Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Propagation Patterns With Omnipolar EGM in Epicardial Multi-Electrode Arrays

Omnipolar Electrogram (OP-EGM) is a recently proposed technique to characterize myocardial propagation in multi-electrode catheters regardless of the angle between propagation direction and catheter bipolar. This work aims to evaluate the accuracy of atrial propagation parameters obtained with OP-EGM in sinus rhythm (SR) and in different patterns of atrial fibrillation (AF). Real and simulated unipolar electrograms (u-EGMs) were used in this study. For both types of data, conduction velocity was obtained for each clique of 4 neighbour electrodes using OP-EGM. As a reference, conduction velocity was also computed from local activation times (LATs) using a linear propagation model.Analysis of simulated data showed that conduction velocity had good concordance with propagation patterns for both estimations, although the LAT-based errors were lower in most of the cases. When conduction velocity magnitude (CV) was 1 mm/ms, its estimation errors (expressed as mean ± std) calculated with OP-EGM and from LATs were 0.053 ± 0.005 mm/ms and 0.003 ±2.1 ×10−5 mm/ms, respectively, when focus was at 30 mm from the bottom of the tissue slice, while propagation direction angular errors were 6.64 ± 4.3°and 4.35 ± 2.8°. In real data, maps obtained with OP-EGM presented smoother and more coherent patterns than those based on LATs.
Conductivity and Permittivity in Plasma With Nonequilibrium Electron Distribution Function

Electron conductivity and dielectric permittivity are essential parameters that determine the external characteristics of laboratory and space plasmas in terms of electromagnetic (EM) wave propagation. In most problems, <inline-formula> <tex-math notation="LaTeX">$\sigma $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> are calculated using the elementary theory under the assumption that the electron distribution function (EDF) is under equilibrium (Maxwellian). But, in real plasma, the EDF is, as a rule, non-Maxwellian. Therefore, the form of EDF can significantly affect the characteristics of various processes involving electrons. In this article, the calculations of the correction coefficients for conductivity and permittivity in argon were performed in a wide frequency range for typical two-temperature EDFs, with which almost any EDF in the laboratory and ionospheric plasma can be approximated. The results show that the form of EDF has a significant effect on the conductivity and the permittivity in plasma. Most substantial differences are observed under conditions when the frequency of the EM wave is less than the transport electron–gas collision frequency of electrons.
EdgeInsight: Characterizing and Modeling the Performance of Machine Learning Inference on the Edge and Cloud

The Internet-of-Things (IoT) is growing in importance enabling an increasing number of scientific, industrial, and societal applications. At the same time, the computational capabilities of IoT and edge devices are rapidly improving making them viable for machine learning. Thus, the deployment of machine learning models on the edge is becoming a critical capability. However, such deployments are challenging as edge devices are more resource-constrained than clouds and cannot elastically scale on demand. Moving the application to the cloud can provide more computational power, but raises other challenges, such as security, reliability, and bandwidth. The integration of edge and cloud computing resources is often essential for many applications allowing them to choose the best configuration with respect to their requirements and characteristics, such as data rates and computational complexity. To understand the trade-offs between edge and cloud computing the assessment of different cloud and edge configurations is required. In this paper, we present EdgeInsight, a framework for characterizing and modeling of the inference performance of edge and cloud infrastructures. EdgeInsight enables system builders to size and fine-tune infrastructure parameters for different workloads and applications. We use EdgeInsight to qualitatively and quantitatively study edge and cloud deployment configuration for deep learning inference. Our evaluation shows, that edge inference can outperform cloud inference when model architecture and accuracy, inference framework and pre-processing parameters are carefully selected.
A Preliminary Study on the Feasibility of Detecting Global Acute Cerebral Ischemia by the MIPS Method

Acute cerebral ischemia is an important clinical disease that is usually detected by magnetic resonance imaging or computed tomography. The magnetic inductive phase shift (MIPS) is a new method for detecting cerebral diseases, which is non-invasive, miniaturized, and low-cost. A total of 25 rabbits were studied using a two-coil sensor with a 0.3–200 MHz frequency range, and all the subjects were measured for 1 hour. Based on the rabbit acute cerebral ischemia model, the rabbits were divided into unilateral ligation, bilateral ligation, and non-ligation groups. The results showed that the average MIPS values of the non-ligation, unilateral ligation, and bilateral ligation group were −0.195 ± 0.079°, −4.873 ± 1.042°, and −9.165 ± 2.862° respectively. MIPS distinguished different severities of cerebral ischemia in rabbits with statistical significance (p < 0.05). Laser Doppler flowmetry (LDF) was used as the gold standard for collecting cerebral blood flow data. The strong correlation between the LDF measurements and the phase shift suggested that the phase shift reflects blood flow changes in the brain. Overall, these results suggest that the MIPS detection method has the potential to provide early detection of global cerebral ischemia. Furthermore, this method effectively distinguished different severities of cerebral ischemia.
A New Graphical Method for Reporting Performance Results of a Diagnostic Test

