Reviewerly/exHarmony huggingface dataset:
Deep HDR Imaging via A Non-Local Network

One of the most challenging problems in reconstructing a high dynamic range (HDR) image from multiple low dynamic range (LDR) inputs is the ghosting artifacts caused by the object motion across different inputs. When the object motion is slight, most existing methods can well suppress the ghosting artifacts through aligning LDR inputs based on optical flow or detecting anomalies among them. However, they often fail to produce satisfactory results in practice, since the real object motion can be very large. In this study, we present a novel deep framework, termed NHDRRnet, which adopts an alternative direction and attempts to remove ghosting artifacts by exploiting the non-local correlation in inputs. In NHDRRnet, we first adopt an Unet architecture to fuse all inputs and map the fusion results into a low-dimensional deep feature space. Then, we feed the resultant features into a novel global non-local module which reconstructs each pixel by weighted averaging all the other pixels using the weights determined by their correspondences. By doing this, the proposed NHDRRnet is able to adaptively select the useful information (e.g., which are not corrupted by large motions or adverse lighting conditions) in the whole deep feature space to accurately reconstruct each pixel. In addition, we also incorporate a triple-pass residual module to capture more powerful local features, which proves to be effective in further boosting the performance. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed NDHRnet in terms of suppressing the ghosting artifacts in HDR reconstruction, especially when the objects have large motions.
Automatic retrosynthetic route planning using template-free models

Retrosynthetic pathway planning using a template-free model coupled with heuristic Monte Carlo tree search.
Deeply-Supervised Networks With Threshold Loss for Cancer Detection in Automated Breast Ultrasound

ABUS, or Automated breast ultrasound, is an innovative and promising method of screening for breast examination. Comparing to common B-mode 2D ultrasound, ABUS attains operator-independent image acquisition and also provides 3D views of the whole breast. Nonetheless, reviewing ABUS images is particularly time-intensive and errors by oversight might occur. For this study, we offer an innovative 3D convolutional network, which is used for ABUS for automated cancer detection, in order to accelerate reviewing and meanwhile to obtain high detection sensitivity with low false positives (FPs). Specifically, we offer a densely deep supervision method in order to augment the detection sensitivity greatly by effectively using multi-layer features. Furthermore, we suggest a threshold loss in order to present voxel-level adaptive threshold for discerning cancer vs. non-cancer, which can attain high sensitivity with low false positives. The efficacy of our network is verified from a collected dataset of 219 patients with 614 ABUS volumes, including 745 cancer regions, and 144 healthy women with a total of 900 volumes, without abnormal findings. Extensive experiments demonstrate our method attains a sensitivity of 95% with 0.84 FP per volume. The proposed network provides an effective cancer detection scheme for breast examination using ABUS by sustaining high sensitivity with low false positives. The code is publicly available at https://github.com/nawang0226/abus_code.
Ant Lion Optimization: Variants, Hybrids, and Applications

Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.
Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification

Teachable Machine (teachablemachine.withgoogle.com) is a web-based GUI tool for creating custom machine learning classification models without specialized technical expertise. (Machine learning, or ML, lets systems learn to analyze data without being explicitly programmed.) We created it to help students, teachers, designers, and others learn about ML by creating and using their own classification models. Its broad uptake suggests it has empowered people to learn, teach, and explore ML concepts: People have created curriculum, tutorials, and other resources using Teachable Machine on topics like AI ethics at institutions including the Stanford d.school, NYU's Interactive Telecommunications Program, the MIT Media Lab, as well as creative experiments. Users in 201 countries have created over 125,000 classification models. Here we outline the project and its key contributions of (1) a flexible, approachable interface for ML classification models without ML or coding expertise, (2) a set of technical and design decisions that can inform future interactive machine learning tools, and (3) an example of how structured learning content surrounding the tool supports people accessing ML concepts.
Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-Training

