Reviewerly/exHarmony huggingface dataset:
Development of Computable Phenotype to Identify and Characterize  Transitions in Acuity Status in Intensive Care Unit

Background: In the United States, 5.7 million patients are admitted annually to intensive care units (ICU), with costs exceeding $82 billion. Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by the time constraints imposed on healthcare providers. Methods: Using the University of Florida Health (UFH) Integrated Data Repository as Honest Broker, we created a database with electronic health records data from a retrospective study cohort of 38,749 adult patients admitted to ICU at UF Health between 06/01/2014 and 08/22/2019. This repository includes demographic information, comorbidities, vital signs, laboratory values, medications with date and timestamps, and diagnoses and procedure codes for all index admission encounters as well as encounters within 12 months prior to index admission and 12 months follow-up. We developed algorithms to identify acuity status of the patient every four hours during each ICU stay. Results: We had 383,193 encounters (121,800 unique patients) admitted to the hospital, and 51,073 encounters (38,749 unique patients) with at least one ICU stay that lasted more than four hours. These patients requiring ICU admission had longer median hospital stay (7 days vs. 1 day) and higher in-hospital mortality (9.6% vs. 0.4%) compared with those not admitted to the ICU. Among patients who were admitted to the ICU and expired during hospital admission, more deaths occurred in the ICU than on general hospital wards (7.4% vs. 0.8%, respectively). Conclusions: We developed phenotyping algorithms that determined patient acuity status every four hours while admitted to the ICU. This approach may be useful in developing prognostic and clinical decision-support tools to aid patients, caregivers, and providers in shared decision-making processes regarding resource use and escalation of care.
Interpretation of Deep Temporal Representations by Selective  Visualization of Internally Activated Nodes

Recently deep neural networks demonstrate competitive performances in classification and regression tasks for many temporal or sequential data. However, it is still hard to understand the classification mechanisms of temporal deep neural networks. In this paper, we propose two new frameworks to visualize temporal representations learned from deep neural networks. Given input data and output, our algorithm interprets the decision of temporal neural network by extracting highly activated periods and visualizes a sub-sequence of input data which contributes to activate the units. Furthermore, we characterize such sub-sequences with clustering and calculate the uncertainty of the suggested type and actual data. We also suggest Layer-wise Relevance from the output of a unit, not from the final output, with backward Monte-Carlo dropout to show the relevance scores of each input point to activate units with providing a visual representation of the uncertainty about this impact.
On The Problem of Relevance in Statistical Inference

This paper is dedicated to the "50 Years of the Relevance Problem" - a long-neglected topic that begs attention from practical statisticians who are concerned with the problem of drawing inference from large-scale heterogeneous data.
GEVO: GPU Code Optimization using Evolutionary Computation

GPUs are a key enabler of the revolution in machine learning and high performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of the GPU programs in the Rodinia benchmark suite and the machine learning models, SVM and ResNet18, on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48% and by as much as 412% over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51.08%. For the machine learning workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24X) and the a9a income prediction (2.93X) datasets with no loss of model accuracy. GEVO achieves 1.79X kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1% model accuracy reduction.
BabyAI++: Towards Grounded-Language Learning beyond Memorization

Despite success in many real-world tasks (e.g., robotics), reinforcement learning (RL) agents still learn from tabula rasa when facing new and dynamic scenarios. By contrast, humans can offload this burden through textual descriptions. Although recent works have shown the benefits of instructive texts in goal-conditioned RL, few have studied whether descriptive texts help agents to generalize across dynamic environments. To promote research in this direction, we introduce a new platform, BabyAI++, to generate various dynamic environments along with corresponding descriptive texts. Moreover, we benchmark several baselines inherited from the instruction following setting and develop a novel approach towards visually-grounded language learning on our platform. Extensive experiments show strong evidence that using descriptive texts improves the generalization of RL agents across environments with varied dynamics.
An investigation of phone-based subword units for end-to-end speech  recognition

