Reviewerly/exHarmony huggingface dataset:
A spatial-phase-shift-based defect detection shearography system with independent adjustment of shear amount and spatial carrier frequency

Digital shearography has a wide application in defect detection. Phase-shift-technique-based shearography has a higher phase measurement sensitivity than the real-time shearography. This phase shift technique can be divided into temporal phase shift shearography and spatial phase shift shearography. The latter has the advantage of fast detection and can be used in dynamic detection. In a traditional spatial carrier frequency shearography system, shear amount adjustments and spatial carrier frequency are not completely independent. In some shearography systems, shear amount and spatial carrier frequency are adjusted by rotating mirrors. The amounts of rotation required to obtain the appropriate shear amount and spatial carrier frequency are different, thereby reducing the accuracy of detection results. In other shearography systems, although the two adjustments are divided into two parts, using a rotating mirror to obtain the appropriate shear amount will also introduce additional spatial carrier frequency which will also have some influence on the result. This paper introduces a detection method with independently adjusting shear amount and spatial carrier frequency. Shear amount adjustment is achieved by changing the written image of a spatial light modulator (SLM). No additional spatial carrier frequency is introduced. Spatial carrier frequency is only controlled by the relative position between the dual apertures. Experimental results of undamaged and damaged specimens indicate that this system is suitable for detecting the phase distribution of the deformation and internal defects.
Literature Review of Automatic Text Summarization: Research Trend, Dataset and Method

Automatic text summarization can be defined as the process of presenting one or more text documents while maintaining the main information content using an automatic machine with no more than half the original text or less than the original text. Research in the field of text summarization began in the 1950s and until now there is no system that can produce summaries such as professionals or humans. This paper aims to identify and analyze methods, datasets and trends in automatic text summarization research from 2015 to 2019. The method used a systematic literature review (SLR) about automatic text summarization. The results obtained are that research on automatic text summarization is still relevant to date. The extractive approach is still in demand in the past three years because the extractive is easier than abstractive and the opportunity to combine methods is still open, for example using a neuro computing approach, namely the emergence of a new DQN method (Deep Q-Network) which shows comparable results and even better. The text summarization research trend has also undergone a slight change in the past three years where new things have emerged that are trends that are leading to optimization, how to optimize text summarization performance in order to get high accuracy.
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

The wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., medical) is largely hindered as the private nature of data prohibits it from being shared. To this end, we propose Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN), which allows releasing a sanitized form of the sensitive data with rigorous privacy guarantees. In contrast to prior work, our approach is able to distort gradient information more precisely, and thereby enabling training deeper models which generate more informative samples. Moreover, our formulation naturally allows for training GANs in both centralized and federated (i.e., decentralized) data scenarios. Through extensive experiments, we find our approach consistently outperforms state-of-the-art approaches across multiple metrics (e.g., sample quality) and datasets.
Core – An Optimal Data Placement Strategy in Hadoop for Data Intentitive Applications Based on Cohesion Relation

The tremendous growth of data being generated today is making storage and computing a mammoth task. With its distributed processing capability Hadoop gives an efficient solution for such large data. Hadoop’s default data placement strategy places the data blocks randomly across the nodes without considering the execution parameters resulting in several lacunas such as increased execution time, query latency etc., Also, most of the data required for a task execution may not be locally available which creates data-locality problem. Hence we propose an innovative data placement strategy based on dependency of data blocks across the nodes. Our strategy dynamically analyses the history log and establishes relationship between various tasks and blocks required for each task through Block Dependency Graph (BDG). Then Our CORE-Algorithm re-organizes the HDFS layout by redistributing the data blocks to give an optimal data placement, resulting in improved performance for Big Data sets in distributed environment. This strategy is tested in 20-node cluster with different real-world MR applications. The results conclude that proposed strategy reduces the query execution time by 23%, improves the data locality by 50.7%, compared to default.
C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds

Distributed dataflow systems enable data-parallel processing of large datasets on clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. Yet, selecting appropriate cloud resources for dataflow jobs - that neither lead to bottlenecks nor to low resource utilization - is often challenging, even for expert users such as data engineers. We present C3O, a collaborative system for optimizing data processing cluster configurations in public clouds based on shared historical runtime data. The shared data is utilized for predicting the runtimes of data processing jobs on different possible cluster configurations, using specialized regression models. These models take the diverse execution contexts of different users into account and exhibit mean absolute errors below 3% in our experimental evaluation with 930 unique Spark jobs.
Learning to Infer User Implicit Preference in Conversational Recommendation

