Reviewerly/exHarmony huggingface dataset:
A Comprehensive Benchmark for Single Image Compression Artifact Reduction

We present a comprehensive study and evaluation of existing single image compression artifact removal algorithms using a new 4K resolution benchmark. This benchmark is called the Large-Scale Ideal Ultra high-definition 4K (LIU4K), and it includes including diversified foreground objects and background scenes with rich structures. Compression artifact removal, as a common post-processing technique, aims at alleviating undesirable artifacts, such as blockiness, ringing, and banding caused by quantization and approximation in the compression process. In this work, a systematic listing of the reviewed methods is presented based on their basic models (handcrafted models and deep networks). The main contributions and novelties of these methods are highlighted, and the main development directions are summarized, including architectures, multi-domain sources, signal structures, and new targeted units. Furthermore, based on a unified deep learning configuration ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> same training data, loss function, optimization algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> ), we evaluate recent deep learning-based methods based on diversified evaluation measures. The experimental results show state-of-the-art performance comparisons of existing methods based on both full-reference, non-reference, and task-driven metrics. Our survey gives a comprehensive reference source for future research on single image compression artifact removal and inspires new directions in related fields.
Unsupervised Acoustic Unit Discovery for Speech Synthesis Using Discrete Latent-Variable Neural Networks

For our submission to the ZeroSpeech 2019 challenge, we apply discrete latent-variable neural networks to unlabelled speech and use the discovered units for speech synthesis. Unsupervised discrete subword modelling could be useful for studies of phonetic category learning in infants or in low-resource speech technology requiring symbolic input. We use an autoencoder (AE) architecture with intermediate discretisation. We decouple acoustic unit discovery from speaker modelling by conditioning the AE's decoder on the training speaker identity. At test time, unit discovery is performed on speech from an unseen speaker, followed by unit decoding conditioned on a known target speaker to obtain reconstructed filterbanks. This output is fed to a neural vocoder to synthesise speech in the target speaker's voice. For discretisation, categorical variational autoencoders (CatVAEs), vector-quantised VAEs (VQ-VAEs) and straight-through estimation are compared at different compression levels on two languages. Our final model uses convolutional encoding, VQ-VAE discretisation, deconvolutional decoding and an FFTNet vocoder. We show that decoupled speaker conditioning intrinsically improves discrete acoustic representations, yielding competitive synthesis quality compared to the challenge baseline.
A Survey on Personalized News Recommendation Technology

In the face of massive data on the Internet, users often “lost themselves”. Personalized recommendation technology has made breakthroughs in areas such as e-commerce, advertising, audio and video recommendation in recent years. Due to the inherent characteristics of network news, such as the massive data, heterogeneity, update and change fast, timeliness and strong geographical awareness and so on, the progress of personalized recommendation technology in the field of news lags behind the above areas. And it cannot meet the requirements in news field entirely. Therefore, it is the main task of the current news recommendation system to integrate the existing personalized recommendation technologies into the news recommendation field, to study how to handle massive heterogeneous news data, to construct an optimal user preference model, and to improve the overall performance of the personalized news recommendation. This paper presents the state-of-the-art personalized news recommendation technologies in recent years, and analyzes the advantages and disadvantages of the mainstream technology based on seven main directions. Finally, the open issues in the development of personalized news recommendation technology are analyzed and concluded, hoping to guide the related work in the future.
On Model Stability as a Function of Random Seed

In this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of the model in general.We specifically perform a controlled study on the effect of random seeds on the behaviour of attention, gradientbased and surrogate model based (LIME) interpretations.Our analysis suggests that random seeds can adversely affect the consistency of models resulting in counterfactual interpretations.We propose a technique called Aggressive Stochastic Weight Averaging (ASWA) and an extension called Norm-filtered Aggressive Stochastic Weight Averaging (NASWA) which improves the stability of models over random seeds.With our ASWA and NASWA based optimization, we are able to improve the robustness of the original model, on average reducing the standard deviation of the model's performance by 72%.
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection. In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We also evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher return during training than existing methods.
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets

