Miracle-dz/UniFAR-FTUs huggingface dataset:
A Query Taxonomy Describes Performance of Patient-Level Retrieval from Electronic Health Record Data

Performance of systems used for patient cohort identification with electronic health record (EHR) data is not well-characterized. The objective of this research was to evaluate factors that might affect information retrieval (IR) methods and to investigate the interplay between commonly used IR approaches and the characteristics of the cohort definition structure.We used an IR test collection containing 56 test patient cohort definitions, 100,000 patient records originating from an academic medical institution EHR data warehouse, and automated word-base query tasks, varying four parameters. Performance was measured using B-Pref. We then designed 59 taxonomy characteristics to classify the structure of the 56 topics. In addition, six topic complexity measures were derived from these characteristics for further evaluation using a beta regression simulation.We did not find a strong association between the 59 taxonomy characteristics and patient retrieval performance, but we did find strong performance associations with the six topic complexity measures created from these characteristics, and interactions between these measures and the automated query parameter settings.Some of the characteristics derived from a query taxonomy could lead to improved selection of approaches based on the structure of the topic of interest. Insights gained here will help guide future work to develop new methods for patient-level cohort discovery with EHR data.
The Service of Secure Data Publication and Subscription on Cloud Platforms

With the increase of data volume, the service of data publication and subscription is an efficient method to share massive data. It is impossible to capture and manage data with traditional database tools. Therefore, the cloud platform becomes the most suitable platform for data publication and subscription due to its huge storage, strong computing power and good economical utility. In the process of data publishing and subscription, cloud server as a third party is semi-honest, which will bring serious privacy problems. In this paper, a secure data publishing and subscription scheme based on cloud platform is proposed, which can protect the privacy of data and subscriber interest, and achieve efficient and accurate data publishing and subscription. It can realize that only data subscribers who meet the requirements of access rights can obtain and decrypt the data, and the matching of data subscriber interest and data label can be realized at the same time. In addition, this scheme also supports user cancellation, it can cancel dishonest users in the system.
On Relaxing Failing Queries over RDF Databases

Database users can be frustrated by having an empty answer to a query. In particular, querying RDF triple-stores are prone to failure due to the nature of RDF data. The problem of query relaxation is thus a helpful technique for efficiently querying RDF databases by providing users with alternative answers instead of an empty result. The main shortcoming of most prior researches is that they focus on the relaxation itself rather than the causes of failure. Yet, determining possible explanations, i.e. diagnoses, for an unexpected behavior of the system under observation is crucial to the user. In this paper, we present CADER, a novel approach for computing relaxations for failed queries over RDF databases. We show how the number of required database queries for determining all the possible relaxations can be limited to the search of failed subqueries of the user query. Then, we point out how the hitting set problem can be applied for determining the possible relaxed queries in an efficient way without querying the RDF database. Finally, the efficiency and scalability of CADER against existing solutions are shown through extensive experiments on the well-known RDF benchmarks with a variety of queries of different shapes.
Should we use bridging therapy during switching patients from warfarin to novel oral anticoagulants

Rivaroxaban is a new anticoagulant that was approved by the Food and Drug Administration in 2011 for stroke and systemic embolism prophylaxis in patients with nonvalvular atrial fibrillation. Several characteristics have made rivaroxaban an attractive alternative to warfarin: once-daily dosing, obviating the need for monitoring the international normalized ratio (INR), noninferiority to warfarin for preventing stroke in patients with atrial fibrillation, and comparatively lower risk of intracranial hemorrhage. Both The European Summary of Product Characteristics and the US Prescribing Information recommend to discontinue warfarin and to start rivaroxaban on the following day if the initial INR level is below 3.0, yet many clinics convert patient from warfarin to rivaroxaban when INR level is <2.0 due to increased risk of hemorrhage. Since there are no conventional coagulation measures to reliably demonstrate the level of anticoagulation in patients on rivaroxaban, there is an increased risk of adverse events, especially for patients with high ischemic burden. We report a case of acute ischemic stroke in a patient who switched from warfarin to rivaroxaban (20 mg once daily). A 68-year-old female patient with multiple co-morbidities was admitted to our clinic with ischemic stroke. After 1 week follow-up with parenteral anticoagulation in the neurology clinic, she was discharged with mild deficits and with dabigatran treatment (150 mg twice daily). Novel oral anticoagulants are attractive options for anticoagulation particularly in patients who are incompatible with warfarin therapy. On the other hand, physicians should be alert during switching patients from warfarin to novel oral anticoagulants.
An Active High Frequency Damping Scheme for the Current Control of L Filter-Based Grid-Connected Inverter