Phones and their context-dependent variants have been the standard modeling units for conventional speech recognition systems, while characters and subwords have demonstrated their effectiveness for end-to-end recognition systems. We investigate the use of phone-based subwords, in particular, byte pair encoder (BPE), as modeling units for end-to-end speech recognition. In addition, we also developed multi-level language model-based decoding algorithms based on a pronunciation dictionary. Besides the use of the lexicon, which is easily available, our system avoids the need of additional expert knowledge or processing steps from conventional systems. Experimental results show that phone-based BPEs tend to yield more accurate recognition systems than the character-based counterpart. In addition, further improvement can be obtained with a novel one-pass joint beam search decoder, which efficiently combines phone- and character-based BPE systems. For Switchboard, our phone-based BPE system achieves 6.8\%/14.4\% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6.3\%/13.3\% WER. On Fisher + Switchboard, joint decoding leads to 4.9\%/9.5\% WER, setting new milestones for telephony speech recognition.
Multi-layered Monitoring for Virtual Machines

This chapter describes monitoring methods to achieve both security and reliability in virtualized computer systems. We show how to perform continuous monitoring and leverage information across different layers of a virtualized computer system to detect malicious attacks and accidental failures.
Introduction: Cyber Physical Systems and Healthcare Analytics

Computing systems and computer algorithms making use of measurements derived from cyber physical systems (e.g., smart grids) or biological systems (e.g., humans) have led to several breakthroughs in the recent years. These breakthroughs grounded in novel foundational analytical formations comprise modern-day electric grids, self-driving cars, and precision medicine approaches.
Geologists reveal trends in mineral diversity

Classifying minerals by how they were made could aid scientists in their study of the complex chemistry of other worlds. Geology reference texts define mineral species on the basis of chemical composition and crystal structure. But mineral samples collected in the field often contain trace elements and structural defects that distinguish most specimens from a textbook example. “If you’re interested in comparing Earth with other planets, both within our solar system and beyond, then you really need to think beyond those very simple, idealized structures,” says Robert Hazen , a mineralogist and astrobiologist at the Carnegie Institution for Science. So Hazen and his colleagues devised a new way to categorize minerals: using the imperfections that tell stories about each mineral’s geologic past. Because minerals acquire defects as they form, the isotopic ratios and foreign inclusions trapped within their crystal lattices can help researchers understand the context in which they were
E-Business Software Product Line Methodology Based on SMEs Characteristics

The growth of Small and Medium Enterprises (SMEs) will certainly have a positive influence on economic development in a country. However, many SMEs struggle to survive, grow, and show limited productivity. It was recorded that there were around 63.9% of SMEs in Indonesia whose turnover had decreased by more than 30% during the COVID-19 pandemic. Among the factors that cause the low survival rate of SMEs, a factor that is considered critical is the lack of success in the use of e-business. The use of e-business by SMEs is not a one-size-fits-all solution, because SMEs have various characteristics. In this study, an E-Business Software Product Line Methodology based on SMEs characteristics was proposed using Software Product Line Engineering (SPLE) approach. In general, the proposed methodology was sufficient to describe the aspects needed in building a software development methodology. Aspects that had not been described were those related to software project management. The proposed methodology is useful for building an e-business application platform that can be customized based on SMEs characteristics.
The Development Process and Characteristics of the 2022 Revised the National Curriculum in History - Focusing on the "2022 Revised Open Debate Source Book on the Development of the Draft for the National Curriculum in History“

This study deals with the overall process of the first draft for the National curriculum development research in History and the characteristics of each subject, focusing on the "2022 revised Open Debate source book on the development of the draft for the National curriculum." In 2025, the high school credit system will be fully implemented, and innovative curriculum revision work is needed to prepare for this. The draft development team conducted a total of six studies on draft development, including middle school 'History', high school 'Korean History', optional subjects 'World History', 'East Asia History Travel', 'Exploring Modern World as History', and 'Conversation with History Contents'. First, each subject was developed in connection with elementary, middle, and high schools as elementary researchers conducted research together. In the case of common subjects, the 2018 partial revision the National curriculum in History was maintained. Second, it attempted to reflect the main points emphasized in the General Discussion, such as democratic citizenship education and ecological transformation education. Third, efforts were made to implement an inquiry-oriented curriculum. Fourth, in the case of 'East Asia History Travel', the subject name was modified to suit the career optional subject.
A Critical Review on ‘The Development of Independent National Culture in the Early Joseon Dynasty’ as a Textbook Narrative

