates the advantages of wavelet-based feature extraction and neural networks classification is proposed for hyperspectral image classification. Wavelet networks is a kind of feed-forward neural networks using wavelets as activation function. Both the position and the dilation parameters of the wavelets are optimized as well as the weights of the network during the training phase. The value of wavelet networks lies in their capabilities of optimizing network weights and extracting essential features simultaneously for hyperspectral images classification. In this study, the influence of the learning rate and momentum term during the network training phase is presented, and several initialization modes of wavelet networks were used to test the performance of wavelet networks.

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Più fonti

Tesi sul tema "Network extraction"

McEnnis, Daniel. "On-demand metadata extraction network (OMEN)". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99382.

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OMEN (On-demand Metadata Extraction Network) addresses a fundamental problem in Music Information Retrieval: the lack of universal access to a large dataset containing significant amounts of copyrighted music. This thesis proposes a solution to this problem that is accomplished by utilizing the large collections of digitized music available at many libraries. Using OMEN, libraries will be able to perform on-demand feature extraction on site, returning feature values to researchers instead of providing direct access to the recordings themselves. This avoids copyright difficulties, since the underlying music never leaves the library that owns it. The analysis is performed using grid-style computation on library machines that are otherwise under-used (e.g., devoted to patron web and catalogue use).

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El, Ghoul Aymen. "Phase fields for network extraction from images". Nice, 2010. http://www.theses.fr/2010NICE4075.

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Cette thèse décrit la construction d’un modèle de réseaux non-directionnels (e. G. Réseaux routiers), fondé sur les contours actifs d’ordre supérieur (CAOs) et les champs de phase développés récemment, et introduit une nouvelle famille de champs de phase des CAOs pour les réseaux directionnels (e. G. Réseaux hydrographiques, vaisseaux sanguins). Dans la première partie de cette thèse, nous nous intéressons à l’analyse de la stabilité d’une énergie de type CAOs aboutissant à un « diagramme de phase ». Les résultats permettent une sélection des valeurs des paramètres pour la modélisation de réseaux non-directionnels. Au contraire des réseaux routiers, les réseaux hydrographiques sont directionnels, i. E. Ils contiennent un « flux » monodimensionnel circulant dans chaque branche. Nous développons un modèle de champ de phase non-local de réseaux directionnels, qui, en plus du champ de phase scalaire décrivant une région par une fonction caractéristique lisse et qui interagit non-localement afin que des configurations de réseaux linéiques soient favorisées, introduit un champ vectoriel représentant le « flux » dans les branches du réseau. Ce champ vectoriel est contraint d’être nul à l’extérieur, et de magnitude égale à 1 à l’intérieur du réseau ; circulant dans le sens longitudinal des branches du réseau ; et de divergence très faible. Cela prolonge les branches du réseau ; contrôle la variation de largeur tout au long d’une branche, et forme des jonctions non-symétriques telles que la somme des largeurs entrantes soit approximativement égale à celle des largeurs sortantes. Ce nouveau modèle a été appliqué au problème d’extraction de réseaux hydrographiques à partir d’images satellitaires très haute résolution
This thesis describes the construction of an undirected network (e. G. Road network) model, based on the recently developed higher-order active contours (HOACs) and phase fields, and introduces a new family of phase field HOACs for directed networks (e. G. Hydrographic networks in remote sensing imagery, vascular networks in medical imagery). In the first part of this thesis, we focus on the stability analysis of a HOAC energy leading to a “phase diagram”. The results which are confirmed by numerical experiments enable the selection of parameter values for the modeling of indirectly networks. Hydrographic networks, unlike road networks, are directed, i. E. They carry a unidirectional flow in each branch. This leads to specific geometric properties of the branches and particularly of the junctions that it is useful to capture in model, for network extraction purposes. We thus develop a nonlocal phase field model of directed networks, which, in addition to a scalar field representing a region by its smoothed characteristic function and interacting no locally so as to favor network configurations, contains a vector field representing the “flow” through the network branches. The vector field is strongly encouraged to be zero outside, and of unit magnitude inside the network ; running along the network branches ; and to have a zero divergence. This prolongs network, controls width variation along a branch ; and produces asymmetric junctions for which total incoming branch width approximately equals total outgoing branch width. The new proposed model is applied to the problem of hydrographic network extraction from very high resolution satellite images, and it outperforms the undirected network model

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Swanepoel, Lodewyk. "Network simulation for the effective extraction of IP network statistics / Lodewyk Swanepoel". Thesis, North-West University, 2003. http://hdl.handle.net/10394/231.

