High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structure...
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Although deep learning models for abnormality classification can perform well in screening mammography, the demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unc...
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data visuals (scientific images) display and express various amounts and types of information, and, as the saying goes,“an image is worth 1,000 words.” Based on a review of two studies, a new estimation of how many ...
data visuals (scientific images) display and express various amounts and types of information, and, as the saying goes,“an image is worth 1,000 words.” Based on a review of two studies, a new estimation of how many words an image is actually worth was calculated in an attempt to quantify the complicated biological process of image perception. The result revealed that an image is actually worth more than 30,000 words. This new value estimation provides insight into the power of images. Given that figures, graphs, and data visualizations are types of images commonly used in research and publications, every produced figure is important and must be carefully considered during the publication process.
Forecasting time series that are generated in dynamic environments is challenging due to the characteristic of these series: data generated at high speed and/or large amounts of data which contain multiple variables, ...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
Forecasting time series that are generated in dynamic environments is challenging due to the characteristic of these series: data generated at high speed and/or large amounts of data which contain multiple variables, and have complex seasonality. Forecasting real-time availability of parking spaces can lead to all sorts of benefits including user satisfaction, energy savings and more efficient use of parking spaces. In this paper, we explorer using deep Gated Recurrent Units (GRUs) to forecast multivariate time series in parking lots setting. Predicting the parking availability mainly depends on the multiple seasonal patterns exhibited in the parking lots and by the real-time occupancy data. In particular, we use the heterogeneous data streams that generated from a limited number of existing Internet of Things (IoT) devices which monitor different parking lots, to construct the parking availability data. Then, we employ GRUs on the parking data to provide predictions across a variety of parking lots. Using the proposed approach, we have provided the real-time parking availability information for different time intervals in six parking lots at University of Essex. Although the results indicate the efficiency of applying GRUs to forecast the multivariate time series, these results show that the simple Multilayer Perceptron (MLPs) can perform better than the GRUs models.
In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. ...
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In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream.
Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with pr...
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ISBN:
(纸本)9781665429825
Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings.
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of te...
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ISBN:
(数字)9781728171685
ISBN:
(纸本)9781728171692
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.
The use of short text has become widespread in social media like Twitter and Facebook. Typically, users on social media platforms adopt nonstandard format terms when posting. This introduces challenges for Information...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
The use of short text has become widespread in social media like Twitter and Facebook. Typically, users on social media platforms adopt nonstandard format terms when posting. This introduces challenges for Information Retrieval (IR) and Natural Language Processing (NLP) and standard or classical methods tend not to perform well in this domain. In this paper, we have addressed one of the challenges in IR which is Named Entity Recognition (NER). We introduce a novel probabilistic approach which targets entities occurring in an informal (nonstandard) format within short text. The Probabilistic Named Entity Recognition (PNER) model identifies these entities using cooccurrence patterns. These patterns have been detected using the word cooccurrence embeddings of 278.6 million tweets. The results show an enhancement of 7% on two standard methods when used in combination with PNER. The testing dataset has been created using the standard methods in addition to street names and places taken from the Open Street Map (OSM) database.
In the recent past, many human rights organizations have started using social media to identify, collect and document human rights violations. To manually extract relevant data from the large corpus of this social net...
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ISBN:
(数字)9781728108582
ISBN:
(纸本)9781728108599
In the recent past, many human rights organizations have started using social media to identify, collect and document human rights violations. To manually extract relevant data from the large corpus of this social network data is difficult and time-consuming and expensive. Furthermore, with the advent of technology, the context and significance of the human rights abuses has and will change over time and advice from experts is needed to perform any kind quantitative analysis on this data. There are applications and systems that help structure this data into relevant categories, but detecting underlying latent patterns, finding similar annotated patterns and continuously upgrading the system to perform exploratory analysis requires high maintenance and cost. This paper proposes a solution to address this problem by integrating semi-supervised learning (with Matrix Factorization) and similarity measures algorithms to classify the large unstructured corpus into stories that have been labelled with one or more types of human rights abuses. In the last few decades, recommender systems have come across as powerful machine learning tools to infer from data and provide value-added content. Along the same context, semi-supervised algorithms mitigate situations where there is a relatively small labelled training data, but a large unlabeled data-set. This paper tries to combine both these algorithms to discover patterns in unlabeled victim survivor stories and recommends labels from other similar stories, thus updating the initial labelled set. The efficiency of the algorithm is evaluated using state of art evaluation metrics. Experimental results show a correlation between new and labelled stories. Real-world results show that the algorithm outplays some of in house recommendation algorithms.
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