Reviewerly/exHarmony huggingface dataset:
Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution

Benefiting from the strong capabilities of deep CNNs for feature representation and nonlinear mapping, deep-learning-based methods have achieved excellent performance in single image super-resolution. However, most existing SR methods depend on the high capacity of networks which is initially designed for visual recognition, and rarely consider the initial intention of super-resolution for detail fidelity. Aiming at pursuing this intention, there are two challenging issues to be solved: (1) learning appropriate operators which is adaptive to the diverse characteristics of smoothes and details; (2) improving the ability of model to preserve the low-frequency smoothes and reconstruct the high-frequency details. To solve them, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose on improving the detail fidelity, instead of blindly designing or employing the deep CNNs architectures for merely feature representation in local receptive fields. Particularly, we propose a Hessian filtering for interpretable feature representation which is high-profile for detail inference, a dilated encoder-decoder and a distribution alignment cell to improve the inferred Hessian features in morphological manner and statistical manner respectively. Extensive experiments demonstrate that the proposed methods achieve superior performances over the state-of-the-art methods quantitatively and qualitatively. Code is available at https://github.com/YuanfeiHuang/DeFiAN.
Deep Learning for Software Defect Prediction

Software fault prediction is an important and beneficial practice for improving software quality and reliability. The ability to predict which components in a large software system are most likely to contain the largest numbers of faults in the next release helps to better manage projects, including early estimation of possible release delays, and affordably guide corrective actions to improve the quality of the software. However, developing robust fault prediction models is a challenging task and many techniques have been proposed in the literature. Traditional software fault prediction studies mainly focus on manually designing features (e.g. complexity metrics), which are input into machine learning classifiers to identify defective code. However, these features often fail to capture the semantic and structural information of programs. Such information is needed for building accurate fault prediction models. In this survey, we discuss various approaches in fault prediction, also explaining how in recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features and make accurate predictions.
Autonomous driving path planning algorithm based on improved A* algorithm in unstructured environment

There are two shortcomings in the application of traditional A* algorithm in the path planning of autonomous driving. One is that the vehicle environment description method suitable for the A* algorithm is not given; the other is that the vehicle contours and kinematic constraints are not considered. Therefore, according to the characteristics of unstructured environment, this paper presents an environment description method combining global navigation layer and local planning layer, and proposes a local motion planning algorithm based on the improved A* algorithm for autonomous driving vehicles in unstructured environment. In the improved algorithm, profile collision is avoided by setting redundant security space, and the cost of path curvature is considered in heuristic function design. Compared with the original algorithm, it can improve the smoothness of the path, so as to get a path more satisfied with vehicle motion constraints. Simulation results show that the improved algorithm can avoid vehicle contours collision and output a smoother path.
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume

Recently, end-to-end trainable deep neural networks have significantly improved stereo depth estimation for perspective images. However, 360° images captured under equirectangular projection cannot benefit from directly adopting existing methods due to distortion introduced (i.e., lines in 3D are not projected onto lines in 2D). To tackle this issue, we present a novel architecture specifically designed for spherical disparity using the setting of top-bottom 360° camera pairs. Moreover, we propose to mitigate the distortion issue by (1) an additional input branch capturing the position and relation of each pixel in the spherical coordinate, and (2) a cost volume built upon a learnable shifting filter. Due to the lack of 360° stereo data, we collect two 360° stereo datasets from Matterport3D and Stanford3D for training and evaluation. Extensive experiments and ablation study are provided to validate our method against existing algorithms. Finally, we show promising results on real-world environments capturing images with two consumer-level cameras. Our project page is at https://albert100121.github.io/360SD-Net-Project-Page.
Investigating Severity Thresholds for Test Smells