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This study investigates the extent to which a communication sessions' QoS parameters can be measured through only extracting TCP/IP header data. The effect on these measurements based on the point of header extraction within the network as well as the OSI stack are also investigated. An overview of packet switched networks and packet switched network protocols are given. The disadvantages and advantages of different network architectures and protocols are also given. Different network simulation tools are discussed and compared to find the most appropriate simulation tool for this study. Two network topologies are introduced and sessions are constructed and monitored through only using the TCP/IP header data. Sessions are established and maintained and the results obtained from these sessions are compared and the most appropriate solution is chosen. The results have shown that extracting data at the edge routers for sessions are the most optimal solution. These sessions are established and maintained through using virtual private network technologies and protocols.
Thesis (M.Ing.)--North-West University, Potchefstroom Campus, 2004.

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Kushmerick, Nicholas. "Wrapper induction for information extraction /". Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6867.

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Tong, Dong Ling. "Genetic algorithm-neural network : feature extraction for bioinformatics data". Thesis, Bournemouth University, 2010. http://eprints.bournemouth.ac.uk/15788/.

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With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data.

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Seegmiller, Ray D., Greg C. Willden, Maria S. Araujo, Todd A. Newton, Ben A. Abbott e William A. Malatesta. "Automation of Generalized Measurement Extraction from Telemetric Network Systems". International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581647.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California
In telemetric network systems, data extraction is often an after-thought. The data description frequently changes throughout the program so that last minute modifications of the data extraction approach are often required. This paper presents an alternative approach in which automation of measurement extraction is supported. The central key is a formal declarative language that can be used to configure instrumentation devices as well as measurement extraction devices. The Metadata Description Language (MDL) defined by the integrated Network Enhanced Telemetry (iNET) program, augmented with a generalized measurement extraction approach, addresses this issue. This paper describes the TmNS Data Extractor Tool, as well as lessons learned from commercial systems, the iNET program and TMATS.

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Karaman, Ersin. "Road Network Extraction From High-resolution Multi-spectral Satellite Images". Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615362/index.pdf.

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In this thesis, an automatic road extraction algorithm for multi-spectral images is developed. The developed model extracts elongated structures from images by using edge detection, segmentation and clustering techniques. The study also extracts non-road regions like vegetative fields, bare soils and water bodies to obtain more accurate road map. The model is constructed in a modular approach that aims to extract roads with different characteristics. Each module output is combined to create a road score map. The developed algorithm is tested on 8-band WorldView-2 satellite images. It is observed that, the proposed road extraction algorithm yields 47 % precision and 70 % recall. The approach is also tested on the lower spectral resolution images with four-band, RGB and gray level. It is observed that the additional four bands provide an improvement of 12 % for precision and 3 % for recall. Road type analysis is also in the scope of this study. Roads are classified into asphalt, concrete and unpaved using Gaussian Mixture Models. Other linear objects such as railroads and water canals may also be extracted by this process. An algorithm that classifies drive roads and railroads for very high resolution images is also investigated. It is based on the Fourier descriptors that identify the presence of railroad sleepers. Water canals are also extracted in multi-spectral images by using spectral ratios that employ the near infrared bands. Structural properties are used to distinguish water canals from other water bodies in the image.

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Kumuthini, Judit. "Extraction of genetic network from microarray data using Bayesian framework". Thesis, Cranfield University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442547.

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Wang, Wei. "Event Detection and Extraction from News Articles". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82238.

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Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly.
Ph. D.

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Bródka, Piotr. "Key User Extraction Based on Telecommunication Data". Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5863.

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The number of systems that collect vast amount of data about users rapidly grow during last few years. Many of these systems contain data not only about people characteristics but also about their relationships with other system users. From this kind of data it is possible to extract a social network that reflects the connections between system’s users. Moreover, the analysis of such social network enables to investigate different characteristics of its users and their linkages. One of the types of examining such network is key users extraction. Key users are these who have the biggest impact on other network users as well as have big influence on network evolution. The obtained knowledge about these users enables to investigate and predict changes within the network. So this knowledge is very important for the people or companies who make a profit from the network like telecommunication company. The second important issue is the ability to extract these users as quick as possible, i.e. developed the algorithm that will be time-effective in large social networks where number of nodes and edges is equal few millions.

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Più fonti

Libri sul tema "Network extraction"

Trigueiros, Duarte. Neural network based methods in the extraction of knowledge from accounting and financial data. Norwich: University of East Anglia, 1991.

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Mokhlesabadifarahani, Bita, e Vinit Kumar Gunjan. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-320-0.

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Berry, R. H. The application of neural network based methods to the extraction of knowledge from accounting reports: A classificationstudy. Norwich: School of Information Systems, University of East Anglia, 1991.