Conversational recommender systems (CRS) enable traditional recommender systems to interact with users by asking questions about attributes and recommending items. The attribute-level and item-level feedback of users can be utilized to estimate users' preferences. However, existing works do not fully exploit the advantage of explicit item feedback --- they only use the item feedback in rather implicit ways such as updating the latent user and item representation. Since CRS has multiple chances to interact with users, leveraging the context in the conversation may help infer users' implicit feedback (e.g., some specific attributes) when recommendations get rejected. To address the limitations of existing methods, we propose a new CRS framework called Conversational Recommender with Implicit Feedback (CRIF). CRIF formulates the conversational recommendation scheme as a four-phase process consisting of offline representation learning, tracking, decision, and inference. In the inference module, by fully utilizing the relation between users' attribute-level and item-level feedback, our method can explicitly deduce users' implicit preferences. Therefore, CRIF is able to achieve more accurate user preference estimation. Besides, in the decision module, to better utilize the attribute-level and item-level feedback, we adopt inverse reinforcement learning to learn a flexible decision strategy that selects the suitable action at each conversation turn. Through extensive experiments on four benchmark CRS datasets, we validate the effectiveness of our approach, which significantly outperforms the state-of-the-art CRS methods.
Learning cascaded convolutional networks for blind single image super-resolution

This paper studies the blind super-resolution of real low-quality and low-resolution (LR) images. Existing convolutional network (CNN) based approaches usually learn a single image super-resolution (SISR) model for a specific downsampler (e.g., bicubic downsampling, blurring followed by downsampling). The learned model, however, is tailored to the specific downsampler and fails to super-resolve real LR images which are degraded in more sophisticated and diverse manners. Moreover, the ground-truth high-resolution (HR) of real LR images are generally unavailable. Instead of learning from unpaired real LR-HR images or a specific downsampler, this paper learns blind SR network from a realistic, parametric degradation model by considering blurring, noise, downsampling, and even JPEG compression. In contrast to direct blind reconstruction of HR image, the proposed model adopts a cascaded architecture for noise estimation, blurring estimation, and non-blind SR, which can be jointly end-to-end learned from training data and benefit generalization ability. By taking the bicubicly upscaled LR image as input to non-blind SR, the proposed method can present a single unified model for blind SR with any upscaling factors and varying degradation parameters. Experimental results show that the proposed method performs favorably on synthetic and real LR images.
CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is exploited and the efficiency is hindered by dense per-frame localization. In this paper, we propose Coarse-to-Fine Action Detector (CFAD), an original end-to-end trainable framework for efficient spatio-temporal action localization. The CFAD introduces a new paradigm that first estimates coarse spatio-temporal action tubes from video streams, and then refines the tubes’ location based on key timestamps. This concept is implemented by two key components, the Coarse and Refine Modules in our framework. The parameterized modeling of long temporal information in the Coarse Module helps obtain accurate initial tube estimation, while the Refine Module selectively adjusts the tube location under the guidance of key timestamps. Against other methods, the proposed CFAD achieves competitive results on action detection benchmarks of UCF101-24, UCFSports and JHMDB-21 with inference speed that is 3.3\(\times \) faster than the nearest competitor.
On Lightweight Privacy-preserving Collaborative Learning for Internet of Things by Independent Random Projections

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. This article considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator. Existing distributed machine learning and data encryption approaches incur significant computation and communication overhead, rendering them ill-suited for resource-constrained IoT objects. We study an approach that applies independent random projection at each IoT object to obfuscate data and trains a deep neural network at the coordinator based on the projected data from the IoT objects. This approach introduces light computation overhead to the IoT objects and moves most workload to the coordinator that can have sufficient computing resources. Although the independent projections performed by the IoT objects address the potential collusion between the curious coordinator and some compromised IoT objects, they significantly increase the complexity of the projected data. In this article, we leverage the superior learning capability of deep learning in capturing sophisticated patterns to maintain good learning performance. Extensive comparative evaluation shows that this approach outperforms other lightweight approaches that apply additive noisification for differential privacy and/or support vector machines for learning in the applications with light to moderate data pattern complexities.
Practical Escrow Protocol for Bitcoin

