Reviewerly/exHarmony huggingface dataset:
Predicting post-stroke pneumonia using deep neural network approaches

Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stroke pneumonia prediction models using more advanced machine learning algorithms, specifically deep learning approaches. Using a hospital’s electronic health record(EHR) data from 2007-2017, 13,930 eligible patients with acute ischaemic stroke (AIS) were identified to build and evaluate the models (85% of the patients were used for training, and 15% were used for testing). In total, 1012 patients (7.23%) contracted pneumonia during hospitalization. A number of machine learning methods were developed and compared to predict pneumonia in the stroke population in China. In addition to the classic methods (i.e., logistic regression (LR), support vector machines (SVMs), extreme gradient boosting (XGBoost)), methods based on multiple layer perceptron (MLP) neural networks and recurrent neural network (RNNs) (i.e., attention-augmented gated recurrent unit (GRU)) are also implemented to make use of the temporal sequence information in electronic health record (EHR) systems. Prediction models for pneumonia were built for two time windows, i.e., within 7 days and within 14 days after stroke onset. In particular, pneumonia occurring within the 7-day window is considered highly associated with stroke (stroke-associated pneumonia, SAP). The attention-augmented GRU model achieved the best performance based on an area under the receiver operating characteristic curve (AUC) of 0.928 for pneumonia prediction within 7 days and an AUC of 0.905 for pneumonia prediction within 14 days. This method outperformed the other machine learning-based methods and previously published pneumonia risk score models. Considering that pneumonia prediction after stroke requires a high sensitivity to facilitate its prevention at a relatively low cost (i.e., increasing the nursing level), we also compared the prediction performance using other evaluation criteria by setting the sensitivity to 0.90. The attention-augmented GRU achieved the optimal performance, with a specificity of 0.85, a positive predictive value (PPV) of 0.32 and a negative predictive value (NPV) of 0.99 for pneumonia within 7 days and a specificity of 0.82, a PPV of 0.29 and an NPV of 0.99 for pneumonia within 14 days. The deep learning-based predictive model is feasible for stroke patient management and achieves the optimal performance compared to many classic machine learning methods.
DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.
Verification of smart contracts: A survey

To achieve trust and continuity in the smart contracts-based business process execution, the verification of such smart contracts is mandatory. A blockchain-based smart contract should work as intended before using it. Due to the immutable nature of blockchain, any bugs or errors will become permanent once published and could lead to huge economic losses. To avoid such problems, verification is required to check the correctness and the security of the smart contract. In this survey, we consider the smart contracts and we investigate smart contacts formal verification methods. We also investigate the security assurance for smart contracts using vulnerabilities detection methods. In this context, we provide a detailed overview of the different approaches to verify the smart contracts and we present the used methods and tools. We show a description of each method as well as its advantages and limitations and we draw several conclusions.
Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation

Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation. One of the most common strategies is to translate images from the source domain to the target domain and then align their marginal distributions in the feature space using adversarial learning. However, source-to-target translation enlarges the bias in translated images and introduces extra computations, owing to the dominant data size of the source domain. Furthermore, consistency of the joint distribution in source and target domains cannot be guaranteed through global feature alignment. Here, we present an innovative framework, designed to mitigate the image translation bias and align cross-domain features with the same category. This is achieved by 1) performing the target-to-source translation and 2) reconstructing both source and target images from their predicted labels. Extensive experiments on adapting from synthetic to real urban scene understanding demonstrate that our framework competes favorably against existing state-of-the-art methods.
The Use of Booklet Media in Managing Bullying Behavior at the Lowest Class School

This study discusses the use of booklet media in overcoming bullying behaviour in low-grade elementary schools.This study uses a 4-D research and development (R&D) method: Define, Design, Develop, and Disseminate.The study results obtained validation from experts in the material, media, and language showed that the booklet that was developed met the valid criteria and was suitable for use.The validation test results by elementary school teachers in Bogor Regency, which consisted of 30 booklet teachers, were very relevantto be used, did not need to be revised.A large-scale trial to grade 3 elementary school students in Bogor obtained that the booklet media was valid and suitable for use.The results of students' understanding of bullying at the beginning were shallow, amounting to only 31.03 percent while after learning given using the booklet media rose to 72.41%.Thus this media booklet can be used as early prevention of bullying deviant behaviour.The use of booklet media with all its weaknesses and advantages is expected to positively influence the loss of bullying behaviour among students so that harmony is established within the school environment and realizing the school as a safe, comfortable, and pleasant place.Also, the school, as an educational institution, becomes a place to gain knowledge and develop one's maturity to show that he is intelligent rationally, emotionally, socially, and spiritually.
A machine learning based credit card fraud detection using the GA algorithm for feature selection

