Reviewerly/exHarmony huggingface dataset:
Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding

Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model.
Optimizing Deep Learning Model for Neural Network Topology

In this work, a method of tuning deep learning models using H2O is proposed, where the trained network model is built from samples of selected features from the dataset, in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting its parameters in order to get better accuracy. The number of hidden layers and the number of neurons are the key parameters in each layer of a deep machine-learning network, which have great control on the performance of the algorithm. Hyper-parameter, grid search and random hyper-parameter approaches aid in setting these important parameters. In this paper, a new ensemble strategy is suggested that shows potential to optimize parameter settings and hence save more computational resources throughout the tuning process of the models. The data are collected from several airline datasets to build a deep prediction model to forecast airline passenger numbers. The preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method.
Location-Scale Matching for Approximate Quasi-Order Sampling

Quasi-orders are reflexive and transitive binary relations and have many applications. Examples are the dependencies of mastery among the problems of a psychological test, or methods such as item tree or Boolean analysis that mine for quasi-orders in empirical data. Data mining techniques are typically tested based on simulation studies with unbiased samples of randomly generated quasi-orders. In this paper, we develop techniques for the approximately representative sampling of quasi-orders. Polynomial regression curves are fitted for the mean and standard deviation of quasi-order size as a function of item number. The resulting regression graphs are seen to be quadratic and linear functions, respectively. The extrapolated values for the mean and standard deviation are used to propose two quasi-order sampling techniques. The discrete method matches these location and scale measures with a transformed discrete distribution directly obtained from the sample. The continuous method uses the normal density function with matched expectation and variance. The quasi-orders are constructed according to the biased randomized doubly inductive construction, however they are resampled to become approximately representative following the matched discrete and continuous distributions. In simulations, we investigate the usefulness of these methods. The location-scale matching approach can cope with very large item sets. Close to representative samples of random quasi-orders are constructed for item numbers up to n = 400.
Removal of monotonically increasing or decreasing phase ambiguity in retrieved phase by Riesz transform method in digital interferometric techniques

In this work, a method based on vortex operator, for removal of monotonically increasing or decreasing phase ambiguity in the retrieved phase by the Riesz transform method in digital interferometric techniques is presented. Since in digital interferometric techniques, the phase extraction methods and algorithms are essential because these techniques are being continuously employed in many scientific, industrial, and engineering applications to measure various physical parameters which are encoded as the phase of the fringe pattern. There exist many methods/algorithms for phase extraction from the fringe pattern such as temporal phase-shifting, spatial phase-shifting, fast Fourier transforms (FFT) method, wavelet transform, Hilbert transform etc. In recent years, phase extraction from a single fringe pattern by using the Riesz transform method is developed because of its several advantages. However, the retrieved phase by Riesz transform is affected by π shifts due to the lack of discrimination between positive and negative spatial frequencies. This problem could be resolved by using a vortex operator which filters the data in the frequency domain. We present here some simulated results demonstrating the removal of phase ambiguity in the retrieved phase by Riesz transform method.
Spatio-Temporal Convolutional Neural Network for Frame Rate Up-Conversion

The visual quality of the video is improved by realizing higher resolution and higher frame rate. In order to realize higher frame rate, we propose new frame rate up-conversion method using spatio-temporal convolutional neural network. In recent years, with the development of machine learning techniques such as convolutional neural networks, clearer interpolation frame estimation has been realized. However, with the conventional convolutional neural network method, it is difficult to estimate an accurate interpolation frames for video including complex motion. In order to deal with this problem, we adopted spatio-temporal convolution rather than conventional spatial convolution. Spatio-temporal convolution is thought to be effective for nonlinear motion because it can capture the time change of the motion of the object. We verified the effectiveness of the proposed method by using video data including complex motions such as rotational motion and scaling.
Adaptive windowed Fourier transform filtering method for speckle fringe patterns

