Reviewerly/exHarmony huggingface dataset:
Development of a machine learning model for the prediction of the short-term mortality in patients in the intensive care unit

The aim of this study was to develop and evaluate a machine learning model that predicts short-term mortality in the intensive care unit using the trends of four easy-to-collect vital signs. The primary training cohort included 1968 patients at the Veterans Health Service Medical Center. The external validation cohort comprised 409 patients at Seoul National University Hospital. Datasets of heart rate, systolic blood pressure, diastolic blood pressure, and peripheral capillary oxygen saturation (SpO2) measured every hour for 10 h were used. The performances of mortality prediction models generated using five machine learning algorithms, Random Forest (RF), XGboost, perceptron, convolutional neural network, and Long Short-Term Memory, were calculated and compared using area under the receiver operating characteristic curve (AUROC) values and an external validation dataset. The machine learning model generated using the RF algorithm showed the best performance. Its AUROC was 0.922, which is much better than the 0.8408 of the Acute Physiology and Chronic Health Evaluation II. The machine learning model developed using SpO2 showed the best performance (AUROC, 0.89). This simple yet powerful new mortality prediction model could be useful for early detection of probable mortality and appropriate medical intervention, especially in rapidly deteriorating patients.
Will the Metaverse Be the Future of the Internet?

In the past two years, the Metaverse, which has become popular, has attracted a large amount of capital, like Alphabet, META, Tencent, Bytedance.They have all invested a lot of people and money in the development of the Metaverse.But what is the Metaverse, what can the Metaverse bring to the table, and what is the future of the Metaverse?This article studies the history and origin of the concept of the Metaverse, combines the views published by previous people to explore the role of Metaverse for today's society, and discusses in more depth the research and application of metaverse technology by these tech giants.This article discusses some questions.The first one is how the metaverse is being used by technology companies.Also, the mutual relations like the impact of the current environment on the development of the Metaverse, and the impact of the Metaverse on current social environment.However, some of the problems are not quite clear when we look at them in this day and age, and the Metaverse that people imagine has not yet been realized due to technical shortcomings and software and hardware defects.With the development of time, many technical problems may be solved in the future, and we expect to see the complete Metaverse.
An Enhanced Lightweight Convolutional Neural Network for Ship Detection in Maritime Surveillance System

With the extensive application of artificial intelligence, ship detection from optical satellite remote sensing images using deep learning technology can significantly improve detection accuracy. However, the existing methods usually have complex models and huge computations, which makes them difficult to deploy on resource-constrained devices such as satellites. To solve this problem, this paper proposes an enhanced lightweight ship detection model called ShipDetectionNet to replace the standard convolution with improved convolution units. The improved convolution unit is implemented by applying depthwise separable convolution to replace standard convolution and further using the pointwise group convolution to replace the point convolution in depthwise separable convolution. In addition, the Attention mechanism is incorporated into the convolution unit to ensure detection accuracy. Compared with the latest YOLOv5s, our model has a comparable performance in mean Average Precision (mAP), while the number of parameters and the model size are reduced by 14.18&#x0025; and 13.14&#x0025;, respectively. Compared to five different lightweight detection models, the proposed ShipDetectionNet is more competent for ship detection tasks. In addition, the ShipDetectionNet is evaluated on four challenging scenarios, demonstrating its generalizability and effectiveness.
The Pure Poet: How Good is the Subjective Credibility and Stylistic Quality of Literary Short Texts Written with an Artificial Intelligence Tool as Compared to Texts Written by Human Authors?

Vivian Emily Gunser, Steffen Gottschling, Birgit Brucker, Sandra Richter, Dîlan Canan Çakir, Peter Gerjets. Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022). 2022.
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models