Abstract The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.
Using Wikis And Weblogs To Support Reflective Learning In An Introductory Engineering Design Course

An observation and a pedagogical challenge often found in project-based design courses is that students see what they have produced but they do not see what they have learned.This paper presents preliminary findings from an NSF-sponsored research project which experiments with the use of weblogs and wiki environments, two open source tools, to facilitate student integration and synthesis of learning in Designing the Human Experience, an introductory freshman seminar on design engineering at Stanford University.Coupled with Folio Thinking, a coached process of creating learning portfolios and supporting reflection, this study explores how the combination of this innovative pedagogy along with these new forms of social software can positively influence students' knowledge, awareness, and skills in design engineering. Weblogs and WikisSocial software designed to support group interaction has evolved, since the appearance in the 1960s of multi-user computers and networks, in a variety of forms such as multi-player games, chat rooms, instant messaging, and bulletin boards.More recently, weblogs (or blogs) and wikis (web pages that any user can edit) have captured the imagination of members of both the corporate world and higher education community as valuable knowledge management and group communication tools.Schofield (2003) suggests that the rapid rise of interest in software to support group interaction can be attributed to an emerging web-based platform based on blogs, wikis, and RSS feeds (a format for syndicating news and content), on ease of use, and on the ubiquity of web access. 1 In the professional and personal worlds, social interactions increasingly occur and move fluidly between virtual and face-to-face environments.This is particularly true for today's college students who have been described by Prensky (2001) as "digital natives" of the world and languages of computers, video games and the Internet. 2Higher education is only now beginning to explore the potential educational value of blogs and wikis as a means to promote deeper learning and integration of learning experiences from inside and outside the classroom (Williams & Jacobs, 2004). 3 Folio Thinking and ReflectionFolio Thinking is an instructional method grounded in the process of students creating learning portfolios.Learning portfolios are purposeful collections of artifacts that represent the learning experiences of the portfolio owner, who might be an individual or a group of individuals-students, project teams, faculty, an academic program, or an institution.The Folio Thinking pedagogical approach is designed to enhance self-awareness by enabling students to make their knowledge explicit and visible for themselves as well as for others.
The language of generalization.

Language provides simple ways of communicating generalizable knowledge to each other (e.g., "Birds fly," "John hikes," and "Fire makes smoke"). Though found in every language and emerging early in development, the language of generalization is philosophically puzzling and has resisted precise formalization. Here, we propose the first formal account of generalizations conveyed with language that makes quantitative predictions about human understanding. The basic idea is that the language of generalization expresses that an event or a property occurs relatively often, where what counts as relatively often depends upon one's prior expectations. We formalize this simple idea in a probabilistic model of language understanding, which we test in 3 diverse case studies: generalizations about categories (generic language), events (habitual language), and causes (causal language). We find that the model explains the gradience in human endorsements that has perplexed previous attempts to formalize this swath of linguistic expressions. This work opens the door to understanding precisely how abstract knowledge is learned from language. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning

Rapid progress has been made in the field of reading comprehension and question answering, where several systems have achieved human parity in some simplified settings.However, the performance of these models degrades significantly when they are applied to more realistic scenarios, where answers are involved with various types, multiple text strings are correct answers, or discrete reasoning abilities are required.In this paper, we introduce the Multi-Type Multi-Span Network (MTMSN), a neural reading comprehension model that combines a multi-type answer predictor designed to support various answer types (e.g., span, count, negation, and arithmetic expression) with a multi-span extraction method for dynamically producing one or multiple text spans.In addition, an arithmetic expression reranking mechanism is proposed to rank expression candidates for further confirming the prediction.Experiments show that our model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results.Source code 1 is released to facilitate future work.
Quantum optimization with a novel Gibbs objective function and ansatz architecture search

The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansatz for the quantum circuit architecture, together with a prescription for choosing the variational parameters of the circuit. We propose modifications to both. First, we define the Gibbs objective function and show that it is superior to the energy expectation value for use as an objective function in tuning the variational parameters. Second, we describe an Ansatz Architecture Search (AAS) algorithm for searching the discrete space of quantum circuit architectures near the QAOA to find a better ansatz. Applying these modifications for a complete graph Ising model results in a $244.7\%$ median relative improvement in the probability of finding a low-energy state while using $33.3\%$ fewer two-qubit gates. For Ising models on a 2d grid we similarly find $44.4\%$ median improvement in the probability with a $20.8\%$ reduction in the number of two-qubit gates. This opens a new research field of quantum circuit architecture design for quantum optimization algorithms.
Adaptive frequency median filter for the salt and pepper denoising problem

