Augmented Framework Research Articles

IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis

IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis

Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.

Generative data augmentation by conditional inpainting for multi-class object detection in infrared images

Multi-class object detection in infrared images is important in military and civilian use. Deep learning methods can obtain high accuracy but require a large-scale dataset. We propose a generative data augmentation framework DOCI-GAN, for infrared multi-class object detection with limited data. Contributions of this paper are four-folds. Firstly, DOCI-GAN is designed as a conditional image inpainting framework, yielding paired infrared multi-class object image and annotation. Secondly, a text-to-image converter is formulated to transform text-format object annotations to bounding box mask images, leading the augmentation to be mask-image-to-raw-image translation. Thirdly, a multiscale morphological erosion-based loss is created to alleviate the intensity inconsistency between inpainted local backgrounds and global background. Finally, for generating diverse images, artificial multi-class object annotations are integrated with real ones during augmentation. Experimental results demonstrated that DOCI-GAN augments dataset with high-quality infrared multi-class object images, consequently improving the accuracy of object detection baselines.

Remove First, Detect Later: A Counter-intuitive Approach For Detecting Radio Frequency Interference in Radio Sky Imagery

Abstract The modern era has witnessed a rapid uptake of technological use - from air travel to mobile cellphones. Technological advancement has however come at the cost of radio spectrum crowding and as such the efficient detection of radio frequency interference (RFI) from radio sky images has become more paramount. Detecting RFI is a complex task that blends semantic segmentation and anomaly detection, further complicated by the limited availability of public datasets with accurate ground truth labels. Recent studies show that deep learning models improve RFI detection compared to current state-of-the-art tools. However, many astronomers are hesitant to adopt these models, possibly due to the dependence of these models on noisy labels from existing tools when accurate ground truth labels are largely unavailable in the public domain. This study argues that utilizing large weakly labelled training datasets yields lower performance than appropriately employing a modest set of expertly annotated samples. Further, RFDL (Remove First Detect Later), an augmented deep learning framework, is proposed. By first, counter-intuitively, removing RFI with inpainting, RFDL feeds the difference between the original and inpainted images into existing detection models. RFDL’s performance is benchmarked against current state-of-the-art deep learning methods and the prevelant AOFlagger pipeline, using AUROC, AUPRC, and F1 score metrics. It is shown that RFDL significantly outperforms the state-of-the-art whilst only necessitating the use of 20 expertly labelled images.

A meta-contrastive learning with data augmentation framework for zero-shot stance detection

Zero-shot stance detection (ZSSD) identifies the stances of targets that have not been encountered during the testing phase. Most of the existing efforts are dedicated to enhancing the generalizability of models while ignoring data issues such as data scarcity and targets that are not explicitly mentioned. Therefore, we consider approaching this task from both the data and model perspectives and propose a meta-contrastive learning with data augmentation framework. We first use a generation model to generate target keyphrases for enhancing the original text. Then, we utilize a meta-learning technique that incorporates contrastive learning for improving the generalizability of the model, enabling it to better adapt to unknown targets. The experimental results obtained on three benchmark datasets prove that our framework achieves outstanding performance improvements in the ZSSD task. Our code is available at https://github.com/qifen37/MCLDA.

Peering Through the Fog of Uncertainty: Out-of-Sample Forecasts of Post-Pandemic Tourism

Abstract This paper uses an augmented gravity model framework to investigate the historical impact of infectious diseases on international tourism and develops an out-of-sample prediction model. Using bilateral tourism flows among 38,184 pairs of countries during the period 1995–2017, I compare the forecasting performance of alternative specifications and estimation methods. These computations confirm the statistical and economic significance of infectious-disease episodes in forecasting international tourism flows. Including infectious diseases in the model brings a significant improvement in forecast accuracy relative to the standard gravity model. The magnitude of these effects, however, is likely to be much greater in the case of COVID-19, which is a highly contagious virus that has spread fast throughout populations across the world.