Relations between words are governed by hierarchical structure rather than linear ordering.Sequence-to-sequence (seq2seq) models, despite their success in downstream NLP applications, often fail to generalize in a hierarchysensitive manner when performing syntactic transformations-for example, transforming declarative sentences into questions.However, syntactic evaluations of seq2seq models have only observed models that were not pre-trained on natural language data before being trained to perform syntactic transformations, in spite of the fact that pre-training has been found to induce hierarchical linguistic generalizations in language models; in other words, the syntactic capabilities of seq2seq models may have been greatly understated.We address this gap using the pre-trained seq2seq models T5 and BART, as well as their multilingual variants mT5 and mBART.We evaluate whether they generalize hierarchically on two transformations in two languages: question formation and passivization in English and German.We find that pre-trained seq2seq models generalize hierarchically when performing syntactic transformations, whereas models trained from scratch on syntactic transformations do not.This result presents evidence for the learnability of hierarchical syntactic information from non-annotated natural language text while also demonstrating that seq2seq models are capable of syntactic generalization, though only after exposure to much more language data than human learners receive. Train, dev, test Generalization Structure Question Formation PassivizationNo RC/PP quest: some xylophones have remembered my yak.→ have some xylophones remembered my yak?passiv: your quails amused some vulture.→ some vulture was amused by your quails.RC/PP on object quest: my zebras have amused some walrus who has waited.→ have my zebras amused some walrus who has waited?passiv: some tyrannosaurus entertained your quail behind your newt.→ your quail behind your newt was entertained by some tyrannosaurus.RC/PP on subject quest: my vultures that our peacock hasn't applauded haven't read.→ haven't my vultures that our peacock hasn't applauded read?passiv: the zebra upon the yak confused your orangutans.→ your orangutans were confused by the zebra upon the yak.
Teleconsultation as an advanced practice nursing during the COVID-19 pandemic based on Roy and Chick-Meleis

ABSTRACT Teleconsultation can be classified as an advanced practice nursing that requires nurses’ clinical reasoning based on a consistent theoretical framework to use in the nursing process. Our study conducted a theoretical-reflective analysis, based on Callista Roy’s Adaptation Model of Nursing and Chick-Meleis’ Transition Theory, about the contribution of teleconsultation as an advanced practice nursing in the care of older adults with chronic diseases during the COVID-19 pandemic. We reflect on this in two moments: “nursing theories and dealing with COVID-19” and “ways of adapting to new care models and advanced practice nursing,” based on communication and information technologies. The worsening of the pandemic in Brazil changed life cycles, health/disease and organizational processes, demanding the development of an adaptive-transactional state by users and health care providers. Thus, information and communication technologies combined with advanced practice nursing can relieve social distancing and its repercussions on health care.
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation

Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner.Although many previous studies try to incorporate global information into NMT models, there still exist limitations on how to effectively exploit bidirectional global context.In this paper, we propose a Confidence Based Bidirectional Global Context Aware (CB-BGCA) training framework for NMT, where the NMT model is jointly trained with an auxiliary conditional masked language model (CMLM).The training consists of two stages:(1) multi-task joint training; (2) confidence based knowledge distillation.At the first stage, by sharing encoder parameters, the NMT model is additionally supervised by the signal from the CMLM decoder that contains bidirectional global contexts.Moreover, at the second stage, using the CMLM as teacher, we further pertinently incorporate bidirectional global context to the NMT model on its unconfidently-predicted target words via knowledge distillation.Experimental results show that our proposed CB-BGCA training framework significantly improves the NMT model by +1.02, +1.30 and +0.57BLEU scores on three large-scale translation datasets, namely WMT'14 Englishto-German, WMT'19 Chinese-to-English and WMT'14 English-to-French, respectively.
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting

In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e.g., policy changes, conflicts, or pandemics.Class imbalance and drift can sometimes be mitigated by resampling the training data to simulate (or compensate for) a known target distribution, but what if the target distribution is determined by unknown future events?Instead of simply resampling uniformly to hedge our bets, we focus on the underlying optimization algorithms used to train such document classifiers and evaluate several group-robust optimization algorithms, initially proposed to mitigate grouplevel disparities.Reframing group-robust algorithms as adaptation algorithms under concept drift, we find that Invariant Risk Minimization and Spectral Decoupling outperform samplingbased approaches to class imbalance and concept drift, and lead to much better performance on minority classes.The effect is more pronounced the larger the label set.
Why only Micro-F1? Class Weighting of Measures for Relation Classification

Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F 1 , macro-F 1 or AUC.In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets.We introduce a framework for weighting schemes, where existing schemes are extremes, and two new intermediate schemes.We show that reporting results of different weighting schemes better highlights strengths and weaknesses of a model.
CIPP-SAW Application as an Evaluation Tool of E-Learning Effectiveness

