scholarly journals Interpretable Rumor Detection in Microblogs by Attending to User Interactions

2020 ◽  
Vol 34 (05) ◽  
pp. 8783-8790 ◽  
Author(s):  
Ling Min Serena Khoo ◽  
Hai Leong Chieu ◽  
Zhong Qian ◽  
Jing Jiang
Keyword(s):  
State Of The Art ◽  
Attention Mechanism ◽  
Data Sets ◽  
Long Distance ◽  
User Interactions ◽  
Data Set ◽  
Structure Information ◽  
Attention Model ◽  
Tree Models ◽  
Rumor Detection

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.

scholarly journals A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension

2020 ◽  
Vol 17 (4) ◽  
pp. 1323 ◽  
Author(s):  
Bin Wang ◽  
Xuejie Zhang ◽  
Xiaobing Zhou ◽  
Junyi Li
Keyword(s):  
Reading Comprehension ◽  
Clinical Medicine ◽  
State Of The Art ◽  
Practical Application ◽  
Medical Field ◽  
Long Distance ◽  
Data Set ◽  
Attention Model ◽  
Dilated Convolution ◽  
Machine Reading

The machine comprehension research of clinical medicine has great potential value in practical application, but it has not received sufficient attention and many existing models are very time consuming for the cloze-style machine reading comprehension. In this paper, we study the cloze-style machine reading comprehension in the clinical medical field and propose a Gated Dilated Convolution with Attention (GDCA) model, which consists of a gated dilated convolution module and an attention mechanism. Our model has high parallelism and is capable of capturing long-distance dependencies. On the CliCR data set, our model surpasses the present best model on several metrics and obtains state-of-the-art result, and the training speed is 8 times faster than that of the best model.

SANTM: Efficient Self-attention-driven Network for Text Matching

2022 ◽  
Vol 22 (3) ◽  
pp. 1-21
Author(s):  
Prayag Tiwari ◽  
Amit Kumar Jaiswal ◽  
Sahil Garg ◽  
Ilsun You
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
The State ◽  
Attention Mechanism ◽  
Matching Problems ◽  
Attention Model ◽  
Extra Information ◽  
Textual Entailment ◽  
Benchmark Datasets ◽  
Text Matching

Self-attention mechanisms have recently been embraced for a broad range of text-matching applications. Self-attention model takes only one sentence as an input with no extra information, i.e., one can utilize the final hidden state or pooling. However, text-matching problems can be interpreted either in symmetrical or asymmetrical scopes. For instance, paraphrase detection is an asymmetrical task, while textual entailment classification and question-answer matching are considered asymmetrical tasks. In this article, we leverage attractive properties of self-attention mechanism and proposes an attention-based network that incorporates three key components for inter-sequence attention: global pointwise features, preceding attentive features, and contextual features while updating the rest of the components. Our model follows evaluation on two benchmark datasets cover tasks of textual entailment and question-answer matching. The proposed efficient Self-attention-driven Network for Text Matching outperforms the state of the art on the Stanford Natural Language Inference and WikiQA datasets with much fewer parameters.

Limitations on digital filtering of the DNAG magnetic data set for the conterminous U.S.

Geophysics ◽  
1993 ◽  
Vol 58 (9) ◽  
pp. 1281-1296 ◽  
Author(s):  
V. J. S. Grauch
Keyword(s):  
Spatial Frequency ◽  
North American ◽  
Digital Filtering ◽  
Magnetic Data ◽  
Data Sets ◽  
Frequency Content ◽  
Long Distance ◽  
High Quality ◽  
Data Set ◽  
The U.S