Existing analysis work in machine reading comprehension (MRC) is largely concerned with evaluating the capabilities of systems. However, the capabilities of datasets are not assessed for benchmarking language understanding precisely. We propose a semi-automated, ablation-based methodology for this challenge; By checking whether questions can be solved even after removing features associated with a skill requisite for language understanding, we evaluate to what degree the questions do not require the skill. Experiments on 10 datasets (e.g., CoQA, SQuAD v2.0, and RACE) with a strong baseline model show that, for example, the relative scores of the baseline model provided with content words only and with shuffled sentence words in the context are on average 89.2% and 78.5% of the original scores, respectively. These results suggest that most of the questions already answered correctly by the model do not necessarily require grammatical and complex reasoning. For precise benchmarking, MRC datasets will need to take extra care in their design to ensure that questions can correctly evaluate the intended skills.
A Novel Attention Cooperative Framework for Automatic Modulation Recognition

Modulation recognition plays an indispensable role in the field of wireless communications.In this paper, a novel attention cooperative framework based on deep learning is proposed to improve the accuracy of the automatic modulation recognition (AMR).Within this framework, a convolutional neural network (CNN), a recurrent neural network (RNN), and a generative adversarial network (GAN) are constructed to cooperate in AMR.A cyclic connected CNN (CCNN) is designed to extract spatial features of the received signal, and a bidirectional RNN (BRNN) is constructed for obtaining temporal features.To take full advantage of the complementarity and relevance between the spatial and temporal features, a fusion strategy based on global average and max pooling (GAMP) is proposed.To deal with different influence levels of the signal feature maps, we present the attention mechanism in this framework to realize recalibration.Besides, modulation recognition based on deep learning requires numerous data for training purposes, which is difficult to achieve in practical AMR applications.Therefore, an auxiliary classification GAN (ACGAN) is developed as a generator to expand the training set, and we modify the loss function of ACGAN to accommodate the processing of the actual in-phase and quadrature (I/Q) signal data.Considering the difference in distribution between generated data and real data, we propose a novel auxiliary weighing loss function to achieve higher recognition accuracy.Experimental results on the dataset RML2016.10ashow that the proposed framework outperforms existing deep learning-based approaches and achieves 94% accuracy at high signal to noise ratio (SNR).
DeepNeuro: an open-source deep learning toolbox for neuroimaging

Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.
MRI Manufacturer Shift and Adaptation: Increasing the Generalizability of Deep Learning Segmentation for MR Images Acquired with Different Scanners

Purpose To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy. Materials and Methods This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n = 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation. Paired Wilcoxon signed rank tests were performed. Results The segmentation CNNs exhibited a significant performance drop when applied to datasets from different manufacturers (Dice reduced from 89.7% ± 2.3 [standard deviation] to 68.7% ± 10.8, P < .05, from 90.6% ± 2.1 to 59.5% ± 13.3, P < .05, from 89.2% ± 2.3 to 64.1% ± 12.0, P < .05, for manufacturer 1, 2, and 3, respectively). After manufacturer adaptation, the segmentation performance was significantly improved (from 68.7% ± 10.8 to 84.3% ± 6.2, P < .05, from 72.4% ± 10.2 to 85.7% ± 6.5, P < .05, for manufacturer 2 and 3, respectively). Quantitative LV function parameters were also significantly improved. For LVEF, the manufacturer adaptation increased the Pearson correlation from 0.005 to 0.89 for manufacturer 2 and from 0.77 to 0.94 for manufacturer 3. Conclusion A segmentation CNN well trained on datasets from one MRI manufacturer may not generalize well to datasets from other manufacturers. The proposed manufacturer adaptation can largely improve the generalizability of a deep learning segmentation tool without additional annotation. Supplemental material is available for this article. Keywords: Image Postprocessing, MR-Imaging, Segmentation © RSNA, 2020
Deep Learning-Based Cryptanalysis of Lightweight Block Ciphers

