Reviewerly/exHarmony huggingface dataset:
Real three‐dimensional cardiac imaging using leading‐edge holographic display

Abstract Understanding three‐dimensional cardiac anatomy is fundamental for the practice of clinical cardiology. However, if three‐dimensional images are displayed on two‐dimensional monitors, they fail to provide depth perception. Currently, novel technologies, including the three‐dimensional printing, three‐dimensional monitors/projectors, and virtual reality applications can provide real three‐dimensionality with depth perception. However, their relatively high cost and limited user‐friendliness prevent their wide application. We introduce novel and commercially available holographic display, which allows multiple observers to see the full‐color holographic images simultaneously without any specific glasses and headgear. This leading‐edge technology is immediately applicable in both educational and clinical settings.
Engaging Rural High School Students in STEM Utilizing Graduate Mentors, Virtual Reality and Case‐based Learning

High school students exposed to early mentorship opportunities and targeted development of critical thinking skills are better prepared for success in higher education. Rural high school students can face barriers that limit access to content experts and scholastic growth. Here we demonstrate the use of a virtual reality (VR) curriculum as a novel method of instruction, fostering mentorship and the development of critical thinking skills. Graduate mentors remotely connected with high school students to facilitate learning using a virtual, dissectible cadaver and volumized medical imaging (CT and MRI). The curriculum features weekly case-based learning exercises, providing students an opportunity to develop problem solving, critical thinking and oral presentation skills. We hypothesized that VR is an effective method of connecting high school students to graduate mentors and novel STEM learning opportunities, while the VR case-based curriculum enhances critical thinking skill and helps prepare students to succeed in higher education. Qualitative data assessed critical thinking, problem-solving skills, student motivation, mentorship engagement, and satisfaction utilizing the VR program as compared to 2D methods. Quantitative data assessed progression of student spatial abilities during the semester. Preliminary findings suggest an improvement in student interest and engagement while promoting intellectual engagement with graduate mentors. Research on the implementation of virtual reality in education is in its early stages, but there is a growing need to investigate the effectiveness of technology in overcoming barriers to learning among high school students. This course is an early exploration of how VR can enhance STEM teaching, improve student learning, and prepare students for success in higher education.
EmoDNN: understanding emotions from short texts through a deep neural network ensemble

The knowledge obtained from emotions via online communities is substantially valuable in various domains, including social management, resource planning, politics, and market predictions. Affective computing, as a multi-aspect realm, aims to exploit emotion-pertinent details from various contents via connecting artificial intelligence to cognitive science. The hidden personality cues in daily brief contents can reveal the cognitive aspect of authors and uncover both similarities and contrasts between them. However, the main challenge lies in devising a cognition-aware algorithm to trace emotional cues in brief contents. To solve the challenge, we develop a novel framework that infers the cognitive aspect of individuals. We propose a deep ensemble method, supplied with a novel dropout algorithm, that aggregates outcomes from various classifiers to extract emotions from short texts. We employ a new embedding approach to enrich emotion-relevant features, collectively assembled via lexicons and attention actuates, resulting in a preferable set of vectors. The experimental results show that our proposed framework can achieve better accuracy in recognizing emotions versus other trending competitors. We empirically observe that detecting emotion latent cues via relying on personality features can effectively distinguish short text authors. Furthermore, the deep learning models overcome conventional methods, including the SVM, categorization, and heuristic rules.
Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification

The classification of text is one of the key areas of research for natural language processing.Most of the organizations get customer reviews and feedbacks for their products for which they want quick reviews to action on them.Manual reviews would take a lot of time and effort and may impact their product sales, so to make it quick these organizations have asked their IT to leverage machine learning algorithms to process such text on a real-time basis.Gated recurrent units (GRUs) algorithms which is an extension of the Recurrent Neural Network and referred to as gating mechanism in the network helps provides such mechanism.Recurrent Neural Networks (RNN) has demonstrated to be the main alternative to deal with sequence classification and have demonstrated satisfactory to keep up the information from past outcomes and influence those outcomes for performance adjustment.The GRU model helps in rectifying gradient problems which can help benefit multiple use cases by making this model learn long-term dependencies in text data structures.A few of the use cases that follow aresentiment analysis for NLP.GRU with RNN is being used as it would need to retain long-term dependencies.This paper presents a text classification technique using a sequential word embedding processed using gated recurrent unit sigmoid function in a Recurrent neural network.This paper focuses on classifying text using the Gated Recurrent Units method that makes use of the framework for embedding fixed size, matrix text.It helps specifically inform the network of long-term dependencies.We leveraged the GRU model on the movie review dataset with a classification accuracy of 87%.
Evaluation of Complementary Filter Method in Increasing the Performance of Motion Tracking Gloves for Virtual Reality Games

