Reviewerly/exHarmony huggingface dataset:
A Recurrent Neural Network Model for Predicting Activated Partial Thromboplastin Time After Treatment With Heparin: Retrospective Study

Anticoagulation therapy with heparin is a frequent treatment in intensive care units and is monitored by activated partial thromboplastin clotting time (aPTT). It has been demonstrated that reaching an established anticoagulation target within 24 hours is associated with favorable outcomes. However, patients respond to heparin differently and reaching the anticoagulation target can be challenging. Machine learning algorithms may potentially support clinicians with improved dosing recommendations.This study evaluates a range of machine learning algorithms on their capability of predicting the patients' response to heparin treatment. In this analysis, we apply, for the first time, a model that considers time series.We extracted patient demographics, laboratory values, dialysis and extracorporeal membrane oxygenation treatments, and scores from the hospital information system. We predicted the numerical values of aPTT laboratory values 24 hours after continuous heparin infusion and evaluated 7 different machine learning models. The best-performing model was compared to recently published models on a classification task. We considered all data before and within the first 12 hours of continuous heparin infusion as features and predicted the aPTT value after 24 hours.The distribution of aPTT in our cohort of 5926 hospital admissions was highly skewed. Most patients showed aPTT values below 75 s, while some outliers showed much higher aPTT values. A recurrent neural network that consumes a time series of features showed the highest performance on the test set.A recurrent neural network that uses time series of features instead of only static and aggregated features showed the highest performance in predicting aPTT after heparin treatment.
Learning to Detect Semantic Boundaries with Image-Level Class Labels

This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different classes, our task is formulated as a multiple instance learning (MIL) problem, where pixels on a line segment connecting areas of two different classes are regarded as a bag of boundary candidates. Moreover, we design a new neural network architecture that can learn to estimate semantic boundaries reliably even with uncertain supervision given by the MIL strategy. Our network is used to generate pseudo semantic boundary labels of training images, which are in turn used to train fully supervised models. The final model trained with our pseudo labels achieves an outstanding performance on the SBD dataset, where it is as competitive as some of previous arts trained with stronger supervision.
Comment recommendation based on graph bidirectional aggregation networks

The function of “Comment” is widely used on the Internet with high efficiency and convenience. However, the quality of comments may have different effects depending on the preferences of the viewers. High-quality comments that users like will promote communication between users and bring positive effects, while low-quality comments that users dislike will hinder communication between users, even spread negative emotions and affect user experience. To avoid the influence, a comment recommendation by link prediction is implemented. Meanwhile, the quality of input features affects the recommendation results. For this reason, a link prediction model based on graph bidirectional aggregation networks (GBAN) is proposed. It constructs user features by text information of comment, adds attention mechanism to consider the impact of different followers in social networks, and modifies the feature representation of user nodes by the reverse aggregation mechanism. Finally, the link prediction method is applied to make recommendations based on the scores of edges among nodes. Experimental results show that the proposed method can make the recommendation performance better. • A graph bidirectional aggregation network is proposed for strengthing node representation capabilities. • Use multi-task learning to fuse node embedding and link prediction models. • Alleviate the impact of online trolls on the daily life of netizens based on the proposed method.
Exploring the role of mobile game-based apps towards a smart learning city environment – the innovation of EduCITY

Purpose This paper provides a general review related to a wider project, aimed at developing a mobile game-based app on Education for Sustainable Development within a smart learning city environment. It aims to address guidelines for a new action-oriented transformative pedagogy that is essential for the development of key competencies for sustainable development based on literature review and on the exploration of a previously created mobile game-based app – the EduPARK app. Design/methodology/approach This work gives continuity to the developed work, through a qualitative interpretive methodology of case study. Its main objective is to analyze the opinion of Higher Education students, who have experienced the EduPARK app, in what concerns the impact of the educational strategy into: (1) learning value, (2) authentic learning, (3) lifelong learning, and (4) conservation and sustainable habits. Data collection instruments involve students’ reflections triangulated to a questionnaire. Findings The study finds that the potential for promoting conservation and sustainable habits is widely recognized by app users, although they mention that this aspect can be further explored. This legitimates the relevance of the new mission – EduCITY, which focuses on enhancing sustainability key skills of citizens who participate in extension game-based activities on strategic paths in the city. Originality/value This paper comprises a set of guidelines and best practices for educational stakeholders and decision makers in order to enhance a wider integration of this outdoor mobile innovative approach in education, promoting smart and sustainable attitudes of citizens within the cities.
A Graph Learning Based Framework for Billion-Scale Offline User Identification

