scholarly journals Multiview Active Learning for Scene Classification with High-Level Semantic-Based Hypothesis Generation

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Tuozhong Yao ◽  
Wenfeng Wang ◽  
Yuhong Gu ◽  
Qiuguo Zhu

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms.

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Tuozhong Yao ◽  
Wenfeng Wang ◽  
Yuhong Gu

Multiview active learning (MAL) is a technique which can achieve a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. In this paper, we present a new deep multiview active learning (DMAL) framework which is the first to combine multiview active learning and deep learning for annotation effort reduction. In this framework, our approach advances the existing active learning methods in two aspects. First, we incorporate two different deep convolutional neural networks into active learning which uses multiview complementary information to improve the feature learnings. Second, through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. The experiments with two challenging image datasets demonstrate that our proposed DMAL algorithm can achieve promising results than several state-of-the-art active learning algorithms.


2021 ◽  
Vol 4 (1) ◽  
pp. 3-14
Author(s):  
Zdzislaw Polkowski ◽  
◽  
Sambit Kumar Mishra ◽  

In a general scenario, the approaches linked to the innovation of large-scaled data seem ordinary; the informational measures of such aspects can differ based on the applications as these are associated with different attributes that may support high data volumes high data quality. Accordingly, the challenges can be identified with an assurance of high-level protection and data transformation with enhanced operation quality. Based on large-scale data applications in different virtual servers, it is clear that the information can be measured by enlisting the sources linked to sensors networked and provisioned by the analysts. Therefore, it is very much essential to track the relevance and issues with enormous information. While aiming towards knowledge extraction, applying large-scaled data may involve the analytical aspects to predict future events. Accordingly, the soft computing approach can be implemented in such cases to carry out the analysis. During the analysis of large-scale data, it is essential to abide by the rules associated with security measures because preserving sensitive information is the biggest challenge while dealing with large-scale data. As high risk is observed in such data analysis, security measures can be enhanced by having provisioned with authentication and authorization. Indeed, the major obstacles linked to the techniques while analyzing the data are prohibited during security and scalability. The integral methods towards application on data possess a better impact on scalability. It is observed that the faster scaling factor of data on the processor embeds some processing elements to the system. Therefore, it is required to address the challenges linked to processors correlating with process visualization and scalability.


Author(s):  
P. Baumann ◽  
V. Merticariu ◽  
A. Dumitru ◽  
D. Misev

With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights &ndash; yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets &ndash; ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". <br><br> With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.


Concussion ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. CNC76 ◽  
Author(s):  
James Mooney ◽  
Mitchell Self ◽  
Karim ReFaey ◽  
Galal Elsayed ◽  
Gustavo Chagoya ◽  
...  

Sports-related concussion has been examined extensively in collision sports such as football and hockey. However, historically, lower-risk contact sports such as soccer have only more recently garnered increased attention. Here, we review articles examining the epidemiology, injury mechanisms, sex differences, as well as the neurochemical, neurostructural and neurocognitive changes associated with soccer-related concussion. From 436 titles and abstracts, 121 full texts were reviewed with a total of 64 articles identified for inclusion. Concussion rates are higher during competitions and in female athletes with purposeful heading rarely resulting in concussion. Given a lack of high-level studies examining sports-related concussion in soccer, clinicians and scientists must focus research efforts on large-scale data gathering and development of improved technologies to better detect and understand concussion.


Author(s):  
P. Baumann ◽  
V. Merticariu ◽  
A. Dumitru ◽  
D. Misev

With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights &ndash; yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets &ndash; ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". &lt;br&gt;&lt;br&gt; With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.


2006 ◽  
Vol 361 (1467) ◽  
pp. 519-523 ◽  
Author(s):  
Konrad U Foerstner ◽  
Christian von Mering ◽  
Peer Bork

Environmental sequencing, also dubbed metagenomics, is increasingly being used to obtain insights into organismal communities in diverse habitats, and has a variety of potential applications foreseeable in biotechnology and medicine. The first public large-scale data provide already a wealth of information hidden in vast amounts of fragmented pieces of DNA from unknown species residing in these environments. Comparative sequence analysis is essential for the interpretation of such data. However, different layers of complexity that are intrinsic to each sample require the establishment of some baselines for comparison: how to normalize for the differences in phylogenetic and functional diversity, how to avoid biases from incomplete data, and how to deal with differences in species dominance or genome sizes? Here we discuss a few of these items and delineate some simple discriminative sequence properties for four distinct habitats.


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