Reviewerly/exHarmony huggingface dataset:
Ethical Implementation of the Learning Healthcare System with Blockchain Technology

Introduction: A learning healthcare system (LHCS), the paradigm in which healthcare organization and delivery continuously maximizes and seamlessly incorporates new knowledge, has yet to be attained. In the ethical framework proposed for the LHCS, all healthcare stakeholders are responsible for contributing to learning, including patients, who would primarily fulfill this obligation by allowing their health data to be used for research. While patients will ultimately benefit from a LHCS, requiring individuals to give up some rights to control and privacy of their health data before reciprocal benefits are established is a critical barrier to implementation, particularly given erosion of public trust from increasingly common health data breaches and scandals. Objective: We propose that blockchain technology complemented by secure computation methods can foster implementation of a LHCS by minimizing upfront patient-facing compromises with unsurpassed data security and privacy, and by optimizing the system’s fulfillment of its obligations to respect patients through transparency, engagement, and accountability. Findings: We demonstrate how a blockchain-enabled LHCS could foster patient willingness to contribute to learning by providing desired security and control over health data. Also, secure computation methods could enable meta-analysis without exposing individual-level data, thus allowing the system to protect patients’ privacy while simultaneously learning from their data. The transparency and immutability of blockchain ledgers would also support the public’s trust in the system by allowing patients to audit and oversee which of their data are used, how they are used, and by whom. Further, blockchain communities are community-governed peer-to-peer networks in which sharing builds mutually beneficial value, offering a model for engaging patients as LHCS stakeholders. Lastly, smart contracts could be used to ensure accountability of the system by embedding feedback mechanisms by which patients directly and automatically realize benefits of sharing their data. Discussion: Blockchain technology united with secure computational methods represents an opportunity to overcome ethical barriers to transitioning to a LHCS via enhanced security, privacy and transparency for health data and capacity to optimize patient engagement and accountability. We close with limitations of current blockchain technology and next steps.
Case-Base Maintenance: An Approach Based on Active Semi-Supervised Learning

Case-Base Maintenance (CBM) becomes of great importance when implementing a Computer-Aided Diagnostic (CAD) system using Case-Based Reasoning (CBR). Since it is essential for the learning to avoid the case-base degradation, this work aims to build and maintain a quality case base while overcoming the difficulty of assembling labeled case bases, traditionally assumed to exist or determined by human experts. The proposed approach takes advantage of large volumes of unlabeled data to select valuable cases to add to the case base while monitoring retention to avoid performance degradation and to build a compact quality case base. We use machine learning techniques to cope with this challenge: an Active Semi-Supervised Learning approach is proposed to overcome the bottleneck of scarcity of labeled data. In order to acquire a quality case base, we target its performance criterion. Case selection and retention are assessed according to three combined sampling criteria: informativeness, representativeness, and diversity. We support our approach with empirical evaluations using different benchmark data sets. Based on experimentation, the proposed approach achieves good classification accuracy with a small number of retained cases, using a small training set as a case base.
An Automated Bengali Text Summarization Technique Using Lexicon-Based Approach

There is enough resources for English to process and obtain summarize documents. But this thing is not directly applicable for Bengali language as there is lots of complexity in Bengali, which is not same to English in the context of grammar and sentence structure. Again, doing this for Bengali is harder as there is no established tool to facilitate research work. But this necessary as 26 crore people use this language. So, we have gone for a new approach Bengali document summarization. Here, the system design has been completed by preprocessing the i/p (input) doc, tagging the word, replacing pronoun, sentence ranking, respectively. Pronoun replacement has been added here to minimize the rate of swinging pronoun in the output summary. As the pronoun replacement, we have gone ranking sentences according to sentence frequency, numerical figures (both in digit and word version) and document title. Here, if the sentence has any word that exists in title also taken into our account. The similarity between two sentences has been checked to deduct one as that causes less redundancy. The numerical figure also makes an impact, so they were also identified. We have taken over 3000 newspaper and books documents words has been trained according to grammar. And two documents have been checked by the design system to evaluate the efficiency of designed summarizer. From the evaluation system, it is been found that the recall, precision, F-score are 0.70 as it is 70%, 0.82 as it is 82%, 0.74 as it is 74%, respectively.
A digital image correlation-aided sampling moiré method for high-accurate in-plane displacement measurements

