Reviewerly/exHarmony huggingface dataset:
Extraction of risk factors for cardiovascular diseases from Chinese electronic medical records

Early prevention of cardiovascular diseases (CVDs) can effectively prevent later loss of health, and the detection of CVDs risk factors is a simple method to achieve early prevention. Personal health records play a prominent role in the field of health information extraction because of their factuality and reliability. This present study describes how to extract risk factors for CVDs from Chinese electronic medical records (CEMRs). The extraction process involves two tasks: (a) CVDs risk factor recognition and (b) risk factor time and assertion classification. We considered risk factor recognition as a named entity recognition (NER) task and time and assertion classification as a textual classification task. An information extraction pipeline system consisting of NER and textual classification modules with machine learning models was developed. In the risk factor recognition module, bidirectional long short term memory (BLSTM) with extra risk factor textual feature input was built, as well, convolutional neural networks (CNNs) with risk factor type and section label input and support vector machine (SVM) were built for time and assertion classification. We have achieved the best performance of risk factor recognition with F1 value of 0.9609, time and assertion classification with F1 of 0.9812 and 0.9612, respectively. The experimental results showed that our system achieved a high performance and can extract risk factors from CEMRs efficiently. The proposed system is the first system for CVDs risk factors extraction from CEMRs and shows competition to risk factor extraction systems that developed on English EMRs. Further, its good performance should have a strong influence on CVDs prevention.
Movie Recommendation via Markovian Factorization of Matrix Processes

The success of the probabilistic matrix factorization (PMF) model has inspired the rapid development of collaborative filtering algorithms, among which timeSVD++ has demonstrated great performance advantage in solving the movie rating prediction problem. Allowing the model to evolve over time, timeSVD++ accounts for “concept drift” in collaborative filtering by heuristically modifying the quadratic optimization problem derived from the PMF model. As such, timeSVD++ no longer carries any probabilistic interpretation. This lack of frameworks makes the generalization of timeSVD++ to other collaborative filtering problems rather difficult. This paper presents a new model family termed Markovian factorization of matrix process (MFMP). On one hand, MFMP models, such as timeSVD++, are capable of capturing the temporal dynamics in the dataset, and on the other hand, they also have clean probabilistic formulations, allowing them to adapt to a wide spectrum of collaborative filtering problems. Two simple example models in this family are introduced for the prediction of movie ratings using time-stamped rating data. The experimental study using MovieLens dataset demonstrates that the two models, although simple and primitive, already have comparable or even better performance than timeSVD++ and a standard tensor factorization model.
ADMM attack

Many recent studies demonstrate that state-of-the-art Deep neural networks (DNNs) might be easily fooled by adversarial examples, generated by adding carefully crafted and visually imperceptible distortions onto original legal inputs through adversarial attacks. Adversarial examples can lead the DNN to misclassify them as any target labels. In the literature, various methods are proposed to minimize the different lp norms of the distortion. However, there lacks a versatile framework for all types of adversarial attacks. To achieve a better understanding for the security properties of DNNs, we propose a general framework for constructing adversarial examples by leveraging Alternating Direction Method of Multipliers (ADMM) to split the optimization approach for effective minimization of various lp norms of the distortion, including l0, l1, l2, and l∞ norms. Thus, the proposed general framework unifies the methods of crafting l0, l1, l2, and l∞ attacks. The experimental results demonstrate that the proposed ADMM attacks achieve both the high attack success rate and the minimal distortion for the misclassification compared with state-of-the-art attack methods.
Pseudo-polarimetric Method for Dense Haze Removal

The scattering and absorption by the existing particles in turbid media is severely degrading the image quality and decreasing the visibility distance, especially in dense turbid media. In this paper, we investigate the polarization property of hazy images in dense turbid media, and we propose a simple pseudo-polarimetric dehazing method for dense haze removal, which only requires a single image captured by the detector. By our proposed method, two orthogonal polarization subimages are derived from the single original image, and then, based on these two subimages, one can obtain the recovered image by using the traditional polarimetric method. The real-word experimental results verify that, for the dense haze, our method based on a single image could have a comparable or better performance compared to the traditional polarimetric dehazing method with multiple images captured at different polarization states.
An Efficient Framework for Remote Sensing Parallel Processing: Integrating the Artificial Bee Colony Algorithm and Multiagent Technology