A virtual high frequency damping (VHD) method is proposed in this paper for the improving of L filter-based grid-connected inverter’s stable margin as well as its output current waveform performance. The precise frequency domain model of system, which involves pulse width modulation (PWM) and digital control delay, is sophisticated studied. Based on which, the quantitative relationship between the proportional (P) control gain and the inductance value as well as the digital control delay is revealed, and the system global control gain margin is obtained. Furthermore, the stability problems caused by the larger value of control gain under the traditional control method and a current sampling prediction (CP) method are studied, respectively. Therefore, the guidelines for the designing of system’s virtual damping is provided, and VHD method as well as its phase compensation approach are thereby proposed to attenuate the oscillation of system output current, in particular of the high frequency oscillation, and to improve system performance in terms of expanding the current loop operating bandwidth and increasing the system stable margin. The simulation and experimental results both clearly validate the correctness and feasibility of the proposed VHD method.
A Comprehensive Analysis of Privacy-preserving Techniques in Deep learning based Disease Prediction Systems

With the rise in demand for deep learning models due to its ability to learn features from data, and predict, it is widely used in disease prediction systems. However, as patient medical records are considered to be highly confidential due to them consisting of personal information, its privacy-preservation is of prime importance. Conventional privacy-preserving techniques often tend to hinder the utilitarian aspect of the system. In this paper we carry out a comprehensive analysis of privacy-preserving techniques for disease prediction systems that use deep learning along with a comparison of the different privacy-preserving techniques. This paper also discusses the existing privacy-preserving approaches in deep learning. They are cryptographic approaches, attribute-based encryptions, homomorphic encryptions and other hybrid approaches.
SC-BiCapsNet: A Sentiment Classification Model Based on Bi-Channel Capsule Network

Most text sentiment classification methods have the shortcomings of insufficient semantic understanding and low classification accuracy. To solve these problems, a sentiment classification model based on capsule network (SC-BiCapsNet) was proposed in this paper. In the stage of word vectorization, the G-Word model is presented to improve the ability of text information representation. In the stage of text semantic representation, the COL-Att model is presented to optimize the semantic coding structure and enhance the effect of text semantic expression. In the stage of text classification, the capsule network is devised, and its core dynamic routing algorithm can increase the weight value of important features, thus improving the accuracy of classification results. This paper evaluates the performance of SC-BiCapsNet model on two text datasets, IMDB and NLPCC 2014 dataset. The experimental results demonstrate that our method outperforms the results of other related models on IMDB dataset and gets the highest accuracy of 0.9238. Meanwhile, comparing with the best system in NLPCC2014-Task II, the F1-scsore of SC-BiCapsNet has increased by at least 1.5%, which proves the validity of SC-BiCapsNet. In the future work, we will try to reduce the time consumption and improve the generalization ability of the model.
Utilization of blockchain for mitigating the distributed denial of service attacks

Distributed Denial of Service (DDoS) attacks cause devastating effects on the web services and hence harm the digital availability. The DDoS attackers use vulnerabilities exposed through new networking technologies like wireless, mobile, IoT, and associated protocol weaknesses for bringing down the networks and servers. Owing to availability of easily available tools and botnet armies, the DDoS attack incidences in the internet world are increasing day by day. Several techniques have been proposed by the researchers against DDoS attacks. This paper concentrates on the utilization of one of the latest and most promising technologies, that is, blockchain technology against DDoS attacks. The blockchain technology is rapidly finding use in various applications ranging from financial to gaming; this is because of its stable, decentralized, and secure architecture. The DDoS solutions based on blockchain are still in infancy and some solutions provide only architectural details without bothering about the implementation details. This paper presents a study of the blockchain‐based DDoS solutions. It also compares the existing blockchain‐based techniques against DDoS attacks and analyses them. This paper facilitates the development of future research proposals in this emerging area of blockchain technology.
Appearance Flow Completion for Novel View Synthesis

Novel view synthesis from sparse and unstructured input views faces challenges like the difficulty with dense 3D reconstruction and large occlusion. This paper addresses these problems by estimating proper appearance flows from the target to input views to warp and blend the input views. Our method first estimates a sparse set 3D scene points using an off‐the‐shelf 3D reconstruction method and calculates sparse flows from the target to input views. Our method then performs appearance flow completion to estimate the dense flows from the corresponding sparse ones. Specifically, we design a deep fully convolutional neural network that takes sparse flows and input views as input and outputs the dense flows. Furthermore, we estimate the optical flows between input views as references to guide the estimation of dense flows between the target view and input views. Besides the dense flows, our network also estimates the masks to blend multiple warped inputs to render the target view. Experiments on the KITTI benchmark show that our method can generate high quality novel views from sparse and unstructured input views.
Intelligent Cache Pollution Attacks Detection for Edge Computing Enabled Mobile Social Networks

