Reviewerly/exHarmony huggingface dataset:
Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model

Radiology reports contain a diverse and rich set of clinical abnormalities documented by radiologists during their interpretation of the images. Comprehensive semantic representations of radiological findings would enable a wide range of secondary use applications to support diagnosis, triage, outcomes prediction, and clinical research. In this paper, we present a new corpus of radiology reports annotated with clinical findings. Our annotation schema captures detailed representations of pathologic findings that are observable on imaging ("lesions") and other types of clinical problems ("medical problems"). The schema used an event-based representation to capture fine-grained details, including assertion, anatomy, characteristics, size, and count. Our gold standard corpus contained a total of 500 annotated computed tomography (CT) reports. We extracted triggers and argument entities using two state-of-the-art deep learning architectures, including BERT. We then predicted the linkages between trigger and argument entities (referred to as argument roles) using a BERT-based relation extraction model. We achieved the best extraction performance using a BERT model pre-trained on 3 million radiology reports from our institution: 90.9–93.4% F1 for finding triggers and 72.0–85.6% F1 for argument roles. To assess model generalizability, we used an external validation set randomly sampled from the MIMIC Chest X-ray (MIMIC-CXR) database. The extraction performance on this validation set was 95.6% for finding triggers and 79.1–89.7% for argument roles, demonstrating that the model generalized well to the cross-institutional data with a different imaging modality. We extracted the finding events from all the radiology reports in the MIMIC-CXR database and provided the extractions to the research community.
Current developments and future directions in archaeological science

Dissolution of redox-active metal oxides plays a fundamental role in the environment and technological applications. However, dissolution mechanisms remain poorly understood at nanoscale, particularly the interplay between acidic and reductive ...Iron-based redox-active minerals are ubiquitous in soils, sediments, and aquatic systems. Their dissolution is of great importance for microbial impacts on carbon cycling and the biogeochemistry of the lithosphere and hydrosphere. Despite its widespread ...
Anticaustics in a Fabry–Perot interferometer

We address the response of a Fabry-Perot interferometer to a monochromatic point source. We calculate the anticaustics (that is, the virtual wavefronts of null path difference) resulting from the successive internal reflections occurring in the system. They turn out to be a family of ellipsoids (or hyperboloids) of revolution, which allows us to reinterpret the operation of the Fabry-Perot interferometer from a geometrical point of view that facilitates comparison with other apparently disparate arrangements, such as Young's double slit.
A Novel Adaptive FCM with Cooperative Multi-Population Differential Evolution Optimization

Fuzzy c-means (FCM), the fuzzy variant of the popular k-means, has been used for data clustering when cluster boundaries are not well defined. The choice of initial cluster prototypes (or the initialization of cluster memberships), and the fact that the number of clusters needs to be defined a priori are two major factors that can affect the performance of FCM. In this paper, we review algorithms and methods used to overcome these two specific drawbacks. We propose a new cooperative multi-population differential evolution method with elitism to identify near-optimal initial cluster prototypes and also determine the most optimal number of clusters in the data. The differential evolution populations use a smaller subset of the dataset, one that captures the same structure of the dataset. We compare the proposed methodology to newer methods proposed in the literature, with simulations performed on standard benchmark data from the UCI machine learning repository. Finally, we present a case study for clustering time-series patterns from sensor data related to real-time machine health monitoring using the proposed method. Simulation results are promising and show that the proposed methodology can be effective in clustering a wide range of datasets.
Intelligent Material Data Preparation Mechanism Based on Ensemble Learning for AI‐Based Ceramic Material Analysis

Abstract Research interest in ceramic materials increased due to their extensive environmental, biomedical, and electronic applications. Increased demand for ceramics with specialized experimental conditions and limited resources has resulted in a higher cost for scientific practices and applications. Enormous material data is accumulated from traditional and high‐tech experimentation, but the manual recording process has shown inconsistencies in the analysis results. Recently, application based on artificial intelligence (AI) and machine learning has been able to address the issues of traditional scientific experiments in material science. However, no machine learning mechanisms are proposed for sophisticated data preparation and AI‐based discovery application of ceramics. This paper proposed an intelligent material data preparation mechanism based on ensemble learning for AI‐assisted material screening and discovery. The current method can potentially resolve the problems of missing and inconsistent material data. As a case study, a material data preparation platform for ceramic material data pre‐processing is developed. For performance evaluation of the proposed mechanism, machine learning regression models are trained before and after the imputation techniques applied to the data. Performance analysis shows that the ensemble model of deep learning network (DNN) and automated machine learning (autoML) performed better as compared to previously reported imputation approaches.
Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing

With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.
A modularity design approach to behavioral research with immersive virtual reality: A SkyrimVR-based behavioral experimental framework

Virtual reality (VR) has been shown to be a potential research tool, yet the gap between traditional and VR behavioral experiment systems poses a challenge for many behavioral researchers. To address the challenge posed, the present study first adopted a modularity design strategy and proposed a five-module architectural framework for a VR behavioral experiment system that aimed to reduce complexity and costs of development. Applying the five-module architectural framework, the present study developed the SkyrimVR-based behavioral experimental framework (SkyBXF) module, a basic experimental framework module that adopted and integrated the classic human behavior experiment structure (i.e., session-block-trial model) with the modifiable VR massive gaming franchise The Elder Scrolls V: Skyrim VR. A modified version of previous behavioral research to investigate the effects of masked peripheral vision on visually-induced motion sickness in an immersive virtual environment was conducted as a proof of concept to showcase the feasibility of the proposed five-module architectural framework and the SkyBXF module developed. Behavioral data acquired through the case study were consistent with those from previous behavioral research. This indicates the viability of the proposed five-module architectural framework and the SkyBXF module developed, and provides proof that future behavioral researchers with minimal programming proficiency, 3D environment development expertise, time, personnel, and resources may reuse ready-to-go resources and behavioral experiment templates offered by SkyBXF to swiftly establish realistic virtual worlds that can be further customized for experimental need on the go.
COHETS: A highlight extraction method using textual streams of streaming videos

As more and more conversation-oriented streaming videos become available, streaming platforms have gradually taken the place of traditional media for people to access information. Still, conversation-oriented streaming videos are often lengthy and people are reluctant to view the whole video. In this research, we investigated highlight extraction on conversation-oriented streaming videos. Previous highlight extraction methods analyzed visual features of videos and are therefore unable to deal with conservation-oriented streaming videos whose highlights are related to streamer discourses and viewer responses. For this reason, the proposed highlight extraction method called COHETS does not evaluate visual features but rather simultaneously examines textual streams of streamer discourses and viewer messages to extract meaningful highlights. Experiments based on real world streaming data demonstrate that streamer discourses and viewer responses via their feedback messages are useful for extracting highlights of conversation-oriented streaming videos. Also, our designed position enrichment and message attention techniques effectively distill the embeddings of the two textual streams and lead to extraction results that are superior to those of state-of-the-art deep learning-based highlight extraction methods and extraction-based text summarization methods.
Dynamic <scp>FERNet</scp> : Deep learning with optimal feature selection for face expression recognition in video

Summary This work concentrates on the recognition of facial emotion from video sequences with deep learning. Once the input video is converted into frames, the face detection is performed on each frame using the viola–jones face detection algorithm. Then, the feature extraction is performed by three well‐performing feature extraction techniques like modified local directional pattern, spatio‐temporal features, and scale‐invariant feature transforms. The extracted features from all the frames of the video are concatenated. To reduce the feature‐length for decreasing the training complexity, and enhance the recognition performance, the optimal feature selection is accomplished with the distance‐based tunicate swarm algorithm. These selected features are processed to an innovative deep learning model termed a heuristically modified recurrent neural network. The same D‐TSA improves the performance of RNN by optimally tuning its hidden neurons. Experimental results on a widely used benchmark dataset and manually collected dataset show that the classification performance is improved using spatio‐temporal features, SIFT, M‐LDP, and optimal feature selection, and thus, the proposed model with HM‐RNN outperforms the other existing models.
Imposing temporal consistency on deep monocular body shape and pose estimation