The magnetic data set compiled for the Decade of North American Geology (DNAG) project presents an important digital data base that can be used to examine the North American crust. The data represent a patchwork from many individual airborne and marine magnetic surveys. However, the portion of data for the conterminous U.S. has problems that limit the resolution and use of the data. Now that the data are available in digital form, it is important to describe the data limitations more specifically than before. The primary problem is caused by datum shifts between individual survey boundaries. In the western U.S., the DNAG data are generally shifted less than 100 nT. In the eastern U.S., the DNAG data may be shifted by as much as 300 nT and contain regionally shifted areas with wavelengths on the order of 800 to 1400 km. The worst case is the artificial low centered over Kentucky and Tennessee produced by a series of datum shifts. A second significant problem is lack of anomaly resolution that arises primarily from using survey data that is too widely spaced compared to the flight heights above magnetic sources. Unfortunately, these are the only data available for much of the U.S. Another problem is produced by the lack of common observation surface between individual pieces of the U.S. DNAG data. The height disparities introduce variations in spatial frequency content that are unrelated to the magnetization of rocks. The spectral effects of datum shifts and the variation of spatial frequency content due to height disparities were estimated for the DNAG data for the conterminous U.S. As a general guideline for digital filtering, the most reliable features in the U.S. DNAG data have wavelengths roughly between 170 and 500 km, or anomaly half‐widths between 85 and 250 km. High‐quality, large‐region magnetic data sets have become increasingly important to meet exploration and scientific objectives. The acquisition of a new national magnetic data set with higher quality at a greater range of wavelengths is clearly in order. The best approach is to refly much of the U.S. with common specifications and reduction procedures. At the very least, magnetic data sets should be remerged digitally using available or newly flown long‐distance flight‐line data to adjust survey levels. In any case, national coordination is required to produce a consistent, high‐quality national magnetic map.

scholarly journals A review of forecasting techniques for large datasets

2008 ◽  
Vol 203 ◽  
pp. 109-115 ◽  
Author(s):  
Jana Eklund ◽  
George Kapetanios
Keyword(s):  
State Of The Art ◽  
Large Data ◽  
Large Datasets ◽  
Large Data Sets ◽  
Multiple Models ◽  
Small Subset ◽  
Data Sets ◽  
Single Model ◽  
Data Set ◽  
Forecasting Techniques

This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a small subset or the whole data set is being forecast. In particular, we provide brief descriptions of the methods and short recommendations where appropriate, without going into detailed discussions of their merits or demerits.

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

scholarly journals Constructing a Lightweight Key-Value Store Based on the Windows Native Features

2019 ◽  
Vol 9 (18) ◽  
pp. 3801 ◽  
Author(s):  
Hyuk-Yoon Kwon
Keyword(s):  
State Of The Art ◽  
Main Idea ◽  
Real Data ◽  
Data Sets ◽  
Parameter Setting ◽  
Data Set ◽  
Multi Level ◽  
Windows Registry ◽  
Best Parameter ◽  
Better Than

In this paper, we propose a method to construct a lightweight key-value store based on the Windows native features. The main idea is providing a thin wrapper for the key-value store on top of a built-in storage in Windows, called Windows registry. First, we define a mapping of the components in the key-value store onto the components in the Windows registry. Then, we present a hash-based multi-level registry index so as to distribute the key-value data balanced and to efficiently access them. Third, we implement basic operations of the key-value store (i.e., Get, Put, and Delete) by manipulating the Windows registry using the Windows native APIs. We call the proposed key-value store WR-Store. Finally, we propose an efficient ETL (Extract-Transform-Load) method to migrate data stored in WR-Store into any other environments that support existing key-value stores. Because the performance of the Windows registry has not been studied much, we perform the empirical study to understand the characteristics of WR-Store, and then, tune the performance of WR-Store to find the best parameter setting. Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. That is, WR-Store becomes much more efficient than the other key-value stores as the size of data set increases. In addition, we show that the performance of WR-Store is maintained even in the case of intensive registry workloads where 1000 processes accessing to the registry actively are concurrently running.

scholarly journals Supervised and Unsupervised Neural Approaches to Text Readability

