Reviewerly/exHarmony huggingface dataset:
Learning Shape Representations for Person Re-Identification under Clothing Change

Person re-identification (re-ID) aims to recognize instances of the same person contained in multiple images taken across different cameras. Existing methods for re-ID tend to rely heavily on the assumption that both query and gallery images of the same person have the same clothing. Unfortunately, this assumption may not hold for datasets captured over long periods of time. To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning method which is able to generate a shape-based feature representation that is invariant to clothing. We call our model the Clothing Agnostic Shape Extraction Network (CASE-Net). CASE-Net learns a representation of a person that depends primarily on shape via adversarial learning and feature disentanglement. Quantitative and qualitative results across 5 datasets (Div-Market, Market1501, three large-scale datasets under clothing changes) show our approach makes significant improvements over prior state-of-the-art approaches.
Deep Image Compositing

Image compositing is a task of combining regions from different images to compose a new image. A common use case is background replacement of portrait images. To obtain high quality composites, professionals typically manually perform multiple editing steps such as segmentation, matting and foreground color decontamination, which is very time consuming even with sophisticated photo editing tools. In this paper, we propose a new method which can automatically generate high-quality image compositing with-out any user input. Our method can be trained end-to-end to optimize exploitation of contextual and color information of both foreground and background images, where the com-positing quality is considered in the optimization. Specifically, inspired by Laplacian pyramid blending, a dense-connected multi-stream fusion network is proposed to effectively fuse the information from the foreground and back-ground images at different scales. In addition, we intro-duce a self-taught strategy to progressively train from easy to complex cases to mitigate the lack of training data. Experiments show that the proposed method can automatically generate high-quality composites and outperforms existing methods both qualitatively and quantitatively.
Anomaly Detection-Based Unknown Face Presentation Attack Detection

Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users. These detectors are of practical importance as they are shown to generalize well to new attack types. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection where both classifier and feature representations are learned together end-to-end. First, we introduce a pseudo-negative class during training in the absence of attacked images. The pseudo-negative class is modeled using a Gaussian distribution whose mean is calculated by a weighted running mean. Secondly, we use pairwise confusion loss to further regularize the training process. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task as shown in our ablation study. We perform extensive experiments on four publicly available datasets: Replay-Attack, Rose-Youtu, OULU-NPU and Spoof in Wild to show the effectiveness of the proposed approach over the previous methods. Code is available at: https://github.com/yashasvi97/IJCB2020_anomaly.
Digital Tracing during the COVID-19 Pandemic: User Appraisal, Emotion, and Continuance Intention

This study explores how people appraise the use of contact tracing apps during the novel coronavirus (COVID-19) pandemic in South Korea. Despite increasing attention paid to digital tracing for health disasters, few studies have empirically examined user appraisal, emotion, and their continuance intention to use contact tracing apps for disaster management during an infectious disease outbreak. A mixed-method approach combining qualitative and quantitative inquiries was employed. In the qualitative study, by conducting interviews with 25 people who have used mobile apps for contact tracing, the way users appraise contact tracing apps for COVID-19 was explored. In the quantitative study, using data collected from 506 users of the apps, the interplay among cognitive appraisal (threats and opportunities) and its association with user emotion, and continuance intention was examined. The findings indicate that once users experience loss emotions, such as anger, frustration, and disgust, they are not willing to continue using the apps. App designers should consider providing technological affordances that enable users to have a sense of control over the technology so that they do not experience loss emotions. Public policymakers should also consider developing measures that can balance public health and personal privacy.
How Reduced Data Precision and Degree of Parallelism Impact the Reliability of Convolutional Neural Networks on FPGAs