An escrow protocol for Bitcoin allows fair trading using bitcoins. To ensure fairness, the existing proposals made various trade-offs between trust, privacy, and efficiency. In this work, we evaluate the existing escrow protocols for cryptocurrency and propose a practical escrow protocol for Bitcoin that is: (a) computationally efficient; (b) round efficient; and (c) privacy-preserving. The core component of our escrow protocol for Bitcoin is a new verifiably encrypted ECDSA scheme, which may be of independent interest. Furthermore, we implement the escrow protocol for Bitcoin in Bitcoin mainnet, demonstrating the feasibility of our protocol.
A Generative Model for Generic Light Field Reconstruction

Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to capture the data distribution of light field patches. We develop a generative model conditioned on the central view of the light field and incorporate this as a prior in an energy minimization framework to address diverse light field reconstruction tasks. While pure learning-based approaches do achieve excellent results on each instance of such a problem, their applicability is limited to the specific observation model they have been trained on. On the contrary, our trained light field generative model can be incorporated as a prior into any model-based optimization approach and therefore extend to diverse reconstruction tasks including light field view synthesis, spatial-angular super resolution and reconstruction from coded projections. Our proposed method demonstrates good reconstruction, with performance approaching end-to-end trained networks, while outperforming traditional model-based approaches on both synthetic and real scenes. Furthermore, we show that our approach enables reliable light field recovery despite distortions in the input.
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data

With the increasing popularity of deep learning on edge devices, compressing large neural networks to meet the hardware requirements of resource-constrained devices became a significant research direction. Numerous compression methodologies are currently being used to reduce the memory sizes and energy consumption of neural networks. Knowledge distillation (KD) is among such methodologies and it functions by using data samples to transfer the knowledge captured by a large model (teacher) to a smaller one (student). However, due to various reasons, the original training data might not be accessible at the compression stage. Therefore, data-free model compression is an ongoing research problem that has been addressed by various works. In this paper, we point out that catastrophic forgetting is a problem that can potentially be observed in existing data-free distillation methods. Moreover, the sample generation strategies in some of these methods could result in a mismatch between the synthetic and real data distributions. To prevent such problems, we propose a data-free KD framework that maintains a dynamic collection of generated samples over time. Additionally, we add the constraint of matching the real data distribution in sample generation strategies that target maximum information gain. Our experiments demonstrate that we can improve the accuracy of the student models obtained via KD when compared with state-of-the-art approaches on the SVHN, Fashion MNIST and CIFAR100 datasets.
A motion model based on recurrent neural networks for visual object tracking

Object tracking algorithms typically leverage on either or both appearance and motion features of target(s). It is common in multi-object tracking to use both features, whereas the role of motion features in single-object trackers has less been explored. Based on the Long Short-Term Memory (LSTM) architecture of recurrent neural networks, we train a novel motion model to be incorporated into the off-the-shelf single-object trackers. The developed model predicts the target location in each frame based on the history of processed motion features in a few prior frames. This aids the tracking algorithm in dynamically updating the search region location, as apposed to static or probabilistic region settings. We incorporate the model into three state-of-the-art CNN-based trackers, namely GOTURN, SiamFC, and DiMP and illustrate the tracking performance enhancements on popular benchmarks. Significant improvements are achieved specially on the sequences rendering challenging situations such as Low Resolution, Out-of-Plane Rotation, Motion Blur, Fast Motion, and Occlusion. The motion model has a low computational cost and complies with the real-time execution of the base trackers.
Extending contour level set model for multi-class image segmentation with Application to Breast Thermography Images

Extraction of target objects with the desired intensity value in multi-region images is a challenging task. This paper presents a new modification to the region-based level set method called Extending Contour Level Set (ECLS) model. In classical level set methods, the intensity of each pixel is compared with both average intensities inside and outside the contour; therefore, image domain is segmented into two regions with the most difference between average intensities. In multi-region images, there is no reason the selected region belongs to the desired one. In our approach, with the assumption that the initial contour is located inside the target object, outside-contour pixels are allocated to the inside-contour region if they have an intensity value close to the inside-contour average intensity. The ECLS model is compared with well-known level set methods such as the Chan-Vese model, the local binary fitting energy model, the geodesic level set model, the local statistical active contour model, and the local and global fitting image model. Simulation results indicate the remarkable advantages of the proposed method in terms of accuracy and computational complexity. To show the ability of the ECLS model in practice, a two-step approach is proposed to segment breast thermography images. In the first step, the ECLS model is exploited to produce a semi-final region. Then, in the second step, a new Controlled Chan-Vese (CCV) model is presented leading to accurate results. The CCV model controls the distance between the average intensities inside and outside the contour based on the image statistics. Comparison with other segmentation methods shows the superiority of the proposed approach. In addition, statistical measures are calculated to demonstrate the significant capabilities of the proposed method.
Evidential Deep Neural Networks for Uncertain Data Classification