This study aims to identify textbook narratives and critically examine their influence through textbook analysis on the National Culture of the early Joseon Dynasty. This approach stems from my research interest in trying to deconstruct any textbook narratives which provides frameworks of organizing specific topics in instructional explanation as well as units in history textbooks. To foster historical thinking and deconstructive reading, it seems to be necessary to expose the characteristics of textbook narratives which would lead to reveal the constructing process and reality of textbook narratives. Specifically, the analysis was made with focus on 'the Development of Independent National Culture in the early Joseon Dynasty' which plays a role of a textbook narrative throughout a series of National Curriculum revisions and textbook revisions. Such a textbook narrative was identified to provide a framework of decide, exclude, and describe historical facts to create a particular story of the National Culture of the early Joseon Dynasty. Compared to previous history textbook reviews focusing on factual errors, exaggeration, and biased descriptions, this study can be characterized to emphasize the way to read in a deconstructive way based on close examination of textbook narratives. Since the textbook narratives are to reproduce corresponding to public demands of present values and civic engagement, this study strongly suggests that students need to learn how to read historical documents argumentatively not by avoiding existing textbook narratives but by actively deconstructing them.
Description Perspective for New Narratives in the History of Joseon during Its Latter Part in Middle and High School History Textbooks under the Revised Curriculum of 2015

The current middle and high school history textbooks under the Revised Curriculum of 2015 are the first history textbooks developed amid the growing social interest in the importance of authorized history textbooks after the regime's attempt at replacing old history textbooks with new government-designated ones. Today, authorized history textbooks are oriented toward the development of textbooks to present diverse perspectives reflecting the changing situations of the times and the research achievements of the academic circles instead of textbooks containing only a single perspective under the guidance of the government. The description perspective of a textbook serves as a starting line to establish what and how to look at history for students. In the current history textbooks, the perspective for the description of political history finds the accountability of the ruined nation in the failure of political management from factional politics to power politics. The economic and cultural history is described from the perspective of development with the symbolic elements of Western modernization set as a standard of pursuit. The social history describes the social class system and the rural community society from the perspective of discrimination and conflict. The current history textbooks keep almost the same perspectives and descriptions as the old government-designated textbooks within their larger framework of depicting the aspects of Joseon in its latter part. History textbooks need to have a narrative transition and offer students a chance to understand the times from more diverse perspectives so that they can align with the goals oriented by today's history education. The descriptions of political history should help students figure out the various patterns of political management and changes during the latter part of Joseon according to historical causal relations. The descriptions of economic and cultural history should focus on development as an objective phenomenon based on proof and comparison and present reasonable historical meanings accordingly. The descriptions of social history should move students closer to the reality of Joseon society during its latter part based on the coexistence of conflict and integration. These proposals will hopefully lead to description changes in history textbooks through collaborations among researchers, teachers, and people concerned.
Method of Constructing the Contents of the History Supplementary Textbook for Systematic History Education

In the field of history education, discussions concerning the affiliation have been mainly focused on the curriculum of Korean history. Most researchers may agree that teaching needs to be organized by school level. However, history education that meets students' level of understanding may be more effective in differentiating the teaching methods rather than organizing the teaching content differently at each school level. It may be in this context that a variety of teaching and learning methods have recently been emphasized in school education, which values student activities more than unilateral lectures by teachers. Since the seventh revision of the curriculum, more sections have been made to allow students to solve problems on their own, such as "exploration activities," as textbooks began to include feed, guidance and explanation of terms in addition to the text. In the current 2015 revised curriculum, these efforts have become more prominent, adding activities such as writing history. Finding relevant materials is essential for such activities, and if history textbooks are produced that contain abundant materials needed for these student activities, they will be able to increase the utilization of the supplementary materials in class. In the meantime, the history textbook has been composed of maps and visual materials, faithful to the name of "substantive maps and paintings." However, because the latest textbooks contain most of the necessary maps and visuals, the supplementary textbooks, which include a number of such overlapping materials, are now seen to have served its purpose. It is necessary to create an auxiliary textbook that presents students' learning activities to the level of school level and includes data to carry them out according to the level of their understanding.
Projector-based Augmented Reality support for shop-floor programming of industrial robot milling operations

