Reviewerly/exHarmony huggingface dataset:
Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning

Electronic Health Records (EHRs)-based surveillance systems are being actively developed for detecting adverse drug reactions (ADRs), but this is being hindered by the difficulty of extracting data from unstructured records. This study performed the analysis of ADRs from nursing notes for drug safety surveillance using the temporal difference method in reinforcement learning (TD learning).Nursing notes of 8,316 patients (4,158 ADR and 4,158 non-ADR cases) admitted to Ajou University Hospital were used for the ADR classification task. A TD(λ) model was used to estimate state values for indicating the ADR risk. For the TD learning, each nursing phrase was encoded into one of seven states, and the state values estimated during training were employed for the subsequent testing phase. We applied logistic regression to the state values from the TD(λ) model for the classification task.The overall accuracy of TD-based logistic regression of 0.63 was comparable to that of two machine-learning methods (0.64 for a naïve Bayes classifier and 0.63 for a support vector machine), while it outperformed two deep learning-based methods (0.58 for a text convolutional neural network and 0.61 for a long short-term memory neural network). Most importantly, it was found that the TD-based method can estimate state values according to the context of nursing phrases.TD learning is a promising approach because it can exploit contextual, time-dependent aspects of the available data and provide an analysis of the severity of ADRs in a fully incremental manner.
Predicting the Growth of Morphological Families from Social and Linguistic Factors

We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as "trump", "antitrumpism", and "detrumpify", in social media.We introduce the novel task of Morphological Family Expansion Prediction (MFEP) as predicting the increase in the size of a morphological family.We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark.Our experiments demonstrate very good performance on MFEP.
Data-driven algorithm design

The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. Although there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. We model the problem of identifying a good algorithm from data as a statistical learning problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, and our results identify conditions under which these approaches are guaranteed to perform well. We interpret our results in the contexts of learning greedy heuristics, instance feature-based algorithm selection, and parameter tuning in machine learning.
Organization Type and Size Based Identification of Requirements Change Management Challenges in Global Software Development

Global Software Development (GSD) is adopted by organizations to develop quality software at relatively low cost.Requirement Change Management (RCM) plays a key role in overall success of a GSD project.The objective of this study is to identify the challenges of RCM process by adopting systematic literature review (SLR) and validate them by employing questionnaire survey approach in real world practices.A total of 25 challenges were identified through SLR and empirical study.We have further classified the identified challenges into client and vendor organizations with an aim to understand RCM challenges in the context of both types of GSD organizations.The identified challenges were also categorized into three core types of the organization size (small, medium, large), that highlights the significance of each challenge for specific organizational size.The results indicate that there is a moderate correlation between the ranking of these challenges in the literature and the survey results.The finding of this study has the potential to help the GSD organizations in addressing the problems related with RCM in GSD projects.
An FPGA Based Accelerator for Clustering Algorithms With Custom Instructions

Clustering algorithms are becoming popular and widely applied in many academic fields, such as machine learning, pattern recognition, and artificial intelligence. It has posed significant challenges to accelerate the algorithms due to the explosive data scale and wide variety of applications. However, previous studies mainly focus on the raw speedup with insufficient attention to the flexibility of the accelerator to support various applications. In order to accelerate different clustering algorithms in one accelerator, in this article, we design an accelerating framework based on FPGA for four state-of-the-art clustering methods, including K-means, PAM, SLINK, and DBSCAN algorithms. Moreover, we provide both euclidean and Manhattan distances as similarity metrics in the accelerator design paradigm. Moreover, we provide a custom instruction set to operate the accelerators within each application. In order to evaluate the performance and hardware cost of the accelerator, we constructed a hardware prototype on the state-of-the-art Xilinx FPGA platform. Experimental results demonstrate that the accelerator framework is able to achieve up to 23× speedup than Intel Xeon processor, and is 9.46× more energy efficient than NVIDIA GTX 750 GPU accelerators.
A Hybrid Solution Method for the Multi-Service Location Set Covering Problem

The Multi-Service Location Set Covering Problem is an extension of the well-known Set Covering Problem. It arises in practical applications where a set of physical locations need to be equipped with services to satisfy demand within a certain area, while minimizing costs. In this paper we formulate the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, apply the hybrid framework of the D-Wave quantum annealer to solve it, and investigate the feasibility of this approach. To improve the often suboptimal initial solutions found on the D-Wave system, we develop a hybrid quantum/classical optimization algorithm that starts from the seed solution and iteratively creates small subproblems that are more efficiently solved on the D-Wave but often still converge to feasible and improved solutions of the original problem. Finally we suggest some opportunities for increasing the accuracy and performance of our algorithm.
A novel meta-matching approach for ontology alignment using grasshopper optimization

