Reviewerly/exHarmony huggingface dataset:
Hierarchical Interaction Networks with Rethinking Mechanism for Document-Level Sentiment Analysis

Document-level Sentiment Analysis (DSA) is more challenging due to vague semantic links and complicate sentiment information. Recent works have been devoted to leveraging text summarization and have achieved promising results. However, these summarization-based methods did not take full advantage of the summary including ignoring the inherent interactions between the summary and document. As a result, they limited the representation to express major points in the document, which is highly indicative of the key sentiment. In this paper, we study how to effectively generate a discriminative representation with explicit subject patterns and sentiment contexts for DSA. A Hierarchical Interaction Networks (HIN) is proposed to explore bidirectional interactions between the summary and document at multiple granularities and learn subject-oriented document representations for sentiment classification. Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation. We extensively evaluate our proposed models on three public datasets. The experimental results consistently demonstrate the effectiveness of our proposed models and show that HIN-SR outperforms various state-of-the-art methods.
Why We Are Losing the War Against COVID-19 on the Data Front and How to Reverse the Situation

With over 117 million COVID-19–positive cases declared and the death count approaching 3 million, we would expect that the highly digitalized health systems of high-income countries would have collected, processed, and analyzed large quantities of clinical data from patients with COVID-19. Those data should have served to answer important clinical questions such as: what are the risk factors for becoming infected? What are good clinical variables to predict prognosis? What kinds of patients are more likely to survive mechanical ventilation? Are there clinical subphenotypes of the disease? All these, and many more, are crucial questions to improve our clinical strategies against the epidemic and save as many lives as possible. One might assume that in the era of big data and machine learning, there would be an army of scientists crunching petabytes of clinical data to answer these questions. However, nothing could be further from the truth. Our health systems have proven to be completely unprepared to generate, in a timely manner, a flow of clinical data that could feed these analyses. Despite gigabytes of data being generated every day, the vast quantity is locked in secure hospital data servers and is not being made available for analysis. Routinely collected clinical data are, by and large, regarded as a tool to inform decisions about individual patients, and not as a key resource to answer clinical questions through statistical analysis. The initiatives to extract COVID-19 clinical data are often promoted by private groups of individuals and not by health systems, and are uncoordinated and inefficient. The consequence is that we have more clinical data on COVID-19 than on any other epidemic in history, but we have failed to analyze this information quickly enough to make a difference. In this viewpoint, we expose this situation and suggest concrete ideas that health systems could implement to dynamically analyze their routine clinical data, becoming learning health systems and reversing the current situation.
CRPN: A cascaded classification and regression DNN framework for seismic phase picking*

Current deep neural networks (DNN) used for seismic phase picking are becoming more complex, which consumes much computing time without significant accuracy improvement. In this study, we introduce a cascaded classification and regression framework for seismic phase picking, named as the classification and regression phase net (CRPN), which contains two convolutional neural network (CNN) models with different complexity to meet the requirements of accuracy and efficiency. The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0, respectively. The second stage of CRPN is used for high precision classification and regression. The regression is designed to reduce the time difference between the probability maximum and the real arrival time. After being trained using 500,000 P and S phases, the CRPN can process 400 hours’ seismic data per second, whose sampling rate is 1 Hz and 25 Hz for the two stages, respectively, on a Nvidia K2200 GPU, and pick 93% P and 89% S phases with the error being reduced by 0.1s after regression correction.
Lessons from Latin America

research-article Lessons from Latin America: embracing horizontality to reconstruct HCI as a pluriverse Share on Authors: Marisol Wong-Villacres Escuela Superior Politécnica del Litoral Escuela Superior Politécnica del LitoralView Profile , Adriana Alvarado Garcia Georgia Institute of Technology Georgia Institute of TechnologyView Profile , Karla Badillo-Urquiola University of Central Florida University of Central FloridaView Profile , Mayra Donaji Barrera Machuca Simon Fraser University Simon Fraser UniversityView Profile , Marianela Ciolfi Felice KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile , Laura S. Gaytán-Lugo Universidad de Colima Universidad de ColimaView Profile , Oscar A. Lemus Indiana University Bloomington Indiana University BloomingtonView Profile , Pedro Reynolds-Cuéllar MIT Media Lab MIT Media LabView Profile , Monica Perusquía-Hernández NTT Communication Science Laboratories NTT Communication Science LaboratoriesView Profile Authors Info & Affiliations InteractionsVolume 28Issue 2March - April 2021 pp 56–63https://doi.org/10.1145/3447794Published:03 March 2021 0citation1,011DownloadsMetricsTotal Citations0Total Downloads1,011Last 12 Months1,011Last 6 weeks67 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Student perceptions of a mobile learning application for English Oral Presentations: the case of EOPA