With the rapid advances of wireless technologies and popularization of smart mobile devices, edge-enabled mobile social networks (MSNs) have emerged as a promising network paradigm for mobile users to deliver, share, and exchange contents with each other. By leveraging edge caching technology, various content services can be provided to mobile users for improving their quality of experience (QoE). However, edge caching is vulnerable to cache pollution attacks (CPAttacks) with the result of disruptive content delivery. To tackle this problem, we propose a hidden Markov model (HMM) based CPAttack detection scheme in edge-enabled MSNs. Specifically, we first present the CPAttack model based on observations of attacking behaviors. According to the CPAttack model, the caching state of the edge device is characterized by two parameters-content request rate and cache missing rate. Then, with observation sequence constructed by caching states, we develop an HMM-based detection algorithm to distinguish the CPAttack in the approximately time-invariant content request process. To deal with the lack of training data and dynamic of caching states, an adaptive HMM (AHMM) based algorithm is designed to detect the CPAttack in the time-varying content request process. The simulation results demonstrate that the proposed scheme can efficiently improve edge devices’ abilities to sense the CPAttack.
The Effect of Dexamethasone in Tramadol Induced Nausea and Vomiting

We aimed to investigate the efficacy of a single dose bolus of dexamethasone on tramadol induced nausea and vomiting in our study. After approval was taken from ethics committee and patients, a total of 60 ASA I-II patients who were planned to undergo total abdominal hysterectomy (TAH) under general anesthesia were included in this study. A patient-controlled analgesia device was explained for the patients with preoperative visit. After the non-premedicated patients were taken in the operation room, the routine monitorization was performed. Induction was provided with 1μg/kg of fentanyl, 2 mg/kg propofol and 0.1 mg/kg vecuronium. 6-8% concentration of desflurane in a mixture of 50% air and 50% O2 was used for maintenance of anesthesia. When the incision was started to be closed, the patients were randomized into two groups by envelope method. 8 mg iv dexamethasone (2 mL) was given for Group D (n=30), iv Saline solution (2 mL) was given for Group K (n=30). After the incision was closed, a loading dose of intravenous tramadol 1 mg/kg was administered in both groups. The patients were taken in the postanesthesia care unit by extubating following the antagonism of muscle relaxant at the end of surgery. The patient-controlled analgesia device was scheduled to be as infusion: no, bolus: 12 mg, lock-out time: 10 min, 24 hour dosing limit: 400 mg. The pain and nausea and vomiting scores, additional analgesic and antiemetic requirements, the total amount of tramadol consumption were recorded at post-operative recovery and postoperative 2, 4, 6, 12 and 24 hours. Although the incidence of nausea and vomiting, and pain scores at 2 and 4 hours were not statistically significant, they were lower in Gorup D compared to Group K (p>0.05). 14 patients in Group K required additional antiemetics and 12 patients in Group D required additional antiemetics (p>0.05). Six patients in Group K required additional analgesics and 4 patients in Group D required additional analgesics. Although the total amount of tramadol consumption was not statistically significant, it was lower in Group D compared to Group K. It was concluded that a single bolus dose of dexamethasone 8 mg has not reduced tramadol induced nausea and vomiting in patients who were planned to undergo TAH.
Development and performance comparison of MPI and Fortran Coarrays within an atmospheric research model

Amini-application of The Intermediate Complexity Research (ICAR) Model offers an opportunity to compare the costs and performance of the Message Passing Interface (MPI) versus coarray Fortran, two methods of communication across processes. The application requires repeated communication of halo regions, which is performed with either MPI or coarrays. The MPI communication is done using non-blocking two-sided communication, while the coarray library is implemented using a one-sided MPI or OpenSHMEM communication backend. We examine the development cost in addition to strong and weak scalability analysis to understand the performance costs.
Automatic target tracking with time-delayed measurements for unmanned surface vehicles

This paper presents the automatic target detection and tracking of marine obstacles for unmanned surface vehicles (USVs). For practical applications with a USV, the automatic detection of surrounding obstacles is a crucial capability, and marine radars have been commonly used to detect and estimate the motion of obstacles. However, their tracking performance degrades when a target is approaching at a high relative velocity due to their relatively low sampling rate. This study addresses the automatic target tracking of marine obstacles by considering time-delayed measurements provided by a marine radar. The relative position information between a USV and nearby obstacles is obtained using the radar sensor, and the obstacles’ motion including position, course, and speed is estimated using an extended Kalman filter (EKF)-based tracking filter by compensating the measurement delay. To validate the feasibility of the proposed method, a real-sea experiment was conducted using a USV and the results are presented.
A Shared Task for the Digital Humanities Chapter 3: Description of Submitted Guidelines and Final Evaluation Results