Learning to navigate in a visual environment following natural-language instructions is a challenging task, because the multimodal inputs to the agent are highly variable, and the training data on a new task is often limited. In this paper, we present the first pre-training and fine-tuning paradigm for vision-and-language navigation (VLN) tasks. By training on a large amount of image-text-action triplets in a self-supervised learning manner, the pre-trained model provides generic representations of visual environments and language instructions. It can be easily used as a drop-in for existing VLN frameworks, leading to the proposed agent PREVALENT. It learns more effectively in new tasks and generalizes better in a previously unseen environment. The performance is validated on three VLN tasks. On the Room-to-Room benchmark, our model improves the state-of-the-art from 47\% to 51\% on success rate weighted by path length. Further, the learned representation is transferable to other VLN tasks. On two recent tasks, vision-and-dialog navigation and ``Help, Anna!'', the proposed PREVALENT leads to significant improvement over existing methods, achieving a new state of the art.
Cars Can’t Fly Up in the Sky: Improving Urban-Scene Segmentation via Height-Driven Attention Networks

This paper exploits the intrinsic features of urban-scene images and proposes a general add-on module, called height-driven attention networks (HANet), for improving semantic segmentation for urban-scene images. It emphasizes informative features or classes selectively according to the vertical position of a pixel. The pixel-wise class distributions are significantly different from each other among horizontally segmented sections in the urban-scene images. Likewise, urban-scene images have their own distinct characteristics, but most semantic segmentation networks do not reflect such unique attributes in the architecture. The proposed network architecture incorporates the capability exploiting the attributes to handle the urban scene dataset effectively. We validate the consistent performance (mIoU) increase of various semantic segmentation models on two datasets when HANet is adopted. This extensive quantitative analysis demonstrates that adding our module to existing models is easy and cost-effective. Our method achieves a new state-of-the-art performance on the Cityscapes benchmark with a large margin among ResNet101 based segmentation models. Also, we show that the proposed model is coherent with the facts observed in the urban scene by visualizing and interpreting the attention map. Our code and trained models are publicly available.
Augment Your Batch: Improving Generalization Through Instance Repetition

Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling for a fixed budget of optimization steps. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently.
A systematic literature review of personalized learning terms

Abstract Learning is a natural human activity that is shaped by personal experiences, cognitive awareness, personal bias, opinions, cultural background, and environment. Learning has been defined as a stable and persistent change in what a person knows and can do. Learning is formed through an individual’s interactions, including the conveyance of knowledge and skills from others and experiences. So, learning is a personalized experience that allows one to expand their knowledge, perspective, skills, and understanding. Therefore, personalized learning models can help to meet individual needs and goals. Furthermore, to personalize the learning experience, technology integration can play a crucial role. This paper provides a review of the recent research literature on personalized learning as technology is changing how learning can be effectively personalized. The emphasis is on the terms used to characterize learning as those can suggest a framework for personalized and will eventually be used in meta-analyses of research on personalized learning, which is beyond the scope of this paper.
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers

Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem from a sequence-to-sequence correspondence perspective to replace cost volume construction with dense pixel matching using position information and attention. This approach, named STereo TRansformer (STTR), has several advantages: It 1) relaxes the limitation of a fixed disparity range, 2) identifies occluded regions and provides confidence estimates, and 3) imposes uniqueness constraints during the matching process. We report promising results on both synthetic and real-world datasets and demonstrate that STTR generalizes across different domains, even without fine-tuning.
Embedding Propagation: Smoother Manifold for Few-Shot Classification

Few-shot classification is challenging because the data distribution of the training set can be widely different to the test set as their classes are disjoint. This distribution shift often results in poor generalization. Manifold smoothing has been shown to address the distribution shift problem by extending the decision boundaries and reducing the noise of the class representations. Moreover, manifold smoothness is a key factor for semi-supervised learning and transductive learning algorithms. In this work, we propose to use embedding propagation as an unsupervised non-parametric regularizer for manifold smoothing in few-shot classification. Embedding propagation leverages interpolations between the extracted features of a neural network based on a similarity graph. We empirically show that embedding propagation yields a smoother embedding manifold. We also show that applying embedding propagation to a transductive classifier achieves new state-of-the-art results in miniImagenet, tieredImagenet, Imagenet-FS, and CUB. Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16% points. The proposed embedding propagation operation can be easily integrated as a non-parametric layer into a neural network. We provide the training code and usage examples at https://github.com/ElementAI/embedding-propagation .
Lichtenberg algorithm: A novel hybrid physics-based meta-heuristic for global optimization