This study investigated student perceptions and opinions on an English Oral Presentation Application (EOPA), a mobile learning application designed to assist English learners develop oral presentations and online collaboration skills. This study was part of a Design Based Research (DBR) project which incorporated an adapted Technology Acceptance Model (TAM) questionnaire to gain insights on students’ perceptions towards EOPA. In addition, focus-group interviews were used to discover the external and internal factors involved in this type of mobile learning. Participants were 30 Taiwanese undergraduate students who took part in a six-week training course. The questionnaire results indicated that 37% of students found EOPA to be useful for learning, but 33% also found it inconvenient to use. Furthermore, attitudes towards learning suggested they had some difficulties using the app. To gain a deeper understanding of why students use EOPA and to discover other app-related learning problems, the interviews were explored using a Complex Dynamic Systems approach to find the attractor and repeller dynamic states in the learning system. These included the language and presentation skills, the multimedia learning material, app design, online collaboration, anxiety, and limited self-regulation skills. The findings will be used to refine EOPA and the oral presentation learning framework. The study demonstrates how the discovery of different dynamic states can be used to improve mobile learning design.
Optimizing Embedding-Related Quantum Annealing Parameters for Reducing Hardware Bias

Quantum annealers have been designed to propose near-optimal solutions to NP-hard optimization problems. However, the accuracy of current annealers such as the ones of D-Wave Systems, Inc., is limited by environmental noise and hardware biases. One way to deal with these imperfections and to improve the quality of the annealing results is to apply a variety of pre-processing techniques such as spin reversal (SR), anneal offsets (AO), or chain weights (CW). Maximizing the effectiveness of these techniques involves performing optimizations over a large number of parameters, which would be too costly if needed to be done for each new problem instance. In this work, we show that the aforementioned parameter optimization can be done for an entire class of problems, given each instance uses a previously chosen fixed embedding. Specifically, in the training phase, we fix an embedding E of a complete graph onto the hardware of the annealer, and then run an optimization algorithm to tune the following set of parameter values: the set of bits to be flipped for SR, the specific qubit offsets for AO, and the distribution of chain weights, optimized over a set of training graphs randomly chosen from that class, where the graphs are embedded onto the hardware using E. In the testing phase, we estimate how well the parameters computed during the training phase work on a random selection of other graphs from that class. We investigate graph instances of varying densities for the Maximum Clique, Maximum Cut, and Graph Partitioning problems. Our results indicate that, compared to their default behavior, substantial improvements of the annealing results can be achieved by using the optimized parameters for SR, AO, and CW.
Machine Learning Model Cards Transparency Review : Using model card toolkit

In our day to day life, we rely on information that is provided by product makers to make rightful choices such as the nutritional content of food, warnings in medications, strength parameters of a constructed road, etc but when it comes to AI there's has not been any such provided information. The machine learning models are very often distributed without a proper clear understanding of how it functions, i.e. under what conditions would it perform the best and most consistently, whether or not it has blind spots, and, if so, then where are they.Model cards are a very recent and hot topic of research. In Machine Learning (ML), transparency with model cards is relevant as it affects a wide range of domains, from health care to finance and jobs, etc. This research paper presents the importance of model cards and transparency issues.
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI

