Reviewerly/exHarmony huggingface dataset:
Covering Arrays to Support the Process of Feature Selection in the Random Forest Classifier

The Random Forest (RF) algorithm consists of an assembly of base decision trees, constructed from Bootstrap subsets of the original dataset. Each subset is a sample of instances (rows) by a random subset of features (variables or columns) of the original dataset to be classified. In RF, pruning is not applied in the generation of base trees and in the classification process of a new record, each tree issues a vote enabling the selected class to be defined, as that with the most votes. Bearing in mind that in the state of the art it is defined that random feature selection for constructing the Bootstrap subsets decreases the quality of the results achieved with RF, in this work the integration of covering arrays (CA) in RF is proposed to solve this situation, in an algorithm called RFCA. In RFCA, the number N of rows of the CA defines the lowest number of base trees that require to be generated in RF and each row of the CA defines the features that each Bootstrap subset will use in the creation of each tree. To evaluate the new proposal, 32 datasets available in the UCI repository are used and compared with the RF available in Weka. The experiments show that the use of a CA of strength 2 to 7 obtains promising results in terms of accuracy.
Using of Probabilistic-Statistical Characteristics in the Interpretation of Electrical Survey Monitoring Observations

It is within the Verkhnekamsk salt deposit, on the potentially dangerous sections of the mine fields the geologic-geophysical monitoring, which includes the electroprospecting researches by the methods NF and SEP on three spans of the power line AB is carried out regularly. The analysis of statistical characteristics of values of potentials of the natural field and apparent resistance by means of the theory of estimates allows to increase significantly the volume of useful information and more accurately to trace the features of a geological structure which are implicitly expressed in the observed fields. For a more distinct allocation of the hidden regularities of change of amplitude of the apparent resistance (AR) field, a fast wavelet-transformation (FWT) of discrete values of AR by means of the HAAR_2 program was executed previously. The statistical characteristics of SEP values and potential of NF were calculated by various methods realized in the COSCAD 2D software package (in the sliding window, in one-dimensional and two-dimensional dynamic windows), and with different sizes of windows. The statistical characteristics of values of NF potential were combined in turn with the statistics of AR obtained at AB 100, 200 and 400 m, i.e. three multi-attribute spaces were formed. Their structure was analyzed by means of various methods of non-standard classification. The using of procedures of non-standard classification allowed to break the analyzed sets on homogeneous, by formal mathematical criteria, the classes spatially answering to sites of possible engineering-geological complications, that it is extremely difficult by results of only the qualitative analysis of field observations.
Learning Robust Embedding Representation With Hybrid Loss for Classification and Verification

This paper presents a method of building an embedding representation via deep metric learning, which works well in both classification and verification problems. The embedding is built via a proposed hybrid loss, which consists of a softmax loss and a Euclidean-metric loss. The hybrid loss explores the tradeoff, balancing the discriminativeness and invariance. In deep metric learning, a softmax loss is proposed for classification and a Euclidean-metric loss is responsible for verification. We apply the proposed loss and the softmax loss to the well-known deep models, such as VGG-16 and ResNet-50, respectively. In addition, the performances are evaluated by two datasets (CIFAR and Market1501), in which the CIFAR is for classification and the Market1501 is for verification. The results indicate that the embedding learned from the proposed loss does improve the performance on both the classification and verification tasks.
Parallel Learning Algorithms of Local Support Vector Regression for Dealing with Large Datasets

New parallel algorithms of local support vector regression (local SVR), called kSVR, krSVR are proposed in this paper to efficiently handle the prediction task for large datasets. The learning strategy of kSVR performs the regression task with two main steps. The first one is to partition the training data into k clusters, followed which the second one is to learn the SVR model from each cluster to predict the data locally in the parallel way on multi-core computers. The krSVR learning algorithm trains an ensemble of T random kSVR models for improving the generalization capacity of the kSVR alone. The performance analysis in terms of the algorithmic complexity and the generalization capacity illustrates that our kSVR and krSVR algorithms are faster than the standard SVR for the non-linear regression on large datasets while maintaining the high correctness in the prediction. The numerical test results on five large datasets from UCI repository showed that proposed kSVR and krSVR algorithms are efficient compared to the standard SVR. Typically, the average training time of kSVR and krSVR are 183.5 and 43.3 times faster than the standard SVR; kSVR and krSVR also improve 62.10%, 63.70% of the relative prediction correctness compared to the standard SVR, respectively.
Sparsely Connected Convolutional Layers in CNNs for Liver Segmentation in CT