Convolutional neural networks (CNNs) are becoming attractive alternatives to traditional image-processing algorithms in self-driving vehicles for automotive, military, and aerospace applications. The high computational demand of state-of-the-art CNN architectures requires the use of hardware acceleration on parallel devices. Field-programmable gate arrays (FPGAs) offer a great level of design flexibility, low power consumption, and are relatively low cost, which make them very good candidates for efficiently accelerating neural networks. Unfortunately, the configuration memories of SRAM-based FPGAs are sensitive to radiation-induced errors, which can compromise the circuit implemented on the programmable fabric and the overall reliability of the system. Through neutron beam experiments, we evaluate how lossless quantization processes and subsequent data precision reduction impact the area, performance, radiation sensitivity, and failure rate of neural networks on FPGAs. Our results show that an 8-bit integer design can deliver over six times more fault-free executions than a 32-bit floating-point implementation. Moreover, we discuss the tradeoffs associated with varying degrees of parallelism in a neural network accelerator. We show that, although increased parallelism increases radiation sensitivity, the performance gains generally outweigh it in terms of global failure rate.
Context-aware saliency detection for image retargeting using convolutional neural networks

Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual information and low-level features such as texture remain as intact as possible to the human visual system. At the same time, the output image may have different dimensions. Thus, simple methods such as scaling and cropping are not adequate for this purpose. In recent years, researchers have tried to improve the existing retargeting methods, and they have introduced new ones. However, a specific method cannot be utilized to retarget all types of images. In other words, different images require different retargeting methods. Image retargeting has a close relationship to image saliency detection, which is a relatively new image processing task. Earlier saliency detection methods were based on local and global but low-level image information. These methods are called bottom-up processes. On the other hand, newer approaches are top-down and mixed methods that consider the high level and semantic knowledge of the image too. In this paper, we introduce the proposed methods in both saliency detection and retargeting. For the saliency detection, the use of image context and semantic segmentation are examined, and a novel mixed bottom-up and top-down saliency detection method is introduced. After saliency detection, a modified version of an existing retargeting technique is utilized for retargeting the images. The results suggest that the proposed image retargeting pipeline has excellent performance compared to other tested methods. Also, the subjective evaluations on the Pascal dataset can be used as a retargeting quality assessment dataset for further research.
Lexical density and diversity in dissertation abstracts: Revisiting English L1 vs. L2 text differences

This study investigated lexical density and diversity differences in English as L1 vs L2 academic writing of EFL, ESL, and English L1 postgraduate students to compare their lexical proficiency in EFL vs. English L1 academic settings. A corpus of 210 dissertation abstracts was analysed using three natural language processing tools [LCA, TAALED, and Coh-Metrix] where the effects of text length and topic were controlled. In doing so, we examined the relationship between 15 lexical indices and the construct-distinctiveness of lexical density and diversity. The measure-testing process also assesses the effectiveness of each measure in a pair/group of closely-related measures (in terms of the quantification methods) in capturing lexical diversity differences of these texts. This is to obtain a small number of unique measures that capture lexical diversity as an indicator of lexical proficiency and to assist future writing researchers in the measure-selection process in the face of a multitude of available measures. The findings have important implications for writing assessment and research on lexical indicators of writing proficiency, materials development in EFL academic settings especially for thesis/dissertation writing modules, and a possible contribution of ESL academic immersion programmes in approximating English L1 and L2 proficiency.
DANCE : A Deep Attentive Contour Model for Efficient Instance Segmentation

Contour-based instance segmentation methods are attractive due to their efficiency. However, existing contour-based methods either suffer from lossy representation, complex pipeline or difficulty in model training, resulting in sub-par mask accuracy on challenging datasets like MS-COCO. In this work, we propose a novel deep attentive contour model, named DANCE, to achieve better instance segmentation accuracy while remaining good efficiency. To this end, DANCE applies two new designs: attentive contour deformation to refine the quality of segmentation contours and segment-wise matching to ease the model training. Comprehensive experiments demonstrate DANCE excels at deforming the initial contour in a more natural and efficient way towards the real object boundaries. Effectiveness of DANCE is also validated on the COCO dataset, which achieves 38.1% mAP and outperforms all other contour-based instance segmentation models. To the best of our knowledge, DANCE is the first contour-based model that achieves comparable performance to pixel-wise segmentation models. Code is available at https://github.com/lkevinzc/dance.
Multi-Loss Weighting with Coefficient of Variations

Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The final performance is sensitive to choosing the correct (relative) weights for these losses. Finding a good set of weights is often done by adopting them into the set of hyper- parameters, which are set using an extensive grid search. This is computationally expensive. In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . The proposed method incorporates a measure of uncertainty to balance the losses, and as a result the loss weights evolve during training without requiring another (learning based) optimisation. In contrast to many loss weighting methods in literature, we focus on single-task multi-loss problems, such as monocular depth estimation and semantic segmentation, and show that multi-task approaches for loss weighting do not work on those single-tasks. The validity of the approach is shown empirically for depth estimation and semantic segmentation on multiple datasets.
Beyond Identity: What Information Is Stored in Biometric Face Templates?

Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user's privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to accurately state its prediction confidence. This allows us to make more sophisticated statements about the attribute predictability. The results demonstrate that up to 74 attributes can be accurately predicted from face templates. Especially non-permanent attributes, such as age, hairstyles, haircolors, beards, and various accessories, found to be easily-predictable. Since face recognition systems aim to be robust against these variations, future research might build on this work to develop more understandable privacy preserving solutions and build robust and fair face templates.
Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning

The most significant factor to consider during private information transmission through the internet (i.e., insecure channel) is security.So, to keep this data from unauthorized access during transmission, steganography is used.Steganography is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, text, etc.Current image steganography methods work as follows; it assigns cover image then embeds the secret message within it by pixels' modifications, creating the resultant stego-image.These modifications allow steganalysis algorithms to detect the embedded secret message.So, a coverless data hiding concept is proposed to solve this problem.Coverless does not mean that the secret message will be transmitted without using a cover file, or the cover file can be discarded.Instead, the secret message will be embedded by generating a cover file or a secret message mapping.In this paper, a novel, highly robust coverless image steganography method based on optical mark recognition (OMR) and rule-based machine learning (RBML) is proposed.
From generalized zero-shot learning to long-tail with class descriptors

Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes. Often, classes can be accompanied by side information like textual descriptions, but it is not fully clear how to use them for learning with unbalanced long-tail data. Such descriptions have been mostly used in (Generalized) Zero-shot learning (ZSL), suggesting that ZSL with class descriptions may also be useful for long- tail distributions.We describe Dragon, a late-fusion architecture for long-tail learning with class descriptors. It learns to (1) correct the bias towards head classes on a sample- by-sample basis; and (2) fuse information from class- descriptions to improve the tail-class accuracy. We also introduce new benchmarks CUB-LT, SUN-LT, AWA-LT for long-tail learning with class-descriptions, building on existing learning-with-attributes datasets and a version of Imagenet-LT with class descriptors. Dragon outperforms state-of-the-art models on the new benchmark. It is also a new SoTA on existing benchmarks for GFSL with class descriptors (GFSL-d) and standard (vision-only) long-tailed learning ImageNet-LT, CIFAR-10, 100, and Places365-LT.
kalepy: a Python package for kernel density estimation, sampling and plotting

Kernel Density Estimation' or 'KDE' (Parzen, 1962;Rosenblatt, 1956) is a type of nonparametric density estimation (David W. Scott, 2015) that improves upon the traditional 'histogram' approach by, for example, i) utilizing the exact location of each data point (instead of 'binning'), ii) being able to produce smooth distributions with continuous and meaningful derivatives, and iii) removing the arbitrary offset of an initial bin edge.The kalepy package presents a Python KDE implementation designed for broad applicability by including numerous features absent in other packages.kalepy provides optional weightings, reflecting boundary conditions, support for an arbitrary number of dimensions, numerous kernel (i.e., window) functions, built-in plotting, and built-in resampling.
High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions

Accurate monitoring and operation of solar power systems require high-resolution solar radiation measurements and precise separation models. This study aims to improve the accuracy of classic diffuse fraction-clearness index piecewise separation models by applying data-driven classifications of sky conditions. This is achieved through a novel outlier-insensitive clustering algorithm and shape prescriptive modeling, applied to 1-, 10-, 30-, and 60-min ground measurements from 4 different locations in the MENA region. This study shows that classifications of sky conditions are not uniform among the selected locations even though all stations fall in the arid desert climate category. This highlights the importance of extracting the sky conditions from measurements rather than using available classifications in the literature. The selection of the number of clusters has to undergo optimization. The number of clusters is also a function of the time resolution. One of the selected locations shows four optimal clusters for 1-min data and six clusters for 60-min data. All developed piecewise separation models show high accuracy and stability with the mean bias errors approaching zero values and the mean absolute errors ranging between 8.7 and 11.8%. The models also outperform existing ones and have good generalization capabilities under the same climate classification.
Pruning by Training: A Novel Deep Neural Network Compression Framework for Image Processing

Filter pruning for a pre-trained convolutional neural network is most normally performed through human-made constraints or criteria such as norms, ranks, etc. Typically, the pruning pipeline comprises two-stage: first learn a sparse structure from the original model, then optimize the weights in the new prune model. One disadvantage of using human-made criteria to prune filters is that the design and selection of threshold criteria depend on complicated prior knowledge. Besides, the pruning process is less robust due to the impact of directly regularizing on filters. To address the problems mentioned, we propose an effective one-stage pruning framework: introducing a trainable collaborative layer to jointly prune and learn neural networks in one go. In our framework, we first add a binary collaborative layer for each original filter. Then, a new type of gradient estimator - asymptotic gradient estimator is first introduced to pass the gradient in the binary collaborative layer. Finally, we simultaneously learn the sparse structure and optimize the weights from the original model in the training process. Our evaluation results on typical benchmarks, CIFAR and ImageNet, demonstrate very promising results against other state-of-the-art filter pruning methods.
Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective

Recent decades have witnessed a trend that control-theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. Computational methods can be modeled as a controller and searching the equilibrium point of a dynamical system is identical to solving an algebraic equation. Thus, absorbing mature technologies in control theory and integrating it with neural dynamics models can lead to new achievements. This work makes progress along this direction by applying control-theoretical techniques to construct new recurrent neural dynamics for manipulating a perturbed nonstationary quadratic program (QP) with time-varying parameters considered. Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. This work shows how iterative computational methods for solving nonstationary QP can be revisited, designed, and analyzed in a control framework. A modified Newton iteration model and an improved gradient-based neural dynamics are established by referring to the superior structural technology of the presented recurrent neural dynamics, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to show the eminence of the proposed models in solving perturbed nonstationary QP.
Continental-scale spatial distribution of chromium (Cr) in China and its relationship with ultramafic-mafic rocks and ophiolitic chromite deposit

With the increasing demands for Cr following China's new infrastructure projects, ascertaining the concentration and distribution of Cr is urgent and meaningful for prospecting Cr deposit and relieving Cr shortage in China. The China Geochemical Baselines (CGB) project provides continental-scale Cr data from 3382 top and 3380 deep catchment sediment/alluvial soil samples and 11605 rock samples. Statistical analysis shows that the median Cr value (baseline) of China is 50 μg/g. The ultramafic and mafic rocks respectively have median Cr values of 1609 and 129 μg/g based on 52 and 551 samples, much higher than those in intermediate rocks (47 μg/g on 991 samples) and felsic rocks (7.0 μg/g on 2558 samples). Parent rock types, especially ultramafic–mafic rocks, predominantly governed the distribution of Cr enrichments in the catchment sediment/alluvial soil of China. High Cr concentrations (>75th percentile) are closely associated with lithology and Cr mineralization, presented in regions where ultramafic rocks, basalts and black shales widely outcropped, such as Changbaishan basalts in Northeast China, Emeishan basalts in South China and black shales along the middle-lower Yangtze River. After eliminating the disturbances of regional background values, the chromium geochemical anomalous centers (>97.5th percentile) are well consistent with the distribution of two-types Cr deposits, especially with the widely-distributed ophiolitic podiform-type Cr metallogenic belts in China. The nation-wide Cr concentration and distribution would provide giant basic data for further exploration prospecting, environmental and hydrogeological studies.