Ontology alignment is a fundamental task to support information sharing and reuse in heterogeneous information systems. Optimizing the combination of matchers by evolutionary algorithms to align ontology is an effective method. However, such methods have two significant shortcomings: weights need to be set manually to combine matchers, and a reference alignment is required during the optimization process. In this paper, a meta-matching approach GSOOM for automatically configuring weights and threshold using grasshopper optimization algorithm (GOA) has been proposed. In this approach, the ontology alignment problem is modeled as optimizing individual fitness of GOA. A fitness function is proposed, which includes two goals: maximizing the number of matching and the similarity score. Since it does not require an expert to provide a reference alignment, it is more suitable for real-world scenarios. To demonstrate the advantages of the approach, we conduct exhaustive experiments tasks on several standard datasets and compare its performance to other state-of-the-art methods. The experimental results illustrate that our approach is more efficiently and is significantly superior to other metaheuristic-based methods.
Artificial intelligence and engineering education

Journal of Engineering EducationVolume 109, Issue 3 p. 358-361 GUEST EDITORIAL Artificial intelligence and engineering education Aditya Johri, Corresponding Author johri@gmu.edu Department of Information Sciences and Technology, George Mason University, Fairfax, Virginia, USA Correspondence Aditya Johri, Department of Information Sciences and Technology, George Mason University, 4400 University Drive, MS 1G8, Fairfax, VA 22030. Email: johri@gmu.eduSearch for more papers by this author Aditya Johri, Corresponding Author johri@gmu.edu Department of Information Sciences and Technology, George Mason University, Fairfax, Virginia, USA Correspondence Aditya Johri, Department of Information Sciences and Technology, George Mason University, 4400 University Drive, MS 1G8, Fairfax, VA 22030. Email: johri@gmu.eduSearch for more papers by this author First published: 19 May 2020 https://doi.org/10.1002/jee.20326 Funding information: U.S. National Science Foundation, Grant/Award Numbers: 1707837, 1712129 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume109, Issue3July 2020Pages 358-361 RelatedInformation
Experiment and Availability Analytical Model of Cloud Computing System Based on Backup Resource Sharing and Probabilistic Protection Guarantee

A probabilistic protection guarantee enables a cloud provider to improve the availability of their cloud computing system in a cost-efficient manner.A backup resource allocation strategy based on the probabilistic protection guarantee reduces the total amount of required backup computation resources by allowing multiple virtual machines to share the same backup computation resources of a physical machine.There have been no experimental studies that investigate the impact of applying the probabilistic protection guarantee to a cloud computing framework in real use.This paper presents an experiment of failure recovery on the cloud computing system in which the backup computation resources are shared by multiple virtual machines.We implement a prototype cloud system by using the OpenStack framework to demonstrate the failure recovery scenario according to the backup resource allocation strategy.We develop an availability analytical model for the backup resource allocation strategy.Based on the analytical model, we present case studies which derive the availability of cloud system by using the measurement results of the experiment.
Ion Transport and Reordering in a 2D Trap Array

Abstract Scaling quantum information processors is a challenging task, requiring manipulation of a large number of qubits with high fidelity and a high degree of connectivity. For trapped ions, this can be realized in a 2D array of interconnected traps in which ions are separated, transported, and recombined to carry out quantum operations on small subsets of ions. Here, functionality of a junction connecting orthogonal linear segments in a 2D trap array to reorder a two‐ion crystal is demonstrated. The secular motion of the ions experiences low energy gain and the internal qubit levels maintain coherence during the reordering process, therefore demonstrating a promising method for providing all‐to‐all connectivity in a large‐scale, 2D or 3D trapped‐ion quantum information processor.
Validating Uncertainty in Medical Image Translation

Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative values. Recent work has shown that using dropout during training and testing can provide estimates of uncertainty. In this work, we investigate using dropout to estimate epistemic and aleatoric uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.
Gender identification for Egyptian Arabic dialect in twitter using deep learning models