Convolutional neural networks are currently the best working solution for automatic liver segmentation. Generally, each convolutional layer processes all feature maps from the previous layer. We show that the introduction of sparsely connected convolutional layers into the U-Net architecture can benefit the quality of liver segmentation and results in the increase of the dice coeffcient by 0:32% and a reduction of the mean surface distance by 3:84 mm on the LiTS data. Evaluation on the IRCAD data set with the application of post-processing showed a 0:70% higher Dice coeffcient and a 0:26 mm lower mean surface distance.
MFBS: Multiple Factor Bandwidth Strategy Scheme for Anonymity Assessment

This paper proposes a multiple factor bandwidth strategy (MFBS), an anonymity assessment scheme based on bandwidth strategy. We first analyzed the path selection algorithm mechanism based on bandwidth strategy and anonymity constraint conditions and then elaborated the overall architecture and the key module design of the MFBS scheme. A detailed design was carried out so that it can be applied for the evaluation of Tor's anonymous communication system. Finally, according to the running data in the node resource file in the anonymous network, we analyzed anonymity from different dimensions. By evaluating the bandwidth consumed by Tor in the actual network, the anonymity of the Tor could be calculated, and a more comprehensive anonymity assessment could be performed.
Designing an IMS-LD Model for Sharing Space of Learning Management System

The context of this work is that of designing an IMS-LD model for sharing space of Learning Management System (LMS). Our work is specifically in the field or seeking to promote, by means of information technology from a distance. Our approach is to first think about the conditions for creating a real sharing space for LMS between learners, and designing the IT environment that supports this space. In this paper, we try to adapt the IMS-LD model with a sharing model for LMS based on the traditional pedagogy and the social constructivism. This adaptation will go through three stages. Firstly, the development of an LMS model. Secondly, the study of correspondence between the developed model and IMS-LD model and their transformation to IMS-LD model.
Designing a User-Experience-First, Privacy-Respectful, High-Security Mutual-Multifactor Authentication Solution

The rush for improved security, particularly in banking, presents a frightening erosion of privacy. As fraud and theft rise, anti-fraud techniques subject user privacy, identity, and activity to ever-increasing risks. Techniques like behavioral analytics, biometric data exchange, persistent device identifiers, GPS/geo-fencing, knowledge-based authentication, on-line user activity tracking, social mapping and browser fingerprinting secretly share, profile, and feed sensitive user data into backend anti-fraud systems. This is usually invisible, and usually without user consent or awareness. It is also, unfortunately, necessary, partly because contemporary authentication is increasingly ineffective against modern attacks, but mostly because the idea of “usable” is confused with “invisible” most of the time. In the mind of a CISO, “stronger authentication” means a slower, less convenient, and more complicated experience for the user. Security and privacy tend to lose most battles against usability, particularly when friction impacts customer adoption or increases support costs.
Features of Forming Students’ Reflective Position While Studying Mathematics at University by Means of Information and Communication Technologies

Modern professional education takes into account the didactic potential of disciplines and modern educational technologies while searching, developing and implementing new resources and mechanisms in the process of preparing a competitive creative personality with the need for continuous self-improvement and self-development. Studying and analyzing the multicomponent composition of professional training, researchers note the pedagogical potential of information and communication technologies and mathematical disciplines as well as skills of self-determination, self-organization and reflection in the process of forming professional competence. Thus, the purpose of the article is to study features of forming students’ reflective position while studying mathematics at university by means of information and communication technologies. The leading method is the design of a methodical system for teaching mathematics at university, including didactic tools which contribute to increasing students’ interest to the subject, as well as the development of skills in self-determination, self-organization and self-analysis. As a result of the research, the authors of the article defined the didactic potential of information and communication technologies in the process of forming students’ reflective position while studying mathematics. They pointed out methodical aspects of including various forms of interaction of subjects of the educational process while studying mathematics at university through the use of information and communication technologies. Moreover, the authors developed the method “I am learning myself” in order to form students’ reflective position.
The influence of cap rock composition on hydrocarbon seep type in the Zagros oil fields, a study using Advanced Spaceborne Thermal Emission and Reflection Radiometer mineral map

