Reviewerly/exHarmony huggingface dataset:
Pathways to Increasing Trust in Public Health Data

Digital tools make it easier to collect data about patients closer to where they live, understand their health needs better, and treat them faster, thereby saving lives. Community Health Workers (CHWs) are increasingly collecting digital data through the course of care delivery. However, despite the widespread championing of digital health tools, the full impact of these technologies has not been realized because CHW-collected data is often considered low quality and unreliable for data-driven decision-making. At a systemic level, mistrust in the quality of the data (a lack of so-called ‘data trust’) limits the potential impact. The primary objective of this research program was to identify inconsistent or problematic data (IoP) occurring across a digitally enabled CHW health system in order to recommend changes in digital health tools and processes that might increase trust in CHW-collected data. In this exploratory study, data were analyzed using a variety of statistical and data science approaches including clustering algorithms, histograms, box plots, and Sankey diagrams. IoP were identified from a suite of 160 tests internally developed to identify IoP at the health platform level. As anticipated, data exhibited issues with accuracy, completeness, and timeliness across digital health forms. While each IoP issue identified could be individually remediated, recommendations provided are centered on a platform-wide (and tool agnostic) approach to data quality in community health.
Predicting Game Difficulty and Engagement Using AI Players

This paper presents a novel approach to automated playtesting for the prediction of human player behavior and experience. It has previously been demonstrated that Deep Reinforcement Learning (DRL) game-playing agents can predict both game difficulty and player engagement, operationalized as average pass and churn rates. We improve this approach by enhancing DRL with Monte Carlo Tree Search (MCTS). We also motivate an enhanced selection strategy for predictor features, based on the observation that an AI agent's best-case performance can yield stronger correlations with human data than the agent's average performance. Both additions consistently improve the prediction accuracy, and the DRL-enhanced MCTS outperforms both DRL and vanilla MCTS in the hardest levels. We conclude that player modelling via automated playtesting can benefit from combining DRL and MCTS. Moreover, it can be worthwhile to investigate a subset of repeated best AI agent runs, if AI gameplay does not yield good predictions on average.
A Survey of Sim-to-Real Transfer Techniques Applied to Reinforcement Learning for Bioinspired Robots

The state-of-the-art reinforcement learning (RL) techniques have made innumerable advancements in robot control, especially in combination with deep neural networks (DNNs), known as deep reinforcement learning (DRL). In this article, instead of reviewing the theoretical studies on RL, which were almost fully completed several decades ago, we summarize some state-of-the-art techniques added to commonly used RL frameworks for robot control. We mainly review bioinspired robots (BIRs) because they can learn to locomote or produce natural behaviors similar to animals and humans. With the ultimate goal of practical applications in real world, we further narrow our review scope to techniques that could aid in sim-to-real transfer. We categorized these techniques into four groups: 1) use of accurate simulators; 2) use of kinematic and dynamic models; 3) use of hierarchical and distributed controllers; and 4) use of demonstrations. The purposes of these four groups of techniques are to supply general and accurate environments for RL training, improve sampling efficiency, divide and conquer complex motion tasks and redundant robot structures, and acquire natural skills. We found that, by synthetically using these techniques, it is possible to deploy RL on physical BIRs in actuality.
Recommendation Algorithm Using Clustering-Based UPCSim (CB-UPCSim)

One of the well-known recommendation systems is memory-based collaborative filtering that utilizes similarity metrics. Recently, the similarity metrics have taken into account the user rating and user behavior scores. The user behavior score indicates the user preference in each product type (genre). The added user behavior score to the similarity metric results in more complex computation. To reduce the complex computation, we combined the clustering method and user behavior score-based similarity. The clustering method applies k-means clustering by determination of the number of clusters using the Silhouette Coefficient. Whereas the user behavior score-based similarity utilizes User Profile Correlation-based Similarity (UPCSim). The experimental results with the MovieLens 100k dataset showed a faster computation time of 4.16 s. In addition, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values decreased by 1.88% and 1.46% compared to the baseline algorithm.
A Survey of Vision-Based Transfer Learning in Human Activity Recognition

Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research.
Asymmetric watermarking scheme for color images using cascaded unequal modulus decomposition in Fourier domain

In this paper, an asymmetric cryptosystem with unequal modulus decomposition in the Fourier domain is presented. The input-colour image is decomposed into its red, green, and blue components. Each component is bounded with random phase mask and undergoes Fourier Transform followed by unequal modulus decomposition. One of resulting masks acts as first private key and other one is again Fourier Transformed and undergoes unequal modulus decomposition. Further two masks are obtained, where one acts as second private key and other is phase truncated to obtain encrypted image. Encrypted image is attenuated by a factor and appended with host image to obtain watermarked image. Numerical simulations on MATLAB are performed for authenticating and validating proposed scheme. Statistical, correlation distribution, information entropy and histogram analyses are performed to demonstrate scheme efficacy. The results illustrate that the scheme resists classical cryptographic, special and occlusion attacks. The proposed scheme is also highly sensitive to its private keys and attenuation factor.
Random Blur Data Augmentation for Scene Text Recognition