Reporting diagnostic performance results using standard performance measures, such as: sensitivity, specificity, and predictive values, have been a standard practice for decades. Issues with reporting using only numerical values include: 1) often only a subset of performance measures are reported, thus results could be misinterpreted and misused, 2) difficult to visualize the complex relationships of the reported performance measures. To overcome these shortcomings, a graphical presentation has been developed to further improve the test results reporting.The 2-dimentional performance graph uses line segment, area, ratio of line segments, ratio of areas, and sum of areas to represent most of the commonly used performance measures in this single graph.Advantages of the new graphic presentation are: 1) large number of performance measures can be presented and visualized simultaneously in a single graph, 2) allow the complex relationships of all performance measures to be understood more easily, 3) reduce the need to memorize some of the complex formulas for computing the performance measures, and 4) a great teaching tool in explaining the relationships of the commonly used performance measures.
An Optimal Stacking Ensemble for Remaining Useful Life Estimation of Systems Under Multi-Operating Conditions

Remaining useful life (RUL) estimation is expected to provide appropriate maintenance for components or systems in industry to improve the reliability of the systems. Most data-based methods are limited to a single model, which is susceptible to various factors like environmental variability and diversity of operating conditions. In this paper, we propose an optimal stacking ensemble method combining different learning algorithms as meta-learners to mitigate the impact of multi-operating conditions. The selection of meta-learners follows a multi-objective evolutionary algorithm named non-dominated sorting genetic algorithms-II to balance the two conflicting objectives in terms of accuracy and diversity. Then the eventually evolved meta-learners are integrated by the meta-classifier for RUL estimation. In addition, a long-short-term feature extraction strategy is proposed to capture more degradation information from lifecycle data dynamically. Extensive experiments are performed on aero-engine dataset and battery dataset provided by NASA, which achieves the higher prognostic accuracy compared with the single models and existing methods.
Heart Rate Variability Analysis Assessment for Asthma Control Stratification

Autonomic nervous system (ANS) has been suggested to play a major role in the pathogenesis of asthma. This hypothesis has motivated large research, revealing a reduced modulation of the heart rate in subjects with uncontrolled asthma, when compared to asthmatics with controlled symptomatology. In this work, we assessed ANS activity through heart rate variability analysis in a group of asthmatics classified attending to the control of their symptoms. This information was later used for training a logistic regression classifier aiming at differentiating between the levels of control in asthmatic patients. The accuracy of the classifier improved when including ANS information (71.77%, versus 64.73% when only clinical parameters were considered), suggesting that ANS assessment could contribute to better non-invasive asthma monitoring.
Effectiveness of Usability & Performance Features for Web Credibility Evaluation

Unreliable web pages, such as fake news, have become an unavoidable problem. To tackle this problem, recent researches have adopted both content and social features to predict the credibility of the web pages; however, the accuracy is almost saturated. In this paper, we propose the adoption of Google Lighthouse features to predict web page credibility. Our experimental results show that the proposed method achieves an increased accuracy of 7.9% in comparison with state-of-the-art methods.
A Bi-Objective Approach for Product Recommendations

We propose a bi-objective formulation for product recommendations. Our formulation goes beyond traditional recommendations by capturing two conflicting objectives: utility that serves customers’ interests, and profit margin, a business-oriented goal. To satisfy the needs of our business partners, we formulate a new problem, namely generating a result containing all sets of k products such that there does not exist any other set of k products that dominates the returned sets, i.e., whose cumulative values for each objective is higher than a set of k products in the result. We study properties of k-Pareto sets that enable us to reduce the number of candidates, as well as the number of dominance tests between candidate sets. We develop a dynamic programming algorithm that leverages those properties to prune the space of solutions. We generalize traditional measures of recommendation accuracy to be applicable to sets of k products. Our experiments on a large set of real customer transactions validate the need for a bi-objective optimization to reconcile customer and business interests, and the scalability of our solution.
Unsupervised Author Disambiguation using Heterogeneous Graph Convolutional Network Embedding