The effectiveness level of e-learning implementation in the learning process at health colleges is very important for all users to know.Things that can be done to measure the level of effectiveness accurately is to carry out evaluation activities using computerized tools.One of the innovations found in this research was a computer-based evaluation application called the CIPP-SAW application.This application is formed by combining an educational evaluation model called CIPP (Context-Input-Process-Product) with a decision support system method called SAW (Simple Additive Weighting).Based on those situations, this research aimed to provide an overview of the user interface design and workings of the CIPP-SAW application used in evaluating the effectiveness of e-learning implemented in health colleges (case study in Bali province).This research was a development study using Borg & Gall's design, which focused on the preliminary field test and main product revision stages.The subjects involved in the field trial of the CIPP-SAW application were 64 respondents.The respondents included: two informatics experts, two educational evaluation experts, 30 students, and 30 lecturers from several health colleges in Bali province.Data collection tools in the form of questionnaires, interview guidelines, and photo documentation.The analysis technique used was descriptive quantitative which compares the effectiveness level of the CIPP-SAW application with the effectiveness standard which refers to a scale of five.The results showed that the effectiveness level of the CIPP-SAW application was 87.521%, so it was in a good category.
Optimizing the Local Search Algorithm for Generating S-Boxes

Nonlinear substitutions (S-boxes) are an important component of modern symmetric cryptography algorithms. They complicate symmetric transformations and introduce nonlinearity in the input-output relationship, which provides resistance to some cryptanalysis techniques. S-boxes generation can be done in a number of ways. However, the most promising are heuristic techniques. On the one hand, the generated S-boxes look like random settings, which complicates algebraic cryptanalysis. On the other hand, heuristic search makes it possible to achieve high nonlinearity and δ-uniformity, which complicates linear and differential cryptanalysis. In this article, we will explore the simplest local search algorithm for generating S-boxes. To assess the efficiency of the algorithm, the concept of a track of a cost function is introduced in the article. We optimize the algorithm parameters and show that with the chosen parameters it is possible to reliably form S-boxes with nonlinearity 104.
Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract)

Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. Unlike the worst-case a priori guarantees of differential privacy, Fisher information loss measures leakage with respect to specific examples, attributes, or sub-populations within the dataset. We motivate Fisher information loss through the Cramer-Rao bound and delineate the implied threat model. We provide efficient methods to compute Fisher information loss for output-perturbed generalized linear models. Finally, we empirically validate Fisher information loss as a useful measure of information leakage.
Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledge

Conversational Causal Emotion Entailment aims to detect causal utterances for a non-neutral targeted utterance from a conversation. In this work, we build conversations as graphs to overcome implicit contextual modelling of the original entailment style. Following the previous work, we further introduce the emotion information into graphs. Emotion information can markedly promote the detection of causal utterances whose emotion is the same as the targeted utterance. However, it is still hard to detect causal utterances with different emotions, especially neutral ones. The reason is that models are limited in reasoning causal clues and passing them between utterances. To alleviate this problem, we introduce social commonsense knowledge (CSK) and propose a Knowledge Enhanced Conversation graph (KEC). KEC propagates the CSK between two utterances. As not all CSK is emotionally suitable for utterances, we therefore propose a sentiment-realized knowledge selecting strategy to filter CSK. To process KEC, we further construct the Knowledge Enhanced Directed Acyclic Graph networks. Experimental results show that our method outperforms baselines and infers more causes with different emotions from the targeted utterance.
The Development of UMS Building Catalogue Information System

Universitas Muhammadiyah Surakarta (UMS) as one of the biggest private university in Indonesia has a large number of new students per year. However, most new students often get confused to identify and even do not know the location of the buildings that handle various activities in the early period of lectures, such as student orientation, English pre-tests, etc. This is due to lack of information provided by the university to the new enrolled students. To overcome this problem, this work aims to develop a web-based building catalogue by implementing a framework, namely Ionic. This system is built to help the new students getting the building-related information in UMS and find out the location of the building. By following the waterfall model to build the system, the results show that we have successfully developed the building catalogue information system. The main page of the information system displays a list of buildings that are within the scope of UMS, starting from Building A to Building L completed with photos and detailed information about the building. The system is helpful for new students to get the information of all buildings in UMS.
Fault Diagnosis of Coal Mill Based on Kernel Extreme Learning Machine with Variational Model Feature Extraction