Sampling moiré method (SMM) is widely used for measuring high-accurate displacements and strains. However, it provides accurate measurement only when the displacement is smaller than half of grating pitch due to defects on phase difference calculation. To overcome the shortage of SMM, we improve the performance of SMM by adapting the integer-pixel digital image correlation (DIC). The improved SMM is capable of measuring much larger displacements than half of the grating pitch with high accuracy. Through computer simulation, we analysed the displacement measurement accuracy of the improved SMM under various grating pitches and sampling pitches with multiple noise levels. Then the proper range of sampling pitch was obtained. Two independent experiments with short- and long-range sight-distances, respectively, were conducted to validate the accuracy of the improved SMM successfully. In conclusion, with a mean measurement accuracy of up to 1/350 of a pitch, the improved SMM can be used for measuring high-accurate in-plane displacement whether the displacement is greater than a half of grating pitch or not.
Acsnet: Adaptive Cross-Scale Network with Feature Maps Refusion for Vehicle Density Detection

We investigate vehicle density detection from traffic surveillance. This task is rather challenging, mainly due to the low-resolution of data and large-scale variance of vehicles. The main result is that by learning cross-scale features, high-quality vehicle density maps can be attainable. Our main technical contribution is a learning model, called Adaptive Cross-Scale Network (ACSNet), that can learn cross-scale features from traffic surveillance data with low-resolution and large scale variance of vehicles. ACSNet consists of 1) a series of cross-scale feature extraction blocks with dense bypassing paths for harvesting spatial information, 2) an attention block for learning from appropriate scales, and 3) a structural similarity index for learning from occlusion scenes. We assess our ACSNet on two benchmark datasets, and extensive empirical evidence shows that our ACSNet performs favor-ably against the state-of-the-art methods.
Locally Weighted Non-Parametric Modeling of Ship Maneuvering Motion Based on Sparse Gaussian Process

This paper explores a fast and efficient method for identifying and modeling ship maneuvering motion, and conducts a comprehensive experiment. Through the ship maneuvering test, the dynamics interaction between ship and the environment is obtained. Then, the LWL (Locally Weighted Learning algorithm) underlying architecture is constructed by sparse Gaussian Process to reduce the data requirements of LWL-based ship maneuvering motion modeling and to improve the performance for LWL. On this basis, a non-parametric model of ship maneuvering motion is established based on the locally weighted sparse Gaussian Process, and the traditional mathematical model of ship maneuvering motion is replaced by the generative model. This generative model considers the hydrodynamic effects of ships, and reduces the sensitivity of local weighted learning to sample data. In addition, matrix operations are transferred to the auxiliary platform to optimize the calculation performance of the method. Finally, the simulation results of ship maneuvering motion indicate that this method has the characteristics of efficiency, rapidity and universality, and its accuracy conforms to engineering practice.
Deep Learning for Face Detection: Recent Advancements

Various applications like face analysis, recognition, reidentification exist where the use of Face Detection is necessary as their preprocessing algorithm in the pipeline. There has been extensive studies done in the domain of Face Detection in the past, and various robust algorithms have been proposed and evaluated on different datasets. Such techniques are also deployed in various applications. Although it may seem that this domain is very old and much work must have been done in it, there is still room for improvement. Previous studies have targeted issues like facial poses, expressions, scales of images and occlusions, and have achieved good accuracy. In recent years, work on advanced issues like low-resolution images, usage of proposed anchors, scale-invariance of models, minimization of model size, have been explored and various solutions have been proposed. In this paper, we will discuss the state-of-the-art publications in this domain, what issues they are targeting and what technologies they are using.
Hybrid homotopy-PSO global searching approach with multi-kernel extreme learning machine for efficient source identification of DNAPL-polluted aquifer