People share same names in real world. When a digital library user searches for an author name, he may see a mixture of publications by different authors who have the same name. Making distinctions between them is an important prerequisite to improve the quality of services and contents in digital libraries. The general task of author disambiguation is to associate publications which belong to an identical name or names with highly similar spellings to different people entities. In recent years, many researches have been conducted to solve this challenging task. However, some works rely heavily on external knowledge bases and manually annotated data. Some unsupervised learning based works require complex feature engineering. In this paper, we propose a novel and efficient author disambiguation framework which needs no labeled data. We first construct a publication heterogeneous network for each ambiguous name. Then, we use our proposed heterogeneous graph convolutional network embedding method that encodes both graph structure and node attribute information to learn publication representations. After that, we propose a graph enhanced clustering method for name disambiguation that can greatly accelerate the clustering process and need not require the number of distinct persons. Our framework can be continually retrained and applied on incremental disambiguation task when new publications are put in. Experimental results on two datasets show that our framework clearly performs better than several state-of-the-art methods for author disambiguation.
Q-Learning With Kalman Filters

In this investigation, we discuss a game of pursuit-evasion, or a hunter-prey problems using Q-learning ramework. This has always been a popular research subject in the field of robotics where a hunter moves around in pursuit a prey. We involve Kalman filters to estimate the prey’s status (location and velocity) and learn Q-values based on the estimated status. We evaluate our approach by convergence of Q-values and capturing steps.
CTC-Attention based Non-Parametric Inference Modeling for Clinical State Progression

Predictive modeling of patient state to state medical conditions in ICU is a critical yet challenging task in health informatics and machine learning. Prior critical stages from the same ICU admission may contribute differently to the next stages. That said, stages are interdependent, and disease progression is a multi-step temporal observation. In this paper, we formally name this problem as “Clinical State Progression Prediction (CSPP). ” Conventional temporal modeling may fit well to predictions of fixed size observations and number of stages, but have troubles and less flexibility when addressing CSPP. To that end, we cast this problem as multi-label learning on time series data in which each stage is marked by a label. The implementation of entire framework includes two phases. In learning, an RNN based Encoder-Decoder deep model is developed for basic temporal modeling. In addition, Attention mechanism and Connectionist Temporal Classification (CTC) are integrated to explicitly model the temporal dependency as well as monotonic relation between input time series and output label space. In inference, based on the observed multi-stage labels, a non-parametric retrieval is carried out first to build up the reference patient records. Then, based on CTC-Attention learning model, consistent progressions are computed and ranked to contribute to the prediction of the clinical state progression in the next few hours. Extensive experiments on MIMIC III and Parkinson datasets demonstrate that the proposed predictive modeling for CSPP outperforms state-of-the-art works on Sepsis, Kidney-Sepsis-Mortality, Heart-Sepsis-Mortality, and Parkinson Progression.
Analogy-based Common-Sense Knowledge for Opinion-Target Identification and Aggregation

The development of Web 2.0 has improved people's ability to share their opinions. These opinions serve as an important piece of knowledge for other reviewers. To figure out what the opinions is all about, an automatic system of analysis is needed. Aspect-based sentiment analysis is the most important research topic conducted to extract reviewers-opinions about certain attribute, for instance opinion-target (aspect). In aspect-based tasks, the identification of the implicit aspect such as aspects implicitly implied in a review, is the most challenging task to accomplish. However, this paper strives to identify the implicit aspects based on hierarchical algorithm incorporated with common-sense knowledge by means of dimensionality reduction.
A Location Independent Machine Learning Approach for Early Fake News Detection

The spread of fake news on the internet is presenting increasing threats to national security, with the potential to incite public unrest and violence. However, detecting fake news is challenging as they are intentionally written to mislead. Some current methods cannot detect fake news early and require external information like the source to assess articles. To tackle these challenges and improve the generalizability of the models, we adopted a text-based location-independent machine learning approach. It employed two types of machine learning models. The first is the bag-of-words model, made more robust by stacking two levels of models. The second is neural networks that utilize pre-trained GloVe word embeddings, including (a) one-dimensional convolutional neural network (CNN) and (b) bidirectional long short-term memory network (BiLSTM). All models were assessed on various metrics (accuracy, recall, precision and F1), and achieved over 90% on the test set, making this an effective location-independent approach to detect fake news at an early stage without reliance on external information.
Prediction of Sepsis from Clinical Data Using Long Short-Term Memory and eXtreme Gradient Boosting

Developing an objective and efficient computer-aided tool for early detection of sepsis has become a promising research topic. In this paper, we present two methods for early prediction of sepsis from clinical data. One is neural network-based method and the other is eXtreme Gradient Boosting (XGBoost) based method. Considering the temporal relationship between clinical data from sepsis patients in the ICU, we built a Long Short-Term Memory (LSTM) network to extract the intrinsic relation between different indicators in clinical data and meanwhile model the temporal dependencies, which only uses the previous information not future information to predict the results. Neural networks have made great achievements in unstructured data, such as image processing and speech processing, while traditional machine learning methods are better at processing structured data than neural networks. Thus, we trained an XGBoost model on the pre-processed data for improving the prediction accuracy. In official phase, we only used the first seven vital signs in our network, on test set A, the LSTM-based method has the utility score is 0.267 and the score of XGBoost-based method is 0.392. We submit the latter method as the final entry and the official final test utility score is 0.313. Our team name is CQUPT_Just_Try, and the ranking is 15th.
Performance of Ergodic and Outage Capacities in the Presence of Random Blockage Under Generalized Fading Channels