Most of the traditional cryptanalytic technologies often require a great amount of time, known plaintexts, and memory. This paper proposes a generic cryptanalysis model based on deep learning (DL), where the model tries to find the key of block ciphers from known plaintext-ciphertext pairs. We show the feasibility of the DL-based cryptanalysis by attacking on lightweight block ciphers such as simplified DES, Simon, and Speck. The results show that the DL-based cryptanalysis can successfully recover the key bits when the keyspace is restricted to 64 ASCII characters. The traditional cryptanalysis is generally performed without the keyspace restriction, but only reduced-round variants of Simon and Speck are successfully attacked. Although a text-based key is applied, the proposed DL-based cryptanalysis can successfully break the full rounds of Simon32/64 and Speck32/64. The results indicate that the DL technology can be a useful tool for the cryptanalysis of block ciphers when the keyspace is restricted.
Augmented Reality for Learning Mathematics: A Systematic Literature Review

In the last decade, the usage of Augmented Reality (AR) has proliferated especially in the education sector. However, only limited articles have systematically reviewed the research trends in the implementation of AR for learning mathematics. Thus, this paper presents a systematic and comprehensive analysis of the research trend for the period between the year 2015–2019. From a leading indexing Scopus database, this review has identified only 19 journal articles based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. These works of literature were analyzed and grouped into eight themes to illustrate the trends of these research focus: AR apps types, AR apps development tools, research contribution cat-egories, benefits of AR for math, learning problems, testing methods, and math subtopics. Moreover, this synthesized review on learning of mathematics through AR could benefit researchers and educators, thereby suggesting avenues for future research.
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

The \textit{transition matrix}, denoting the transition relationship from clean labels to noisy labels, is essential to build \textit{statistically consistent} classifiers in label-noise learning. Existing methods for estimating the transition matrix rely heavily on estimating the noisy class posterior. However, the estimation error for \textit{noisy class posterior} could be large due to the randomness of label noise. The estimation error would lead the transition matrix to be poorly estimated. Therefore, in this paper, we aim to solve this problem by exploiting the divide-and-conquer paradigm. Specifically, we introduce an \textit{intermediate class} to avoid directly estimating the noisy class posterior. By this intermediate class, the original transition matrix can then be factorized into the product of two easy-to-estimate transition matrices. We term the proposed method the \textit{dual $T$-estimator}. Both theoretical analyses and empirical results illustrate the effectiveness of the dual $T$-estimator for estimating transition matrices, leading to better classification performances.
On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment

Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer), with the most pronounced benefits accruing to low-resource languages.However, recent work has shown that this approach can degrade performance on high-resource languages, a phenomenon known as negative interference.In this paper, we present the first systematic study of negative interference.We show that, contrary to previous belief, negative interference also impacts low-resource languages.While parameters are maximally shared to learn language-universal structures, we demonstrate that language-specific parameters do exist in multilingual models and they are a potential cause of negative interference.Motivated by these observations, we also present a meta-learning algorithm that obtains better cross-lingual transferability and alleviates negative interference, by adding languagespecific layers as meta-parameters and training them in a manner that explicitly improves shared layers' generalization on all languages.Overall, our results show that negative interference is more common than previously known, suggesting new directions for improving multilingual representations. 1
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking

Attribution methods assess the contribution of inputs to the model prediction.One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed without affecting the prediction.Though conceptually simple, erasure's objective is intractable and approximate search remains expensive with modern deep NLP models.Erasure is also susceptible to the hindsight bias: the fact that an input can be dropped does not mean that the model 'knows' it can be dropped.The resulting pruning is over-aggressive and does not reflect how the model arrives at the prediction.To deal with these challenges, we introduce Differentiable Masking.DIFFMASK learns to maskout subsets of the input while maintaining differentiability.The decision to include or disregard an input token is made with a simple model based on intermediate hidden layers of the analyzed model.First, this makes the approach efficient because we predict rather than search.Second, as with probing classifiers, this reveals what the network 'knows' at the corresponding layers.This lets us not only plot attribution heatmaps but also analyze how decisions are formed across network layers.We use DIFFMASK to study BERT models on sentiment classification and question answering.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation Under Hand-Object Interaction