Groundwater pollution source identification (GPSI), which is critical for taking effective measures to protect groundwater resources, assess risks, and design remediation strategies, typically involves the solution of a nonlinear and ill-posed inverse problem. Regarding the inversion of dense non-aqueous phase liquid (DNAPL) sources, the special characteristics of pollutants render related research more complex. In the present study, homotopy-based optimization inverse theory and multi-kernel extreme learning machine (MK-ELM) were combined for efficiently solving GPSI problem while estimating aquifer parameters at a DNAPL-polluted site. The extreme learning machine incorporating multi kernels and whose parameters are obtained by means of a genetic algorithm (GA) was embedded in an optimization model for GPSI to replace the multiphase flow simulation model and to mitigate the considerable computational burdens of inversion iteration. The hybrid homotopy-particle swarm optimization (PSO) algorithm was constructed as a more efficient method for segmentally searching the global optimum in wide areas with low dependence on initial values. Results showed that the application of GA-based MK-ELM and hybrid homotopy-PSO effectively accomplish the simultaneous identification of source characteristics and aquifer parameters. The MK-ELM approximate the outputs of multiphase flow simulation model sufficiently with the certainty coefficient (R2) increased to 0.9982, whereas the mean relative error was limited to 1.5168%. Compared to the widely used PSO algorithm, the hybrid homotopy-PSO algorithm significantly reduced the mean relative error of identification results from 6.77% to 2.89%.
An adaptive Gaussian mixture method for nonlinear uncertainty propagation in neural networks

Using neural networks to address data-driven problems often entails dealing with uncertainties. However, the propagation of uncertainty through a network's nonlinear layers is usually a bottleneck, since the existing techniques designed to transmit Gaussian distributions via moment estimation are not capable of predicting non-Gaussian distributions. In this study, a Gaussian-mixture-based uncertainty propagation scheme is proposed for neural networks. Given that any input uncertainty can be characterized as a Gaussian mixture with a finite number of components, the developed scheme actively examines each mixture component and adaptively split those whose fidelity in representing uncertainty is deteriorated by the network's nonlinear activation layers. A Kullback–Leibler criterion that directly measures the nonlinearity-induced non-Gaussianity in post-activation distributions is derived to trigger splitting and a set of high-precision Gaussian splitting libraries is established. Four uncertainty propagation examples on dynamic systems and data-driven applications are demonstrated, in all of which the developed scheme exhibited exemplary fidelity and efficiency in predicting the evolution of non-Gaussian distributions through both recurrent and multi-layer neural networks.
A new approach of bipolar valued fuzzy set theory applied on semigroups

In this paper, we generalize the concept of interval-valued bipolar fuzzy sets (IVBFSs) and define interval-valued bipolar fuzzy subsemigroups (IVBF-subsemigroup) and interval-valued bipolar fuzzy left (right, two-sided) ideals (IVBF-left [right, two-sided] ideals) over semigroups, which is a generalization of the concept of an bipolar valued fuzzy set (BVFS) in a semigroup. The purpose of this paper is to deal with the algebraic structure of semigroups by applying IVBFS theory. We give characterizations of different classes of (intra-regular, left [right] regular, regular, semisimple) semigroups by the properties of their IVBF-ideals. We also characterize these classes in terms of IVBF-left ideals, IVBF-right ideals, and IVBF-two-sided ideals. In this respect, we prove that a semigroup is regular if and only if for every IVBF-right ideal A ˜ = μ A P ˜ , μ A N ˜ and every IVBF-left ideal B ˜ = μ B P ˜ , μ B N ˜ over S , we have A ˜ ∩ B ˜ = A ˜ ⊙ B ˜ . Further, we characterize intra-regular and regular semigroups and prove that a semigroup is intra-regular and regular if and only if for every IVBF-left ideal A ˜ = μ A P ˜ , μ A N ˜ and every IVBF-right ideal B ˜ = μ B P ˜ , μ B N ˜ over S we have A ˜ ∩ B ˜ ≼ A ˜ ⊙ B ˜ .
Locality preserving dense graph convolutional networks with graph context-aware node representations