2021 ◽  
Vol 47 (1) ◽  
pp. 141-179
Author(s):  
Matej Martinc ◽  
Senja Pollak ◽  
Marko Robnik-Šikonja
Keyword(s):  
State Of The Art ◽  
Comprehensive Analysis ◽  
Language Models ◽  
Feature Engineering ◽  
Data Sets ◽  
Data Set ◽  
Systematic Comparison ◽  
Current State ◽  
Text Readability ◽  
Unsupervised Approach

Abstract We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.

scholarly journals Diabetic Retinopathy detection through integration of Deep Learning classification framework

2017 ◽  
Author(s):  
Alexander Rakhlin
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
State Of The Art ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Diabetic Retinopathy Screening ◽  
Classification Framework ◽  
Ongoing Project ◽  
Retinopathy Screening

AbstractThis document represents a brief account of ongoing project for Diabetic Retinopathy Detection (DRD) through integration of state-of the art Deep Learning methods. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in multiple fields of computer vision including medical imaging, and we bring their power to the diagnosis of eye fundus images. For training our models we used publicly available Kaggle data set. For testing we used portion of Kaggle data withheld from training and Messidor-2 reference standard. Neither withheld Kaggle images, nor Messidor-2 were used for training. For Messidor-2 we achieved sensitivity 99%, specificity 71%, and AUC 0.97. These results close to recent state-of-the-art models trained on much larger data sets and surpass average results of diabetic retinopathy screening when performed by trained optometrists. With continuous development of our Deep Learning models we expect to further increase the accuracy of the method and expand it to cataract and glaucoma diagnostics.

scholarly journals Graph Contextualized Self-Attention Network for Session-based Recommendation

2019 ◽  
Author(s):  
Chengfeng Xu ◽  
Pengpeng Zhao ◽  
Yanchi Liu ◽  
Victor S. Sheng ◽  
Jiajie Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Attention Mechanism ◽  
The Self ◽  
Graph Structure ◽  
Convolutional Network ◽  
Attention Network ◽  
Attention Model ◽  
Sequence Modeling ◽  
Real World Datasets

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming).  Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences.  In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN).  Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.

scholarly journals Outlier Mining in High Throughput Screening Experiments

2002 ◽  
Vol 7 (4) ◽  
pp. 341-351 ◽  
Author(s):  
Michael F.M. Engels ◽  
Luc Wouters ◽  
Rudi Verbeeck ◽  
Greet Vanhoof
Keyword(s):  
Data Mining ◽  
Biological Activity ◽  
High Throughput ◽  
High Throughput Screening ◽  
False Negative ◽  
Data Sets ◽  
Data Set ◽  
Structure Information ◽  
Screening Experiments ◽  
Mining Procedure

A data mining procedure for the rapid scoring of high-throughput screening (HTS) compounds is presented. The method is particularly useful for monitoring the quality of HTS data and tracking outliers in automated pharmaceutical or agrochemical screening, thus providing more complete and thorough structure-activity relationship (SAR) information. The method is based on the utilization of the assumed relationship between the structure of the screened compounds and the biological activity on a given screen expressed on a binary scale. By means of a data mining method, a SAR description of the data is developed that assigns probabilities of being a hit to each compound of the screen. Then, an inconsistency score expressing the degree of deviation between the adequacy of the SAR description and the actual biological activity is computed. The inconsistency score enables the identification of potential outliers that can be primed for validation experiments. The approach is particularly useful for detecting false-negative outliers and for identifying SAR-compliant hit/nonhit borderline compounds, both of which are classes of compounds that can contribute substantially to the development and understanding of robust SARs. In a first implementation of the method, one- and two-dimensional descriptors are used for encoding molecular structure information and logistic regression for calculating hits/nonhits probability scores. The approach was validated on three data sets, the first one from a publicly available screening data set and the second and third from in-house HTS screening campaigns. Because of its simplicity, robustness, and accuracy, the procedure is suitable for automation.

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