Reviewerly/exHarmony huggingface dataset:
Labeled from Unlabeled: Exploiting Unlabeled Data for Few-shot Deep HDR Deghosting

High Dynamic Range (HDR) deghosting is an indispensable tool in capturing wide dynamic range scenes without ghosting artifacts. Recently, convolutional neural networks (CNNs) have shown tremendous success in HDR deghosting. However, CNN-based HDR deghosting methods require collecting large datasets with ground truth, which is a tedious and time-consuming process. This paper proposes a pioneering work by introducing zero and few-shot learning strategies for data-efficient HDR deghosting. Our approach consists of two stages of training. In stage one, we train the model with few labeled (5 or less) dynamic samples and a pool of unlabeled samples with a self-supervised loss. We use the trained model to predict HDRs for the unlabeled samples. To derive data for the next stage of training, we propose a novel method for generating corresponding dynamic inputs from the predicted HDRs of unlabeled data. The generated artificial dynamic inputs and predicted HDRs are used as paired labeled data. In stage two, we finetune the model with the original few labeled data and artificially generated labeled data. Our few-shot approach outperforms many fully-supervised methods in two publicly available datasets, using as little as five labeled dynamic samples.
Compression Helps Deep Learning in Image Classification

The impact of JPEG compression on deep learning (DL) in image classification is revisited. Given an underlying deep neural network (DNN) pre-trained with pristine ImageNet images, it is demonstrated that, if, for any original image, one can select, among its many JPEG compressed versions including its original version, a suitable version as an input to the underlying DNN, then the classification accuracy of the underlying DNN can be improved significantly while the size in bits of the selected input is, on average, reduced dramatically in comparison with the original image. This is in contrast to the conventional understanding that JPEG compression generally degrades the classification accuracy of DL. Specifically, for each original image, consider its 10 JPEG compressed versions with their quality factor (QF) values from {100,90,80,70,60,50,40,30,20,10}. Under the assumption that the ground truth label of the original image is known at the time of selecting an input, but unknown to the underlying DNN, we present a selector called Highest Rank Selector (HRS). It is shown that HRS is optimal in the sense of achieving the highest Top k accuracy on any set of images for any k among all possible selectors. When the underlying DNN is Inception V3 or ResNet-50 V2, HRS improves, on average, the Top 1 classification accuracy and Top 5 classification accuracy on the whole ImageNet validation dataset by 5.6% and 1.9%, respectively, while reducing the input size in bits dramatically—the compression ratio (CR) between the size of the original images and the size of the selected input images by HRS is 8 for the whole ImageNet validation dataset. When the ground truth label of the original image is unknown at the time of selection, we further propose a new convolutional neural network (CNN) topology which is based on the underlying DNN and takes the original image and its 10 JPEG compressed versions as 11 parallel inputs. It is demonstrated that the proposed new CNN topology, even when partially trained, can consistently improve the Top 1 accuracy of Inception V3 and ResNet-50 V2 by approximately 0.4% and the Top 5 accuracy of Inception V3 and ResNet-50 V2 by 0.32% and 0.2%, respectively. Other selectors without the knowledge of the ground truth label of the original image are also presented. They maintain the Top 1 accuracy, the Top 5 accuracy, or the Top 1 and Top 5 accuracy of the underlying DNN, while achieving CRs of 8.8, 3.3, and 3.1, respectively.
Yoga Pose Detection Using Deep Learning Techniques

Yoga aims at development of all-round personality, it is synchronization of mind, body and the spirit. Improper postures / in-correct way of doing Yoga can result in serious damage to one’s physique and brain. In ancient time, it used to be performed only under the supervision of an accomplished Guru (teacher). In this busy world with time and place constraints, it is difficult to locate an accomplished Guru. Therefore, it is essential to adopt the right Yoga procedure at the very beginning. Deep Learning models can be trained to detect Yoga postures and be able to provide feedback / corrections if needed. OpenPose was the first real-time multi-person system which brought revolutionary change in the field of pose estimation. In this paper using OpenPose we have created a model which estimates a given human pose. We have compared the results obtained by the model for 2D and 3D points of the image and determined if adding more features to the dataset actually increases the accuracy of the model or not. Furthermore, we have put forward a simple neural network model that efficiently analyses the input image and conveys if the pose performed in the image is correct.
Comparison of Breast Cancer and Skin Cancer Diagnoses Using Deep Learning Method