Aiming at the typical faults in the coal mills operation process, the kernel extreme learning machine diagnosis model based on variational model feature extraction and kernel principal component analysis is offered. Firstly, the collected signals of vibration and loading force, corresponding to typical faults of coal mill, are decomposed by variational model decomposition, and the intrinsic model functions at different scales are obtained. Then, the eigenvectors consisting of feature energy and sample entropy in these functions are respectively calculated, and the kernel principal component analysis is used for noise removal and dimensionality reduction. Finally, the kernel extreme learning machine model is trained and tested with the dimension reduced feature vector as input and the corresponding coal mill state as output. The results show that the variational model decomposition extraction can improve the input features of the model compared with the single eigenvector model, and the kernel principal component analysis method can significantly reduce the information redundancy and the correlation of eigenvectors, which can effectively save time and cost, and improve the prediction performance of the model to some extent. The establishment of this model provides a new idea for the study of coal mill fault diagnosis.
Path Planning with Potential Field-Based Obstacle Avoidance in a 3D Environment by an Unmanned Aerial Vehicle

In this paper we address the problem of path planning in an unknown environment with an aerial robot. The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 3D sensors, such as LiDARs. It performs obstacle avoidance in real time and on an on-board computer. We present a novel algorithm based on the conventional Artificial Potential Field (APF) that corrects the planned trajectory to avoid obstacles. To this end, our modified algorithm uses a rotation-based component to avoid local minima. The smooth trajectory following, achieved with the MPC tracker, allows us to quickly change and re-plan the UAV trajectory. Comparative experiments in simulation have shown that our approach solves local minima problems in trajectory planning and generates more efficient paths to avoid potential collisions with static obstacles compared to the original APF method.
Federated Split Learning Model for Industry 5.0: A Data Poisoning Defense for Edge Computing

Industry 5.0 provides resource-efficient solutions compared to Industry 4.0. Edge Computing (EC) allows data analysis on edge devices. Artificial intelligence (AI) has become the focus of interest in recent years, particularly in industrial applications. The coordination of AI at the edge will significantly improve industry performance. This paper integrates AI and EC for Industry 5.0 to defend against data poisoning attacks. A hostile user or node injects fictitious training data to distort the learned model in a data poisoning attack. This research provides an effective data poisoning defense strategy to increase the learning model’s performance. This paper developed a novel data poisoning defense federated split learning, DepoisoningFSL, for edge computing. First, a defense mechanism is proposed against data poisoning attacks. Second, the optimal parameters are determined for improving the performance of the federated split learning model. Finally, the performance of the proposed work is evaluated with a real-time dataset in terms of accuracy, correlation coefficient, mean absolute error, and root mean squared error. The experimental results show that DepoisoningFSL increases the performance accuracy.
A Space-efficient Optical Computing Chip Based on Diffractive Neural Network

A the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing is demonstrated and achieved ~10-fold reduction in both footprint and energy consumption. It has high potential in optical-artificial-intelligence.
Minimal Trap Spaces of Logical Models are Maximal Siphons of Their Petri Net Encoding

Boolean modelling of gene regulation but also of post-transcriptomic systems has proven over the years that it can bring powerful analyses and corresponding insight to the many cases where precise biological data is not sufficiently available to build a detailed quantitative model. This is even more true for very large models where such data is frequently missing and led to a constant increase in size of logical models à la Thomas. Besides simulation, the analysis of such models is mostly based on attractor computation, since those correspond roughly to observable biological phenotypes. The recent use of trap spaces made a real breakthrough in that field allowing to consider medium-sized models that used to be out of reach. However, with the continuing increase in model-size, the state-of-the-art computation of minimal trap spaces based on prime-implicants shows its limits as there can be a huge number of implicants. In this article we present an alternative method to compute minimal trap spaces, and hence complex attractors, of a Boolean model. It replaces the need for prime-implicants by a completely different technique, namely the enumeration of maximal siphons in the Petri net encoding of the original model. After some technical preliminaries, we expose the concrete need for such a method and detail its implementation using Answer Set Programming. We then demonstrate its efficiency and compare it to implicant-based methods on some large Boolean models from the literature.