A Unified Search Framework for Data Augmentation and Neural Architecture on Small-Scale Image Data Sets

Data augmentation is an effective technique to enrich the training data’s diversity and reduce the risk of overfitting. However, different datasets have distinct preferences on various augmentation techniques. Recently, automated data augmentation (auto-augmentation), which could engineer augmentation policy automatically, drew a growing interest. Previous auto-augmentation methods usually utilize a Density Matching strategy, which highly depends on a large data scale to ensure a precise policy evaluation. When facing small-scale datasets, it usually achieves an inferior performance. To address the problem, an improved method named Augmented Density Matching is proposed by augmenting the train data with policies uniformly sampled from a prior distribution, making the policy evaluation more precise. Moreover, we propose a unified search framework for data augmentation and neural architecture (USAA) by formulating the search processes with one formulation. As a result, both optimal augmentation policy and neural architecture could be obtained within one round of search process. Extensive experiments have been conducted on a bag of medical datasets with general small scales, and the results show that the proposed Augmented Density Matching and USAA can outperform the state-of-the-art auto-augmentation and AutoML approaches, respectively.

A PEM-based augmented IBDR framework and its evaluation in contemporary distribution systems

Demand response (DR) is an attractive concept that invites customers’ active participation in the distribution sector by means of price elasticity of demand (PED). It not only enhances customers’ demand sensitivity but also improves technicalities and economics related to both the utility and demand sides. This paper emphasizes the combined effect of price-based DR (PBDR) and incentive-based DR (IBDR) with the inclusion of PED. The elasticity phenomenon, when applied with incentives as in IBDR, changes the demand-consumption pattern as compared to individual DR. Moreover, the demand variation due to only incentives leads to incentive elasticity, which needs to be studied carefully; then only the impact of individual DR and augmented DR (PBDR and IBDR combined) can be understood analytically. In this work, IBDR models are tested on considered pricing schemes along with a new proposed pricing scheme to evaluate the systems’ technical and economical parameters. A standard IEEE 33 bus distribution system has been chosen for the assessment of suggested models and to compare them to the existing ones. Furthermore, these models are descriptively evaluated from both the utility and consumer perspectives.

The distance decay effect and spatial reach of spillovers

This paper quantifies and graphically illustrates the distance decay effect and spatial reach of spillover effects derived from a spatial Durbin (SD) model with parameterized spatial weight matrices. Building on attributes of the concept of spatial autocorrelation developed by Arthur Getis, we adopt a distance-based negative exponential spatial weight matrix and parameterize it by a decay parameter that is different for each spatial lag in this model, both of the regressand and of all regressors. The quantification and illustration are applied to the spatially augmented neoclassical growth framework, which we estimate using data for 266 NUTS-2 regions in the EU over the period 2000–2018. We find distance decay parameters ranging from 0.233 to 2.224 and spatial reaches ranging from 700 to more than 1500 km for the different growth determinants in this model. These wide ranges highlight the restrictiveness of the conventional SD model based on one common spatial weight matrix for all spatial lags.

A distributed collaborative platform for multi-stakeholder multi-level management of renovation projects

To meet European energy-saving and decarbonisation goals, the annual renovation rate of buildings requires to be at least twice its current level; an aspiration to which the use of innovative and automated solutions can contribute. This paper presents such a solution, the RINNO Retrofitting Manager (RRM) which is part of a large, ambitious research and development project (RINNO) that aims to provide an augmented intelligence-enabled framework for deep, energy-focused retrofitting of buildings. The RRM uses web-service technologies to rationalise the retrofitting process and optimise the delivery of renovation works, while making data readily accessible through an integrated set of role-based user interfaces. The RRM is designed and developed as an open distributed system, that is extensible and portable, by implementing a collaborative research and development approach. The RRM platform implements a multi-level, multi-stakeholder planning approach. It addresses the dearth, insufficiency, and isolation of existing renovation tools by enhancing collaboration, interoperability, and data security, and avoiding information loss and misunderstanding. Employing the Unified Theory of Acceptance and Use of Technology (UTAUT) model, tests conducted with users from independent construction organisations confirmed the RRM's satisfactory performance, ease of deployment, and overall suitability for the management of renovation projects. While this research provides a free collaborative platform for managing renovation projects that can be used by all building retrofit stakeholders in Europe, it also introduces a set of web-services that can be easily reused by third-party developers and integrated into their software tools.

