Reviewerly/exHarmony huggingface dataset:
Line Structured Light 3D Sensing With Synchronous Color Mapping

A line structured light measurement system is proposed which can obtain both the 3D geometrical and color information of objects. The profile sensor of the system is consisted of a black-and-white camera and an infrared laser line projector. A high precision calibration method is presented for the range sensor by use of a planar target. Then, the point coordinates of the measurement profile can be computed according to center coordinates of laser stripe and the calibrated sensor parameters. The wavelength of the laser projector is 850nm which is out of the spectrum response range of the color camera. Thus, the projected laser line would not affect the color acquisition. The transformation matrix between the two camera coordinate systems is determined and then optimized based on the invariance of cross ratio. By use of the transformation matrix, each point on the profile can be projected onto the image plane of the color camera, and the color information can be obtained synchronously. The measurement results show that the mean radius error of a ceramic ball can reach 0.009mm, and the mean mapping error of profile points on color image plane is 0.16pixel. The 3D measurement results of the textured objects further validate the proposed system.
A medical treatment based scoring model to detect abusive institutions

Medical abuse refers to a type of abnormal medical practice which is not in compliance with qualitative or ethical standards, such as excessive prescription or overbilling of medical services. Detection of such medical abuses is crucial, especially for the patients and insurance providers, because they become subject to the extra payments incurred. As a result, insurance providers hire medical experts in order to review claims manually, yet through examination is almost impossible due to the volume of the claims filed. A typical approach is to select institutions on suspicion of abusive practices and to manually review all claims involving suspect institutions. In this light, several studies have developed models designed to extract institution-level variables. However, since these variables are at an institution-level, the model cannot account for different types of abuse practiced by individual institutions, hence degrading the accuracy of the prediction model. At the same time, these variables contain information too simple to construct an effective scoring model. In this study, we propose a model that scores the degree of abuse practiced by institutions at the treatment-level, which is the lowest level of data that can be obtained from a medical claim. Our model is the first to use such fine-grained information to construct a model for scoring the abuse by medical institutions. The proposed model consists of two stages: Training a deep neural network with embedding layers for categorical variables, and scoring the abuse degree for each treatment with the model. Then, we aggregate the resulting abuse score of each treatment and the claim amount associated with each treatment by an institution which we define as the abuse score of the institution. We test our model using real-world claim data submitted to the Health Insurance Review and Assessment (HIRA) in 2016. We also compare the performance of the proposed model against the scoring model HIRA has been using, which computes the abuse score of an institution by using institution-level variables as proposed in past literature. Experiment results show that the proposed model represents the degree of medical abuse better. In addition, the results suggest that the reviewers may examine through the claims by at most 6.1 times more efficiently than when using the scoring model with institution-level variables.
Detect Review Manipulation by Leveraging Reviewer Historical Stylometrics in Amazon, Yelp, Facebook and Google Reviews

Consumers now check reviews and recommendations before consuming any services or products. But traders try to shape reviews and ratings of their merchandise to gain more consumers. Seldom they attempt to manage their competitor's review and recommendation. These manipulations are hard to detect by standard lookup from an everyday consumer, but by thoroughly examining, customers can identify these manipulations. In this paper, we try to mimic how a specialist will look to detect review manipulation and came up with algorithms that are compatible with significant and well known online services. We provide a historical stylometry based methodology to detect review manipulations and supported that with results from Amazon, Yelp, Google, and Facebook.
The two-sample problem via relative belief ratio

This paper deals with a new Bayesian approach to the two-sample problem. For two independent samples, the interest is to determine whether the two samples are generated from the same population. At first, two Dirichlet processes are considered as priors for the true distributions generated the data. Then the concentration of the posterior distribution of the distance between the two processes is compared to the concentration of the prior distribution of the distance between the two processes through the relative belief ratio. Many theoretical properties of the procedure have been developed. Several examples have been discussed to illustrate the approach.
Gesture Controlled Hexacopter using MEMS Technology

In today's world, gesture-controlled technology is one of the most advanced research topics for the Unmanned Aerial Vehicles (UAV' `s). Hand gestures or body postures are the most natural and common form for humans to communicate or interact with the machines. This research paper presents the detail use of hand gestures for controlling the motions of a hexa-copter using Microelectromechanical system (MEMS) technology. This work consists of four main parts, such as data exchange through RF wireless communication, processor (controller), 6-axis MEMS Sensor and kinematics of drone. Precise rotation and speed of the Hexacopter has been successfully verified using gestures of the user.
A real-time microearthquakes-detector based on voice activity detection and endpoint detection: An approach to Cotopaxi Volcano