Test smells are poor design decisions implemented in test code, which can have an impact on the effectiveness and maintainability of unit tests. Even though test smell detection tools exist, how to rank the severity of the detected smells is an open research topic. In this work, we aim at investigating the severity rating for four test smells and investigate their perceived impact on test suite maintainability by the developers. To accomplish this, we first analyzed some 1,500 open-source projects to elicit severity thresholds for commonly found test smells. Then, we conducted a study with developers to evaluate our thresholds. We found that (1) current detection rules for certain test smells are considered as too strict by the developers and (2) our newly defined severity thresholds are in line with the participants' perception of how test smells have an impact on the maintainability of a test suite. Preprint [https://doi.org/10.5281/zenodo.3744281], data and material [https://doi.org/10.5281/zenodo.3611111].
Learning To Blindly Assess Image Quality In The Laboratory And Wild

Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images. Similarly, BIQA models optimized for images captured in the wild cannot adequately handle synthetically distorted images. To face the cross-distortion-scenario challenge, we develop a BIQA model and an approach of training it on multiple IQA databases (of different distortion scenarios) simultaneously. A key step in our approach is to create and combine image pairs within individual databases as the training set, which effectively bypasses the issue of perceptual scale realignment. We compute a continuous quality annotation for each pair from the corresponding human opinions, indicating the probability of one image having better perceptual quality. We train a deep neural network for BIQA over the training set of massive image pairs by minimizing the fidelity loss. Experiments on six IQA databases demonstrate that the optimized model by the proposed training strategy is effective in blindly assessing image quality in the laboratory and wild, outperforming previous BIQA methods by a large margin.
Mitigating turnover with code review recommendation

Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turnover tend to disrupt and increase workloads for developers. In this work, we suggest that through code review recommendation we can distribute knowledge and mitigate turnover with minimal impacton the development process. We evaluate review recommenders in the context of ensuring expertise during review, Expertise, reducing the review workload of the core team, CoreWorkload, and reducing the Files at Risk to turnover, FaR. We find that prior work that assigns reviewers based on file ownership concentrates knowledge on a small group of core developers increasing risk of knowledge loss from turnover by up to 65%. We propose learning and retention aware review recommenders that when combined are effective at reducing the risk of turnover by -29% but they unacceptably reduce the overall expertise during reviews by -26%. We develop the Sofia recommender that suggests experts when none of the files under review are hoarded by developers, but distributes knowledge when files are at risk. In this way, we are able to simultaneously increase expertise during review with a ΔExpertise of 6%, with a negligible impact on workload of ΔCoreWorkload of 0.09%, and reduce the files at risk by ΔFaR -28%. Sofia is integrated into GitHub pull requests allowing developers to select an appropriate expert or "learner" based on the context of the review. We release the Sofia bot as well as the code and data for replication purposes.
Unsupervised Anomaly Detection in IoT Systems for Smart Cities

Anomaly detection is critical in the Internet of Things (IoT) systems due to its wide applications for building smart cities, such as quality control in manufacturing, intrusion detection in system security, fault detection in system monitoring. Many existing schemes are problem specific and supervised approaches, which require domain knowledge and tremendous data labeling efforts. In this paper, we investigate unsupervised anomaly detection on multidimensional time series data in IoT systems, and develops a GRU-based Gaussian Mixture VAE scheme, called GGM-VAE. In particular, we employ Gated Recurrent Unit (GRU) cells to discover the correlations among time series data, and use Gaussian Mixture priors in the latent space to characterize the multimodal data. Several previous works assume simple distributions for Gaussian Mixture priors, resulting in insufficient ability to fully capture the data patterns. To overcome this issue, we design a model selection mechanism during the training process under the guidance of Bayesian Inference Criterion (BIC) to find the model which can well estimate the distribution in the Gaussian Mixture latent space. We conduct extensive simulations on four datasets and observe that our proposed scheme outperforms the state-of-the-art anomaly detection schemes and achieves up to 47.88% improvement in F1 scores on average.
Fakd: Feature-Affinity Based Knowledge Distillation for Efficient Image Super-Resolution