Spatial-Temporal Augmentation for Crime Prediction (Student Abstract)

Crime prediction stands as a pivotal concern within the realm of urban management due to its potential threats to public safety. While prior research has predominantly focused on unraveling the intricate dependencies among urban regions and temporal dynamics, the challenges posed by the scarcity and uncertainty of historical crime data have not been thoroughly investigated. This study introduces an innovative spatial-temporal augmented learning framework for crime prediction, namely STAug. In STAug, we devise a CrimeMix to improve the ability of generalization. Furthermore, we harness a spatial-temporal aggregation to capture and incorporate multiple correlations covering the temporal, spatial, and crime-type aspects. Experiments on two real-world datasets underscore the superiority of STAug over several baselines.

Semantic-Aware Data Augmentation for Text-to-Image Synthesis

Data augmentation has been recently leveraged as an effective regularizer in various vision-language deep neural networks. However, in text-to-image synthesis (T2Isyn), current augmentation wisdom still suffers from the semantic mismatch between augmented paired data. Even worse, semantic collapse may occur when generated images are less semantically constrained. In this paper, we develop a novel Semantic-aware Data Augmentation (SADA) framework dedicated to T2Isyn. In particular, we propose to augment texts in the semantic space via an Implicit Textual Semantic Preserving Augmentation, in conjunction with a specifically designed Image Semantic Regularization Loss as Generated Image Semantic Conservation, to cope well with semantic mismatch and collapse. As one major contribution, we theoretically show that Implicit Textual Semantic Preserving Augmentation can certify better text-image consistency while Image Semantic Regularization Loss regularizing the semantics of generated images would avoid semantic collapse and enhance image quality. Extensive experiments validate that SADA enhances text-image consistency and improves image quality significantly in T2Isyn models across various backbones. Especially, incorporating SADA during the tuning process of Stable Diffusion models also yields performance improvements.

Proportional Representation in Metric Spaces and Low-Distortion Committee Selection

We introduce a novel definition for a small set R of k points being "representative" of a larger set in a metric space. Given a set V (e.g., documents or voters) to represent, and a set C of possible representatives, our criterion requires that for any subset S comprising a theta fraction of V, the average distance of S to their best theta*k points in R should not be more than a factor gamma compared to their average distance to the best theta*k points among all of C. This definition is a strengthening of proportional fairness and core fairness, but - different from those notions - requires that large cohesive clusters be represented proportionally to their size. Since there are instances for which - unless gamma is polynomially large - no solutions exist, we study this notion in a resource augmentation framework, implicitly stating the constraints for a set R of size k as though its size were only k/alpha, for alpha > 1. Furthermore, motivated by the application to elections, we mostly focus on the "ordinal" model, where the algorithm does not learn the actual distances; instead, it learns only for each point v in V and each candidate pairs c, c' which of c, c' is closer to v. Our main result is that the Expanding Approvals Rule (EAR) of Aziz and Lee is (alpha, gamma) representative with gamma

Deep learning in the classification and recognition of cardiac activity patterns

Electrocardiography is an examination performed frequently in patients experiencing symptoms of heart disease. Upon a detailed analysis, it has shown potential to detect and identify various activities. In this article, we present a deep learning approach that can be used to analyze ECG signals. Our research shows promising results in recognizing activity and disease patterns with nearly 90% accuracy. In this paper, we present the early results of our analysis, indicating the potential of using deep learning algorithms in the analysis of both onedimensional and two–dimensional data. The methodology we present can be utilized for ECG data classification and can be extended to wearable devices. Conclusions of our study pave the way for exploring live data analysis through wearable devices in order to not only predict specific cardiac conditions, but also a possibility of using them in alternative and augmented communication frameworks.