Abstract Accurate and temporally consistent modeling of human bodies is essential for a wide range of applications, including character animation, understanding human social behavior, and AR/VR interfaces. Capturing human motion accurately from a monocular image sequence remains challenging; modeling quality is strongly influenced by temporal consistency of the captured body motion. Our work presents an elegant solution to integrating temporal constraints during fitting. This increases both temporal consistency and robustness during optimization. In detail, we derive parameters of a sequence of body models, representing shape and motion of a person. We optimize these parameters over the complete image sequence, fitting a single consistent body shape while imposing temporal consistency on the body motion, assuming body joint trajectories to be linear over short time. Our approach enables the derivation of realistic 3D body models from image sequences, including jaw pose, facial expression, and articulated hands. Our experiments show that our approach accurately estimates body shape and motion, even for challenging movements and poses. Further, we apply it to the particular application of sign language analysis, where accurate and temporally consistent motion modelling is essential, and show that the approach is well-suited to this kind of application.
Judgment aggregation, discursive dilemma and reflective equilibrium: Neural language models as self-improving doxastic agents

Neural language models (NLMs) are susceptible to producing inconsistent output. This paper proposes a new diagnosis as well as a novel remedy for NLMs' incoherence. We train NLMs on synthetic text corpora that are created by simulating text production in a society. For diagnostic purposes, we explicitly model the individual belief systems of artificial agents (authors) who produce corpus texts. NLMs, trained on those texts, can be shown to aggregate the judgments of individual authors during pre-training according to sentence-wise vote ratios (roughly, reporting frequencies), which inevitably leads to so-called discursive dilemmas: aggregate judgments are inconsistent even though all individual belief states are consistent. As a remedy for such inconsistencies, we develop a self-training procedure-inspired by the concept of reflective equilibrium-that effectively reduces the extent of logical incoherence in a model's belief system, corrects global mis-confidence, and eventually allows the model to settle on a new, epistemically superior belief state. Thus, social choice theory helps to understand why NLMs are prone to produce inconsistencies; epistemology suggests how to get rid of them.
Architecture, operation, and implementation for Blockchain-driven turnkey project under I4.0 workshop based on RAMI 4.0

• Blockchain-driven turnkey project information reference model is developed. • Standardization of reference architectures and reference solutions. • The operation mechanism and implementation guidance framework of the model are established. • A turnkey project example of customized production line is established. • The application value and performance indicators of the architecture are verified. As the turnkey project adheres to the concept of one-stop delivery, it puts forward higher requirements for rapid construction of manufacturing systems. During the implementation of the turnkey project in the I4.0 workshop, there is a lack of unified construction and delivery standards under the system configuration framework that meets information security. Therefore, according to the I4.0 manufacturing requirements of node scalability, trusted delivery and standard information modeling in the turnkey process, the paper proposes a reference information architecture of blockchain-driven turnkey project to establish a turnkey service system with AAS as the delivery core: (1) Take RAMI4.0 and I4.0 component as the engineering construction framework and supporting technology, by analyzing the bottleneck of blockchain in solving the I4.0 workshop, integrating the open blockchain architecture interoperability characteristics of BC-Based smart contract and Tangle-Based extensible IIoT-node, and establish complete solution to turnkey project information architecture under the I4.0 workshop; (2) Combined with the current turnkey project, this paper proposes and studies the AAS modeling method supporting blockchain verification and the delivery constraint mechanism based on smart contract, and designs the blockchain-based architecture, operation and implementation process with AAS as the delivery core; (3) Through the development and integration of key technologies, an experimental development and operation case of the turnkey project of the I4.0 workshop customized production line is given, which provides theoretical and technical references for more SMEs to quickly integrate into the turnkey project system.
Automated documentation of almost identical movements in the context of dementia diagnostics