(Re-)Programming industrial robots today still remains an intricate process requiring expert knowledge. Therefore enterprises often avoid modifications to existing robot cells, even if processing could be further improved. This contribution presents a projector-based Augmented Reality support system that supports shop-floor-level inspection and intuitive modification of milling robot operations. The system enables visualization of the programmed milling processes directly on top of the workpiece and allows even for non-experts to verify or change the robotic work program directly in the work cell if needed. Technical challenges and proposed solutions are discussed, and the developed demonstrator system is presented. The system was tested in an industrial shop-floor work environment processing large-scale metal workpieces with high production tolerances, making it an ideal candidate for evaluating the practicability of this AR system for quick and user-friendly robot work program modifications.
Point Pattern Feature Matching Method Based on Polar Coordinate

Infrared label point feature matching is one of the basic problems in the application direction of computer vision, pattern recognition and robot navigation and positioning. First, the center of the label point set and the weighted distance center are obtained in Cartesian coordinates, then the Cartesian coordinates are converted into polar coordinates to construct the position information feature vector of the label point set. Finally, using the method of spatial geometric constraints to compare each point in the label code to the feature vector, the matching result of the label code point set is obtained. The contributions of this paper are as follows: 1. A feature vector of label point set based on polar coordinate transformation is constructed, which makes the feature vector invariant to rotation, translation and illumination. 2. Using the combination of feature vector and spatial geometric constraints to screen label point pairs can effectively overcome the adverse effects of outlier points and noise. The final experiments demonstrate the effectiveness and robustness of the polar coordinate feature matching of label codes.
COMPARISON OF AUTOREGRESSIVE MODEL WITH MISSING DATA TREATED USING ORDINARY LEAST SQUARES AND INTERPOLATION WITH WEIGHTING METHOD

Bandung is committed to contributing to the achievement of the Sustainable Development Goals (SDGs) in Indonesia. One of the efforts that can be made to support the 13th pillar of SDGS regarding climate change is to forecast the air temperature of Bandung City in the future. One of the models that can be used for forecasting air temperature data in Bandung is the Autoregressive (AR) model. Based on BMKG data, often the time series data obtained has missing data. Therefore, in order to do a good time series analysis, it is necessary to make an effort to correct the missing data. The purpose of this research was to examine the procedure for overcoming missing data in the AR model using the Ordinary Least Squares (OLS) method and Interpolation with Weighting, which was applied to forecasting the average air temperature data in the city of Bandung. The research methodology followed the Box-Jenkins 3-step procedure. The first-order AR estimation parameter model was estimated using the OLS method and then used to overcome missing data using both methods with weighting using R software. Both methods resulted in an estimated value of 0.9991 and the same Mean Average Percentage Error (MAPE) value of 2,459% with very accurate criteria. Therefore, to overcome the missing data on the average air temperature data in the city of Bandung with a parameter estimator close to one, we got the same result for both methods.
Intelligent mobile robot using MobileN et on the limited-resource embedded platform

Aiming at the problem of number character recognition on a limited-resource embedded platform, an intelligent mobile robot based on a MobileNet network is presented in this paper. The control system takes low-power chip MSP430F5529 as the main control core. The vision system uses an OpenMV 4 H7 Plus vision sensor to obtain image information and recognize numbers through the TensorFlow Lite model. The tracking system uses a grayscale sensor and an infrared tube to obtain path information, the speed control system measures the speed in real-time through a high-precision GMAR encoder, and the incremental PID algorithm is used to control the speed of the motor to achieve precise speed control and tracking. By adjusting the height of the visual sensor and rationally laying out the car body structure, the car's image recognition ability and flexibility are enhanced. An experiment system was set up in the 2021 T. I. Cup National Undergraduate Electronic Design Contest background. Through multiple debugging and experiments, the recognition accuracy rate reached more than 98%, the average inference time was 193ms, and the average speed in the whole course was about 11.6 cm/s. The comparison with template matching, YOLOX-Tiny, and other models and the actual experiment verify that the robot can complete the fast, stable, and accurate identification of numbers images and the robot control system is stable. Moreover, the entire system cost is lower very cost-effective.