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Supervised and unsupervised pattern recognition: Feature extraction and computational intelligence. Boca Raton, Fla: CRC Press, 2000.

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Semantic network analysis: Techniques for extracting, representing and querying media content. Charleston, SC: BookSurge, 2008.

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DNIS 2010 (2010 Aizu Daigaku). Databases in networked information systems: 6th international workshop, DNIS 2010, Aizu-Wakamatsu, Japan, March 29-31, 2010 : proceedings. Berlin: Springer, 2010.

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Gunjan, Vinit Kumar, e Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer, 2015.

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Gunjan, Vinit Kumar, e Bita Mokhlesabadifarahani. EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction. Springer London, Limited, 2015.

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Bianconi, Ginestra. Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.001.0001.

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Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; spreading of diseases, opinions and ideas might take advantage of multilayer network topology and spread even when its single layers cannot sustain an epidemic when taken in isolation; diffusion on multilayer transportation networks can significantly speed up with respect to diffusion on single layers; finally, the interplay between multiplexity and controllability of multilayer networks is a problem with major consequences in financial, transportation, molecular biology and brain networks. This field is one of the most prosperous recent developments of Network Science and Data Science. Multilayer networks include multiplex networks, multi-slice temporal networks, networks of networks, interdependent networks. Multilayer networks are characterized by having a highly correlated multilayer network structure, providing a significant advantage for extracting information from them using multilayer network measures and centralities and community detection methods. The multilayer network dynamics (including percolation, epidemic spreading, diffusion, synchronization, game theory and control) is strongly affected by the multilayer network topology. This book will present a comprehensive account of this emerging field.

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Irarrazaval, Felipe, e Martín Arias-Loyola. Resource Peripheries in the Global Economy: Networks, Scales, and Places of Extraction. Springer International Publishing AG, 2021.

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Capitoli di libri sul tema "Network extraction"

Yang, Cheng, Chuan Shi, Zhiyuan Liu, Cunchao Tu e Maosong Sun. "Network Embedding for Social Relation Extraction". In Network Embedding, 135–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01590-8_10.

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Wen, Dong, Zhenhao Wei, Yanhong Zhou, Yanbo Sun, Fengnian Li e Jiewei Li. "Spatial Complex Brain Network". In EEG Signal Processing and Feature Extraction, 267–86. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_13.

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Gao, Zhongke, Yuxuan Yang e Qing Cai. "Temporal Complex Network Analysis". In EEG Signal Processing and Feature Extraction, 287–300. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_14.

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Rajeh, Stephany, Marinette Savonnet, Eric Leclercq e Hocine Cherifi. "Modularity-Based Backbone Extraction in Weighted Complex Networks". In Network Science, 67–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97240-0_6.

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Hamasaki, Masahiro, Yutaka Matsuo, Keisuke Ishida, Yoshiyuki Nakamura, Takuichi Nishimura e Hideaki Takeda. "Community Focused Social Network Extraction". In The Semantic Web – ASWC 2006, 155–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11836025_16.

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Nguyen, Vu Duc, Yang-Wai Chow e Willy Susilo. "Attacking Animated CAPTCHAs via Character Extraction". In Cryptology and Network Security, 98–113. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35404-5_9.

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Liu, ChunYang, WenBo Sun, WenHan Chao e WanXiang Che. "Convolution Neural Network for Relation Extraction". In Advanced Data Mining and Applications, 231–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53917-6_21.

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Carlini, Nicholas, Matthew Jagielski e Ilya Mironov. "Cryptanalytic Extraction of Neural Network Models". In Advances in Cryptology – CRYPTO 2020, 189–218. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56877-1_7.

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Buccella, Pietro, Camillo Stefanucci, Maher Kayal e Jean-Michel Sallese. "Extraction Tool for the Substrate Network". In Analog Circuits and Signal Processing, 97–112. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74382-0_5.

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Benediktsson, Jon A., e Johannes R. Sveinsson. "Feature Extraction for Neural Network Classifiers". In Neurocomputation in Remote Sensing Data Analysis, 97–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59041-2_11.

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Atti di convegni sul tema "Network extraction"

Fang, Songtao, Zhenya Huang, Ming He, Shiwei Tong, Xiaoqing Huang, Ye Liu, Jie Huang e Qi Liu. "Guided Attention Network for Concept Extraction". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/200.