In the use of Virtual Reality-based video games, users need additional devices to interact, one of which is a Motion Tracking Glove. The Motion Tracking Glove is one of the enhancements that users can use to interact with objects in VR video games. To get the angle value, an accelerometer sensor is used in the MPU6050 module. However, the problem that arises is the accuracy of the sensor because VR demands a low error rate. The purpose of this study is to improve the accuracy of the angular value of the accelerometer sensor value with a complementary filter. Complementary filters can increase the accuracy of the accelerometer sensor by combining its value with the gyroscope sensor value. The Motion Tracking Glove is built using the Arduino Nano and the MPU6050 module to capture angles that move according to hand movements, to connect and exchange data to the main VR device, the Motion Tracking Glove using the Bluetooth module. The results are RMSE 0.6 and MAPE 2.5% with a static Motion Tracking Glove position without movement. In sending Motion Tracking Glove data using the Bluetooth module, the resulting delay time when sending ranges from 0.1 second to 0.4 seconds by trying to move the Motion Tracking Glove from 0 degrees to 90 degrees and back to 0 degrees.
Team ReadMe at CMCL 2021 Shared Task: Predicting Human Reading Patterns by Traditional Oculomotor Control Models and Machine Learning

This system description paper describes our participation in CMCL 2021 shared task on predicting human reading patterns.Our focus in this study is making use of well-known, traditional oculomotor control models and machine learning systems.We present experiments with a traditional oculomotor control model (the EZ Reader) and two machine learning models (a linear regression model and a recurrent network model), as well as combining the two different models.In all experiments we test effects of features well-known in the literature for predicting reading patterns, such as frequency, word length and word predictability.Our experiments support the earlier findings that such features are useful when combined.Furthermore, we show that although machine learning models perform better in comparison to traditional models, combination of both gives a consistent improvement for predicting multiple eye tracking variables during reading.
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game

To achieve sample efficiency in reinforcement learning (RL), it necessitates efficiently exploring the underlying environment. Under the offline setting, addressing the exploration challenge lies in collecting an offline dataset with sufficient coverage. Motivated by such a challenge, we study the reward-free RL problem, where an agent aims to thoroughly explore the environment without any pre-specified reward function. Then, given any extrinsic reward, the agent computes the policy via a planning algorithm with offline data collected in the exploration phase. Moreover, we tackle this problem under the context of function approximation, leveraging powerful function approximators. Specifically, we propose to explore via an optimistic variant of the value-iteration algorithm incorporating kernel and neural function approximations, where we adopt the associated exploration bonus as the exploration reward. Moreover, we design exploration and planning algorithms for both single-agent MDPs and zero-sum Markov games and prove that our methods can achieve $\widetilde{\mathcal{O}}(1 /\varepsilon^2)$ sample complexity for generating a $\varepsilon$-suboptimal policy or $\varepsilon$-approximate Nash equilibrium when given an arbitrary extrinsic reward. To the best of our knowledge, we establish the first provably efficient reward-free RL algorithm with kernel and neural function approximators.
Electronic Architecture solves acoustical problems with coupled volumes that cannot be easily remedied using architectural treatments

There are a multitude of venues throughout the world supporting music and performing arts with architecture that incorporates coupled volumes. It may be a large balcony—or seating under a large balcony—loge or under loge seating, box seats, or opera boxes. One of the most common coupled volumes in such venues is the performance stage. When that stage is outdoors, another set of challenges arises. In worship spaces, this can be a “platform” for musicians with a choir loft, or a chancel with choir and organ, or a bimah. For many years, we have been successfully employing Electronic Architecture to solve acoustical problems that are all but impossible to resolve with architectural treatments alone. In this presentation, we will describe solutions implemented in several venues that substantially improve the listening experience.
Joint Representations of Texts and Labels with Compositional Loss for Short Text Classification