Offline user identification is a scenario that users use their bio-information like faces as identification in offline venues, which has been applied in many offline scenarios such as verification in banks, check-in in hotels and making a purchase in offline merchants. In such a scenario, designing an identification approach to do extremely accurate offline user identification is critical. Most scenarios use faces to identify users and previous algorithms are mainly based on visual features and computer-vision models. However, due to the large variations such as pose, illumination and occlusions in offline scenarios, it remains a challenging problem for existing computer-vision algorithms to get a satisfying accuracy in real-world scenarios. Furthermore, billion-scale candidate users also require high efficiency and high accuracy for the approach.
Demystify Hyperparameters for Stochastic Optimization with Transferable Representations

This paper studies the convergence and generalization of a large class of Stochastic Gradient Descent (SGD) momentum schemes, in both learning from scratch and transferring representations with fine-tuning. Momentum-based acceleration of SGD is the default optimizer for many deep learning models. However, there is a lack of general convergence guarantees for many existing momentum variants in conjunction withstochastic gradient. It is also unclear how the momentum methods may affect thegeneralization error. In this paper, we give a unified analysis of several popular optimizers, e.g., Polyak's heavy ball momentum and Nesterov's accelerated gradient. Our contribution is threefold. First, we give a unified convergence guarantee for a large class of momentum variants in thestochastic setting. Notably, our results cover both convex and nonconvex objectives. Second, we prove a generalization bound for neural networks trained by momentum variants. We analyze how hyperparameters affect the generalization bound and consequently propose guidelines on how to tune these hyperparameters in various momentum schemes to generalize well. We provide extensive empirical evidence to our proposed guidelines. Third, this study fills the vacancy of a formal analysis of fine-tuning in literature. To our best knowledge, our work is the first systematic generalizability analysis on momentum methods that cover both learning from scratch and fine-tuning. Our codes are available https://github.com/jsycsjh/Demystify-Hyperparameters-for-Stochastic-Optimization-with-Transferable-Representations .
Graph-based Multilingual Language Model

The large-scale nature of product catalog and the changing demands of customer queries makes product search a challenging problem. The customer queries are ambiguous and implicit. They may be looking for an exact match of their query, or a functional equivalent (i.e., substitute), or an accessory to go with it (i.e., complement). It is important to distinguish these three categories from merely classifying an item for a customer query as relevant or not. This information can help direct the customer and improve search applications to understand the customer mission. In this paper, we formulate search relevance as a multi-class classification problem and propose a graph-based solution to classify a given query-item pair as exact, substitute, complement, or irrelevant (ESCI). The customer engagement (clicks, add-to-cart, and purchases) between query and items serve as a crucial information for this problem. However, existing approaches rely purely on the textual information (such as BERT) and do not sufficiently focus on the structural relationships. Another challenge in including the structural information is the sparsity of such data in some regions. We propose Structure-Aware multilingual LAnguage Model (SALAM), that utilizes a language model along with a graph neural network, to extract region-specific semantics as well as relational information for the classification of query-product pairs. Our model is first pre-trained on a large region-agnostic dataset and behavioral graph data and then fine-tuned on region-specific versions to address the sparsity. We show in our experiments that SALAM significantly outperforms the current matching frameworks on the ESCI classification task in several regions. We also demonstrate the effectiveness of using a two-phased training setup (i.e., pre-training and fine-tuning) in capturing region-specific information. Also, we provide various challenges and solutions for using the model in an industrial setting and outline its contribution to the e-commerce engine.
Enhancing Teachers’ Digital Competence for Professional Development in Distance Mode