The heavy speckle noise in speckle fringe patterns makes it difficult to extract phase from those interferograms. Therefore filtering is essential and necessary. The windowed Fourier transform (WFT) is an effective method to fulfill this task. However, when the WFT is employed, several parameters, such as the window size, the threshold and the passband, need to be chosen carefully. In this paper, the filtering performance of WFT with different combination of those parameters is investigated by simulation firstly. Based on the comprehensive analysis, we then propose a fully adaptive windowed Fourier transform filtering method. In our method, the window size was determined with the help of wavelet transform, then the threshold was dynamically adjusted according to the amplitude of the window Fourier ridge. As a result, the optimum spectrum containing the fringe information could be obtained. At last, the filtered image can be obtained by inverse Fourier transform of such spectrum. The experimental results are presented to validate the method’s potential. The results were also compared with those obtained using contour window filtering and second-order directional partial differential equation filtering method. It can be seen that contour window filtering and second-order directional partial differential equation filtering could damage fringe structure because of deviation on fringe orientation calculation. While our proposed method can preserve the fringe structure as much as possible. As a result, our adaptive WFT method performs better than the other two methods. It provides an alternative way to suppress the speckle noise in the actual applications.
Variational Image Registration for Inhomogeneous-Resolution Pairs

We propose a variational image registration method for a pair of images with different resolutions. Traditional image registration methods match images assuming that the resolutions of the reference and target images are homogeneous. For the registration of inhomogeneous-resolution image pairs, we first introduce a resolution-conversion method to harmonise the resolution of a pair of images using the rational-order pyramid transform.Then, we develop a variational method for image registration using this resolution-conversion method.
Annotation and Automatic Classification of Aspectual Categories

We present the first annotated resource for the aspectual classification of German verb tokens in their clausal context. We use aspectual features compatible with the plurality of aspectual classifications in previous work and treat aspectual ambiguity systematically. We evaluate our corpus by using it to train supervised classifiers to automatically assign aspectual categories to verbs in context, permitting favourable comparisons to previous work.
Increasing the Anonymity of a Social Network Based on Splitting Users into Constant Usefulness and Zero Information Loss

Social networks are a part of today's society. In such networks, privacy-preserving is considered as an important field since the user's identity is usually revealed by inference attacks. Within this context, security system designers use an isomorphic graph to stop attackers by editing nodes and links, so, the attackers cannot access to users' identities when the vertexes and links of the graph are converted into isomorphic parameters. The security system designers employ random links, clustering of the nodes, weight balancing, nodes addition or de-anonymization techniques (nodes labeling) to confuse malicious attackers. But, these techniques have many defects, such as the loss of information and the reduction of usefulness parameters that evaluate the final social network graph. This paper proposes a new method named as a splitting method where three techniques are proposed to improve mentioned parameters and in general, to improve further the network management.
A New Technique for Laser Spot Detection and Tracking by Using Optical Flow and Kalman Filter

Laser spot is widely used in numerous fields such as pointing during presentations, guiding robots in human machine interaction (HCI), and guiding munitions to targets in military, etc.. The interest of this paper is to develop an effective method for laser spot detection and tracking with exploiting fewer features. In this paper, a new technique jointly combined pyramid Lucas–Kanade (PLK) optical flow for detection and extended Kalman filter for accurately tracking laser spot in low-resolution and varying background video frames is presented. By using this new technique, only deploying intensity of laser spot with displacement of laser spot could get a very good result. This technique could be embedded into multi-core processing devices with inexpensive cost for potential high benefits in the future.
A Large-Margin Approach for Multi-Label Classification Based on Correlation Between Labels

Multi label classification is a challenging task in machine learning concerned with assigning a sample to a subset of available label set. Meaning, a sample can belong to multiple labels. Furthermore, high dimensionality of data and complex correlation between labels makes it even more interesting. For this reason, it attracted many researchers in recent years. classifier-chains (CC), one of well-known methods for multi label classification which is based on binary relevance (BR) method, incorporates label correlation by assuming an order for labels and inserting previous label outputs in feature space and achieves higher performance while still retaining relatively low time complexity. But using predicted labels as features might not be very interpretable with regards to integrating label correlation into the model, especially considering there could be different types of features in a dataset. In this paper, we propose an approach for using correlation among labels based on structure of CC by defining a large-margin model between two predicted labels. Thus directly exploiting the correlation between them in a more interpretable way. The proposed approach is evaluated using 9 multi label datasets and 2 evaluation metrics. Empirical experiments show promising results and demonstrate the effectiveness of proposed method against classifier chains algorithm.
The Impact of Data Reduction on Wearable-Based Human Activity Recognition