Remote sensing (RS) image processing can be converted to an optimization problem, which can then be solved by swarm intelligence algorithms, such as the artificial bee colony (ABC) algorithm, to improve the accuracy of the results. However, such optimization algorithms often result in a heavy computational burden. To realize the intrinsic parallel computing ability of ABC to address the computational challenges of RS optimization, an improved multiagent (MA)-based ABC framework with a reduced communication cost among agents is proposed by utilizing MA technology. Two types of agents, massive bee agents and one administration agent, located in multiple computing nodes are designed. Based on the communication and cooperation among agents, RS optimization computing is realized in a distributed and concurrent manner. Using hyperspectral RS clustering and endmember extraction as case studies, experimental results indicate that the proposed MA-based ABC approach can effectively improve the computing efficiency while maintaining optimization accuracy.
An augmented lagrangian method for solving a new variational model based on gradients similarity measures and high order regulariation for multimodality registration

In this work we propose a variational model for multi-modal image registration.It minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer.We first present a theoretical analysis of the proposed model.Then, to solve the model numerically, we use an augmented Lagrangian method (ALM) to reformulate it to a few more amenable subproblems (each giving rise to an Euler-Lagrange equation that is discretized by finite difference methods) and solve iteratively the main linear systems by the fast Fourier transform; a multilevel technique is employed to speed up the initialisation and avoid likely local minima of the underlying functional.Finally we show the convergence of the ALM solver and give numerical results of the new approach.Comparisons with some existing methods are presented to illustrate its effectiveness and advantages.
Embedding Logic Rules Into Recurrent Neural Networks

Incorporating prior knowledge into recurrent neural network (RNN) is of great importance for many natural language processing tasks. However, most of the prior knowledge is in the form of structured knowledge and is difficult to be exploited in the existing RNN framework. By extracting the logic rules from the structured knowledge and embedding the extracted logic rule into the RNN, this paper proposes an effective framework to incorporate the prior information in the RNN models. First, we demonstrate that commonly used prior knowledge could be decomposed into a set of logic rules, including the knowledge graph, social graph, and syntactic dependence. Second, we present a technique to embed a set of logic rules into the RNN by the way of feedback masks. Finally, we apply the proposed approach to the sentiment classification and named entity recognition task. The extensive experimental results verify the effectiveness of the embedding approach. The encouraging results suggest that the proposed approach has the potential for applications in other NLP tasks.
Tchebichef image watermarking along the edge using YCoCg-R color space for copyright protection

Easy creation and manipulation of digital images present the potential danger of counterfeiting and forgery. Watermarking technique which embeds a watermark into the images can be used to overcome these problems and to provide copyright protection. Digital image watermarking should meet requirements, e.g. maintain image quality, difficult to remove the watermark, quality of watermark extraction, and applicable. This research proposes Tchebichef watermarking along the edge based on YCoCg-R color space. The embedding region is selected by considering the human visual characteristics (HVC) entropy. The selected blocks with minimum of HVC entropy values are transformed by Tchebichef moments. The locations of C(0,1), C(1,0), C(0,2) and C(2,0) of the matrix moment are randomly embedded for each watermark bit. The proposed watermarking scheme produces a good imperceptibility by average SSIM value around 0.98. The watermark recovery has greater resistant after several types of attack than other schemes.
VIRTUE

The digitization of museum exhibits has raised the question of how to make these data accessible, particularly in light of the ever growing collections being available. In this demo, we present the VIRTUE system which allows curators to easily set up virtual museum exhibitions of static and dynamic 2D (paintings, photographs, videos, etc.) and 3D artifacts. Visitors may navigate through the virtual rooms, inspect the artifacts and interact with them in novel ways. Participants will be able to use the system by creating their own exhibitions, which they tour as a visitor.
An arabic question classification method based on new taxonomy and continuous distributed representation of words

The unability of search engines to retrieve precise answer for a given question leads research teams to build question answering systems (QAS). These systems provide exact answers of questions formulated in natural languages. Question classification is a crucial task for QAS since finding the correct answer type increases the performance of this latter. The questions taxonomy plays an important role in question classification. A broad range of taxonomies are proposed; most of these are not designed for Arabic questions. The contribution of the paper is twofold. First, we build a taxonomy for open domain Arabic questions. Second, we propose an efficient method for classifying Arabic questions. The basic idea consists of two stages: first, we compute representation of questions according to continuous distributed representation of words which allows to capture syntactic and semantic relations between words. Then, we apply a machine learning approach to classify questions into seven types or categories. We carried out several experiments and compared the proposed method with different state of arts Arabic question classification methods. Experimental results show that the proposed method achieves 90% in terms of accuracy.
The Sensitivity Design of Piezoresistive Acceleration Sensor in Industrial IoT