In this paper, we propose to apply data augmentation approaches that provide more diverse training images, thus helping train more robust deep models for the Scene Text Recognition (STR) task.The data augmentation methods are Random Blur Region (RBR) and Random Blur Units (RBUs).Specifically, we first introduce RBR designed for the STR task.In training, RBR randomly selects a region and sets the pixels in this region with an average value.However, when RBR provides more various training samples for STR, it may make the samples ambiguous and reduce the recognition accuracy.To address the above problem, we also propose RBUs that divides the blur region into several units.Note that the pixels of one unit share the same value.In this way, RBUs can provide additional readable training samples and help train more robust deep models.Extensive experiments on several STR datasets show that RBUs achieve highly competitive performance.Besides, RBUs are complementary to commonly used data augmentation techniques.
Hierarchical Attention Guided Framework for Multi-resolution Collaborative Whole Slide Image Segmentation

Segmentation of whole slide images (WSIs) is an important step for computer-aided cancer diagnosis. However, due to the gigapixel dimension, WSIs are usually cropped into patches for analysis. Processing high-resolution patches independently may leave out the global geographical relationships and suffer slow inference speed while using low-resolution patches can enlarge receptive fields but lose local details. Here, we propose a Hierarchical Attention Guided (HAG) framework to address above problems. Particularly, our framework contains a global branch and several local branches to perform prediction at different scales. Additive hierarchical attention maps are generated by the global branch with sparse constraints to fuse multi-resolution predictions for better segmentation. During the inference, the sparse attention maps are used as the certainty guidance to select important local areas with a quadtree strategy for acceleration. Experimental results on two WSI datasets highlight two merits of our framework: 1) effectively aggregate multi-resolution information to achieve better results, 2) significantly reduce the computational cost to accelerate the prediction without decreasing accuracy.
Extreme Structure from Motion for Indoor Panoramas without Visual Overlaps

This paper proposes an extreme Structure from Motion (SfM) algorithm for residential indoor panoramas that have little to no visual overlaps. Only a single panorama is present in a room for many cases, making the task infeasible for existing SfM algorithms. Our idea is to learn to evaluate the realism of room/door/window arrangements in the topdown semantic space. After using heuristics to enumerate possible arrangements based on door detections, we evaluate their realism scores, pick the most realistic arrangement, and return the corresponding camera poses. We evaluate the proposed approach on a dataset of 1029 panorama images with 286 houses. Our qualitative and quantitative evaluations show that an existing SfM approach completely fails for most of the houses. The proposed approach achieves the mean positional error of less than 1.0 meter for 47% of the houses and even 78% when considering the top five reconstructions. We will share the code and data in https://github.com/aminshabani/extreme-indoor-sfm.
Dynamic-transmission-based recursive filtering algorithm for microseismic event detection under sensor saturations

This paper is dedicated to dealing with the recursive filtering problem for a class of microseismic signal arrival time picking with dynamic transmission mechanism (DTM) and sensor saturation (SS). Firstly, the microseismic wave and its environmental noise are established as an exponentially decaying cyclic wave and a Markov process, respectively, and then the state space expression of the microseismic system is exhibited. Next, the phenomenon of the SS is taken into account to describe the practical microseismic system. Simultaneously, the DTM is introduced to avoid the waste of communication resources and energy, where more valuable measurements are transmitted to the remote filter. The purpose of this paper is to design a filter such that the arrival time of P wave and S wave (ATPSW) of microseismic signals is picked automatically in consideration of the SS and the DTM, and an upper bound of the filtering error covariance matrix is optimized by the designed filter gain at each time instant. Subsequently, a sufficient condition is given to ensure that the filtering error is exponential bounded in the sense of mean square. Finally, the effectiveness of the proposed algorithm is illustrated by a simulation experiment.
Smart Bus and Bus Stop Management System using IoT Technology

In India, many forms of public transportation are convenient for the users, amidst them buses play a key role. An average person in a city roughly spends thirty minutes for bus arrival every single day that merely adds up eight hours a week. Energy consumption by buses has dramatically increased and so the dependency on petroleum with raising pollution. In addition, lack of proper bus transportation data like navigation, timetable, and seat availability in the bus. The practice of paper tickets causing environmental damage by deforestation and also poor services like seating, lighting, shelter in the bus station. Our proposed system aims to provide better bus management that is capable of bus tracking, route scheduling, E-ticketing via IoT, eventually making commuter’s journey peaceful, saving paper through digitalization.
Picture fuzzy tolerance graphs with application

Abstract In this study, the notions of picture fuzzy tolerance graphs, picture fuzzy interval containment graphs and picture fuzzy $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> -tolerance graphs are established. Three special types of picture fuzzy tolerance graphs having bounded representations are introduced and studied corresponding properties of them taking $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> as max, min and sum functions. Also, picture fuzzy proper and unit tolerance graphs are established and some related results are investigated. The class of picture fuzzy $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> -tolerance chaingraphs which is the picture fuzzy $$\phi $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ϕ</mml:mi> </mml:math> -tolerance graphs of a nested family of picture fuzzy intervals are presented. A real-life application in sports competition is modeled using picture fuzzy min-tolerance graph. Also a comparison is given between picture fuzzy tolerance graphs and intuitionistic fuzzy tolerance graphs.
Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study

Unplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans.The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management.Retrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC).Based on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.14 ± 0.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation.This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.
Training Deep Capsule Networks with Residual Connections

Capsule networks are a type of neural network that have recently gained increased popularity. They consist of groups of neurons, called capsules, which encode properties of objects or object parts. The connections between capsules encrypt part-whole relationships between objects through routing algorithms which route the output of capsules from lower level layers to upper level layers. Capsule networks can reach state-of-the-art results on many challenging computer vision tasks, such as MNIST, Fashion-MNIST and Small-NORB. However, most capsule network implementations use two to three capsule layers, which limits their applicability as expressivity grows exponentially with depth []. One approach to overcome such limitation would be to train deeper network architectures, as it has been done for convolutional neural networks with much increased success. In this paper we propose a methodology to train deeper capsule networks using residual connections, which is evaluated on four datasets and three different routing algorithms. Our experimental results show that in fact, performance increases when training deeper capsule networks. The source code is available on https://github.com/moejoe95/res-capsnet.
Blockchain Application Design and Algorithms for Traceability in Pharmaceutical Supply Chain

Blockchain technology has garnered attention from stakeholders in many domains, including healthcare, governance and supply chain management. In the context of healthcare, traceability of pharmaceutical drugs in a transparent yet secure manner can be made faster and efficient with blockchain. This paper presents a blockchain based solution for traceability known as PharmaChain. The traceability is achieved with application design and algorithms which are proposed in the work. The proposed application can be developed using hyperledger fabric deployed on dockers. The chain codes are written in javascript. The pharmaceutical blockchain proposed in this work consists of manufacturer, wholesaler, retailer and consumer. The right for registering a drug into the blockchain is granted to the manufacturers only and the ownership transfer of the drug is stored. This paper highlights the traceability of ownership transfer of the drug and validates its origin.
A Unified Hyper-GAN Model for Unpaired Multi-contrast MR Image Translation

Cross-contrast image translation is an important task for completing missing contrasts in clinical diagnosis. However, most existing methods learn separate translator for each pair of contrasts, which is inefficient due to many possible contrast pairs in real scenarios. In this work, we propose a unified Hyper-GAN model for effectively and efficiently translating between different contrast pairs. Hyper-GAN consists of a pair of hyper-encoder and hyper-decoder to first map from the source contrast to a common feature space, and then further map to the target contrast image. To facilitate the translation between different contrast pairs, contrast-modulators are designed to tune the hyper-encoder and hyper-decoder adaptive to different contrasts. We also design a common space loss to enforce that multi-contrast images of a subject share a common feature space, implicitly modeling the shared underlying anatomical structures. Experiments on two datasets of IXI and BraTS 2019 show that our Hyper-GAN achieves state-of-the-art results in both accuracy and efficiency, e.g., improving more than 1.47 and 1.09 dB in PSNR on two datasets with less than half the amount of parameters.
SAME: Deformable Image Registration Based on Self-supervised Anatomical Embeddings

In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm self-supervised anatomical embedding (SAM), which is capable of computing dense anatomical/semantic correspondences between two images at the pixel level. Our method is named SAM-enhanced registration (SAME), which breaks down image registration into three steps: affine transformation, coarse deformation, and deep deformable registration. Using SAM embeddings, we enhance these steps by finding more coherent correspondences, and providing features and a loss function with better semantic guidance. We collect a multi-phase chest computed tomography dataset with 35 annotated organs for each patient and conduct inter-subject registration for quantitative evaluation. Results show that SAME outperforms widely-used traditional registration techniques (Elastix FFD, ANTs SyN) and learning based VoxelMorph method by at least \(4.7\%\) and \(2.7\%\) in Dice scores for two separate tasks of within-contrast-phase and across-contrast-phase registration, respectively. SAME achieves the comparable performance to the best traditional registration method, DEEDS (from our evaluation), while being orders of magnitude faster (from 45 s to 1.2 s).
A Transformer-Based Hierarchical Variational AutoEncoder Combined Hidden Markov Model for Long Text Generation

The Variational AutoEncoder (VAE) has made significant progress in text generation, but it focused on short text (always a sentence). Long texts consist of multiple sentences. There is a particular relationship between each sentence, especially between the latent variables that control the generation of the sentences. The relationships between these latent variables help in generating continuous and logically connected long texts. There exist very few studies on the relationships between these latent variables. We proposed a method for combining the Transformer-Based Hierarchical Variational AutoEncoder and Hidden Markov Model (HT-HVAE) to learn multiple hierarchical latent variables and their relationships. This application improves long text generation. We use a hierarchical Transformer encoder to encode the long texts in order to obtain better hierarchical information of the long text. HT-HVAE's generation network uses HMM to learn the relationship between latent variables. We also proposed a method for calculating the perplexity for the multiple hierarchical latent variable structure. The experimental results show that our model is more effective in the dataset with strong logic, alleviates the notorious posterior collapse problem, and generates more continuous and logically connected long text.