The persistent natural hydrocarbon seepage in onshore basins challenges observation and exploration technologies, which are required to document and assess these valuable indications of the presence of oil and gas in the subsurface. This paper aims at demonstrating the relationship between the compositional variation of an evaporite cap rock and the types of seeps occurring at the surface. For this purpose, the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data was utilized for mapping mineral variations of a petroleum system in the Zagros oil fields. Relative absorption-band depth (RBD), band rationing and the boosted regression trees (BRT) were applied to enhance and classify the mineral composition of evaporite, sandstone, and marly limestone formations. The gas seeps were associated with the areas of gypsum-bearing evaporite cap rock while oil seeps were mostly associated with calcite and clay zones within the cap rock, which was more prone to fracturing during the tectonic activities of the basin. It is suggested that the application of remote sensing in the oil and gas industry could be widened by detection of sleep-induced alteration to assess the efficiency of cap rock and to evaluate the productivity of reservoirs at a regional scale.
Secure Computation of Private Set Intersection Cardinality With Linear Complexity

PSI and its variants play a major role when the participants want to perform secret operations on their private data sets. The importance of this chapter is twofold. In the first phase, the author presents a size-hiding PSI-CA protocol followed by its authorized variant, APSI-CA, utilizing Bloom filter. All these constructions are proven to be secure in standard model with linear complexity. In the second phase, the author employs Bloom filter to design an efficient mPSI-CA protocol. It achieves fairness using offline semi-trusted third party (arbiter) unlike the most efficient existing protocols. The arbiter is semi-trusted in the sense that he does not have access to the private information of the entities while he will follow the protocol honestly. Proposed mPSI-CA is proven to be secure against malicious adversaries in the random oracle model (ROM) under the decisional Diffie-Hellman (DDH) assumption. It achieves linear complexity.
A minimal solution for plane motion and structure from two perspective views

Plane motion and structure is a classical problem in photogrammetry and computer vision communities. While the methods based on the Singular Value Decomposition of the plane-induced homography matrix have been largely successful, they are subject to the special assumption about the bottom right entry or the determinant sign of the homography matrix. This paper proposes firstly an explicit closed-form solution for the homography using the normalized barycentric coordinates from four image point correspondences in general position. Then we use algebraic method to propose a minimal solution for the plane motion and structure problem. Further, we derive a concise closed-form relation between two solutions of the plane motion and structure. The performance and practicability of our method are validated by testing on synthetic and real data.
Enriching News Articles with Related Search Queries

Enriching the content of news articles with auxiliary resources is a technique often employed by online news services to keep articles up-to-date and thereby increase users' engagement. We address the task of enriching news articles with related search queries, which are extracted from a search engine's query log. Clicking on a recommended query invokes a search session that allows the user to further explore content related to the article. We present a three-phase retrieval framework for query recommendation that incorporates various article-dependent and article-independent relevance signals. Evaluation based on an offline experiment, performed using annotations by professional editors, and a large-scale online experiment, conducted with real users, demonstrates the merits of our approach. In addition, a comprehensive analysis of our online experiment reveals interesting characteristics of the type of queries users tend to click and the nature of their interaction with the resultant search engine results page.
Satisfaction Classes via Cut Elimination

We present a construction of a satisfaction class in an arbitrary countable recursively saturated models of first-order arithmetic. Our construction is fully classical, namely, it employs nothing more than the classical techniques of formal proof theory.
State Estimation based Echolocation Bionics and Image Processing based Target Pattern Recognition

This paper deals with the theoretical aspect of bat echolocation and bionics, and image processing-based target recognition and identification methods.The state estimation methods utilizing the linear rustic filters such as fixed gain and Kalman filters are studied and implemented for echolocation bionics for estimating the LOS distance.A complete mathematical modeling and simulation of bat dynamics and its prey are presented upon which the relative LOS distance is reconstructed with state estimators and less RMS errors.Also, target recognition and identification using Optical, IR Digital Night Vision and Thermal camera is studied and implemented at different environmental conditions to demonstrate the superiority of thermal camera.
Diversity and Progress Controlled Gravitational Search Algorithm for Balancing Load in Cloud

Load scheduling is used to distribute load over the cloud. Various load scheduling techniques are used for efficient functioning of cloud. Recently many search based optimization techniques have been successfully used for the process of load scheduling. These optimization techniques considerably reduces the time which is required to solve the problem of load scheduling in cloud computing. This paper discusses about Diversity and Progress Controlled Gravitational Search Algorithm. The results of proposed approach are compared with Gravitational Search Algorithm and Particle Swarm Optimization. Experimental results confirm that the proposed approach is better than PSO and GSA.
A Multi-sensor Target Recognition Information Fusion Approach Based on Improved Evidence Reasoning Rule