Artificial intelligence applications are of great importance in the solution of cancer, which is one of the biggest health problems of our age. In this study, a study was conducted on deep learning methods that make life important in the early diagnosis of breast cancer and skin cancer, which are among the most common types of cancer worldwide. Breast cancer and skin cancer data were classified as benign and malignant by deep learning methods. While working with the deep learning method, the classification was made using the Convolutional Neural Network (CNN) algorithm. In this classification, the data are divided into benign cancer sets and malignant cancer sets. Finally, the data provided by the logistic regression method were analyzed and success charts were created and both types were compared. As a result, accuracy and loss graphs of both cancer types were formed. The aim of the study is to compare breast cancer and skin cancer with the deep learning method. And some breast cancer and skin cancer diagnoses are confused. In further studies, the basis of differentiating the diagnosis of these two types of cancer from each other was made in this study.
ThunderRW

As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient in-memory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic step-centric programming model named Gather-Move-Update to abstract different RW algorithms. Based on the programming model, we develop the step interleaving technique to hide memory access latency by switching the executions of different random walk queries. In our experiments, we use four representative RW algorithms including PPR, DeepWalk, Node2Vec and MetaPath to demonstrate the efficiency and programming flexibility of ThunderRW. Experimental results show that ThunderRW outperforms state-of-the-art approaches by an order of magnitude, and the step interleaving technique significantly reduces the CPU pipeline stall from 73.1% to 15.0%.
Low-light image enhancement via deep Retinex decomposition and bilateral learning

Low-light images enhancement is a challenging task because enhancing image brightness and reducing image degradation should be considered simultaneously. Although existing deep learning-based methods improve the visibility of low-light images, many of them tend to lose details or sacrifice naturalness. To address these issues, we present a multi-stage network for low-light image enhancement, which consists of three sub-networks. More specifically, inspired by the Retinex theory and the bilateral grid technique, we first design a reflectance and illumination decomposition network to decompose an image into reflectance and illumination maps efficiently. To increase the brightness while preserving edge information, we then devise an attention-guided illumination adjustment network. The reflectance and the adjusted illumination maps are fused and refined by adversarial learning to reduce image degradation and improve image naturalness. Experiments are conducted on our rebuilt SICE low-light image dataset, which consists of 1380 real paired images and a public dataset LOL, which has 500 real paired images and 1000 synthetic paired images. Experimental results show that the proposed method outperforms state-of-the-art methods quantitatively and qualitatively.
A Literature Review on the GDPR, COVID-19 and the Ethical Considerations of Data Protection During a Time of Crisis

Summary Objective: This survey article presents a literature review of relevant publications aiming to explore whether the EU's General Data Protection Regulation (GDPR) has held true during a time of crisis and the implications that arose during the COVID-19 outbreak. Method and Results: Based on the approach taken and the screening of the relevant articles, the results focus on three themes: a critique on GDPR; the ethics surrounding the use of digital health technologies, namely in the form of mobile applications; and the possibility of cross border transfers of said data outside of Europe. Within this context, the article reviews the arising themes, considers the use of data through mobile health applications, and discusses whether data protection may require a revision when balancing societal and personal interests. Conclusions: In summary, although it is clear that the GDPR has been applied through a mixed and complex experience with data handling during the pandemic, the COVID-19 pandemic has indeed shown that it was a test the GDPR was designed and prepared to undertake. The article suggests that further review and research is needed to first ensure that an understanding of the state of the art in data protection during the pandemic is maintained and second to subsequently explore and carefully create a specific framework for the ethical considerations involved. The paper echoes the literature reviewed and calls for the creation of a unified and harmonised network or database to enable the secure data sharing across borders.
(Input) Size Matters for CNN Classifiers

