Reviewerly/exHarmony huggingface dataset:
Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning

We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks. Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate expectation-maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias). The forward pass of the encoder approximates the sufficient statistics of the E-step as the solution to a sparse coding problem, using an iterative proximal gradient algorithm called FISTA. The encoder can be interpreted either as a recurrent neural network or as a deep residual network, with two-sided ReLU nonlinearities in both cases. The M-step is implemented via a two-stage backpropagation. The first stage relies on a linear decoder applied to the encoder and a norm-squared loss. It parallels the dictionary update step in dictionary learning. The second stage updates the regularization parameter by applying a loss function to the encoder that includes a prior on the parameter motivated by Bayesian statistics. We demonstrate in an image-denoising task that CRsAE learns Gabor-like filters and that the EM-inspired approach for learning biases is superior to the conventional approach. In an application to recordings of electrical activity from the brain, we demonstrate that CRsAE learns realistic spike templates and speeds up the process of identifying spike times by 900× compared with algorithms based on convex optimization.
Learning deep edge prior for image denoising

Image restoration is an important technique to deal with the degradation of the image. This paper presents an efficient and trusty denoising scheme, which combines the convolutional neural network (CNN) technique with the traditional variational model, to offer interpretable and high quality reconstructions. In this scheme, CNN, which has proven effectiveness in feature extraction tasks, is adopted to obtain the designed edge features from the noisy images, to be the prior of the reconstruction through an edge regularization. In the proposed denoising model, the total variation (TV) regularization is also adopted for its superior performance in allowing the sharp edges. The solution of the proposed model is obtained by using the Bregman splitting method, with the existence and the uniqueness of the solution also analyzed in this paper. Extensive experiments show that the two regularizations combined in the proposed model are able to fix the staircasing defects effectively and retrieve the fine textures in the recovered images as well, which outperforms the state-of-the-art interpretable denoising methods. Moreover, the proposed edge regularization can be easily extended into other kinds of noise or other restoration tasks, which implies the strong adaptivity of the proposed scheme.
Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework

Background An estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse. Objective The aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse. Methods We developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation). Results Our technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network. Conclusions The proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.
Triple attention network for video segmentation

Video segmentation automatically segments a target object throughout a video and has recently achieved good progress due to the development of deep convolutional neural networks (DCNNs). However, how to simultaneously capture long-range dependencies in multiple spaces remains an important issue in video segmentation. In this paper, we propose a novel triple attention network (TriANet) that simultaneously exploits temporal, spatial, and channel context knowledge by using the self-attention mechanism to enhance the discriminant ability of feature representations. We verify our method on the Shining3D dental, DAVIS16, and DAVIS17 datasets, and the results show our method to be competitive when compared with other state-of-the-art video segmentation methods.
Obstacle avoidance path planning of unmanned submarine vehicle in ocean current environment based on improved firework-ant colony algorithm

In order to solve the unmanned underwater vehicle two-dimensional autonomous path planning problem in the environment affected by ocean current and obstacles, this paper applies the improved Fireworks-Ant Colony Hybrid Algorithm to solve it. Firstly, a two-dimensional Lamb vortex ocean current environment model with randomly distributed obstacles is established, and the circular obstacle is equivalent to a square grid. Then, the mathematical model of path planning is established considering the energy consumption cost, navigation time cost and navigation distance cost. Finally, the improved fireworks-ant colony hybrid algorithm is applied to solve the nonlinear optimization problem, and this algorithm is compared with the basic ant colony algorithm for simulation experiments in the four different marine environments. The experimental results show that this algorithm can quickly find the global optimal solution, and the more complex the environment, the more obvious its advantages. The algorithm proposed in this paper provides a new way for autonomous path planning of underwater vehicles.
Part-of-speech tagging for Arabic tweets using CRF and Bi-LSTM

Over the past few years, Twitter has experienced massive growth and the volume of its online content has increased rapidly. This content has been a rich source for several studies that focused on natural language processing (NLP) research. However, Twitter data pose numerous challenges and obstacles to NLP tasks. For the English language, Twitter has an NLP tool that provides tweet-specific NLP tasks, which present significant opportunities for English NLP research and applications. Part-of-speech (POS) tagging for English tweets is one of the tasks that is offered and facilitated by such a tool. In contrast, only a few attempts have been made to develop POS taggers for Arabic content on Twitter. In this paper, we consider POS tagging, which is one of the NLP tasks that directly affects the performance of other subsequent text processing tasks. We introduce three manually annotated datasets for the POS tagging of Arabic tweets: the ‘Mixed,’ ‘MSA,’ and ‘GLF’ datasets with 3000, 1000, and 1000 Arabic tweets, respectively. In addition, we present an exploratory analysis of the behavior of using hashtags in Arabic tweets, which is a phenomenon that affects the task of POS tagging. We also present two supervised POS taggers that are developed based on two approaches: Conditional Random Fields and Bidirectional Long Short-Term Memory (Bi-LSTM) models. We conclude that the Bi-LSTM-based POS tagger achieves the state-of-the-art results for the ‘Mixed’ dataset with 96.5% accuracy. However, the specific-dialect taggers trained on the ‘MSA’ and ‘GLF’ datasets achieve an accuracy of 95.6% and 95%, respectively. The results for the ‘Mixed’ dataset indicate the effectiveness of developing a joint POS tagger without the need for a dialect-specific POS tagger.
SCAUL: Power Side-Channel Analysis With Unsupervised Learning