Graph convolutional networks (GCNs) have been widely used for representation learning on graph data, which can capture structural patterns on a graph via specifically designed convolution and readout operations. In many graph classification applications, GCN-based approaches have outperformed traditional methods. However, most of the existing GCNs are inefficient to preserve local information of graphs — a limitation that is especially problematic for graph classification. In this work, we propose a locality-preserving dense GCN with graph context-aware node representations. Specifically, our proposed model incorporates a local node feature reconstruction module to preserve initial node features into node representations, which is realized via a simple but effective encoder–decoder mechanism. To capture local structural patterns in neighborhoods representing different ranges of locality, dense connectivity is introduced to connect each convolutional layer and its corresponding readout with all previous convolutional layers. To enhance node representativeness, the output of each convolutional layer is concatenated with the output of the previous layer’s readout to form a global context-aware node representation. In addition, a self-attention module is introduced to aggregate layer-wise representations to form the final graph-level representation. Experiments on benchmark datasets demonstrate the superiority of the proposed model over state-of-the-art methods in terms of classification accuracy.
Uncertainty quantification of a computer model for binary black hole formation

In this paper, a fast and parallelizable method based on Gaussian processes (GPs) is introduced to emulate computer models that simulate the formation of binary black holes (BBHs) through the evolution of pairs of massive stars. Two obstacles that arise in this application are the a priori unknown conditions of BBH formation and the large scale of the simulation data. We address them by proposing a local emulator which combines a GP classifier and a GP regression model. The resulting emulator can also be utilized in planning future computer simulations through a proposed criterion for sequential design. By propagating uncertainties of simulation input through the emulator, we are able to obtain the distribution of BBH properties under the distribution of physical parameters.
Revealing and Concealing Bitcoin Identities: A Survey of Techniques

Bitcoin remains the most widely used cryptocurrency. It has attracted users from tech enthusiasts to commercial investors to criminals, in no small part due to its reputation for anonymity. While not designed primarily for privacy, Bitcoin's architecture contains several provisions that can be exploited by criminals to conduct illegal activity including money laundering and collecting payments from ransomware and scams. Since Bitcoin's creation in 2008, various groups such as law enforcement, lawyers, criminals and privacy-focused Bitcoin users have been locked in a struggle between attempts to reveal hidden Bitcoin users' identities and attempts to keep those identities concealed. We present a survey of the techniques used within Bitcoin to reveal or conceal users' identities. We provide an easy to understand explanations of how these techniques work and provide a cross reference of which revealing techniques are effective for specific concealing techniques.
Predictive XR Telepresence for Robotic Operations in Space

This paper proposes a new method for robotic teleoperation scenarios with significant time delay utilizing Extended Reality (XR) technologies, specifically for Lunar base assembly and maintenance. Traditional teleoperation interfaces interpret commands sent through a desktop computer screen and are limited in providing spatial awareness cues. Additionally, the software often has difficulty in accounting for a time delay or providing intuitive techniques for human-robot interaction (HRI). This paper presents a new XR interface for robotic teleoperation using a predictive 3D simulated virtual environment developed using the SpaceCRAFT platform. SpaceCRAFT is a multiuser Virtual Reality (VR) and Augmented Reality (AR) systems engineering toolbox software that allows for worldwide collaboration on design and test of space systems. SpaceCRAFT provides the operator a virtual interface to the robot represented through three different states, a Virtual Command State (VCS), Predicted Current State (PCS), and Time Delayed State (TDS). To improve situational awareness and the PCS representation of the robot, SpaceCRAFT processes LIDAR and RGB camera data to create a constantly updating 3D point cloud representation of the remote environment. An experiment involving teleoperation of a 3D printed robotic manipulator was run, with a simulated time delay similar to that between the Earth and the Moon. Results demonstrate that the predictive approach provides an effective and flexible method of robot teleoperation that helps achieve successful task completion, even with a large time delay.
Semiotically-grounded distant viewing of diagrams: insights from two multimodal corpora