In this paper, we analyze the performance of the ergodic capacity and outage capacity in the presence of random blockage under the generalized $\eta-\mu$ and $\kappa-\mu$ fading channels. Specifically, exact analytical expressions for the ergodic capacity and outage capacity in the presence of random blockage are derived. Based on the derived expressions, the influence of fading parameters and probability of blocking on the system performance is investigated. The analytical results are validated through Monte-Carlo simulations. The results show that the system performance improves when the fading parameters increase and (or) the probability of blockage decreases.
Building a Reliable and Cost-Effective RTK-GNSS Infrastructure for Precise Positioning of IoT Applications

A precise positioning solution is mandatory for many applications including intelligent transport systems (ITS), precision agriculture, space weather forecasting, disaster rescue and managing unmanned aerial vehicles (UAV). RTK-GNSS is a key technology in providing centimeter-level accuracy for those applications. However, reliable and cost-effective RTK-GNSS applications and infrastructure are still lacking. Thus, in this paper, we discuss how a reliable and cost-saving RTK-GNSS infrastructure can be designed, deployed and accomplished by using available technologies and tools. We developed a prototyping device equipped with RTK-GNSS receiver, cellular modem, sensors, and movable energy supply modules that include a solar panel as a self-sustainable power source. In our design, different sensors are used to monitor and control the physical status of the base station through Internet. We conducted two experiments in different weather and geographical conditions where our result shows that the consistency of the cm-level accuracy is around 91% in both areas. Similarly, the reliability of the system is maintained by the sensors' data that are visualized graphically in real-time through Internet. After analyzing the experimented results, and mechanism of the base station status update and control system, we conclude that the accuracy, feasibility, and reliability can be achieved even by using a low-cost RTK-GNSS receiver with sensors network. We believe that developing this infrastructure will support precise positioning applications and upgrade technological capabilities for generations.
Approximate Querying for the Property Graph Language Cypher

Graph databases are well-suited to managing large, complex, dynamically evolving datasets. However, for data that is irregular and heterogeneous, it may be difficult to formulate queries that precisely capture a user’s information seeking requirements. This points to the need for approximate query processing capabilities that can automatically make changes to a query so as to aid in the incremental discovery of relevant information. In this paper we motivate and explore techniques for providing such capabilities for the Cypher query language. This is the first time that query approximation has been investigated in the context of the property graph data model, which is becoming increasingly prevalent in research and industry.
A Model Checking based Proposal for Mobile Colluding Attack Detection

The application collusion attack is a new form of threat that is becoming widespread in mobile environment. This technique requires that two or more apps cooperate in some way with the aim to perform a malicious action that they are unable to perform independently. In this paper we propose the adoption of model checking to detect whether two or more apps are performing a collusion attack.
A Framework for Fast Simulation of Power Electronic Circuits*

An adaptive multi-resolution framework based on model order reduction to accelerate the simulations of high-fidelity power electronic circuits (PECs) is presented in this paper. The wide span of eigenvalues makes the simulation of PECs prohibitively slow. The proposed framework tackles this problem. We use singular perturbation approximation to extract reduced-order models where the transient component of non-dominant eigenvalues is ignored. Each reduced-order model corresponds to a particular level of accuracy or resolution. The simulation engine is set such that it adaptively switches across different resolutions. This approach is advantageous in the sense that instead of simulating the original system over the entire time-range, a combination of the full and reduced models is simulated. This accelerates the simulation considerably and at the same time ensures that the complete response of the system is obtained. An error bound for the approximation method is derived. The method is illustrated using a DC-DC Buck-boost converter.
Purchase Prediction in Free Online Games via Survival Analysis

In the free online game industry, in-game purchase prediction is an important research topic. On the one hand, knowing the players who are likely or unlikely to pay for the game can help the development of corresponding marketing strategies and incentives. On the other hand, the result of purchase prediction indicates the status of the game operation status and helps the game company allocate a reasonable amount of resources for the game. In this paper, we focus on predicting the purchase probability of paying players and finding important features. We consider two types of features related to players’ playing and purchase behaviors. Then, based on these features we proposed, we use two different models to predict the survival probability for players and analyze the feature importance.