Existing power analysis techniques rely on strong adversary models with prior knowledge of the leakage or training data. We introduce side-channel analysis with unsupervised learning (SCAUL) that can recover the secret key without requiring prior knowledge or profiling (training). We employ an LSTM auto-encoder to extract features from power traces with high mutual information with the data-dependent samples of the measurements. We demonstrate that by replacing the raw measurements with the auto-encoder features in a classical DPA attack, the efficiency, in terms of required number of measurements for key recovery, improves by 10X. Further, we employ these features to identify a leakage model with sensitivity analysis and multi-layer perceptron (MLP) networks. SCAUL uses the auto-encoder features and the leakage model, obtained in an unsupervised approach, to find the correct key. On a lightweight implementation of AES on Artix-7 FPGA, we show that SCAUL is able to recover the correct key with 3,700 power measurements with random plaintexts, while a DPA attack requires at least 17,400 measurements. Using misaligned traces, with an uncertainty equal to 20 percent of the hardware clock cycle, SCAUL is able to recover the secret key with 12,300 measurements while the DPA attack fails to detect the key.
Secure Balance Planning of Off-blockchain Payment Channel Networks

Off-blockchain payment channels can significantly improve blockchain scalability by enabling a large number of micro-payments between two blockchain nodes, without committing every single payment to the blockchain. Multiple payment channels form a payment network, so that two nodes without direct channel connection can still make payments. A critical challenge in payment network construction is to decide how many funds should be deposited into payment channels as initial balances, which seriously influences the performance of payment networks, but has been seldom studied by existing work. In this paper, we address this challenge by designing PnP, a balance planning service for payment networks. Given estimated payment demands among nodes, PnP can decide channel balances to satisfy these demands with a high probability. It does not rely on any trusted third-parties, and can provide strong protection from malicious attacks with low overhead. It obtains these benefits with two novel designs, the cryptographic sortition and the chance-constrained balance planning algorithm. Experimental results on a testbed of 30 nodes show that PnP can enable 30% more payments than other designs.
Attribute-based Propensity for Unbiased Learning in Recommender Systems

Many modern recommender systems train their models based on a large amount of implicit user feedback data. Due to the inherent bias in this data (e.g., position bias), learning from it directly can lead to suboptimal models. Recently, unbiased learning was proposed to address such problems by leveraging counterfactual techniques like inverse propensity weighting (IPW). In these methods, propensity scores estimation is usually limited to item's display position in a single user interface (UI).
Deep Learning for Facial Beauty Prediction

Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.
ET-DeaL: A P2P Smart Contract-based Secure Energy Trading Scheme for Smart Grid Systems

A smart grid (SG) system offers many services to the end-users, such as load management, load forecasting, and energy trading (ET). As data among different devices in SG environment flows through an open channel, i.e., Internet, so, security and privacy always remains a challenging issue. Though many solutions exist for this problem in literature but these solutions are not adequate to handle security, privacy, latency, real-time settlement of ET. Moreover, most of the solutions reported in the literature are based upon the centralized architecture having single point of failure. Motivated from these facts, this paper proposes a scheme ET-DeaL, which is a Smart Contract-based Secure Energy Trading scheme for SG system for peer-to-peer (P2P) ET. ET-DeaL uses Ethereum smart contract (ESC) and Inter Planetary File System (IPFS) for the P2P ET management. Moreover, it manages the energy load of residential houses, industries, and electric vehicles (EVs). In ET-DeaL, security and privacy issues have been resolved using ESC, while storage cost issues are handled with IPFS protocol. We implemented a real time ESC and deploy it in Truffle suite. The security bugs of the ET-DeaL are tested on MyThril open-source tool. Finally, ET-DeaL performance evaluation demonstrates its effectiveness as compared to the traditional systems where it outperforms the existing schemes with respect to various performance evaluation metrics.
Randomized Kaczmarz with averaging

The randomized Kaczmarz (RK) method is an iterative method for approximating the least-squares solution of large linear systems of equations. The standard RK method uses sequential updates, making parallel computation difficult. Here, we study a parallel version of RK where a weighted average of independent updates is used. We analyze the convergence of RK with averaging and demonstrate its performance empirically. We show that as the number of threads increases, the rate of convergence improves and the convergence horizon for inconsistent systems decreases.
A Semantic-Aware Strategy for Automatic Speech Recognition Incorporating Deep Learning Models