The Ring of Fire represents a constant threat in the case of possible eruptions due to its 160 active volcanoes. Consequently, several studies have determined that an increase of tectonic and long period events could indicate the beginning of a volcanic eruptive process. In this context, it is crucial to improve microearthquake detection by using real-time automatic recognition systems. For this purpose, this work aims to improve microearthquake detection based on Voice Activity Detection (VAD) and endpoint detection. Furthermore, we propose the use of signal segmentation, considering the signal-to-noise ratio, and adapting VAD techniques for microearthquake detection. The results tested in real-time have demonstrated the reliability of our detector by obtaining 97.89% precision, 97.10% accuracy, and a value of 0.015 of Balanced Error Rate (BER). We considered a dataset for real-time testing of 744 h of continuous data from Cotopaxi Volcano in Ecuador. In these tests, the MDA detector is 8.89% more precise than the classical STA/LTA detector.
Transferring Pareto Frontiers across Heterogeneous Hardware Environments

Software systems provide user-relevant configuration options called features. Features affect functional and non-functional system properties, whereas selections of features represent system configurations. A subset of configuration space forms a Pareto frontier of optimal configurations in terms of multiple properties, from which a user can choose the best configuration for a particular scenario. However, when a well-studied system is redeployed on a different hardware, information about property value and the Pareto frontier might not apply. We investigate whether it is possible to transfer this information across heterogeneous hardware environments. We propose a methodology for approximating and transferring Pareto frontiers of configurable systems across different hardware environments. We approximate a Pareto frontier by training an individual predictor model for each system property, and by aggregating predictions of each property into an approximated frontier. We transfer the approximated frontier across hardware by training a transfer model for each property, by applying it to a respective predictor, and by combining transferred properties into a frontier. We evaluate our approach by modeling Pareto frontiers as binary classifiers that separate all system configurations into optimal and non-optimal ones. Thus we can assess quality of approximated and transferred frontiers using common statistical measures like sensitivity and specificity. We test our approach using five real-world software systems from the compression domain, while paying special attention to their performance. Evaluation results demonstrate that accuracy of approximated frontiers depends linearly on predictors' training sample sizes, whereas transferring introduces only minor additional error to a frontier even for small training sizes.
Improving Recognition of Thai Handwritten Characters with Deep Convolutional Neural Networks

For handwritten character recognition, a common problem is that each writer has unique handwriting for each character (e.g. stroke, head, loop, and curl). The similarities of handwritten characters in each language is also a problem. These similarities have led to recognition mistakes. This research compared deep Convolutional Neural Networks (CNNs) which were used for handwriting recognition in the Thai language. CNNs were tested with the THI-C68 dataset. This research also compared two training methods, Train from scratch and Transfer learning, by using VGGNet-19 and Inception-ResNet-v2 architectures. The results showed that VGGNet-19 architecture with transfer learning can reduce learning time. Moreover, it also increased recognition efficiency up to 99.20% when tested with 10-fold cross-validation.
Conditionally structured variational Gaussian approximation with importance weights

We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into "global" parameters and "local" latent variables, we define a class of variational approximations that exploit this partitioning and go beyond Gaussian variational approximation. This approximation is motivated by the fact that in many hierarchical models, there are global variance parameters which determine the scale of local latent variables in their posterior conditional on the global parameters. We also consider parsimonious parametrizations by using conditional independence structure and improved estimation of the log marginal likelihood and variational density using importance weights. These methods are shown to improve significantly on Gaussian variational approximation methods for a similar computational cost. Application of the methodology is illustrated using generalized linear mixed models and state space models.
Camera calibration based on the common pole-polar properties between two coplanar circles with various positions