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Concept extraction aims to find words or phrases describing a concept from massive texts. Recently, researchers propose many neural network-based methods to automatically extract concepts. Although these methods for this task show promising results, they ignore structured information in the raw textual data (e.g., title, topic, and clue words). In this paper, we propose a novel model, named Guided Attention Concept Extraction Network (GACEN), which uses title, topic, and clue words as additional supervision to provide guidance directly. Specifically, GACEN comprises two attention networks, one of them is to gather the relevant title and topic information for each context word in the document. The other one aims to model the implicit connection between informative words (clue words) and concepts. Finally, we aggregate information from two networks as input to Conditional Random Field (CRF) to model dependencies in the output. We collected clue words for three well-studied datasets. Extensive experiments demonstrate that our model outperforms the baseline models with a large margin, especially when the labeled data is insufficient.

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Yang, Dongdong, Senzhang Wang e Zhoujun Li. "Ensemble Neural Relation Extraction with Adaptive Boosting". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/630.

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Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equally to the semantic meaning of the sentence. Next via adaptive boosting, we build strategically several such neural classifiers. By ensembling multiple such LSTM classifiers with adaptive boosting, we could build a more effective and robust joint ensemble neural networks based relation extractor. Experiment results on real dataset demonstrate the superior performance of the proposed model, improving F1-score by about 8% compared to the state-of-the-art models.

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Khan, Mohd Jawed, e Pankaj Pratap Singh. "Road Extraction from Remotely Sensed Data: A Review". In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.14.

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Up-to-date road networks are crucial and challenging in computer vision tasks. Road extraction is yet important for vehicle navigation, urban-rural planning, disaster relief, traffic management, road monitoring and others. Road network maps facilitate a great number of applications in our everyday life. Therefore, a systematic review of deep learning approaches applied to remotely sensed imagery for road extraction is conducted in this paper. Four main types of deep learning approaches, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models are presented in this paper. We also compare these various deep learning models applied to remotely sensed imagery to show their performances in extracting road parts from high-resolution remote sensed imagery. Later future research directions and research gaps are described.

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Zheng, Xiaoming, Yan Wang e Mehmet A. Orgun. "Contextual Sub-network Extraction in Contextual Social Networks". In 2015 IEEE Trustcom/BigDataSE/ISPA. IEEE, 2015. http://dx.doi.org/10.1109/trustcom.2015.365.

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Tao Chen, Yue Sun e Shi Jian Zhao. "Constrained principal component extraction network". In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4594025.

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Liu, Jianyi, e Jinghua Wang. "Keyword Extraction Using Language Network". In 2007 International Conference on Natural Language Processing and Knowledge Engineering. IEEE, 2007. http://dx.doi.org/10.1109/nlpke.2007.4368023.

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Xu, Chenglin, Wei Rao, Eng Siong Chng e Haizhou Li. "Time-Domain Speaker Extraction Network". In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9004016.

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Xiao, Ming-Ming, Shun-Zheng Yu e Yu Wang. "Automatic Network Protocol Automaton Extraction". In 2009 Third International Conference on Network and System Security. IEEE, 2009. http://dx.doi.org/10.1109/nss.2009.71.

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Yang, Hang, Haosen Wang, Huang Peng, Deqi Cao, Yuntao Zhang e Yajie Dou. "Network Security Intelligence Information Extraction". In 2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE). IEEE, 2021. http://dx.doi.org/10.1109/mlise54096.2021.00043.

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Salakij, Saran, James A. Liburdy e Deborah V. Pence. "Modeling In-Situ Vapor Extraction During Convective Boiling". In ASME 2009 Fluids Engineering Division Summer Meeting. ASMEDC, 2009. http://dx.doi.org/10.1115/fedsm2009-78522.

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The pressure drop of convective boiling flow may be reduced by extracting vapor locally since the entire generated vapor does not have to travel through the entire channel length. In this study, the theoretical model was developed to simulate a convective boiling flow through a fractal-like branching microchannel network with vapor extraction. The fractal-like branching microchannel network has a porous membrane forming one wall of the channels. Vapor extraction occurs by applying a vacuum across the membrane. Sample predictive local conditions and global results are presented and discussed. The predicting results are classified into two groups: low inlet flow rate-low heat flux and high inlet flow rate-high heat flux. The results show that to increase extracted vapor mass flow rate, either decreasing supplying extracting pressure or increasing permeability of the porous membrane must be applied. As the amount of vapor extracting increases, the reduction in pressure drop across the channel and the exit vapor quality is achieved.

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Rapporti di organizzazioni sul tema "Network extraction"

Intrator, Nathan. A Neural Network for Feature Extraction. Fort Belvoir, VA: Defense Technical Information Center, marzo 1990. http://dx.doi.org/10.21236/ada223059.