Fully convolutional neural networks (CNNs) can process input of arbitrary size by applying a combination of downsampling and pooling. However, we find that fully convolutional image classifiers are not agnostic to the input size but rather show significant differences in performance: presenting the same image at different scales can result in different outcomes. A closer look reveals that there is no simple relationship between input size and model performance (no ‘bigger is better’), but that each network has a preferred input size, for which it shows best results. We investigate this phenomenon by applying different methods, including spectral analysis of layer activations and probe classifiers, showing that there are characteristic features depending on the network architecture. From this we find that the size of discriminatory features is critically influencing how the inference process is distributed among the layers. Based on these findings we are able to derive basic design guidelines for optimizing neural architectures on specific datasets.
Discretized Integrated Gradients for Explaining Language Models

As a prominent attribution-based explanation algorithm, Integrated Gradients (IG) is widely adopted due to its desirable explanation axioms and the ease of gradient computation.It measures feature importance by averaging the model's output gradient interpolated along a straight-line path in the input data space.However, such straight-line interpolated points are not representative of text data due to the inherent discreteness of the word embedding space.This questions the faithfulness of the gradients computed at the interpolated points and consequently, the quality of the generated explanations.Here we propose Discretized Integrated Gradients (DIG), which allows effective attribution along non-linear interpolation paths.We develop two interpolation strategies for the discrete word embedding space that generates interpolation points that lie close to actual words in the embedding space, yielding more faithful gradient computation.We demonstrate the effectiveness of DIG over IG through experimental and human evaluations on multiple sentiment classification datasets.We provide the source code of DIG to encourage reproducible research 1 .
Neural Additive Models: Interpretable Machine Learning with Neural Nets

Deep neural networks (DNNs) are powerful black-box predictors that have achieved impressive performance on a wide variety of tasks. However, their accuracy comes at the cost of intelligibility: it is usually unclear how they make their decisions. This hinders their applicability to high stakes decision-making domains such as healthcare. We propose Neural Additive Models (NAMs) which combine some of the expressivity of DNNs with the inherent intelligibility of generalized additive models. NAMs learn a linear combination of neural networks that each attend to a single input feature. These networks are trained jointly and can learn arbitrarily complex relationships between their input feature and the output. Our experiments on regression and classification datasets show that NAMs are more accurate than widely used intelligible models such as logistic regression and shallow decision trees. They perform similarly to existing state-of-the-art generalized additive models in accuracy, but can be more easily applied to real-world problems.
Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning

International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact for cross-lingual hacker asset detection (CLHAD). CLHAD automatically leverages the knowledge learned from English content to detect hacker assets in non-English dark web platforms. CLHAD encompasses a novel Adversarial deep representation learning (ADREL) method, which generates multilingual text representations using generative adversarial networks (GANs). Drawing upon the state of the art in cross-lingual knowledge transfer, ADREL is a novel approach to automatically extract transferable text representations and facilitate the analysis of multilingual content. We evaluate CLHAD on Russian, French, and Italian dark web platforms and demonstrate its practical utility in hacker asset profiling, and conduct a proof-of-concept case study. Our analysis suggests that cybersecurity managers may benefit more from focusing on Russian to identify sophisticated hacking assets. In contrast, financial hacker assets are scattered among several dominant dark web languages. Managerial insights for security managers are discussed at operational and strategic levels.
Capsule GAN for prostate MRI super-resolution

Prostate cancer is a prevalent disease among adult men. One in seven Canadian men is diagnosed with this cancer in their lifetime. Super-Resolution (SR) can facilitate early diagnosis and potentially save many lives. In this paper, a robust and accurate model is proposed for prostate MRI SR. For the first time, MSG-GAN and CapsGAN are utilized simultaneously for high-scale medical SR. The model is trained on the Prostate-Diagnosis and PROSTATEx datasets. The proposed model outperformed the state-of-the-art prostate SR model in all similarity metrics with substantial margins. For $$8 \times$$ SR, 19.77, 0.60, and 0.79 are achieved for Peak Signal-to-Noise Ratio (PSNR), Structural SIMilarity index metric (SSIM), and Multi-Scale Structural SIMilarity index metric (MS-SSIM), respectively. A new task-specific similarity assessment is introduced as well. A classifier is trained for severe cancer detection. The drop in the accuracy of this model when dealing with super-resolved images is used to evaluate the ability of medical detail reconstruction of the SR models. The proposed model surpassed state-of-the-art work with a 6% margin. The model is also more compact in comparison with the related architecture and has 45% less number of trainable parameters. The proposed SR model is a step towards an efficient and accurate general medical SR platform.
ESET: Threat Report Q2 2020