We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy of state-of-the-art methods can drop, and that they fail mostly on poses absent from the training set. Unfortunately, since the space of hand poses is highly dimensional, it is inherently not feasible to cover the whole space densely, despite recent efforts in collecting large-scale training datasets. This sampling problem is even more severe when hands are interacting with objects and/or inputs are RGB rather than depth images, as RGB images also vary with lighting conditions and colors. To address these issues, we designed a public challenge (HANDS’19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set. More exactly, HANDS’19 is designed (a) to evaluate the influence of both depth and color modalities on 3D hand pose estimation, under the presence or absence of objects; (b) to assess the generalisation abilities w.r.t. four main axes: shapes, articulations, viewpoints, and objects; (c) to explore the use of a synthetic hand models to fill the gaps of current datasets. Through the challenge, the overall accuracy has dramatically improved over the baseline, especially on extrapolation tasks, from 27 mm to 13 mm mean joint error. Our analyses highlight the impacts of: Data pre-processing, ensemble approaches, the use of a parametric 3D hand model (MANO), and different HPE methods/backbones.
Secured Smart Healthcare System: Blockchain and Bayesian Inference Based Approach

The Internet of Healthcare Things (IoHT) is a rising smart ubiquitous structure that interconnects shrewd gadgets, devices, stakeholders (e.g., specialists, patients, caregivers, researcher, and so on.), and infrastructure utilizing smart sensors. Over the past decade, exposure to modern devices and data sensing and analysis strategies has allowed numerous analysts to develop and deliver personalized IoHT services. This brought about an impressive number of examination results tending to the applications, difficulties, and likely arrangements focusing on secured communication inside the IoHT system. Despite sizable efforts devoted to it, secured service provisioning is a first-rate mission yet. This research aims at providing a multi-layer security framework for heterogeneous IoHT systems using bayesian inference based trust management with the digitally signed blockchain network that includes individual node and data protection. The simulation results reveal the feasibility and effectiveness of the proposed dynamic approach regarding malicious node detection.
A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser

Abstract Neural networks are one of the disruptive computing concepts of our time. However, they fundamentally differ from classical, algorithmic computing. These differences result in equally fundamental, severe and relevant challenges for neural network computing using current computing substrates. Neural networks urge for parallelism across the entire processor and for a co-location of memory and arithmetic, i.e. beyond von Neumann architectures. Parallelism in particular made photonics a highly promising platform, yet until now scalable and integratable concepts are scarce. Here, we demonstrate for the first time how a fully parallel and fully implemented photonic neural network can be realized by spatially multiplexing neurons across the complex optical near-field of a semiconductor multimode laser. Discrete spatial sampling defines ∼90 nodes on the surface of a large-area vertical cavity surface emitting laser that is optically injected with the artificial neural networks input information. Importantly, all neural network connections are realized in hardware, and our processor produces results without pre- or post-processing. Input and output weights are realized via the complex transmission matrix of a multimode fiber and a digital micro-mirror array, respectively. We train the readout weights to perform 2-bit header recognition, a 2-bit XOR logical function and 2-bit digital to analog conversion, and obtain <?CDATA $\lt0.9 \times 10^{-3}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>&lt;</mml:mo> <mml:mn>0.9</mml:mn> <mml:mo>×</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:math> and 2.9 × 10 −2 error rates for digit recognition and XOR, respectively. Finally, the digital to analog conversion can be realized with a standard deviation of only 5.4 × 10 −2 . Crucially, our proof-of-concept system is scalable to much larger sizes and to bandwidths in excess of 20 GHz.