Computer vision camera calibration is widely performed using parallel circles. Various cases of two coplanar circles are algebraically explained, proving that the common pole is located at the line at infinity for all relative positions, and the corresponding polar passes through the centers of the two circles. The two common poles of the two coplanar circles are the points at infinity when concentric; one common pole of the two coplanar circles is a point at infinity when nonconcentric. Accordingly, the vanishing line can be obtained by using the common pole-polar properties of two groups of two coplanar circles, and the camera's intrinsic parameters are solved according to the constraints between the image of the circular points and the imaged absolute conic. The camera calibration can be solved using only three images of two coplanar circles. Simulation and experiments verify that the proposed algorithms are effective.
Near-Real-Time Loss Estimates for Future Italian Earthquakes Based on the M6.9 Irpinia Example

The number of fatalities and injured was calculated, using the computer code QLARM and its data set and assuming information about the Irpinia 1980 earthquake became available in near-real-time. The casualties calculated for a point source, an approximate line source and a well-defined line source would have become available about 30 min, 60 min and years after the main shock, respectively. The first estimate would have been satisfactory, indicating the seriousness of the disaster. The subsequent loss estimate after 60 min would have defined the human losses accurately, and the ultimate estimate was most accurate. In 2009, QLARM issued a correct estimate of the number of fatalities within 22 min of the M6.3 L’Aquila main shock. These two results show that the number of casualties and injuries in large and major earthquakes in Italy can be estimated correctly within less than an hour by using QLARM.
Digital Education as a New Vector of Development of Education in the Northern Regions

The problem of training highly qualified teaching staff in the Russian North, which is capable of creating and effectively using promising educational technologies that take into account the specific conditions of work, study, life, national and cultural traditions of the region, is key to the innovative development of its scientific and educational environment and improvement of pedagogical activity. The article shows that working in the conditions of digitalization in educational sphere requires complex transformations both in the school system and in the system of pedagogical education under the influence of progressive digital technologies. Its practical implementation requires changing of goals, organizational forms and technologies of educational activities based on digital technologies, the development of productive strategies aimed at integrating the innovations created in the traditional educational process. The purpose of the article is to substantiate the necessity and timeliness of digital modernization of education, primarily in pedagogics, and ways of its implementation in the Republic of Sakha (Yakutia).
Solar Radiation Forecasting Using Support Vector Regression

Solar energy is the most predominant renewable energy resource available to humankind. To remain depend on it in future, forecasting of solar energy is essential. In this paper, solar potential is forecasted with the help of Support vector regression (SVR) depending on other easily measurable parameters. The parameters like pressure, temperature, humidity are exploited in the prediction of daily global solar radiation. The data used for the study is taken for a period of two year for the location of New Alipore, Kolkata. Two models where developed using RBF kernel and Polynomial kernel function of SVR. The performance of this two models are evaluated with the statistical measures viz, Coefficient of Determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and Root Mean Square Error (RMSE). The result obtained are R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.7976 and RMSE of 1.0564 for training while R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.7845 and RMSE of 1.0532 for testing with RBF kernel. While polynomial kernel gives R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9393 and RMSE of 1.1975 for training while R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9060 and RMSE of 1.1594 for testing.
Ensemble of Multi-headed Machine Learning Architectures for Time-Series Forecasting of Healthcare Expenditures

Machine learning (ML) is increasingly being used in the healthcare domain for time-series predictions. However, the development of multi-headed ML architectures for multivariate time-series predictions has been less explored in the literature concerning healthcare domain. Multi-headed neural network architectures work on the idea that each independent variable (input series) can be handled by a separate neural network model (head) and the output of each of these models (heads) can be combined before a prediction is made about a dependent variable. In this paper, we develop three multi-headed ML architectures and their ensemble to predict patients’ weekly average expenditures on two pain medications. A multi-headed long short-term memory (LSTM) model, a multi-headed convolutional neural network long short-term memory (CNN-LSTM) model, a multi-headed convolutional long short-term memory (ConvLSTM) model, and an ensemble model combining predictions of the three multi-headed models were calibrated to predict patients’ weekly average expenditures on two pain medications. The ensemble model combined the predictions of different multi-headed models using an exponential weight algorithm. Results revealed that the ensemble model outperformed the multi-headed models for both pain medications. Furthermore, the multi-headed CNN-LSTM model outperformed the multi-headed LSTM and ConvLSTM models for one medication; however, the multi-headed LSTM model outperformed the multi-headed CNN-LSTM and ConvLSTM models for the other medication. We highlight the utility of developing multi-headed ML architectures and their ensembles for prediction of patient-related expenditures in the healthcare domain.
Nonuniform Hyper-Network Embedding with Dual Mechanism