With the growing complexity of deep learning methods adopted in practical applications, there is an increasing and stringent need to explain and interpret the decisions of such methods. In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model. To this end, we use a generative model, which acts as a prior for generating data, and traverse its latent space using a novel evolutionary strategy with momentum updates. Our framework is generic because (i) it can employ any underlying generator, e.g.. Variational Auto-Encoders (VAEs) or Generative Adversarial Networks (GANs), and (ii) it can be applied to any input data, e.g. images, text samples or tabular data. Since we use a zero-order optimization method, our framework is model-agnostic, in the sense that the machine learning model that we aim to explain is a black-box. We stress out that our novel framework does not require access or knowledge of the internal structure or the training data of the black-box model. We conduct experiments with two generative models, VAEs and GANs, and synthesize exemplars for various data formats, image, text and tabular, demonstrating that our framework is generic. We also employ our prototype synthetization framework on various black-box models, for which we only know the input and the output formats, showing that it is model-agnostic. Moreover, we compare our framework (available at https://github.com/antoniobarbalau/exemplar) with a model-dependent approach based on gradient descent, proving that our framework obtains equally-good exemplars in a shorter time.
Technological and analytical review of contact tracing apps for COVID-19 management

Role of technology is improving for COVID-19 management all around the world. Usage of mobile applications, web applications, cloud computing, and related technologies have helped many public administrators worldwide manage the current pandemic. Contact tracing applications are such mobile app solutions that are used by more than 100 countries today. This study presents a structured research review-based framework related to multiple contact tracing applications. The various components of the framework are related to technological working, design architecture, and feature analysis of the applications, along with the analysis of the acceptance of such applications worldwide. Also, components focusing on the security features and analysis of these applications based on Data Privacy, Security Vetting, and different attacks have been included in the research framework. Many applications are yet to explore the analytical capabilities of the data generated through contact tracing. The various use-cases identified for these applications are detecting positive case probability, identifying a containment zone in the country, finding regional hotspots, monitoring public events & gatherings, identifying sensitive routes, and allocating resources in various regions during the pandemic. This study will act as a guide for the users researching contact tracings applications using the proposed four-layered framework for their app assessment.
A modified conjugate gradient-based Elman neural network

Elman recurrent network is a representative model with feedback mechanism. Although gradient descent method has been widely used to train Elman network, it frequently leads to slow convergence. According to optimization theory, conjugate gradient method is an alternative strategy in searching the descent direction during training. In this paper, an efficient conjugate gradient method has been presented to reach the optimal solution in two ways: (1) constructing a more effective conjugate coefficient, (2) determining adaptive learning rates in terms of the generalized Armijo search method. Experiments show that the performance of the new algorithm is superior to traditional algorithms, such as gradient descent method and conjugate gradient method. In particular, the new algorithm has better performance than the evolutionary algorithm. Finally, we prove the weak and strong convergence of the presented algorithm, i.e., the gradient norm of the error function with respect to the weight vectors converges to zero and the weight sequence approaches a fixed optimal point.
HHAR-net: Hierarchical Human Activity Recognition using Neural Networks

Activity recognition using built-in sensors in smart and wearable devices provides great opportunities to understand and detect human behavior in the wild and gives a more holistic view of individuals' health and well being. Numerous computational methods have been applied to sensor streams to recognize different daily activities. However, most methods are unable to capture different layers of activities concealed in human behavior. Also, the performance of the models starts to decrease with increasing the number of activities. This research aims at building a hierarchical classification with Neural Networks to recognize human activities based on different levels of abstraction. We evaluate our model on the Extrasensory dataset; a dataset collected in the wild and containing data from smartphones and smartwatches. We use a two-level hierarchy with a total of six mutually exclusive labels namely, "lying down", "sitting", "standing in place", "walking", "running", and "bicycling" divided into "stationary" and "non-stationary". The results show that our model can recognize low-level activities (stationary/non-stationary) with 95.8% accuracy and overall accuracy of 92.8% over six labels. This is 3% above our best performing baseline (HHAR-net is shared as an open source tool at https://github.com/mehrdadfazli/HHAR-Net ).
Binary segmentation of medical images using implicit spline representations and deep learning

We propose a novel approach to image segmentation based on combining implicit spline representations with deep convolutional neural networks. This is done by predicting the control points of a bivariate spline function whose zero-set represents the segmentation boundary. We adapt several existing neural network architectures and design novel loss functions that are tailored towards providing implicit spline curve approximations. The method is evaluated on a congenital heart disease computed tomography medical imaging dataset. Experiments are carried out by measuring performance in various standard metrics for different networks and loss functions. We determine that splines of bidegree (1,1) with 128×128 coefficient resolution performed optimally for 512×512 resolution CT images. For our best network, we achieve an average volumetric test Dice score of close to 92%, which reaches the state of the art for this congenital heart disease dataset.
A weighted multi-feature transfer learning framework for intelligent medical decision making