Short text classification is an important foundation for natural language processing (NLP) tasks. Though, the text classification based on deep language models (DLMs) has made a significant headway, in practical applications however, some texts are ambiguous and hard to classify in multi-class classification especially, for short texts whose context length is limited. The mainstream method improves the distinction of ambiguous text by adding context information. However, these methods rely only the text representation, and ignore that the categories overlap and are not completely independent of each other. In this paper, we establish a new general method to solve the problem of ambiguous text classification by introducing label embedding to represent each category, which makes measurable difference between the categories. Further, a new compositional loss function is proposed to train the model, which makes the text representation closer to the ground-truth label and farther away from others. Finally, a constraint is obtained by calculating the similarity between the text representation and label embedding. Errors caused by ambiguous text can be corrected by adding constraints to the output layer of the model. We apply the method to three classical models and conduct experiments on six public datasets. Experiments show that our method can effectively improve the classification accuracy of the ambiguous texts. In addition, combining our method with BERT, we obtain the state-of-the-art results on the CNT dataset.
AI1 Quantifying Bias in ML-Extracted Variables for Inference in Clinical Oncology

Machine learning (ML) approaches can extract clinically relevant information from electronic health records (EHRs) to be used for research purposes, such as comparative effectiveness analyses. This study assessed the effects of misclassification error in ML-extracted clinical variables when used in statistical analyses.
Education system readiness to switch to a remote mode of work: pandemic lessons reflection

The article actualizes the problem of the education system readiness to switch to a remote mode of work based on research and reflection on the lessons of the coronavirus pandemic in spring 2020; it substantiated the need for digitalization of the educational process through analysis of trends in the development of education and training using information systems; disclosed approaches to the methodology of digitalization of education in such areas as digital didactics, digital transformation, digital education, digital ecosystem; the concept of «readiness for activity or action» is defined, a theoretical analysis of the organizational, motivational, psychological, material and technical, normative readiness of educational organizations and participants in educational relations for activities in the new conditions of distance learning is provided; it describes the results of a study of the readiness of regional education systems, educational organizations, participants in educational relations (teachers, students and their parents) to work in adistance, remote mode; disclosed research methods (online survey, analysis of websites of educational organizations, study and generalization of experience) and the data of the results of the study of normative, material and technical, informational, organizational readiness, the education system for a remote mode of work, the readiness of teaching staff for new working conditions, as well as the readiness of families of students to study in self-isolation; examples of the best practices for organizing the activities of schools and families of students during the period of the forced transition to distance learning, including forms of rapid response, targeted methodological support of educational organizations in the region, psychological assistance to children and parents in the process of transition to new digital relationship; identified and generalized problems, suggested ways of solving in the near future.
Towards Trustworthiness of Electronic Health Record system using Blockchain

The digital archive of the patient's personal health records is the Electronic Health Record (EHR) that has various advantages. For finding the best solution to resolve the associated issues, a detailed study is required to utilize modern technologies and standards so that these issues and errors can be minimized. There are several issues associated with implementing the EHR, including data management, privacy, and patient data security. This research aims to examine the use of the Blockchain in EHR frameworks as per the national and international standards of EHR to reduce the associated issues. A blockchain-based framework can be successful in solving the current challenges in EHR. It allows storing, sharing, managing, controlling, and maintaining patient information between healthcare providers. The study illustrated the related difficulties of integrating EHR and proposed Blockchain as a solution to help manage the records and preserve privacy, confidentiality, usability and protection of patient-related details. This study helps to understand the current challenges better and help in the proper implementation of Blockchain technology in EHR.
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale  Black-Box Optimization

We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the decision variables is infeasible. In this paper, we propose a novel algorithm, coined ZO-BCD, that exhibits favorable overall query complexity and has a much smaller per-iteration computational complexity. In addition, we discuss how the memory footprint of ZO-BCD can be reduced even further by the clever use of circulant measurement matrices. As an application of our new method, we propose the idea of crafting adversarial attacks on neural network based classifiers in a wavelet domain, which can result in problem dimensions of over 1.7 million. In particular, we show that crafting adversarial examples to audio classifiers in a wavelet domain can achieve the state-of-the-art attack success rate of 97.9%.
Clusus: A cyber range for network attack simulations