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Bixby, Robert E., e Robert Fourer. Finding Embedded Network Rows in Linear Programs I: Extraction Heuristics. Fort Belvoir, VA: Defense Technical Information Center, agosto 1986. http://dx.doi.org/10.21236/ada455195.

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Kegelmeyer, Philip W., Timothy M. Shead e Daniel M. Dunlavy. Network and Ensemble Enabled Entity Extraction in Informal Text (NEEEEIT) final report. Office of Scientific and Technical Information (OSTI), settembre 2013. http://dx.doi.org/10.2172/1115263.

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Griffin, Andrew, Sean Griffin, Kristofer Lasko, Megan Maloney, S. Blundell, Michael Collins e Nicole Wayant. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), aprile 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

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Kazmierczak, Ing H. Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge-Based Image Analysis. Fort Belvoir, VA: Defense Technical Information Center, settembre 1988. http://dx.doi.org/10.21236/ada202810.

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Li, Howell, Enrique Saldivar-Carranza, Jijo K. Mathew, Woosung Kim, Jairaj Desai, Timothy Wells e Darcy M. Bullock. Extraction of Vehicle CAN Bus Data for Roadway Condition Monitoring. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317212.

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Obtaining timely information across the state roadway network is important for monitoring the condition of the roads and operating characteristics of traffic. One of the most significant challenges in winter roadway maintenance is identifying emerging or deteriorating conditions before significant crashes occur. For instance, almost all modern vehicles have accelerometers, anti-lock brake (ABS) and traction control systems. This data can be read from the Controller Area Network (CAN) of the vehicle, and combined with GPS coordinates and cellular connectivity, can provide valuable on-the-ground sampling of vehicle dynamics at the onset of a storm. We are rapidly entering an era where this vehicle data can provide an agency with opportunities to more effectively manage their systems than traditional procedures that rely on fixed infrastructure sensors and telephone reports. This data could also reduce the density of roadway weather information systems (RWIS), similar to how probe vehicle data has reduced the need for micro loop or side fire sensors for collecting traffic speeds.

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Canellas, João Vitor, Fabio Ritto, Ricardo Fischer e Paulo Jose Medeiros. What is the best biomaterial for alveolar ridge preservation after tooth extraction? a systematic review and network meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review Protocols, marzo 2020. http://dx.doi.org/10.37766/inplasy2020.3.0005.

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Li, wanlin, jie Yun, siying He, ziqi Zhou e ling He. Effect of different exercise therapies on fatigue in maintenance hemodialysis patients:A Bayesian Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, novembre 2022. http://dx.doi.org/10.37766/inplasy2022.11.0144.

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Review question / Objective: Population: maintenance hemodialysis patients. Intervention: exercise therapy (resistance exercise； aerobic exercise； resistance combined aerobic exercise； muscle relaxation training； Baduanjin ). Comparison: simple routine nursing. Outcome: fatigue； sleep quality. Study design: randomized controlled trial. Eligibility criteria: Inclusion and exclusion criteria： RCT of study type exercise intervention in MHD patients' fatigue； Study subjects： MHD patients ≥18 years old, regardless of gender, nationality or race； The intervention measures were exercise therapy, including resistance exercise, aerobic exercise, resistance combined aerobic exercise, Baduanjin, muscle relaxation training, etc. The control group was conventional nursing measures or the comparison of the above exercise therapy； Outcome indicators： The primary outcome indicator was fatigue score, and the secondary outcome indicator was sleep quality score； Exclusion criteria： Literature using non-exercise intervention; Non-Chinese and English documents; Unable to obtain the full text or repeated publication of literature; The data cannot be extracted or the extraction is incomplete; There are serious defects in the design of the research experiment.

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Balza, Lenin H., Camilo De Los Rios, Alfredo Guerra, Luis Herrera-Prada e Osmel Manzano. Unraveling the Network of the Extractive Industries. Inter-American Development Bank, aprile 2021. http://dx.doi.org/10.18235/0003191.

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This paper analyzes extractive industries in Colombia and their connections to other economic activities in the country. We use detailed social security data on all formal employees to create an industry-relatedness measure using labor flows between industries. Drawing on the vast network analysis literature, we exploit centrality measures to reveal the importance of the extractive sector among Colombian industries. Our results show that extractive industries are well connected within the Colombian industrial network, and that they are central overall and within their clusters. We also find that extractive industries have stronger linkages with manufacturing and agriculture than with other sectors. Finally, a higher relatedness to extractive activities is correlated with lower levels of employment, specially of female workers.

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Weischedel, Ralph. Extracting Dynamic Evidence Networks. Fort Belvoir, VA: Defense Technical Information Center, dicembre 2004. http://dx.doi.org/10.21236/ada429898.

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