The Covid-19 pandemic has brought death, despair, disruption and – for a callous few – opportunity. Cyber criminals have not been furloughed – in fact, they have been busier than ever and have come up with topical new ways of luring victims into parting with their money.
Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.
LargeEA

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). Current EA approaches suffer from scalability issues, limiting their usage in real-world EA scenarios. To tackle this challenge, we propose LargeEA to align entities between large-scale KGs. LargeEA consists of two channels, i.e., structure channel and name channel. For the structure channel, we present METIS-CPS, a memory-saving mini-batch generation strategy, to partition large KGs into smaller mini-batches. LargeEA, designed as a general tool, can adopt any existing EA approach to learn entities' structural features within each mini-batch independently. For the name channel, we first introduce NFF, a name feature fusion method, to capture rich name features of entities without involving any complex training process; we then exploit a name-based data augmentation to generate seed alignment without any human intervention. Such design fits common real-world scenarios much better, as seed alignment is not always available. Finally, LargeEA derives the EA results by fusing the structural features and name features of entities. Since no widely-acknowledged benchmark is available for large-scale EA evaluation, we also develop a large-scale EA benchmark called DBP1M extracted from real-world KGs. Extensive experiments confirm the superiority of LargeEA against state-of-the-art competitors.
Robust echo state network with sparse online learning

Echo state network (ESN) is an effective tool for nonlinear systems modeling. To handle irregular noises or outliers in practical systems and alleviate the overfitting issue, the robust echo state network with sparse online learning (RESN-SOL) is proposed. Firstly, the ε-insensitive loss function is introduced to replace the commonly used quadratic loss function, which is theoretically optimal for Gaussian noise distribution. Secondly, the online gradient descent algorithm is used to calculate the network readout. Notably, the better learning performance can be achieved by the constant learning rate rather than the decreasing step size. Based on this observation, the sparse online learning algorithm (SOL) is proposed, in which the constant step size is used. Particularly, the SOL is able to truncate the small weights in network readout to zero for achieving sparsity. Furthermore, the convergence of RESN-SOL is theoretically analyzed, which implies the tradeoff between learning performance and readout sparsity can be controlled by a predefined sparsity parameter. Finally, the proposed method is verified in two simulated benchmarks and an actual dynamical wastewater treatment system. Experimental results demonstrate that the RESN-SOL exhibits better robustness against outliers, network compactness and modeling accuracy than other existing algorithms.
Multi-scale multi-modal fusion for object detection in autonomous driving based on selective kernel

Fusion object detection using camera and LiDAR information in autonomous driving is still a challenging task, the difference between sensor data increases the difficulty of data fusion. To address this issue, we propose a multi-scale selective kernel fusion(MSSKF) method and demonstrate its practical utility by using LiDAR-camera fusion in object detection network. Specifically, a multi-scale feature fusion module that uses multi-scale convolution to separate the feature expression of multi-modal information and calculates the weight of each modal feature channel is proposed. We use the idea of multi-scale convolution and selection kernel to complete multi-modal fusion in object detection, which is conducive to solving the problem that the image and point cloud fusion are difficult to match due to the difference in data structure, and the complementarity of multi-modal information has been fully utilized. To verify the effectiveness of MSSKF, experiments on the KITTI object detection benchmark dataset are conducted. It has been observed that the proposed method achieves more accurate detection for pedestrians and vehicles, with a 1.6% gain in AP50 compared to the values of the original fusion method, reaching a score of 90.1%, and the mAP reached 60.9%. Experiments show that the proposed method introduces a new optimization idea for multi-modal fusion in the field of autonomous driving object detection, and the fusion detection efficiency is at over 12 fps on a single GPU.