Piezoresistive acceleration sensors are widely used in various fields of the industrial Internet of Things because of their lightweight, fast response, and small size. The structural sensitivity of the sensor affects the accuracy of the measurement. And the sensitivity that the traditional method designs are only a feasible solution, not an optimal solution. Due to the differences in factory processes, the optimization of structural sensitivity is an NP-hard problem. To solve the design problem of structural sensitivity, we adopt the swarm intelligence algorithm in this paper, and we design a model for the structural sensitivity of the piezoresistive acceleration sensor. In addition, an improved grasshopper optimization algorithm (CC-GOA) that combines chaos strategy and Cauchy mutation is proposed to optimize the structural sensitivity of the piezoresistive acceleration sensor, and the structure of the sensor is composed of four beams and mass block. The experiments are compared with six well-known algorithms on 16 benchmark functions to verify the algorithm performance of CC-GOA, and then, the structural sensitivity of the piezoresistive acceleration sensor is optimized by CC-GOA. The results indicate that the piezoresistive acceleration sensor is designed with high sensitivity and superiority.
Modeling and Design of Azimuth-Altitude Dual Axis Solar Tracker for Maximum Solar Energy Generation

The sun tracking system that lets Parabolic Dish or PV panel orthogonal to the sun radiation during the day, can raise the concentrated sun radiation by up to 40%. The fixed Parabolic Dish cannot generally track the sun trajectory, also the single-axis tracking system can follow the sun in the horizontal direction (azimuth angle), while the two-axis tracker tracks the sun path in both azimuth and altitude angles. Dual axis automated control tracking system, which tracks the sun in two planes (azimuth and altitude) to move a Concentrated Parabolic Dish system to the direction of ray diffusion of sun radiation is studied and designed. The designed tracking system constructed of microcontroller or programmable logic control (PLC) with a digital program that operates sun tracker using driver, gear box to control the angular speed and mechanical torque, supports and mountings. Two steeper motors are modelled to guide the parabolic dish panel perpendicular to the sun's beam. In the present study, simulation scheme of two axis sun tracking system has been developed by operating under Matlab/Simulink. The program models and studies the effectiveness of overall system. The designed tracker has been studied with real data of sun trajectory angles (azimuth and altitude) as well as a Direct Normal Irradiation (DNI) to improve the effectiveness of parabolic dish panel by adding the tracking features to those systems according to the present site.©2019. CBIORE-IJRED. All rights reservedArticle History: Received May 18th 2018; Received in revised form October 8th 2018; Accepted January 6th 2019; Available onlineHow to Cite This Article: Shufat, S.A., Kurt, E, and Hancerlioğulları, A. (2019) Modeling and Design of Azimuth-Altitude Dual Axis Solar Tracker for Maximum Solar Energy Generation. Int. Journal of Renewable Energy Development, 8(1), 7-13.https://doi.org/10.14710/ijred.8.1.7-13
Precise Target Selection Techniques in Handheld Augmented Reality Interfaces

Augmented reality (AR) capabilities on handheld mobile devices are increasing; therefore, interaction with a virtual scene is a key feature to explore the full potential of mobile AR.One of the primary interaction tasks is 3D target selection.Because touch screens provide a direct means of interaction with AR content by directly touching and manipulating AR elements, touch-based interaction is a natural and appealing style of input for AR applications.However, developing AR selection techniques for handheld devices using touch screens is not a straightforward task.One issue is that while interacting with AR targets, users occlude the targets with their fingers.Furthermore, because the user's finger covers a large area of the screen, the size of the target users can touch is limited.In addition, in a 3D AR environment, it is difficult to select targets by touch when they are occluded from each other due to differences in spatial depth.In this paper, we present four novels AR selection techniques for handheld mobile devices that can precisely select targets with small sizes even when they are occluded.Then, we present the results of an experimental study in which we evaluated the usability, performance, and error rate of our proposed and existing AR selection techniques.
Ensemble Feature Selection Algorithm

In this paper, we propose a new feature selection algorithm based on ensemble selection.In order to generate the library of models, each model is trained using just one feature.This means each model in the library represents a feature.Ensemble construction returns a well performing subset of features associated to the well performing subset of models.Our proposed approaches are evaluated using eight benchmark datasets.The results show the effectiveness of our ensemble selection approaches.
EnergyQARE