Although the number of Arabic language writers in social media is increasing, the research work targeting Author Profiling (AP) is at the initial development phase. This paper investigates Gender Identification (GI) (male or female) of authors posting Egyptian dialect tweets using Neural Networks (NN) models. Various architectures of NN are explored with extensive parameters' selection such as simple Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Long–Short Term Memory (LSTM), Convolutional Bidirectional Long-Short Term Memory (C-Bi-LSTM) and Convolutional Bidirectional Gated Recurrent Units (C-Bi-GRU) NN which is tuned for the GI problem at hand. The best acquired GI accuracy using C-Bi-GRU multichannel model is 91.37%. It is worth noting that the presence of the bidirectional layer as well as the convolutional layer in the NN models has significantly enhanced the GI accuracy.
Uncertainty in information system development: Causes, effects, and coping mechanisms

Information system development (ISD) projects are an ever-growing field of project management (PM) with their unique features, and project failures in ISD are relatively common. In the broader context of PM, uncertainty is a studied, yet mercurial phenomenon. By contrast, uncertainty in ISD projects has received relatively little attention from scholars, and PM literature has not systematically focused on uncertainty in ISD from a viewpoint other than that of project managers. In order to understand uncertainties in ISD projects, we need to first understand the causes behind them, their effects on everyday ISD work, and share coping mechanisms utilized among industry professionals. In the context of ISD projects, we set out to explore what causes uncertainty, what are the effects of uncertainty, and how software industry professionals cope with uncertainty. We conducted eleven semi-structured interviews with a diverse range of ISD professionals, and analyzed the interviews using conventional content analysis. Our results extend and complement current knowledge on the causes, effects, and coping mechanisms of uncertainty, especially in the context of ISD. Additionally, we present practical considerations on how to implement our findings into ISD industry and education.
Dual Path Denoising Network for Real Photographic Noise

This letter presents a convolutional neural network (CNN) for image denoising, especially for the reduction of real noises. As a network topology, we adopt the dual path network (DPN) that combines the advantages of residual and densely connected networks. Using the DPN as a basic building block, we design a network that connects the DPN in dual path again with an attention mechanism. For efficient denoising of real noise images, we build a training set where noisy images are obtained from a heteroscedastic Gaussian noise model and in-camera pipeline. In addition, we augment the synthetic training set with a relatively small number of real noise data. In the experiments, the proposed method is shown to provide state-of-the-art performance in reducing both synthetic and real noises.
Design of a histopathology image segmentation algorithm for CAD of cancer

In the present world, medical diagnosis systems depend a lot on medical imaging and digital nosology. The digital nosology is leading to computer-aided diagnosis to facilitate the rapid and accurate screening of specimens. In this work, we present a method based on object shaped kernel for histopathology image segmentation to identify and analyze the cell nuclei region. The proposed segmentation technique is a multi-stage procedure, which includes contrast enhancement of the image, nuclei region extraction, nucleus centroid marking, nuclei area refinement and, complex nuclei separation. The performance of proposed method is measured using three standard H&E stained histopathology image datasets and a proposed dataset. The simulation results of the segmentation schemes are evaluated using the F1-score, Hausdorff distance and Jaccard index. The proposed system also introduced a novel performance measure as cumulative factor. The superiority of proposed segmentation system is verified in terms of cumulative factor in comparison to various the state of art methods.
Embedded Identification of Surface Based on Multirate Sensor Fusion With Deep Neural Network

In this letter, we propose a multivariate time-series classification system that fuses multirate sensor measurements within the latent space of a deep neural network. In our network, the system identifies the surface category based on audio and inertial measurements generated from the surface impact, each of which has a different sampling rate and resolution in nature. We investigate the feasibility of categorizing ten different everyday surfaces using a proposed convolutional neural network, which is trained in an end-to-end manner. To validate our approach, we developed an embedded system and collected 60 000 data samples under a variety of conditions. The experimental results obtained exhibit a test accuracy for a blind test dataset of 93%, taking less than 300 ms for end-to-end classification in an embedded machine environment. We conclude this letter with a discussion of the results and future direction of research.
Multimodal Alignment and Attention-Based Person Search via Natural Language Description