Transformative computing provides an emerging technology to data analysis and information processing, but how to effectively connect the data derived from different domains has aroused much of concern. Especially on medical areas, the scarcity of annotated medical data makes it hard to build a robust classification model, thus, the utilization of medical resources from different sources is particularly important. Transfer learning leverages the knowledge gained from the related domain to enhance the computational effectivity on the target domain. In this work, we extend transfer learning with ensemble learning to present a novel Weighted Multi-Feature Hybrid Transfer Learning Framework (W-MHTL) that builds a transformative approach to connect different domains and applies it to medical decision making. Our approach lessens the distribution variances from multiple perspectives by applying variant types of feature-based transfer learning methods. In each feature space, we construct the transfer model by evaluating the correlations and obtain the predicting result from each model. Finally, a feasible ensemble strategy is used to jointly consider each result. We evaluate our approach on synthetic datasets and UCI medical benchmarks, and a cerebral stroke dataset collected from local hospital. The experimental results reveal that our method achieves superior performances with the currently available alternatives.
Adaptive visual servo control law for finite-time tracking to land quadrotor on moving platform using virtual reticle algorithm

This paper proposed an image-based visual servoing (IBVS) control law for a quadrotor that is equipped with a single monocular camera attached to its bottom. For control purposes virtual reticle plane (VRP) algorithm is used to track the relative 3D position of the quadrotor to the tilting and moving target landing platform within the range of the camera’s field of view (FOV). In this article, the landing platform’s tilting motion is considered sinusoidal type oscillatory motion for the overhead camera. For control purposes, an adaptive finite-time control (AFTC) is proposed, based on the finite-time control (FTC) and adaptive approximation of uncertainties. A constructive combination of FTC and adaptive approximation inherits benefits of both to overcome each other’s limitations. The task of the controller is to regulate the position error to zero in time calculated by VRP. The experimental results confirmed the effectiveness of the VRP algorithm to track the desired parameters of the moving target. Finally, simulations are performed to illustrate the effectiveness and improved performance of the proposed AFTC in response time, robustness, and tracking accuracy.
Detecting direct causality in multivariate time series: A comparative study

The concept of Granger causality is increasingly being applied for the characterization of directional interactions in different applications. A multivariate framework for estimating Granger causality is essential in order to account for all the available information from multivariate time series. However, the inclusion of non-informative or non-significant variables creates estimation problems related to the ‘curse of dimensionality’. To deal with this issue, direct causality measures using variable selection and dimension reduction techniques have been introduced. In this comparative work, the performance of an ensemble of bivariate and multivariate causality measures in the time domain is assessed, focusing on dimension reduction causality measures. In particular, different types of high-dimensional coupled discrete systems are used (involving up to 100 variables) and the robustness of the causality measures to time series length and different noise types is examined. The results of the simulation study highlight the superiority of the dimension reduction measures, especially for high-dimensional systems.
Self-attention Enhanced Patient Journey Understanding in Healthcare System

Understanding patients' journeys in healthcare system is a fundamental prepositive task for a broad range of AI-based healthcare applications. This task aims to learn an informative representation that can comprehensively encode hidden dependencies among medical events and its inner entities, and then the use of encoding outputs can greatly benefit the downstream application-driven tasks. A patient journey is a sequence of electronic health records (EHRs) over time that is organized at multiple levels: patient, visits and medical codes. The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes. This paper proposes a novel self-attention mechanism that can simultaneously capture the contextual and temporal relationships hidden in patient journeys. A multi-level self-attention network (MusaNet) is specifically designed to learn the representations of patient journeys that is used to be a long sequence of activities. We evaluated the efficacy of our method on two medical application tasks with real-world benchmark datasets. The results have demonstrated the proposed MusaNet produces higher-quality representations than state-of-the-art baseline methods. The source code is available in https://github.com/xueping/MusaNet.