Automatic Speech Recognition (ASR) is trending in the age of the Internet of Things and Machine Intelligence. It plays a pivotal role in several applications. Conventional models for automatic speech recognition do not yield a high accuracy rate especially in the context of native Indian Languages. This paper proposes a novel strategy to model an ASR and Speaker Recognition system for the Hindi language. A semantic-aware strategy incorporating acoustic modeling is encompassed along with Deep Learning Techniques such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs). MFCC is further imbibed into the model strategically for feature extraction and the RNN for pattern matching and decoding. The proposed strategy has shown a promising performance with the speaker recognition system yielding 97% accuracy and speech recognition system with a very low word error rate of 2.9 when compared to other existing solutions.
Input Redundancy for Parameterized Quantum Circuits

In this paper we deal with a special type of parameterized quantum circuits, the so-called quantum neural networks or QNN's, which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al.\ (Phys.\ Rev.\ A, 2018; see below). The input is encoded into amplitudes of states of qubits. The no-cloning principle of quantum mechanics suggests that there is an advantage in redundantly encoding the input value several times. We follow this suggestion, and prove lower bounds on the number of redundant copies for two types of input encoding. We draw conclusions for the architecture design of QNNs.
The challenges of responding to misinformation during a pandemic: content moderation and the limitations of the concept of harm

Social media have been central in informing people about the COVID-19 pandemic. They influence the ways in which information is perceived, communicated and shared online, especially with physical distancing measures in place. While these technologies have given people the opportunity to contribute to public discussions about COVID-19, the narratives disseminated on social media have also been characterised by uncertainty, disagreement, false and misleading advice. Global technology companies have responded to these concerns by introducing new content moderation policies based on the concept of harm to tackle the spread of misinformation and disinformation online. In this essay, we examine some of the key challenges in implementing these policies in real time and at scale, calling for more transparent and nuanced content moderation strategies to increase public trust and the quality of information about the pandemic consumed online.
DFM: A parameter-shared deep fused model for knowledge base question answering

Currently, Knowledge Base Question Answering (KBQA) is an important research topic in the fields of information retrieval (IR) and natural language processing (NLP). The most common questions asked on the Web are simple questions, which can be answered by a single relational fact in a knowledge base (KB). However, answering simple questions automatically remains a challenging task in the IR and NLP research communities. Based on a review of various studies and a detailed analysis, we surmise that these challenges are primarily related to the following concerns: (1) how to effectively access a large-scale KB; and (2) how to effectively reduce the gap between NL questions and the structured semantics in a KB. Most previous studies have considered these as separate and independent subtasks, subject detection and predicate matching. Here, we propose a deep fused model that combines subject detection and predicate matching under a unified framework. Specifically, we employ a subject detection model to recognize the subject entity in a question, and a multilevel semantic model to learn the semantic representations for questions and predicates. These models share parameters, and can be trained jointly. We evaluated the proposed method on both English and Chinese KBQA datasets. The experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art systems when applied to both datasets.
Multimodal Smart Eyewear for Longitudinal Eye Movement Tracking

Contextual measurements of multiple physiological signals using familiar garments or accessories can provide insights into health and behavior. Recording these signals on or near the face is challenging because most subjects are sensitive to implements placed on the face or head. Here, fabric electrodes and garment design strategies are used synergistically to afford an unobtrusive platform, Chesma, for longitudinal acquisition of physiological signals from a user's face. Chesma contains two novel fabric-based electrodes: a hydrogel electrode (tAgTrode) that retains its ionic character after multiple uses/washes, does not cause skin irritation, and can be continuously operated for upward of 8 h without losing signal integrity. Second is an ion-conductive fabric pressure sensor (Press-ION) that captures pulse waveforms. When sewn onto a lightweight eye mask, these electrodes simultaneously gather complementary electro-oculography and cardiac data, which can enable sleep quality and psychological studies and improve the accuracy and usability of virtual reality headsets.
High tissue contrast image synthesis via multistage attention-GAN: Application to segmenting brain MR scans

Magnetic resonance imaging (MRI) presents a detailed image of the internal organs via a magnetic field. Given MRI’s non-invasive advantage in repeated imaging, the low-contrast MR images in the target area make segmentation of tissue a challenging problem. This study shows the potential advantages of synthetic high tissue contrast (HTC) images through image-to-image translation techniques. Mainly, we use a novel cycle generative adversarial network (Cycle-GAN), which provides a mechanism of attention to increase the contrast within the tissue. The attention block and training on HTC images are beneficial to our model to enhance tissue visibility. We use a multistage architecture to concentrate on a single tissue as a preliminary and filter out the irrelevant context in every stage in order to increase the resolution of HTC images. The multistage architecture reduces the gap between source and target domains and alleviates synthetic images’ artefacts. We apply our HTC image synthesising method to two public datasets. In order to validate the effectiveness of these images we use HTC MR images in both end-to-end and two-stage segmentation structures. The experiments on three segmentation baselines on BraTS’18 demonstrate that joining the synthetic HTC images in the multimodal segmentation framework develops the average Dice similarity scores (DSCs) of 0.8%, 0.6%, and 0.5% respectively on the whole tumour (WT), tumour core (TC), and enhancing tumour (ET) while removing one real MRI channels from the segmentation pipeline. Moreover, segmentation of infant brain tissue in T1w MR slices through our framework improves DSCs approximately 1% in cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) compared to state-of-the-art segmentation techniques. The source code of synthesising HTC images is publicly available.