The teaching profession is dynamic and requires constant development. By introducing and prioratising an effective professional development system, school facilitates the introduction of innovations. The term “professional development” refers to an orderly, continuous, and intensive approach to improving teacher's work efficiency with the ultimate goal of further enhancing student achievement which involves three interrelated stages: study of specific needs, planning and implementation of relevant activities, evaluation of the obtained effect. The most popular activities are trainings with the core concept of digitalization, which involves developing teachers digital competence and skills using digital tools. During the pandemic, the practice of offering various types of online trainings for teacher professional development has gradually increased. Different digital educational platforms were introduced. Above mentioned online trainings/webinars were the best solution to maintain the continuity of teachers' professional development, however, this process posed a number of challenges to the pedagogical community. The purpose of the present work is, to identify the strengths and weaknesses of distance training, evaluate its results and define ways to increase efficiency. Research methods: surveys, interviews, focus groups. The research was conducted in 3 stages: an online survey of teachers, an interview with 14 school principals and a focus group with trainers/representatives of training organizations. Research outcomes: Though teacher development programs were problematic in terms of intensity, outdated topics, teachers lack of digital competence, poor internet connection, lack of implementation and evaluation of the training experience, the flexibility and availability of attendance, access to the materials enhanced teachers readiness and motivation. On the basis of the research and analysis that highlight the current situation recommendations are made which will add practical value to the study.
On the Linear Components Space of S-boxes Generated by Orthogonal Cellular Automata

We investigate S-boxes defined by pairs of Orthogonal Cellular Automata (OCA), motivated by the fact that such CA always define bijective vectorial Boolean functions, and could thus be interesting for the design of block ciphers. In particular, we perform an exhaustive search of all nonlinear OCA pairs of diameter $$d=4$$ and $$d=5$$ , which generate S-boxes of size $$6\times 6$$ and $$8\times 8$$ , respectively. Surprisingly, all these S-boxes turn out to be linear, and thus they are not useful for the design of confusion layers in block ciphers. However, a closer inspection of these S-boxes reveals a very interesting structure. Indeed, we remark that the linear components space of the OCA-based S-boxes found by our exhaustive search are themselves the kernels of linear CA, or, equivalently, polynomial codes. We finally classify the polynomial codes of the S-boxes obtained in our exhaustive search and observe that, in most cases, they actually correspond to the cyclic code with generator polynomial $$X^{b}+1$$ , where $$b=d-1$$ . Although these findings rule out the possibility of using OCA to design good S-boxes in block ciphers, they give nonetheless some interesting insights for a theoretical characterization of nonlinear OCA pairs, which is still an open question in general.
Augmented Creativity: Leveraging Natural Language Processing for Creative Writing

Recent advances have moved natural language processing (NLP) capabilities with artificial intelligence beyond mere grammar and spell-checking functionality.One such new use that has arisen is the ability to suggest new content to writers to inspire new ideas by using "machine-in-the-loop" strategies in creative writing.In order to explore the possibilities of such a strategy, this study provides a model to be adopted in creative writing courses in higher education.An NLP application was created using Python and spaCy and deployed via Streamlit.The AI allowed students to see if their grammar aligned with those principles and techniques taught in class to assist with a deeper understanding of the grammatical aspects of the content and also to improve their creativity as writers.The study at hand seeks to determine the efficacy of a new proprietary NLP on improving understanding of grammar and creativity in student writing.Participants in the study were assessed through surveys and open-ended questions.Findings note that participants agreed the algorithm assisted them in a better understanding of grammar but were not as receptive to assistance in improving their creativity.It should also be noted that the suggestions provided by the algorithm did not necessarily improve the written artifacts submitted in the study.Results indicate that students enjoy using NLP as part of the creative writing process but largely, as with other language processing tools, to assist with grammar and syntax.
SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles

Abstract Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio‐temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio‐temporal ensemble data without re‐training. The latent‐space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet , we are able to perform similarity analyses on large‐scale spatio‐temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in‐depth basic understanding of the underlying temporal evolution of a given ensemble.
Mobile and Multimodal? A Comparative Evaluation of Interactive Workplaces for Visual Data Exploration

