Reviewerly/exHarmony huggingface dataset:
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion detection employs two-stage region proposal based methods trained with centroid or bounding-box annotations. We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging. Specifically, we optimize the anchor configurations using a differential evolution search algorithm. For training, we leverage the response evaluation criteria in solid tumors (RECIST) annotation which are measured in clinical routine. We incorporate dense masks from weak RECIST labels, obtained automatically using GrabCut, into the training objective, which in combination with other advancements yields new state-of-the-art performance. We evaluate our method on the public DeepLesion benchmark, consisting of 32,735 lesions across the body. Our one-stage detector achieves a sensitivity of 90.77% at 4 false positives per image, significantly outperforming the best reported methods by over 5%.
Uses and Reuses of Scientific Data: The Data Creators’ Advantage

Open access to data, as a core principle of open science, is predicated on assumptions that scientific data can be reused by other researchers.We test those assumptions by asking where scientists find reusable data, how they reuse those data, and how they interpret data they did not collect themselves.By conducting a qualitative metaanalysis of evidence on two long-term, distributed, interdisciplinary consortia, we found that scientists frequently sought data from public collections and from other researchers for comparative purposes such as "ground-truthing" and calibration.When they sought others' data for reanalysis or for combining with their own data, which was relatively rare, most preferred to collaborate with the data creators.We propose a typology of data reuses ranging from comparative to integrative.Comparative data reuse requires interactional expertise, which involves knowing enough about the data to assess their quality and value for a specific comparison such as calibrating an instrument in a lab experiment.Integrative reuse requires contributory expertise, which involves the ability to perform the action, such as reusing data in a new experiment.Data integration requires more specialized scientific knowledge and deeper levels of epistemic trust in the knowledge products.Metadata, ontologies, and other forms of curation benefit interpretation for any kind of data reuse.Based on these findings, we theorize the data creators' advantage, that those who create data have intimate and tacit knowledge that can be used as barter to form collaborations for mutual advantage.Data reuse is a process that occurs within knowledge infrastructures that evolve over time, encompassing expertise, trust, communities, technologies, policies, resources, and institutions.
The triple-edged sword of COVID-19: understanding the use of digital technologies and the impact of productive, disruptive, and destructive nature of the pandemic

This opinion paper presents some observations on underlying complexities impacting organizations and societies due to Covid-19, and significant increase in the use of advanced IS/IT. Through the lens of current pandemic – the impact of Covid-19 and use of advanced IS/IT in avoiding loss of productivity, disruption in business operations and destruction as a whole, is studied. These observations offer timely insights on several topics, identifying key issues and recommendations for research and practice.
Modeling and Optimization of Performance and Cost of Serverless Applications

Function-as-a-Service (FaaS) and serverless applications have proliferated significantly in recent years because of their high scalability, ease of resource management, and pay-as-you-go pricing model. However, cloud users are facing practical problems when they migrate their applications to the serverless pattern, which are the lack of analytical performance and billing model and the trade-off between limited budget and the desired quality of service of serverless applications. In this article, we fill this gap by proposing and answering two research questions regarding the prediction and optimization of performance and cost of serverless applications. We propose a new construct to formally define a serverless application workflow, and then implement analytical models to predict the average end-to-end response time and the cost of the workflow. Consequently, we propose a heuristic algorithm named Probability Refined Critical Path Greedy algorithm (PRCP) with four greedy strategies to answer two fundamental optimization questions regarding the performance and the cost. We extensively evaluate the proposed models by conducting experimentation on AWS Lambda and Step Functions. Our analytical models can predict the performance and cost of serverless applications with more than 98 percent accuracy. The PRCP algorithms can achieve the optimal configurations of serverless applications with 97 percent accuracy on average.
The Outer Rim Simulation: A Path to Many-core Supercomputers

Abstract We describe the Outer Rim cosmological simulation, one of the largest high-resolution N -body simulations performed to date, aimed at promoting science to be carried out with large-scale structure surveys. The simulation covers a volume of (4.225 Gpc) 3 and evolves more than one trillion particles. It was executed on Mira, a BlueGene/Q system at the Argonne Leadership Computing Facility. We discuss some of the computational challenges posed by a system such as Mira, a many-core supercomputer, and how the simulation code, Hardware/Hybrid Accelerated Cosmology Code, has been designed to overcome these challenges. We have carried out a large range of analyses on the simulation data and we report on the results as well as the data products that have been generated. The full data set generated by the simulation totals more than 5 PB of data, making curation and handling of the data a large challenge in itself. The simulation results have been used to generate synthetic catalogs for large-scale structure surveys, including Dark Energy Spectroscopic Instrument and eBOSS, as well as experiments on the cosmic microwave background. A detailed catalog for the data challenges of the Large Synoptic Survey Telescope Dark Energy Science Collaboration has been created as well. We publicly release some of the Outer Rim halo catalogs, downsampled particle information, and lightcone data.
Context-aware Cross-level Fusion Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings. In addition, the appearance of camouflaged objects varies significantly, e.g., object size and shape, aggravating the difficulties of accurate COD. In this paper, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net) to address the challenging COD task. Specifically, we propose an Attention-induced Cross-level Fusion Module (ACFM) to integrate the multi-level features with informative attention coefficients. The fused features are then fed to the proposed Dual-branch Global Context Module (DGCM), which yields multi-scale feature representations for exploiting rich global context information. In C2F-Net, the two modules are conducted on high-level features using a cascaded manner. Extensive experiments on three widely used benchmark datasets demonstrate that our C2F-Net is an effective COD model and outperforms state-of-the-art models remarkably. Our code is publicly available at: https://github.com/thograce/C2FNet.
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128 languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107 language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform English-only pretraining when translating English speech into other languages, a setting which favors monolingual pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.
Dependency or Span, End-to-End Uniform Semantic Role Labeling

Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence. End-to-end SRL without syntactic input has received great attention. However, most of them focus on either span-based or dependency-based semantic representation form and only show specific model optimization respectively. Meanwhile, handling these two SRL tasks uniformly was less successful. This paper presents an end-to-end model for both dependency and span SRL with a unified argument representation to deal with two different types of argument annotations in a uniform fashion. Furthermore, we jointly predict all predicates and arguments, especially including long-term ignored predicate identification subtask. Our single model achieves new state-of-the-art results on both span (CoNLL 2005, 2012) and dependency (CoNLL 2008, 2009) SRL benchmarks.
Exploring Duplicated Regions in Natural Images

Duplication of image regions is a common method for manipulating original images, using typical software like Adobe Photoshop, 3DS MAX, etc. In this study, we propose a duplication detection approach that can adopt two robust features based on discrete wavelet transform (DWT) and kernel principal component analysis (KPCA). Both schemes provide excellent representations of the image data for robust block matching. Multiresolution wavelet coefficients and KPCA-based projected vectors corresponding to image-blocks are arranged into a matrix for lexicographic sorting. Sorted blocks are used for making a list of similar point-pairs and for computing their offset frequencies. Duplicated regions are then segmented by an automatic technique that refines the list of corresponding point-pairs and eliminates the minimum offset-frequency threshold parameter in the usual detection method. A new technique that extends the basic algorithm for detecting Flip and Rotation types of forgeries is also proposed. This method uses global geometric transformation and the labeling technique to indentify the mentioned forgeries. Experiments with a good number of natural images show very promising results, when compared with the conventional PCA-based approach. A quantitative analysis indicate that the wavelet-based feature outperforms PCA- or KPCA-based features in terms of average precision and recall in the noiseless, or uncompressed domain, while KPCA-based feature obtains excellent performance in the additive noise and lossy JPEG compression environments.
Multi angle optimal pattern-based deep learning for automatic facial expression recognition

Facial Expression Recognition (FER) plays the vital role in the Human Computer Interface (HCI) applications. The illumination and pose variations affect the FER adversely. The projection of complex 3D actions on the image plane and the inaccurate alignment are the major issues in the FER process. This paper presents the novel Multi-Angle Optimal Pattern-based Deep Learning (MAOP-DL) method to rectify the problem from sudden illumination changes, find the proper alignment of a feature set by using multi-angle-based optimal configurations. The proposed method includes the five major processes as Extended Boundary Background Subtraction (EBBS), Multi-Angle Texture Pattern+STM, Densely Extracted SURF+Local Occupancy Pattern (LOP), Priority Particle Cuckoo Search Optimization (PPCSO) and Long Short-Term Memory -Convolutional Neural Network (LSTM-CNN). Initially, the EBBS algorithm subtracts the background and isolates the foreground from the images which overcome the illumination and pose variation. Then, the MATP-STM extracts the texture patterns and DESURF-LOP extracts the relevant key features of the facial points. The PPCSO algorithm selects the relevant features from the MATP-STM feature set to speed up the classification. The employment of LSTM-CNN predicts the required label for the facial expressions.The major key findings of the proposed work are clear image analysis, effective handling of pose/illumination variations and the facial alignment. The proposed MAOP-DL validates its effectiveness on two standard databases such as CK+ and MMI regarding various metrics and confirm their assurance of wide applicability in recent applications.
What is the Effect of Importance Weighting in Deep Learning

Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning. While the effect of importance weighting is well-characterized for low-capacity misspecified models, little is known about how it impacts over-parameterized, deep neural networks. This work is inspired by recent theoretical results showing that on (linearly) separable data, deep linear networks optimized by SGD learn weight-agnostic solutions, prompting us to ask, for realistic deep networks, for which many practical datasets are separable, what is the effect of importance weighting? We present the surprising finding that while importance weighting impacts models early in training, its effect diminishes over successive epochs. Moreover, while L2 regularization and batch normalization (but not dropout), restore some of the impact of importance weighting, they express the effect via (seemingly) the wrong abstraction: why should practitioners tweak the L2 regularization, and by how much, to produce the correct weighting effect? Our experiments confirm these findings across a range of architectures and datasets.
Multi-view Spectral Clustering Network