When monitoring the health state of people with neurological diseases, it is crucial to detect disease progression and disease-related changes as soon as possible. Such changes can be detected in the performance of everyday activities like eating, drinking, nose blowing, reading, looking at photos, knitting, telephoning, or brushing hair. In order to supervise such everyday activities, this work proposes an approach employing smartwatches in combination with a Recurrent neural network. A smartwatch offers the possibility of integrating sensor technology into a patient’s daily routines in a unobtrusive way. We have developed a machine learning web tool to track motion data using smartwatch sensors. Data from the accelerometer, heart rate sensor, gyroscope, gravity sensor and position sensor are tracked at 20 Hz. Our tool allows a systematic comparison of the performance between a Long Short-Term Memory (LSTM) model and the different sensor systems in classifying twelve nearly identical daily activities. In this paper, we address the problem of caregiver effort in creating daily care documentation for dementia patients. We present an activity classification system that enables automatic classification of daily activities of patients with dementia across a nursing shift. To assist caregivers, we provide an idea for integrating our system into a nursing documentation.
Secure distributed power trading protocol for networked microgrids based on blockchain and elliptic curve cryptography

Abstract The definition of an electricity trading protocol among microgrids is not an easy task to perform due to the grid's increasing requirements. In fact, a trading process must provide security and especially privacy of the buyer and seller identities as well as prices. Also, guaranteeing a financial profit to trading parties is of great importnace. Moreover, matching the full distributed paradigm is required since it will be executed in a distributed system. All these constraints must be satisfied with consideration to the computational costs especially data storage. In this paper, an electricity trading protocol is introduced to ensure: (1) the security and privacy of buyers, sellers, and sales information, (2) the trading process distribution, and (3) the minimisation of the stored data. The protocol builds upon the blockchain technology and the Elliptic Curve Cryptography. To achieve the protocol goals, a new architecture based on a concept of membership of microgrids to families and groups of families is defined. The proposed protocol is implemented using Java programming langauge. An honest‐but‐curious‐adversary security model is applied to a formal case study to prove security of the protocol.
Review of higher education policy during the pandemic: A Spanish perspective

The COVID-19 pandemic has prompted profound changes in Higher Education. Thus, policymakers in different national contexts worldwide needed to design alternative responses to deal with new educational scenarios. In Spain, rethinking educational management in Higher Education remains an issue of current debate. Methodological readjustments towards virtual teaching, the digital divide due to socio-economic and cultural issues of the students, unequal access to education, or a decrease in internationalization have been some of the most significant adversities faced during this time. Amid this crisis, the measures taken by political leaders and university managers played an essential role. This study aims to provide an overview of the policy measures developed during this period, describing some of the most important decisions taken by different universities in Spain. To this end, an exploration based on secondary data from extensive literature reviews is carried out to construct a descriptive analysis of the measures implemented in Spanish universities. The findings highlight the coordinated action of the Spanish university system and the staunch defense of face-to-face teaching. This health crisis has also highlighted the deficits of this level of education and shed light on its future. Spain faces major challenges in the field of higher education. Consequently, the Spanish university system must begin building the foundations for educational innovation and training education professionals without fear of online scenarios. Finally, this article also proposes suggestions to guide policymakers in dealing with future adverse situations.
Galois LCD codes over mixed alphabets

In this work we give a characterization of Galois Linear Complementary Dual codes and Galois-invariant codes over mixed alphabets of finite chain rings, which leads to the study of the Gray image of FpFp[θ]-linear codes, where p∈{2;3} and θ≠θ2=0 that provides LCD codes over Fp.
Semiparametric finite mixture of regression models with Bayesian P-splines

Abstract Mixture models provide a useful tool to account for unobserved heterogeneity and are at the basis of many model-based clustering methods. To gain additional flexibility, some model parameters can be expressed as functions of concomitant covariates. In this Paper, a semiparametric finite mixture of regression models is defined, with concomitant information assumed to influence both the component weights and the conditional means. In particular, linear predictors are replaced with smooth functions of the covariate considered by resorting to cubic splines. An estimation procedure within the Bayesian paradigm is suggested, where smoothness of the covariate effects is controlled by suitable choices for the prior distributions of the spline coefficients. A data augmentation scheme based on difference random utility models is exploited to describe the mixture weights as functions of the covariate. The performance of the proposed methodology is investigated via simulation experiments and two real-world datasets, one about baseball salaries and the other concerning nitrogen oxide in engine exhaust.