Power market operators have recently introduced smart grid demand response (DR), in which electricity consumers regulate their power usage following market requirements. DR helps stabilize the grid and enables integrating a larger amount of intermittent renewable power generation. Data centers provide unique opportunities for DR participation due to their flexibility in both workload servicing and power consumption. While prior studies have focused on data center participation in legacy DR programs such as dynamic energy pricing and peak shaving, this article studies data centers in emerging DR programs, i.e., demand side capacity reserves. Among different types of capacity reserves, regulation service reserves (RSRs) are especially attractive due to their relatively higher value. This article proposes EnergyQARE , the Energy and Q uality-of-Service (QoS) A ware R SR E nabler, an approach that enables data center RSR provision in real-life scenarios. EnergyQARE not only provides a bidding strategy in RSR provision, but also contains a runtime policy that adaptively modulates data center power through server power management and server provisioning based on workload QoS feedback. To reflect real-life scenarios, this runtime policy handles a heterogeneous set of jobs and considers transition time delay of servers. Simulated numerical results demonstrate that in a general data center scenario, EnergyQARE provides close to 50% of data center average power consumption as reserves to the market and saves up to 44% in data center electricity cost, while still meeting workload QoS constraints. Case studies in this article show that the percentages of savings are not sensitive to a specific type of non-interactive workload, or the size of the data center, although they depend strongly on data center utilization and parameters of server power states.
The Limits of Smart Contracts

This essay investigates the potential of smart contracts to replace the legal system as an infrastructure for transactions.It argues that (contract) law remains relevant for most transactions even if they are entirely structured by way of smart contract.The reason for this is that the power of smart contracts to create and enforce obligations against attempts by the legal system to thwart their execution is limited.These limitations are most relevant for obligations to perform certain actions outside the blockchain, but also apply to other obligations contingent on facts outside the records stored on the blockchain.
Mapping Heavy Metal Concentrations in Beach Sands Using GIS and Portable XRF Data

It is necessary to investigate the contamination of beach sands to ensure water safety, as they may contain potentially toxic trace elements. Tourists, oil spills, or replenishing sands can cause beach sand contamination. In this study, heavy metal contamination maps of lead (Pb) and zinc (Zn) were created for Wolpo Beach, on the eastern coast of Korea, using portable X-ray fluorescence and geographic information systems (GIS). Interpolation methods, such as kriging and inverse distance weighting, were used in this study and their results were compared. Understanding the spatial variation of potentially toxic trace elements in beach sand is necessary to determine suitable measures for preventing contamination. Sufficient sand data for understanding spatial patterns can be acquired by using rapid portable X-ray fluorescence analysis. As a result, we could create heavy metal concentration maps for the sand of Wolpo Beach. It was confirmed that the southern part of the target area is more contaminated than the northern part. However, there are no sand areas with highly concentrated heavy metal levels. In addition, no sample data exceed the soil contamination standards. This study demonstrates that portable X-ray fluorescence and geographic information systems can be utilized for investigating and preventing the contamination of beach sands by creating heavy metal concentration maps.
An Enhanced Group Recommender System by Exploiting Preference Relation

With ties among people have been much more closer, making recommendations for groups of users became a more general demand, which facilitates the prevalence of group recommender system (GRS).Existing solutions for GRS are mostly established based on preference feedbacks of absolute form such as ratings, yet neglecting that preference assessment criteria are usually heterogeneous among different members.In this paper, we propose GRS-PR, an enhanced group recommender system by exploiting preference relation.First, a preference relation-based multi-variate extreme learning machine model is formulated to predict unknown preference relations in candidate items.Second, on the basis of predicted results, borda voting rule is employed to generate recommendation results from candidate items.In addition, efficiency, parameter sensitivity, and sparsity tolerance of the GRS-PR are evaluated through a set of experiments.
An experimental evaluation of garbage collectors on big data applications

Popular big data frameworks, ranging from Hadoop MapReduce to Spark, rely on garbage-collected languages, such as Java and Scala. Big data applications are especially sensitive to the effectiveness of garbage collection (i.e., GC), because they usually process a large volume of data objects that lead to heavy GC overhead. Lacking in-depth understanding of GC performance has impeded performance improvement in big data applications. In this paper, we conduct the first comprehensive evaluation on three popular garbage collectors, i.e., Parallel, CMS, and G1, using four representative Spark applications. By thoroughly investigating the correlation between these big data applications' memory usage patterns and the collectors' GC patterns, we obtain many findings about GC inefficiencies. We further propose empirical guidelines for application developers, and insightful optimization strategies for designing big-data-friendly garbage collectors.