Visual Internet of Things (VIoT) has been widely deployed in the field of social security. However, how to enable it to be intelligent is an urgent yet challenging task. In this article, we address the task of searching persons with natural language description query in a public safety surveillance system, which is a practical and demanding technique in VIoT. It is a fine-grained many-to-many cross-modal problem and more challenging than those with the image and the attribute as queries. The existing attempts are still weak in bridging the semantic gap between visual modality from different camera sensors and text modality from natural language descriptions. We propose a deep person search approach with a natural language description query by employing the attention mechanism (AM) and multimodal alignment (MA) method to supervise the cross-modal mapping. Particularly, the AM consists of two self-attention modules and one cross-attention module, where the former aims at learning discriminative representations and the latter supervises each other with their own information to offer accurate guidance to a common space. The MA approach contains three alignment processes with a novel cross-ranking loss function to make different matching pairs separable in a common space. Extensive experiments on large-scale CUHK-PEDES demonstrate the superiority of the proposed approach.
Governance and the capital flight trap in Africa

The study examines the use of governance tools to fight capital flight by reducing the capital flight trap. Two overarching policy syndromes are addressed in the study. It first assesses whether governance is an effective deterrent to the capital flight trap in Africa, before examining what thresholds of government quality are required to fight the capital flight trap in the continent. The following findings are established. Evidence of a capital flight trap is apparent because past values of capital flight have a positive effect on future values of capital flight. The net effects from interactions of the capital flight trap with political stability, regulation quality, economic governance and corruption-control on capital flight are positive. The critical masses at which 'voice and accountability' and regulation quality can complement the capital flight trap to reduce capital flight are respectively, 0.120 and 0.680, which correspond to the best performing countries. Policy implications are discussed.
Forecasting power-efficiency related key performance indicators for modern data centers using LSTMs

Modern HPC and cloud data centers, being mission critical facilities, continue to grow in size and number as the amount of Internet and cloud-based services increases. Moreover, the recent hype in the usage of machine learning based technologies, requiring appropriate infrastructures for data storage and computational power, further pushes the need for modern data centers. This expenditure not only increases the operational costs, but also affects the environment by generating carbon footprints. Due to increased load on supporting power grids, some governmental organizations start to reconsider data center deployment procedures with an increased demand of renewable energy utilization and waste heat recovery. One of the challenges when powering data centers using renewable energy is the intermittent availability of the power. The dynamics of available power must be taken into account to maximize usage of accessible renewable energy while adhering to existing service level agreements as well as to thermal and power requirements. This requires a framework that can continuously match the actual behavior of the target computing infrastructure to the current availability of power and cooling. This paper introduces machine learning based approaches aiming to model various data center energy/power consumption related Key Performance Indicators (KPIs). A validation is performed using the multi-year operational data obtained at Leibniz Supercomputing Centre (LRZ). This framework is used as a building block for achieving a data center infrastructure-aware resource management and scheduling.
S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors

To assess inter-reader agreement for US BI-RADS descriptors using S-Detect: a computer-guided decision-making software assisting in US morphologic analysis. 73 solid focal breast lesions (FBLs) (mean size: 15.9 mm) in 73 consecutive women (mean age: 51 years) detected at US were randomly and independently assessed according to the BI-RADS US lexicon, without and with S-Detect, by five independent reviewers. US-guided core-biopsy and 24-month follow-up were considered as standard of reference. Kappa statistics were calculated to assess inter-operator agreement, between the baseline and after S-Detect evaluation. Agreement was graded as poor (≤ 0.20), moderate (0.21–0.40), fair (0.41–0.60), good (0.61–0.80), or very good (0.81–1.00). 33/73 (45.2%) FBLs were malignant and 40/73 (54.8%) FBLs were benign. A statistically significant improvement of inter-reader agreement from fair to good with the use of S-Detect was observed for shape (from 0.421 to 0.612) and orientation (from 0.417 to 0.7) (p < 0.0001) and from moderate to fair for margin (from 0.204 to 0.482) and posterior features (from 0.286 to 0.522) (p < 0.0001). At baseline analysis isoechoic (0.0485) and heterogeneous (0.1978) echo pattern, microlobulated (0.1161) angular (0.1204) and spiculated (0.1692) margins and combined pattern (0.1549) for posterior features showed the worst agreement rate (poor). After S-Detect evaluation, all variables but isoechoic pattern showed an agreement class upgrade with a statistically significant improvement of inter-reader agreement (p < 0.0001). S-Detect significantly improved inter-reader agreement in the assessment of FBLs according to the BI-RADS US lexicon but evaluation of margin and echo pattern needs to be further improved, particularly isoechoic pattern.