Abstract In this article, we argue for the benefits of combining large-scale analyses of visual materials currently pursued within digital humanities with insights from multimodality research, which is an emerging discipline that studies how human communication relies on appropriate combinations of expressive resources. We show that concepts developed within the field of multimodality research provide appropriate metadata schemes for various modes of expression in large corpora and datasets. We illustrate the proposed approach using a common mode of expression, diagrams, and analyse two recent multimodal diagram corpora using statistical and computational methods. Our results suggest that multimodally-motivated metadata schemes can provide a robust foundation for computational analyses of large corpora and datasets. Even if a corpus or dataset is not designed to support full-blown analyses of multimodal communication, our results imply that multimodality theory can still be used to impose tighter analytical control over a variety of visual materials.
Generic Library Mapping Approach for Trans-Compilation

Cross-platform mobile development is a widely used framework due to its nature of building an app using one development life cycle and deploying it to multiple platforms like Android and iOS. Many cross-platform solutions were recently developed to convert from one platform to another using Trans-compilation approach as Trans-Compiler Android to IOS Conversion (TCAIOSC) and Trans-Compiler Based Mobile Applications code converter. The limitations of these solutions is that they are unable to convert a library function that was not previously included by the developer. In this paper, a generic library mapping approach is proposed, which aims to improve the efficiency and accuracy of the recent systems. To validate the generic library mapping approach, a tool that maps a library in swift to a corresponding library injava is developed. The experimental results shows a substantial improvement to the accuracy of the Trans-Compiler Based Mobile Applications code converter from 76% to 91%.
Multimodal Data Fusion Using Canonical Variates Analysis Confusion Matrix Fusion

Data fusion from a variety of sources requires alignment, association, and analysis. One method to determine the relationship between two variables measuring the same information is a correlation analysis. The canonical variates analysis (CVA) supports the assessments of two sets of data. In this paper, we compare results from the fusion of histograms to that of the fusion of confusion matrices developed from data of the same modality and that of a cross modality. We use the Confusion Matrix Fusion (CMF) approach in the analysis and compare the results for EO/RF fusion. In the analysis, the Experiments, Scenarios, Concept of Operations, and Prototype Engineering (ESCAPE) data set is used for comparison to previous aerospace results.
A constrained optimization approach for cross-domain emotion distribution learning

Emotion distribution learning aims to annotate unlabeled instances with a set of emotion categories and their strengths. Non-negative Matrix Tri-Factorization (NMTF) introduces an association matrix between document clusters and word clusters to help the domain adaptation task in emotion distribution learning. Nevertheless, many prior cross-domain emotion distribution learning methods had two major deficiencies. First, they hypothesize that there is a one-to-one correspondence between document clusters and emotion labels. In their experiments, the number of document clusters depends on the number of labels. Second, the prior work does not endow models with adequate constraints. In the real scenario of cross-domain emotion distribution learning, there are potential constraints that may improve the performance of such models. In order to address these problems, we propose a constrained optimization approach based on NMTF for cross-domain emotion distribution learning. In our model, the relationship between document clusters and emotion labels is not always one-to-one. A novel content-based constraint is also endowed based on the hypothesis that documents belonging to the same clusters must have similar content. We solve the optimization problem by an alternately iterative algorithm and show the proof of convergence. Experiments on 12 real-world cross-domain emotion distribution learning tasks validate the effectiveness of our method.