One crucial step toward improving any pattern recognition model is refining the data (feature extraction) and simplifying it (feature selection) for the classifier. In this paper, we investigate the impact of feature reduction on the performance of HAR. We collected step data from two subjects and answer research questions related to the impact of feature reduction in terms of performance, generalizability and varying classifiers. Our findings indicate feature reduction can reduce the number of features by close to 90%, while only having an impact of 1-2% in model performance. Moreover, we find that feature reduction can impact the generalizability of HAR models. Lastly, we find that feature reduction does not have a major impact on most classifiers examined. Our results are useful for designers of HAR systems to help them optimize their models while ensuring high performance.
Scaling Clean Energy for Data Centres: Trends, Problems, Solutions

As the modular backbone of the technological revolution, data centers are essential. As the infrastructure of the twenty-first century, data centers must align with the challenges of twenty-first-century infrastructure. While data centers are far less power hungry than distributed computing, the technology revolution in the making will choke without a satisfactory solution of clean energy for scaling data centers. Key innovations in predictive intelligence make a world of PUE ratios near one likely. But absolute power draw will continue to grow the footprint of data centers on local power networks and their carbon intensity. Two key factors to overcome the clean energy barrier of scaling data centers are energy optimization via on-site production and intelligent location siting. For humanity to thrive in the digital age, data center infrastructure much be efficient, decarbonized, and resource neutral at scale.
Credit Card Risk Assessment Based on Machine Learning

Our credit card business has developed a rapidly rising stage after a slow market incubation period. The rapid development of the credit card business on one hand led to the rapid development of Chinese economy, on the other hand, however, it brought the bank a large dose of credit risk. The number of credit cards issued by commercial banks is increasing. Customer credits risk issues receiver more attention. Credit rating has become an important technical means for banks to improve their risk management. This paper proposes a machine learning technology to model and analyze credit card users, and the data is oversampled by SMOTE method. Handling of abnormal values and missing values are performed and the variables are standardized. The range of values is made to fall within the same range. Finally, a logistic regression model is established. The random forest algorithm is used to verify its feasibility and effectiveness.
Economic Literacy and Economic Growth

This chapter focuses on the links between general economic literacy and growth in a nation in which both market and political processes affect the level and structure of economic activity. In a narrow sense the general state of economic literacy or understanding is or ought to be irrelevant in terms of the advance of human well-being. Another problem is the counterintuitive nature of even elementary implications of economic analysis. Many in Congress claim to believe that price controls on prescription drugs will make consumers in the medical market better off. Steven Buckles, president of the National Council on Economic Education, one of the groups of organizations that sponsored the Gallup survey, comments that “economic illiteracy is rampant. It has the potential to misshape public opinion on economic issues and lead to policies that have perverse effects on the economy.”
An algorithm based on ABNL-MRF model for SAR image segmentation

Abstract In this letter, a segmentation method is proposed based on an adaptive Bayesian non-local MRF (ABNL-MRF) model. Aimed at the multiplicative speckle existing in SAR images, a patch-similarity measure based on a ratio of probability is introduced firstly. Then non-local similar pixel-blocks are adopted to guide the segmentation of the noisy image. However, non-local method over-smoothes edge regions and makes them inaccurate though it is robust for speckle. A rectification index is designed according to the homogeneity degree of local window. Based on the value of rectification index, a non-local constraint term is adaptively integrated into the potential function. Thus a suitable prior is defined that guarantees both the smoothness of the denoised image and the preservation of its structure. Experimental results demonstrate the very good performance of the proposed method.
Applicability Investigation of Joining Technologies for Diamond Core Bits