description of computational narratology in the former and, on the contrary, the rather lengthy discussion and focus on first-person narrators in the latter. Seven out of eight contributions also cited additional research (cf. endnote 2). Guideline I developed their own approach. Even though the authors refer to certain concepts (cf. Table 3), there is no explicit reference to a theorist. Guideline I conceives of narrative as linguistic representation of a story and focuses on the identification of borders of narratives, introducing the notion of uninterrupted vs. embedded vs. interruptive narrative (referring also to analepsis and prolepsis). Guideline VIII gives its own definition of narratives and focuses on level changes that can be identified with a test (“Let me tell you a story”). References to Narratological Concepts
The Effectiveness of Using Deep Learning Algorithms in Predicting Daily Activities

Predicting Activities of Daily Living (ADL) for elder people could let them live actively, independently and healthy. In this paper, Authors perform a comparative study to address the effectiveness of deep learning algorithms on ADL. As a baseline structure, The Convolutional Neural Networks (CNN's) as a deep learning algorithm is employed to perform the experiments and conducting the comparative study with the very common used traditional machine learning algorithms. Several factors in the CNN are manipulated to gain the best result in predicting the ADL in comparison with the most ML result in this matter. To reduce the threat to validity, very common data set are used in several previous studies in term of ADL prediction is adopted in this paper. The dataset was collected from a wearable chest accelerometer. The total numbers of participants are 15 and they were performing 7 main activities namely standing up, working at the computer, going up downstairs, standing, walking, walking and talking with someone and talking while standing, walking and going up downstairs. Three experiments were conducted in this paper, and CNN provides promising result in term of ADL predictions for the very common data set in this field and ML algorithms.
Theorizing from secondary qualitative data: A comparison of two data analysis methods

Abstract This study aims to compare the analytical processes involved in two theorizing approaches applied to secondary qualitative data. To this end, the two authors individually analyzed the same raw material, one using the grounded theory approach and the other using the general inductive approach. Our comparison of these processes brought out the strengths and weaknesses of each approach. More specifically, this study found that data analysis using the grounded theory approach makes it possible to go beyond the analysis and interpretation resulting from the general inductive approach. Recommendations are made regarding the importance of the conceptual framework when theorizing from qualitative data. Finally, this study highlights relevant ways to use secondary qualitative data.
Automated Pyramid Summarization Evaluation

Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts. A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries. We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods. It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.
Characteristics Features of Hepatocellular Carcinoma in Bangladesh and their Public Health Implications

Background/Objectives: In spite of being a common cancer, little is known about clinical presentation, etiological factors and prognosis of hepatocellular carcinoma (HCC) in Bangladesh. Accordingly, early detection and efficient management of HCC is not available at this country. Materials and methods: Demographical data, etiological factors and clinical presentations of 57 consecutive patients with HCC were analyzed prospectively at the only medical university of Bangladesh. Results: Ninety-one percent patients with HCC were male. The mean age of HCC patients was 45.8 years (standard deviation: 15.3 years; range: 18-75 years). All patients were symptomatic when they first appeared to the physicians, and many of them were unaware that they had been suffering from liver diseases. Hepatitis B surface antigen was detected in 62% and antibody to hepatitis C virus was found in 6% patients with HCC. Most of the patients with HCC had liver cirrhosis (77%). Single HCC nodule was detected in 61% patients. Sixty-four percent patients had intermediate stage of liver cancer. Conclusion: Containment of HCC-related morbidity and mortality and better prognosis of HCC in Bangladesh depends on early diagnosis of hepatitis viruses.
Rate-Loss Mitigation of SC-LDPC Codes Without Performance Degradation

In research on spatially-coupled low-density parity-check (SC-LDPC) codes, rate-loss of SC-LDPC codes is one of the main issues to be addressed. One way to mitigate the rate-loss is to attach additional variable nodes with an irregular degree distribution, where the degree distribution is optimized with a constraint that the belief propagation (BP) threshold should not be degraded by attaching variable nodes. However, it is observed that the degree distribution obtained with the BP threshold constraint induces degradation of the finite-length performance. In order to address the problem, we propose new optimization methods to attach additional variable nodes while minimizing performance degradation. The proposed optimization methods are based on several design techniques including the scaling law, local threshold, expected graph evolution, differential evolution algorithms, the use of a protograph structure, and puncturing codewords. Using the optimized structure for additional variable nodes, the rate-loss of SC-LDPC codes can be reduced by more than 53% without sacrificing the finite-length performance. It is also shown that the rate-loss mitigation can be translated into a performance improvement if the proposed and the conventional SC-LDPC codes are compared at the same code rate.