Uncertain data classification makes it possible to reduce the decision risk through abstaining from classifying uncertain cases. Incorporating this idea into the process of computer aided diagnosis can greatly reduce the risk of misdiagnosis. However, for deep neural networks, most existing models lack a strategy to handle uncertain data and thus suffer the costs of serious classification errors. To tackle this problem, we utilize Dempster-Shafer evidence theory to measure the uncertainty of the prediction output by deep neural networks and thereby propose an uncertain data classification method with evidential deep neural networks (EviNet-UC). The proposed method can effectively improve the recall rate of the risky class through involving the evidence adjustment in the learning objective. Experiments on medical images show that the proposed method is effective to identify uncertain data instances and reduce the decision risk.
Learning Canonical 3D Object Representation for Fine-Grained Recognition

We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image, trained on an image collection without using any ground-truth 3D annotation. We accomplish this by representing an object as a composition of 3D shape and its appearance, while eliminating the effect of camera viewpoint, in a canonical configuration. Unlike conventional methods modeling spatial variation in 2D images only, our method is capable of reconfiguring the appearance feature in a canonical 3D space, thus enabling the subsequent object classifier to be invariant under 3D geometric variation. Our representation also allows us to go beyond existing methods, by incorporating 3D shape variation as an additional cue for object recognition. To learn the model without ground-truth 3D annotation, we deploy a differentiable renderer in an analysis-by-synthesis frame- work. By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object and achieves competitive performance on fine-grained image recognition and vehicle re-identification. We also demonstrate that the performance of 3D shape reconstruction is improved by learning fine-grained shape deformation in a boosting manner.
Minimal Cases for Computing the Generalized Relative Pose using Affine Correspondences

We propose three novel solvers for estimating the relative pose of a multi-camera system from affine correspondences (ACs). A new constraint is derived interpreting the relationship of ACs and the generalized camera model. Using the constraint, we demonstrate efficient solvers for two types of motions assumed. Considering that the cameras undergo planar motion, we propose a minimal solution using a single AC and a solver with two ACs to overcome the degenerate case. Also, we propose a minimal solution using two ACs with known vertical direction, e.g., from an IMU. Since the proposed methods require significantly fewer correspondences than state-of-the-art algorithms, they can be efficiently used within RANSAC for outlier removal and initial motion estimation. The solvers are tested both on synthetic data and on real-world scenes from the KITTI odometry benchmark. It is shown that the accuracy of the estimated poses is superior to the state-of-the-art techniques.
Lithological, structural and hydrothermal alteration mapping utilizing remote sensing datasets: a case study around Um Salim area, Egypt

Abstract Remote sensing datasets have introduced remarkable advancements in mapping rock units, structural elements, and hydrothermal alteration zones. This study applied Landsat Operational Land Imager (OLI) multispectral dataset in discriminating the intricate basement of Um Salim area, Central Eastern Desert (CED), Egypt. Moreover, a panchromatic 15m pixel size band is implemented to extract the study area’s linear structural features. Several image processing methods including False Color Combination (FCC), Band Ratio (BR), Optimum Index Factor (OIF), and Density slicing were utilized in lithological and alteration mapping. The widely used, LINE module of the PCI Geomatica is applied for lineament extraction. Results reasonably discriminate the complicated rock units using selected composites depending on OIF results. A photo-geological map is constructed and shows greater coincidence with recently published maps. Lineaments map and its density revealed the preponderance of NE-SW and WNW-ESE structural trends. The spatial relationship between the resultant hydrothermally-altered zones and the detected structural features strongly recommends further detailed examination for ore deposits within the study area besides manifesting the efficiency of the utilized data and methods.