Abstract For the joining of two metallic components of different material quality, different methods of welding or soldering techniques are used. In this case, these two material qualities are given by the segments containing diamond particles, and thin-walled steel tube. In our previous paper we analysed welded joints with two different kinds of laser sources. In this paper we extend the previous investigations with the analysis of capacitor discharge welded and flame soldered samples. We examine the microstructure of the joints and record the chemical element maps to determine if alloying was formed. Joints are also subjected to fracture and hardness testing. This investigation shows that the applied joining technology meets the increased technical requirements because the joining zone must bear high mechanical and significant thermal loads during usage.
A Data Collection and Analysis System for Managing the Vulnerabilities of Users of an Information System in a Small Business

The relevance of the article is due to consistently high indicators of the number of information security (IS) incidents caused by users of the corporate network and limited financial resources to protect information in small enterprises. Revealed a great interest in theoretical and practical issues of proactive protection of information systems and behavioral analysis - User and Entity Behavi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> oral Analytics (UEBA) to solve the problems of personnel security of information systems. However, despite the intensive development of the UEBA solutions market, they do not sufficiently reflect the context of user behavior and are not used in small enterprises due to resource constraints. To solve these problems, the authors of the article have developed an information and analytical system for the Vulnerability Assessment of Information System Users (EVISU) for a small enterprise. The approach to solving this problem based on the integration of UEBA, Behavioral Biometrics (BB) and Open Source Intelligence (OSINT) technologies is substantiated. The functional novelty of the system is the extension of the monitoring object due to the context of user behavior: the possibility of biometric recognition of persons who have legitimate rights to access information; the ability to monitor and evaluate not only internal but also external user behavior. As a tool to determine the level of user vulnerability, a “Safe User Profile” is proposed. The practical result of the work is the successful testing of the IAS EVISU in a small enterprise for the production of software products.
A Graph Formalism for Time and Memory Efficient Morphological Attribute-Space Connected Filters

Attribute-space connectivity has been put forward as a means of improving image segmentation in the case of overlapping structures. Its main drawback is the huge memory load incurred by mapping a N-dimensional image to an $$(N + \dim (A))$$ -dimensional volume, with $$\dim (A)$$ the dimensionality of the attribute vectors used. In this theoretical paper we introduce a more space and time efficient scheme, by representing attribute spaces for analysis of binary images as a graph rather than a volume. Introducing a graph formalism for attribute-space connectivity opens up the possibility of using attribute-space connectivity on 3D volumes or using more than one attribute dimension, without incurring huge memory costs. Furthermore, the graph formalism does not require quantization of the attribute values, as is the case when representing attribute spaces in terms of $$(N + \dim (A))$$ -dimensional discrete volumes. Efficient processing of high dimensional data produced by multi-sensor detection systems is another advantage of application of our formalism.
Analysing Dissenting Votes Through Electronic Justice From Online Posts to Streets: A Real Case

This paper brings us a vision of what dissenting votes mean in an eSociety, particularly linked to eJustice, and a real case in Spanish jurisdictional process. As soon as judicial decision has an increasing repercussion, our hyper-connected society and social networks on the Internet take a relevant influential role, giving a never before seen feedback to the Judiciary. Easy access to case law, jurisdictional texts and judicial decisions in electronic form are elements with great impact, surpassing traditional publicity. An interesting case of study related to electronic media, and particularly through social networks, has been the dissenting vote of the rape case called “The Wolfpack”, known as “La manada” in Spain, that has crossed the borders of electronic protests to the streets, with demonstrations and reactions against it, particularly from feminist movements. Our hypothesis is that dissenting vote as an eSociety conceptualization has an electronic-digital component whose easy diffusion through the Internet makes it different from the traditional concept. It changes from being an ordinary procedural mechanism within a restricted scope of decision, to opening a debate at citizenship level. It involves nuances on transparency in the digital society, and of course in the idea of Open Justice as part of Open Government initiatives. As a field experience and methodological system, we will use electronic justice ICT tools, such as web-based HJ of the Constitutional Court of Spain and others to explore these considerations analytically.