The Evidence Reasoning (ER) rule extends traditional Dempster-Shafer evidence theory by establishing a new rule to combine multiple pieces of independent evidence with importance and reliability weights. The importance and reliability weight of an evidence source is usually decided by fusion system designers which is subjective. Aiming at solving the evaluation problem of evidence importance and reliability weight in ER rule, a new method is proposed in this paper under the application background of multi-sensor marine target recognition information fusion. The importance weight of evidence source is calculated based on the accuracy of sensor recognition in history observation, while the reliability weight is calculated based on the improved normalized angle distance which measures the conflicting among pieces of evidence. Then the pieces of weighted evidence are combined under ER rule to draw recognition fusion conclusion. The proposed approach improves the ER rule by giving an objective method to measure the importance and reliability weight of evidence. Simulation experiments are conducted, demonstrating that this approach can combine conflicting evidence more effectively. Moreover, compared with other methods, the improved ER rule shares good convergence performance and has higher computational efficiency, which is beneficial for engineering implementation.
Pattern Recognition with Deep Learning

Neural nets fall into the Learning category of our taxonomy. In this chapter, we will expand our neural net toolbox with convolution and pooling layers. A general neural net is shown in Figure 10.1. This is a “deep learning” neural net because it has multiple internal layers. Each layer may have a distinct function and form. In the previous chapter, our multi-layer network had multiple layers, but they were all functionally similar and fully connected.
Semantic Marking Method for Non-text Documents of Website Based on Their Context in Hypertext Clustering

Initial indexing and structuration of information on Internet are the conditions for resolving of the task of an effective search of information that best relates to user’s query now. Mainly they deal with text-based time expensive processing methods. Hyper structured nature of the web is used as an alternate approach for this purpose, but websites also contain information in the non-text format: (images, movies, pdf-files etc.). These documents, first of all, are intended for perception by the person, but not for the automated processing. In this article, we propose the method for the decision of this problem on the way of semantic marking of non-text documents based on their context in hypertext clustering. At the same time, we develop the approach of the context independent semantic clustering of the website with using of web-analytics information, which utilizes internal hypertext structure, user’s behavior statistics and does not require full-text content analysis. For this purpose, we represent the hypertext structure of the site as a graph and apply flow simulation algorithms to produce web clustering. Then we make a semantic description of the clusters by sets of keywords. Non-text documents have hyperlinks to some web clusters, so we consider extracted keywords for relating cluster as its semantic marking. We have checked the suggested method on the example of site sstu.ru.
Interactive Neural Network Robot User Investigation for Medical Image Segmentation

Interactive image segmentation bears the advantage of correctional updates to the current segmentation mask when compared to fully automated systems. Especially in the field of inter-operative medical image processing of a single patient, where a high accuracy is an uncompromisable necessity, a human operator guiding a system towards an optimal segmentation result is a time-efficient constellation benefiting the patient. There are recent categories of neural networks which can incorporate human-computer interaction (HCI) data as additional input for segmentation. In this work, we simulate this HCI data during training with state-of-the-art user models, also called robot users, which aim to act similar to real users given interactive image segmentation tasks. We analyze the influence of chosen robot users, which mimic different types of users and scribble patterns, on the segmentation quality. We conclude that networks trained with robot users with the most spread out seeding patterns generalize well during inference with other robot users.
A Tile-Based Solution Using Cubemap for Viewport-Adaptive 360-degree Video Delivery

360-degree video delivery in Virtual Reality is very challenging due to the fact that 360-degree videos require much higher bandwidth than conventional videos. To overcome this problem, viewport-adaptive streaming has been introduced. In this study, we propose a new adaptation method for tiling-based viewport-adaptive streaming of 360-degree videos. For content preparation, the Cubemap projection format is used, where faces or parts of a face are encoded as tiles. Also, the problem is formulated as an optimization problem, in which each visible tile is weighted based on how that tile overlaps with the viewport. To solve the problem, an approximation algorithm is proposed in this study. An evaluation of the proposed method and reference methods is carried out under different tiling schemes and bandwidths. Experiments show that the Cubemap format with tiling provides a lot of benefits in terms of storage, viewport quality across different viewing directions and bandwidths, and tolerance to prediction errors.
On the correlation of seismic activity to syzygies.

There must be hundreds of papers dedicated to the studies of the relationship between lunar phases and seismic activity on Earth; half of them claim to prove that lunar phases affect seismic activity on Earth, while the other half disprove the same.It is shown here that the lunar phases indeed affect seismic activity on Earth, the correlation between them is especially easily seen in magnitude 8.2 earthquakes and VEI 5 volcanic eruption, but only seasonally.The seasons of correlation last centuries and are intimately connected to the behavior of the Earth's magnetic field.The correlation between the lunar phases and seismic activity may be also easily observed in the seismic activity away from well-formed tectonic lines, e. g. the earthquakes along the East African Rift and high-magnitude deep earthquakes.