Abstract Mobile devices are increasingly being used in the workplace. The combination of touch, pen, and speech interaction with mobile devices is considered particularly promising for a more natural experience. However, we do not yet know how everyday work with multimodal data visualizations on a mobile device differs from working in the standard WIMP workplace setup. To address this gap, we created a visualization system for social scientists, with a WIMP interface for desktop PCs, and a multimodal interface for tablets. The system provides visualizations to explore spatio‐temporal data with consistent WIMP and multimodal interaction techniques. To investigate how the different combinations of devices and interaction modalities affect the performance and experience of domain experts in a work setting, we conducted an experiment with 16 social scientists where they carried out a series of tasks with both interfaces. Participants were significantly faster and slightly more accurate on the WIMP interface. They solved the tasks with different strategies according to the interaction modalities available. The pen was the most used and appreciated input modality. Most participants preferred the multimodal setup and could imagine using it at work. We present our findings, together with their implications for the interaction design of data visualizations.
Exploring the graduates’ perception of English language materials in Vocational High Schools

Instead of continuing their studies, Vocational High School (SMK) students are prepared to work following graduation. Thus, instead of using general English, ESP (English for Specific Purposes) should be integrated in SMK. However, the Indonesian National Curriculum decided to teach both SMA (General High School) and SMK the same general English content. The purpose of this study is to see how graduates feel about the EFL materials they received at SMK. This research involved three SMK graduates as participants. This investigation yielded three recommendations. First, ESP resources should be incorporated to the English topic in SMK without omitting the need for general English materials. Second, SMK classroom activities should place a greater emphasis on practical communication. Third, extensive reading and/or listening activities should be implemented in SMK.DOI: 10.26905/enjourme.v7i1.7445
SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Networks

The effectiveness of Recurrent Neural Networks (RNNs) for tasks such as Automatic Speech Recognition has fostered interest in RNN inference acceleration. Due to the recurrent nature and data dependencies of RNN computations, prior work has designed customized architectures specifically tailored to the computation pattern of RNN, getting high computation efficiency for certain chosen model sizes. However, given that the dimensionality of RNNs varies a lot for different tasks, it is crucial to generalize this efficiency to diverse configurations. In this work, we identify adaptiveness as a key feature that is missing from today’s RNN accelerators. In particular, we first show the problem of low resource utilization and low adaptiveness for the state-of-the-art RNN implementations on GPU, FPGA, and ASIC architectures. To solve these issues, we propose an intelligent tiled-based dispatching mechanism for increasing the adaptiveness of RNN computation, in order to efficiently handle the data dependencies. To do so, we propose Sharp as a hardware accelerator, which pipelines RNN computation using an effective scheduling scheme to hide most of the dependent serialization. Furthermore, Sharp employs dynamic reconfigurable architecture to adapt to the model’s characteristics. Sharp achieves 2×, 2.8×, and 82× speedups on average, considering different RNN models and resource budgets, compared to the state-of-the-art ASIC, FPGA, and GPU implementations, respectively. Furthermore, we provide significant energy reduction with respect to the previous solutions, due to the low power dissipation of Sharp (321 GFLOPS/Watt).
Variable selection in linear-circular regression models

Applications of circular regression models are ubiquitous in many disciplines, particularly in meteorology, biology and geology. In circular regression models, variable selection problem continues to be a remarkable open question. In this paper, we address variable selection in linear-circular regression models where uni-variate linear dependent and a mixed set of circular and linear independent variables constitute the data set. We consider Bayesian lasso which is a popular choice for variable selection in classical linear regression models. We show that Bayesian lasso in linear-circular regression models is not able to produce robust inference as the coefficient estimates are sensitive to the choice of hyper-prior setting for the tuning parameter. To eradicate the problem, we propose a robustified Bayesian lasso that is based on an empirical Bayes (EB) type methodology to construct a hyper-prior for the tuning parameter while using Gibbs Sampling. This hyper-prior construction is computationally more feasible than the hyper-priors that are based on correlation measures. We show in a comprehensive simulation study that Bayesian lasso with EB-GS hyper-prior leads to a more robust inference. Overall, the method offers an efficient Bayesian lasso for variable selection in linear-circular regression while reducing model complexity.
Introduction to Python

This chapter discusses a brief introduction to the python programming language, the core Python language, and important libraries for natural language processing by using different IDEs like Anaconda and Google Co laboratory.