This paper proposes a novel global optimization algorithm called Lichtenberg Algorithm (LA), inspired by the Lichtenberg figures patterns. Optimization is an essential tool to minimize or maximize functions, obtaining optimal results on costs, mass, energy, gains, among others. Actual problems may be multimodal, nonlinear, and discontinuous and may not be minimized by classical analytical methods that depend on the gradient. In this context there are metaheuristics algorithms inspired by natural phenomena to optimize real problems. There is no algorithm that is the worst or the best, but more efficient for a given type of problem. Thus, an unprecedented metaheuristic algorithm was created inspired by the physical phenomenon of radial intra-cloud lightning and Lichtenberg figures, successfully exploiting the fractal power and it is different from many in the literature as it is a hybrid algorithm composed of methods of search based on population and trajectory. Several test functions, including a design problem in a welded beam, were used to verify the robustness and to validate the Lichtenberg Algorithm. In all cases, the results were satisfactory when compared to those in the literature. LA shown to be a powerful optimization tool for both unconstraint optimizations and real problems with linear and nonlinear constraints.
A CenterNet++ model for ship detection in SAR images

Ship detection in SAR images is a challenging task due to two difficulties. (1) Because of the long observation distance, ships in SAR images are small with low resolution, leading to high false negative. (2) Because of the complex onshore background, ships are easily confused with other objects with similar appearance. To solve these problems, we propose an effective and stable single-stage detector called CenterNet++. Our model mainly consists of three modules, i.e., feature refinement module, feature pyramids fusion module, and head enhancement module. Firstly, to address small objects detection problem, we design a feature refinement module for extracting multi-scale contextual information. Secondly, feature pyramids fusion module is developed for generating more powerful semantic information. Finally, to alleviate the impact of complex background, head enhancement module is proposed for a balance between foreground and background. To prove the effectiveness and robustness of the proposed method, we make extensive experiments on three popular SAR image datasets, i.e., AIR-SARShip, SSDD, SAR-Ship. The experimental results show that our CenterNet++ reaches state-of-the-art performance on all datasets. In addition, compared with the baseline CenterNet, the proposed method achieves a remarkable accuracy improvement with negligible increase in time cost.
Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

This paper presents the HASOC track and its two parts. HASOC is dedicated to evaluate technology for finding Offensive Language and Hate Speech. HASOC is creating test collections for languages with few resources and English for comparison. The first track within HASOC has continued work from 2019 and provided a testbed of Twitter posts for Hindi, German and English. The second track within HASOC has created test resources for Tamil and Malayalam in native and Latin script. Posts were extracted mainly from Youtube and Twitter. Both tracks have attracted much interest and over 40 research groups have participated as well as described their approaches in papers. In this overview, we present the tasks, the data and the main results.
hOCBS: A privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design

In the context of blockchain technology, “off-chain” refers to computation or data that is structurally external to the blockchain network. Off-Chain Blockchain Systems (OCBS) enable this information processing and management through distributed software architecture where the blockchain network interacts with off-chain resources. Hence, OCBS are a critical data governance component in the design of enterprise blockchain solutions, resulting in extensive research and development exploring the interplay between on-chain and off-chain storage and computation and efforts to evaluate their performance relative to other information management systems. Key features of OCBS’ are their ability to improve scalability, reduce data storage requirements, and enhance data privacy, all extremely critical issues to enable broader blockchain adoption. These OCBS features map well to the needs of the healthcare industry, particularly due to the need to manage various types of medical, consumer, and other health-related data. However, different types of health data are also subject to stringent regulatory, security and legal requirements, a key factor limiting blockchain adoption in the sector. In response, there is a critical need to better align OCBS design features to different types of healthcare data management and their respective governance and privacy regimes. This article first reviews the characteristics of different constructs of OCBS. It then proposes a modular hybrid privacy-preserving framework leveraging off-chain and on-chain blockchain system design applied to three different reference models that illustrate how blockchain can enhance healthcare information management. Through this privacy-preserving framework we hope to liberate healthcare data by enabling sharing, sovereignty and enhanced trust.
Big data driven supply chain design and applications for blockchain: An action research using case study approach