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Sources of Algeria's economic growth, 1979–2019: Augmented growth accounting framework and growth regression method

This paper examines the sources of economic growth in Algeria, studying the key drivers of their slow and weak economic performance, during the period of 1979–2019 from the perspective of the augmented growth accounting framework and the growth regression method. More specifically, the paper uses the augmented growth accounting framework to decompose the aggregate growth of an individual economy to determine the contribution of various inputs and the total factor productivity. The growth regression method, then, links the growth performance of a given country to a set of economic, social, and institutional variables to identify the deep drivers of its economic growth. Results of the accounting framework show that Algeria's growth performance has been weak and modest for decades. This is due, mainly, to labor growth with weak gain in capital accumulation and to sharp losses in the total factor productivity growth. Furthermore, results of the econometrics study stress the importance of the political, institutional, and economic variables and the macroeconomic stability as potential factors that may affect growth in Algeria. These findings highlight the need for policymakers to identify and to target these determinants to improve the long‐run economic growth in Algeria.

Rethinking Cost-Sensitive Classification in Deep Learning via Adversarial Data Augmentation

Cost-sensitive classification is critical in applications where misclassification errors widely vary in cost. However, overparameterization poses fundamental challenges to the cost-sensitive modeling of deep neural networks (DNNs). The ability of a DNN to fully interpolate a training data set can render a DNN, evaluated purely on the training set, ineffective in distinguishing a cost-sensitive solution from its overall accuracy maximization counterpart. This necessitates rethinking cost-sensitive classification in DNNs. To address this challenge, this paper proposes a cost-sensitive adversarial data augmentation (CSADA) framework to make overparameterized models cost sensitive. The overarching idea is to generate targeted adversarial examples that push the decision boundary in cost-aware directions. These targeted adversarial samples are generated by maximizing the probability of critical misclassifications and used to train a model with more conservative decisions on costly pairs. Experiments on well-known data sets and a pharmacy medication image (PMI) data set, made publicly available, show that our method can effectively minimize the overall cost and reduce critical errors while achieving comparable overall accuracy. Funding: Research reported in this publication was supported by the National Library of medicine of the National Institutes of Health in the United States under award number R01LM013624. Data Ethics & Reproducibility Note: This paper abides by data ethics requirements. Data used are publicly available online at https://deepblue.lib.umich.edu/data/concern/data_sets/6d56zw997 . Codes to replicate the results of this paper are available at https://doi.org/10.24433/CO.2139841.v1 .

Capabilities Comparison Of The Augmented Reality Application Development Frameworks On The Android Platform

In the implementation of augmented reality, the natural process of human-computer interaction continues to be a challenge, specifically to reduce the complexity in the effort of using and providing comfortability. Therefore, it is necessary to search for methods and tools to simplify the complex process of building Augmented Reality applications. Vuforia is a multi-platform that has long been used as an Augmented Reality application development framework. Android and iOS platform frameworks are the new alternatives following the development of cellular technology. Furthermore, a comparative capability was conducted between Vuforia and ARCore on the Android platform. The general performance and the ability to understand the environment include working in horizontal and vertical planes, the ability to work based on lighting conditions, and the distance of the camera during the tests. The results showed that ARCore is superior to Vuforia in almost all testing metrics. However, in overexposed or too dim lighting and at very close surfaces, Vuforia is slightly superior but not essential. Therefore, it can be concluded that ARCore's capability is better than Vuforia.

MaGNIFIES: Manageable GAN Image Augmentation Framework for Inspection of Electronic Systems

MaGNIFIES: Manageable GAN Image Augmentation Framework for Inspection of Electronic Systems

Rationalizing Graph Neural Networks with Data Augmentation

Graph rationales are representative subgraph structures that best explain and support the graph neural network (GNN) predictions. Graph rationalization involves the joint identification of these subgraphs during GNN training, resulting in improved interpretability and generalization. GNN is widely used for node-level tasks such as paper classification and graph-level tasks such as molecular property prediction. However, on both levels, little attention has been given to GNN rationalization and the lack of training examples makes it difficult to identify the optimal graph rationales. In this work, we address the problem by proposing a unified data augmentation framework with two novel operations on environment subgraphs to rationalize GNN prediction. We define the environment subgraph as the remaining subgraph after rationale identification and separation. The framework efficiently performs rationale–environment separation in the representation space for a node’s neighborhood graph or a graph’s complete structure to avoid the high complexity of explicit graph decoding and encoding. We conduct experiments on 17 datasets spanning node classification, graph classification, and graph regression. Results demonstrate that our framework is effective and efficient in rationalizing and enhancing GNNs for different levels of tasks on graphs.