This report documents the design and implementation of Clusus, a cyber range to provide students with a safe isolated environment to learn about cyber security and computer networks. This Bachelor project was proposed by the TU Delft cyber security group. During a two week research phase currently existing solutions were evaluated and requirements for the system were determined. Based on these requirements and research into the capabilities of cloud providers a design was proposed. In the second phase of the project was the implementation of the design. The chosen design consists of three major components, a central server that handles the communication between the learning management system and an individual exercise, a containerized exercise, and finally a program that builds these containers. The containerized exercise consists of all virtual machines required for an exercise and a monitoring program that reports progress to the central server.
Comparison Analysis of Chinese University MOOC(Massive Open Online Course) and K-MOOC(Korea Massive Open Online Course) from the Standpoint of Learners

This study compared the difference between Chinese University MOOC(Massive Open Online Courses) and K-MOOC(Korea Massive Open Online Course) based on the contents and classifications of the courses, the course service structures, and the evaluation methods from the standpoint of learners through literature survey and comparison analysis.The study findings revealed that the Chinese University MOOC provides variety contents to students, schools, and also companies; the classification of K-MOOC courses is done according to level of difficulty and language.Also, both the Chinese MOOC and K-MOOC course service structures platforms have no quizzes and multi-language subtitles within the videos.Furthermore, the study findings revealed that the evaluation methods have no standards and have very different difficulty level to finish a course.Basing on study findings, there is a need for adding various contents to K-MOOC, classifying courses clearly in Chinese University MOOC, adding multi-language subtitles and quizzes in course videos to both platforms.
Spatial Trends in Mineral Abundances across Tyrrhena Terra on Mars derived from Geomorphological and Mineralogical Mapping

&amp;lt;p&amp;gt;Tyrrhena Terra hosts an intriguing variety of aqueously altered materials accompanied by unaltered mafic rocks. Our study region extends from the southern rim of the Isidis impact basin, including the Libya Montes region, southward to the Hellas Basin rim (Fig. 1). The NW part is dominated by lava flows from Syrtis Major that grade southwards into the TT highlands, dissected by fluvial channels and overprinted by abundant impact craters. These landforms together with lobate and fan-shaped deposits within impact craters are evidence for a variable history of erosion and deposition. Ancient phyllosilicate-rich materials have been exposed and uplifted from the subsurface, as they often occur in crater ejecta and central crater uplifts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our previous studies used CRISM spectral data together with CTX, HiRISE, and HRSC images as well as their derived topography data to create geomorphological maps of the southern Isidis region and Tyrrhena Terra. These datasets were used to map and characterize the types and occurrences of phyllosilicates, chlorite, opal, zeolites, carbonates, olivines, and pyroxenes and to assess the relationships between selected aqueous outcrops and surface features.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this work, we build on these results by seeking correlations between aqueous mineral detections with our geomorphological map to assess 1) whether or not there are relationships between specific units and mineral occurrences, and 2) if there are trends across the study region in terms of mineral occurrence and abundance.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The mineralogical map originates from a study that spans not only the inter-Isidis-Hellas region, but also extends northwards to Nili Fosse and westwards to Terra Sabea. The focus of that study was on the metamorphic- and hydrothermally-related alteration history using CRISM targeted and mapping data, including hundreds of calibrated MTRDR images. These mineral detections were available to us as a mapped shape file, enabling us to assess the minerals in context with the geomorphological map. We utilized ESRI&amp;amp;#8217;s ArcGIS system and conducted multiple statistical queries in terms of mineral occurrence/type versus map unit in order to reveal possible trends within and across the study region.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Fe/Mg-phyllosilicates are the dominant aqueous mineral type within the study region and are more abundant in the central region compared to the proximity of either the Isidis or Hellas impact basin. Chlorites increase in abundance with distance from both impact basins, which could be an indication of hydrothermal processes from geothermal flux. The large Hellas impact event appears to have produced more varied temperatures and water chemistries, resulting in increased mineral variability near its rim.&amp;lt;/p&amp;gt;
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision

Low-precision training has become a popular approach to reduce compute requirements, memory footprint, and energy consumption in supervised learning. In contrast, this promising approach has not yet enjoyed similarly widespread adoption within the reinforcement learning (RL) community, partly because RL agents can be notoriously hard to train even in full precision. In this paper we consider continuous control with the state-of-the-art SAC agent and demonstrate that a naive adaptation of low-precision methods from supervised learning fails. We propose a set of six modifications, all straightforward to implement, that leaves the underlying agent and its hyperparameters unchanged but improves the numerical stability dramatically. The resulting modified SAC agent has lower memory and compute requirements while matching full-precision rewards, demonstrating that low-precision training can substantially accelerate state-of-the-art RL without parameter tuning.