Blockchain appears to still be nascent in its growth and a relatively untapped asset. This research investigates the need of blockchain in Industry 4.0 environment from Big Data perspective in supply chain management. The research method used in this study involves a combination of an Action Research method and Case Study research. More specifically, the action research method was applied in two industry case studies that implemented and tested the designed architecture in a global logistics environment. Case Study A examined the blockchain application in cross-border cargo movements whereas Case Study B investigated the application in a liquid chemical logistics company serving to petroleum industries. Our research analysis has identified that the Case A subject had disconnected systems and services for blockchain wherein the big data interactions had failed (failure case). Whereas in Case B, the company has achieved nearly 25% increase in revenue through its customer service after the blockchain implementation and thereby reduction in paperwork and carbon emissions (success case). This research contributes to the advancement of the body of knowledge to big data and blockchain by identifying key implementation guideline and issues for blockchain in supply chain management. Further, action-based research coupled with a case study approach has been used to evaluate the application aspects of the architecture's scalability and functionality of bigdata and blockchain in supply chain management.
Multi-Band Melgan: Faster Waveform Generation For High-Quality Text-To-Speech

In this paper, we propose multi-band MelGAN, a much faster waveform generation model targeting to high-quality text-to-speech. Specifically, we improve the original MelGAN by the following aspects. First, we increase the receptive field of the generator, which is proven to be beneficial to speech generation. Second, we substitute the feature matching loss with the multi-resolution STFT loss to better measure the difference between fake and real speech. Together with pre-training, this improvement leads to both better quality and better training stability. More importantly, we extend MelGAN with multi-band processing: the generator takes mel-spectrograms as input and produces sub-band signals which are subsequently summed back to full-band signals as discriminator input. The proposed multi-band MelGAN has achieved high MOS of 4.34 and 4.22 in waveform generation and TTS, respectively. With only 1.91M parameters, our model effectively reduces the total computational complexity of the original MelGAN from 5.85 to 0.95 GFLOPS. Our Pytorch implementation can achieve a real-time factor of 0.03 on CPU without hardware specific optimization.
Small Object Detection using Context and Attention

There are many limitations applying object detection algorithm on various environments. Specifically, detecting small objects is still challenging because they have low-resolution and limited information. We propose an object detection method using context for improving accuracy of detecting small objects. The proposed method uses additional features from different layers as context by concatenating multi-scale features. We also propose object detection with attention mechanism which can focus on the object in image, and it can include contextual information from target layer. Experimental results shows that proposed method also has higher accuracy than conventional SSD on detecting small objects. Moreover, for $300 \times 300$ input, we achieved 78.1% Mean Average Precision (mAP) on the PASCAL VOC2007 test set.
What serverless computing is and should become

The evolution that serverless computing represents, the economic forces that shape it, why it could fail, and how it might fulfill its potential.
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

We present a large-scale study on unsupervised spatiotemporal representation learning from videos. With a unified perspective on four recent image-based frameworks, we study a simple objective that can easily generalize all these methods to space-time. Our objective encourages temporally-persistent features in the same video, and in spite of its simplicity, it works surprisingly well across: (i) different unsupervised frameworks, (ii) pre-training datasets, (iii) downstream datasets, and (iv) backbone architectures. We draw a series of intriguing observations from this study, e.g., we discover that encouraging long-spanned persistency can be effective even if the timespan is 60 seconds. In addition to state-of-the-art results in multiple benchmarks, we report a few promising cases in which unsupervised pre-training can outperform its supervised counterpart. Code will be made available at https://github.com/facebookresearch/SlowFast.