Convolutional neural networks (CNNs) have been widely used in image super-resolution (SR). Most existing CNN-based methods focus on achieving better performance by designing deeper/wider networks, while suffering from heavy computational cost problem, thus hindering the deployment of such models in mobile devices with limited resources. To relieve such problem, we propose a novel and efficient SR model, named Feature Affinity-based Knowledge Distillation (FAKD), by transferring the structural knowledge of a heavy teacher model to a lightweight student model. To transfer the structural knowledge effectively, FAKD aims to distill the second-order statistical information from feature maps and trains a lightweight student network with low computational and memory cost. Experimental results demonstrate the efficacy of our method and the effectiveness over other knowledge distillation based methods in terms of both quantitative and visual metrics.
A Novel Hybrid Model for Solar Radiation Forecasting Using Support Vector Machine and Bee Colony Optimization Algorithm: Review and Case Study

Abstract This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m2, Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m2 and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m2 and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM.
A cloud-aided privacy-preserving multi-dimensional data comparison protocol

With the emergence of mobile social networks, users can make friends or negotiate projects at any time and anywhere in social communities through their mobile devices (such as smartphones, iPads). The two sides of making friends or cooperation want to know the comparison results between each other in many aspects. Aiming at the requirements of these interactive applications in social networks and considering the limited-resource setting of mobile users, in this paper, we propose a cloud-aided privacy-preserving multi-dimensional data comparison protocol. We introduce a novel comparison method for two multi-dimensional data. Because of the blinded processing trick in our comparison method and the Paillier encryption, the proposed protocol can realize our comparison method without revealing users’ data and the comparison results. Besides, a fog device and a cloud server conduct most of the computations. Furthermore, users pack their multi-dimensional data into one data using Horner Rule, making finishing the comparison of two multi-dimensional data through one-round system communication. Therefore, the presented protocol can significantly lower the computation and communication costs of the system, especially that of the user-side. Security analysis is inducted to validate security properties. Moreover, performance evaluations illustrate the computation and communication efficiency of the proposed protocol.
Biased ReLU neural networks

Neural networks (NN) with rectified linear units (ReLU) have been widely implemented since 2012. In this paper, we describe an activation function called the biased ReLU neuron (BReLU), which is similar to the ReLU. Based on this activation function, we propose the BReLU NN (BRNN). The structure of the BRNN is similar to that of the ReLU network. However, the difference between the two is that the BReLU introduces several biases for each input variable. This allows the BRNN to divide the input space into a greater number of linear regions and improve network flexibility. The BRNN parameters to be estimated are the weight matrices and the bias parameters of the BReLU neurons. The weights are obtained using the backpropagation method. Moreover, we propose a method to compute the bias parameters of the BReLU neurons. In this method, batch normalization is applied to the BRNN, and the variance and mean of the input variables are obtained. Based on these two parameters, the bias parameters are estimated. In addition, we investigate the flexibility of the BRNN. Specifically, we study the number of linear regions and provide the upper bound for the maximum number of linear regions. The results indicate that for the same input dimension, the BRNN divides the input space into a greater number of linear regions than the ReLU network. This explains to a certain extent why the BRNN has the superior approximation ability. Experiments are carried out using five datasets, and the results verify the effectiveness of the proposed method.
Class-Aware Modality Mix and Center-Guided Metric Learning for Visible-Thermal Person Re-Identification

Visible thermal person re-identification (VT-REID) is an important and challenging task in that 1) weak lighting environments are inevitably encountered in real-world settings and 2) the inter-modality discrepancy is serious. Most existing methods either aim at reducing the cross-modality gap in pixel- and feature-level or optimizing cross-modality network by metric learning techniques. However, few works have jointly considered these two aspects and studied their mutual benefits. In this paper, we design a novel framework to jointly bridge the modality gap in pixel- and feature-level without additional parameters, as well as reduce the inter- and intra-modalities variations by a center-guided metric learning constraint. Specifically, we introduce the Class-aware Modality Mix (CMM) to generate internal information of the two modalities for reducing the modality gap in pixel-level. In addition, we exploit the KL-divergence to further align modality distributions on feature-level. On the other hand, we propose an efficient Center-guided Metric Learning (CML) method for decreasing the discrepancy within the inter- and intra-modalities, by enforcing constraints on class centers and instances. Extensive experiments on two datasets show the mutual advantage of the proposed components and demonstrate the superiority of our method over the state of the art.
Amora: Black-box Adversarial Morphing Attack

Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective joint dictionary learning pipeline to obtain a proprietary optical flow field for each attack. Our extensive evaluation on two popular FR systems demonstrates the effectiveness of our adversarial morphing attack at various levels of morphing intensity with smiling facial expression manipulations. Both open-set and closed-set experimental results indicate that a novel black-box adversarial attack based on local deformation is possible, and is vastly different from additive noise attacks. The findings of this work potentially pave a new research direction towards a more thorough understanding and investigation of image-based adversarial attacks and defenses.
Closing the translation gap: AI applications in digital pathology

Recent advances in artificial intelligence show tremendous promise to improve the accuracy, reproducibility, and availability of medical diagnostics across a number of medical subspecialities. This is especially true in the field of digital pathology, which has recently witnessed a surge in publications describing state-of-the-art performance for machine learning models across a wide range of diagnostic applications. Nonetheless, despite this promise, there remain significant gaps in translating applications for any of these technologies into actual clinical practice. In this review, we will first give a brief overview of the recent progress in applying AI to digitized pathology images, focusing on how these tools might be applied in clinical workflows in the near term to improve the accuracy and efficiency of pathologists. Then we define and describe in detail the various factors that need to be addressed in order to successfully close the “translation gap” for AI applications in digital pathology.
Distance Learning at Russian Universities: “Step Forward, Two Steps Back”?

This article discusses the problems associated with the possible widespread introduction of distance learning in the practice of Russian higher education. The authors discuss and analyze in detail the advantages and disadvantages of distance learning in comparison with the traditional contact learning, involving “live” communication between teacher and trainees. The paper states that the disadvantages of learning generally prevail over the advantages. It is concluded that distance learning can become predominant only in completely unique situations, in which “live” communication between people in general and teachers and students in particular, for one reason or another, should be minimized or even eliminated altogether.
Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection

Abstract Deep learning (DL), which involves powerful black box predictors, has achieved a remarkable performance in medical image analysis, such as segmentation and classification for diagnosis. However, in spite of these successes, these methods focus exclusively on improving the accuracy of point predictions without assessing the quality of their outputs. Knowing how much confidence there is in a prediction is essential for gaining clinicians' trust in the technology. In this article, we propose an uncertainty estimation framework, called MC‐DropWeights, to approximate Bayesian inference in DL by imposing a Bernoulli distribution on the incoming or outgoing weights of the model, including neurones. We demonstrate that by decomposing predictive probabilities into two main types of uncertainty, aleatoric and epistemic, using the Bayesian Residual U‐Net (BRUNet) in image segmentation. Approximation methods in Bayesian DL suffer from the “mode collapse” phenomenon in variational inference. To address this problem, we propose a model which Ensembles of Monte‐Carlo DropWeights by varying the DropWeights rate. In segmentation, we introduce a predictive uncertainty estimator, which takes the mean of the standard deviations of the class probabilities associated with every class. However, in classification, we need an alternative approach since the predictive probabilities from a forward pass through the model does not capture uncertainty. The entropy of the predictive distribution is a measure of uncertainty, but its exponential depends on sample size. The plug‐in estimate in mutual information is subject to sampling bias. We propose Jackknife resampling, to correct for sample bias, which improves estimating uncertainty quality in image classification. We demonstrate that our deep ensemble MC‐DropWeights method, using the bias‐corrected estimator produces an equally good or better result in both quantified uncertainty estimation and quality of uncertainty estimates than approximate Bayesian neural networks in practice.