Network embedding which aims to learn the low-dimensional representations for vertices in networks has been extensively studied in recent years. Although there are various models designed for networks with different properties and different structures for different tasks, most of them are only applied to normal networks which only contain pairwise relationships between vertices. In many realistic cases, relationships among objects are not pairwise and such relationships can be better modeled by a hyper-network in which each edge can connect an uncertain number of vertices. In this article, we focus on two properties of hyper-networks: nonuniform and dual property. In order to make full use of these two properties, we firstly propose a flexible model called Hyper2vec to learn the embeddings of hyper-networks by applying a biased second order random walk strategy to hyper-networks in the framework of Skip-gram. Then, we combine the features of hyperedges by considering the dual hyper-networks to build a further model called NHNE based on 1D convolutional neural networks, and train a tuplewise similarity function for the nonuniform relationships in hyper-networks. Extensive experiments demonstrate the significant effectiveness of our methods for hyper-network embedding.
Non-Intrusive Detection of Adversarial Deep Learning Attacks via Observer Networks

Deep learning models are known to be vulnerable to specifically crafted adversarial inputs that are quasi-imperceptible to humans. We propose a novel method to detect adversarial inputs, by augmenting the main classification network with multiple binary detectors (observer networks) which take inputs from the hidden layers of the original network (convolutional kernel outputs) and classify the input as clean or adversarial. During inference, the detectors are treated as a part of an ensemble network and the input is deemed adversarial if at least half of the detectors classify it as so. The proposed method addresses the trade-off between accuracy of classification on clean and adversarial samples, as the original classification network is not modified during the detection process. The use of multiple observer networks makes attacking the detection mechanism non-trivial even when the attacker is aware of the victim classifier. We achieve a 99.5 percent detection accuracy on the MNIST dataset and 97.5 percent on the CIFAR-10 dataset using the Fast Gradient Sign Attack in a semi-white box setup. The number of false positive detections is a mere 0.12 percent in the worst case scenario.
Detection of rotational symmetry in curves represented by the slope chain code

We present a new approach based on the Slope Chain Code to determine whether a curve is rotational symmetrical and its order of symmetry. The proposed approach works for open and closed perfectly symmetrical or quasi-symmetrical 2D curves. Simple operations on the SCC and its invariant properties are central to our methodology. To evaluate the proposed methodology, we use 1400 curves from a public database. For the symmetrical/asymmetrical classification task, a recall (R) of 0.86, a balanced accuracy (BA) of 0.92, and a precision (P) of 0.87 were obtained. For the quasi-symmetrical/quasi-asymmetrical classification task, R=0.77, BA=0.83, and P=0.70 were obtained. For the order of rotational symmetry detection task, the following performance was achieved: R=0.97, BA=0.98, and P=0.95 for a symmetrical set of curves, and R=0.98, BA=0.98, and P=0.90 for a quasi-symmetrical set of curves. We conclude our presentation demonstrating the usefulness of our methodology with three practical applications
Mathematical framework for algorithm analysis

Though nature-inspired algorithms have been shown to be effective, we still lack some in-depth understanding of these algorithms. This chapter attempts to provide a unified framework to analyze algorithms from different perspectives with some mathematical rigor. Both convergence and stability are analyzed, together with some algorithmic complexity. Some key issues concerning benchmarking and comparison of nature-inspired algorithms are also highlighted.
A Survey on Integrating Cloud Computing with Blockchain

The Cloud computing technology is renowned since the decade. Even though it is well established, there are some challenges present. Some of the primary challenges can be solved by integrating cloud computing with Blockchain technology. Blockchain technology is an emerging topic well known for its immutability, and the security that is providing to the data. In this paper with the aid of Blockchain Technology, we will address some of the approaches to the problems of cloud computing.
Research on Virtualization Security in Cloud Computing

Abstract Cloud computing services, with its advantages of flexible configuration, efficient utilization of resources and easy management, have achieved rapid development and wide application, in recent years. However, the security issues in cloud computing are also becoming increasingly prominent, at same time, cloud computing security has become an important factor hindering the development of cloud computing. The virtualization technology is one of the core technologies of cloud computing, meanwhile, virtualization security is the core issue of cloud computing security as well. This article introduces the mainstream virtualization technology, reviews the security threats of virtualization in cloud computing, and puts forward the corresponding countermeasures.