In this article, the authors propose an adaptive frequency median filter (AFMF) to remove the salt and pepper noise. AFMF uses the same adaptive condition of adaptive median filter (AMF). However, AFMF employs frequency median to restore grey values of the corrupted pixels instead of the median of AMF. The frequency median can exclude noisy pixels from evaluating a grey value of the centre pixel of the considered window, and it focuses on the uniqueness of grey values. Hence, the frequency median produces a grey value closer to the original grey value than the one by the median of AMF. Therefore, AFMF outperforms AMF. In experiments, the authors tested the proposed method on a variety of natural images of the MATLAB library, as well as the TESTIMAGES data set. Additionally, they also compared the denoising results of AFMF to the ones of other state-of-the-art denoising methods. The results showed that AFMF denoises more effectively than other methods.
The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence

Abstract Real-world experience is typically multimodal. Evidence indicates that the facilitation in the detection of multisensory stimuli is modulated by the perceptual load, the amount of information involved in the processing of the stimuli. Here, we used a realistic virtual reality environment while concomitantly acquiring Electroencephalography (EEG) and Galvanic Skin Response (GSR) to investigate how multisensory signals impact target detection in two conditions, high and low perceptual load. Different multimodal stimuli (auditory and vibrotactile) were presented, alone or in combination with the visual target. Results showed that only in the high load condition, multisensory stimuli significantly improve performance, compared to visual stimulation alone. Multisensory stimulation also decreases the EEG-based workload. Instead, the perceived workload, according to the “NASA Task Load Index” questionnaire, was reduced only by the trimodal condition (i.e., visual, auditory, tactile). This trimodal stimulation was more effective in enhancing the sense of presence, that is the feeling of being in the virtual environment, compared to the bimodal or unimodal stimulation. Also, we show that in the high load task, the GSR components are higher compared to the low load condition. Finally, the multimodal stimulation (Visual-Audio-Tactile—VAT and Visual-Audio—VA) induced a significant decrease in latency, and a significant increase in the amplitude of the P300 potentials with respect to the unimodal (visual) and visual and tactile bimodal stimulation, suggesting a faster and more effective processing and detection of stimuli if auditory stimulation is included. Overall, these findings provide insights into the relationship between multisensory integration and human behavior and cognition.
Reformulating HOI Detection as Adaptive Set Prediction

Determining which image regions to concentrate is critical for Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on either detected human and object pairs or pre-defined interaction locations, which limits learning of the effective features. In this paper, we reformulate HOI detection as an adaptive set prediction problem, with this novel formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with parallel instance and interaction branches. To attain this, we map a trainable interaction query set to an interaction prediction set with transformer. Each query adaptively aggregates the interaction-relevant features from global contexts through multi-head co-attention. Besides, the training process is supervised adaptively by matching each ground-truth with the interaction prediction. Furthermore, we design an effective instance-aware attention module to introduce instructive features from the instance branch into the interaction branch. Our method outperforms previous state-of-the-art methods without any extra human pose and language features on three challenging HOI detection datasets. Especially, we achieve over 31% relative improvement on a large scale HICO-DET dataset. Code is available at https://github.com/yoyomimi/AS-Net.
SimMatch: Semi-supervised Learning with Similarity Matching

Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simulta-neously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity re-lationship respected to other instances. Next, we instanti-ated a labeled memory buffer to fully leverage the ground truth labels on instance-level and bridge the gaps between the semantic and instance similarities. Finally, we proposed the unfolding and aggregation operation which allows these two similarities be isomorphically transformed with each other. In this way, the semantic and instance pseudo-labels can be mutually propagated to generate more high-quality and reliable matching targets. Extensive ex-perimental results demonstrate that SimMatch improves the performance of semi-supervised learning tasks across dif-ferent benchmark datasets and different settings. Notably, with 400 epochs of training, SimMatch achieves 67.2%, and 74.4% Top-1 Accuracy with 1% and 10% labeled examples on ImageNet, which significantly outperforms the baseline methods and is better than previous semi-supervised learning frameworks.
Fuzzy Ontology and LSTM-based Text Mining: A Transportation Network Monitoring System for Assisting Travel

Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.
Evaluating Style Transfer for Text

Remi Mir, Bjarke Felbo, Nick Obradovich, Iyad Rahwan. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.