Multi-view clustering aims to cluster data from diverse sources or domains, which has drawn considerable attention in recent years. In this paper, we propose a novel multi-view clustering method named multi-view spectral clustering network (MvSCN) which could be the first deep version of multi-view spectral clustering to the best of our knowledge. To deeply cluster multi-view data, MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function, where the local invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches. In addition, we enforce and reformulate an orthogonal constraint as a novel layer stacked on an embedding network for two advantages, i.e. jointly optimizing the neural network and performing matrix decomposition and avoiding trivial solutions. Extensive experiments on four challenging datasets demonstrate the effectiveness of our method compared with 10 state-of-the-art approaches in terms of three evaluation metrics.
HMEAE: Hierarchical Modular Event Argument Extraction

Xiaozhi Wang, Ziqi Wang, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Hierarchical Contextualized Representation for Named Entity Recognition

Named entity recognition (NER) models are typically based on the architecture of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the modeling of single input prevent the full utilization of global information from larger scope, not only in the entire sentence, but also in the entire document (dataset). In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation. In sentence-level, we take different contributions of words in a single sentence into consideration to enhance the sentence representation learned from an independent BiLSTM via label embedding attention mechanism. In document-level, the key-value memory network is adopted to record the document-aware information for each unique word which is sensitive to similarity of context information. Our two-level hierarchical contextualized representations are fused with each input token embedding and corresponding hidden state of BiLSTM, respectively. The experimental results on three benchmark NER datasets (CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset) show that we establish new state-of-the-art results.
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing

We frame the task of machine translation evaluation as one of scoring machine translation output with a sequence-to-sequence paraphraser, conditioned on a human reference.We propose training the paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot translation task (e.g., Czech to Czech).This results in the paraphraser's output mode being centered around a copy of the input sequence, which represents the best case scenario where the MT system output matches a human reference.Our method is simple and intuitive, and does not require human judgements for training.Our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT 2019 segment-level shared metrics task in all languages (excluding Gujarati where the model had no training data).We also explore using our model for the task of quality estimation as a metric-conditioning on the source instead of the reference-and find that it significantly outperforms every submission to the WMT 2019 shared task on quality estimation in every language pair.
BigEarthNet-MM: A Large-Scale, Multimodal, Multilabel Benchmark Archive for Remote Sensing Image Classification and Retrieval [Software and Data Sets]

This article presents the multimodal BigEarthNet (BigEarthNet-MM) benchmark archive consisting of 590,326 pairs of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sentinel-1</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sentinel-2</i> image patches to support deep learning (DL) studies in multimodal, multilabel remote sensing (RS) image retrieval and classification. Each pair of patches in BigEarthNet-MM is annotated with multilabels provided by the CORINE Land Cover (CLC) map of 2018 based on its thematically most detailed level-3 class nomenclature. Our initial research demonstrates that some CLC classes are challenging to accurately describe by considering only (single-date) BigEarthNet-MM images. In this article, we also introduce an alternative class nomenclature as an evolution of the original CLC labels to address this problem. This is achieved by interpreting and arranging the CLC level-3 nomenclature based on the properties of BigEarthNet-MM images in a new nomenclature of 19 classes. In our experiments, we show the potential of BigEarthNet-MM for multimodal, multilabel image retrieval and classification problems by considering several state-of-the-art DL models.
Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit

Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e., loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which were exploited in February 2020. This paper is the first to explore the implication of transaction atomicity and flash loans for the nascent decentralized finance (DeFi) ecosystem. We show quantitatively how transaction atomicity increases the arbitrage revenue. We moreover analyze two existing attacks with ROIs beyond 500k%. We formulate finding the attack parameters as an optimization problem over the state of the underlying Ethereum blockchain and the state of the DeFi ecosystem. We show how malicious adversaries can efficiently maximize an attack profit and hence damage the DeFi ecosystem further. Specifically, we present how two previously executed attacks can be “boosted” to result in a profit of 829.5k USD and 1.1M USD, respectively, which is a boost of 2.37 $$\times $$ and 1.73 $$\times $$ , respectively.
Using Virtual Reality Head-Mounted Displays in Schools with Autistic Children: Views, Experiences, and Future Directions

This article seeks to place children on the autism spectrum at the center of a study examining the potential of virtual reality (VR) head-mounted displays (HMDs) used in classrooms. In doing so, we provide data that address 3 important and often overlooked research questions in the field of autism and technology, working in school-based settings with 31 autistic children from 6 to 16 years of age. First, what type of VR HMD device (and experiences therein) are preferred by children on the autism spectrum using HMDs (given possible sensory concerns). Second, how do children on the autism spectrum report the physical experience, enjoyment, and potential of VR HMDs in their classrooms? Finally, we were interested in exploring what children on the autism spectrum would like to use VR in schools for? Through a mixed methods approach, we found that costly and technologically advanced HMDs were preferred (namely: HTC Vive). In addition, HMDs were reported as being enjoyable, physically and visually comfortable, easy to use, and exciting, and children wanted to use them again. They identified several potential usages for HMDs, including relaxing/feeling calm, being able to explore somewhere virtually before visiting in the real world, and to develop learning opportunities in school. We discuss these findings in the context of VR in classrooms, in addition to considering limitations and implication of our findings.