Reviewerly/exHarmony huggingface dataset:
Segmentation of Venous Vessel in MRI using Transferred Convolutional Neural Network

Accurate segmentation of pancreatic venous vessels (PVV) is of great significance for clinical pancreatic cancer radiotherapy. Magnetic resonance imaging (MRI) can provide qualitative assessment of PVV through visualization. Therefore, there is a strong need for an automated segmentation method that can be directly applied to the segmentation of PVV in MR images. In this paper, we develop a deep learning based method with distance and contour regularized level set evolution (DCRLSE) model refinement for automatic detection and segmentation of PVV. The proposed method consists of two main steps: (1) venous vessel localization and segmentation using a dual convolutional neural network (DualCNN) (2) refinement of the initial segmentation with DLRSE model. As PBV is very challenging to segment due to the high inter-patient anatomical variability in both shape and size, the learned weights from other cases are utilized and finetuned to be applied on new cases to obtain consistent and accurate segmentation results. The proposed method was evaluated on 40 MRI scans from a clinical thoracic-abdomen dynamic MRI dataset. Dice similarity coefficient (DSC), sensitivity, specificity, and modified Hausdorff distance (MHD) were calculated to evaluate the segmentation performance of the proposed method. An average sensitivity: 87.60%, specificity: 99.60%, DSC: 86.3%, and MHD: 0.0991mm were obtained. Comparison with other state-of-the-art segmentation methods demonstrate that the proposed method can provide more accurate and consistent segmentation results of PVV in MR images.
Order-Independent Structure Learning of Multivariate Regression Chain Graphs

This paper deals with multivariate regression chain graphs (MVR CGs), which were introduced by Cox and Wermuth in the nineties to represent linear causal models with correlated errors. We consider the PC-like algorithm for structure learning of MVR CGs, a constraint-based method proposed by Sonntag and Peña in 2012. We show that the PC-like algorithm is order-dependent, because the output can depend on the order in which the variables are given. This order-dependence is a minor issue in low-dimensional settings. However, it can be very pronounced in high-dimensional settings, where it can lead to highly variable results. We propose two modifications of the PC-like algorithm that remove part or all of this order-dependence. Simulations under a variety of settings demonstrate the competitive performance of our algorithms in comparison with the original PC-like algorithm in low-dimensional settings and improved performance in high-dimensional settings.
Fast Nearest-Neighbor Classifier Based on Sequential Analysis of Principal Components

In this paper we improve the speed of the nearest neighbor classifiers of a set of points based on sequential analysis of high-dimensional feature vectors. Each input object is associated with a sequence of principal component scores of aggregated features extracted by deep neural network. The number of components in each element of this sequence is dynamically chosen based on explained proportion of total variance for the training set. We propose to process the next element with higher explained variance only if the decision for the current element is unreliable. This reliability is estimated by matching of the ratio of the minimum distance and all other distances with a certain threshold. Experimental study for face recognition with the Labeled Faces in the Wild and YouTube Faces datasets demonstrates the decrease of running time up to 10 times when compared to conventional instance-based learning.
Noun Compositionality Detection Using Distributional Semantics for the Russian Language

In this paper, we present the first gold-standard corpus of Russian noun compounds annotated with compositionality information. We used Universal Dependency treebanks to collect noun compounds according to part of speech patterns, such as ADJ-NOUN or NOUN-NOUN and annotated them according to the following schema: a phrase can be either compositional, non-compositional, or ambiguous (i.e., depending on the context it can be interpreted both as compositional or non-compositional). Next, we conduct a series of experiments to evaluate both unsupervised and supervised methods for predicting compositionality. To expand this manually annotated dataset with more non-compositional compounds and streamline the annotation process we use active learning. We show that not only the methods, previously proposed for English, are easily adapted for Russian, but also can be exploited in active learning paradigm, that increases the efficiency of the annotation process.
Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking

The likelihood determined by the distance between measurements and predicted states of targets is widely used in many filters for data association. However, if the actual motion model of targets is not coincided with the preset dynamic motion model, this criterion will lead to poor performance when close-space targets are tracked. For rigid target tracking task, the structure of rigid targets can be exploited to improve the data association performance. In this paper, the structure of the rigid target is represented as a hypergraph, and the problem of data association is formulated as a hypergraph matching problem. However, the performance of hypergraph matching degrades if there are missed detections and clutter. To overcome this limitation, we propose a joint probabilistic hypergraph matching labeled multi-Bernoulli (JPHGM-LMB) filter with all undetected cases being considered. In JPHGM-LMB, the likelihood is built based on group structure rather than the distance between predicted states and measurements. Consequently, the probability of each target associated with each measurement (joint association probabilities) can be obtained. Then, the structure information is integrated into LMB filter by revising each single target likelihood with joint association probabilities. However, because all undetected cases is considered, proposed approach is usable in real time only for a limited number of targets. Extensive simulations have demonstrated the significant performance improvement of our proposed method.
A Computational Framework Towards Medical Image Explanation

In this paper, a unified computational framework towards medical image explanation is proposed to promote the ability of computers on understanding and interpreting medical images. Four complementary modules are included, such as the construction of Medical Image-Text Joint Embedding (MITE) based on large-scale medical images and related texts; a Medical Image Semantic Association (MISA) mechanism based on the MITE multimodal knowledge representation; a Hierarchical Medical Image Caption (HMIC) module that is visually understandable to radiologists; and a language-independent medical imaging report generation prototype system by integrating the HMIC and transfer learning method. As an initial study of automatic medical image explanation, preliminary experiments were carried out to verify the feasibility of the proposed framework, including the extraction of large scale medical image-text pairs, semantic concept detection from medical images, and automatic medical imaging reports generation. However, there is still a great challenge to produce medical image interpretations clinically usable, and further research is needed to empower machines explaining medical images like a human being.
A Specific Iterative Closest Point Algorithm for Estimating Radar System Errors

A common radar system calibration approach is to use civil aviation automatic dependent surveillance-broadcast (ADS-B) data to register errors. Considering the temporal and spatial uncertainties in radar system observation data, a specific iterative closest point (SICP) algorithm is proposed for estimating two-dimensional (2D) radar system errors. Radar system errors consist of the measurement deviations for the slant range and azimuth of the target and are spatially reflected by the difference between the observed and actual (ADS-B-observed) positions of the same target. Thus, the SICP algorithm is used to register the tracks corresponding to radar and ADS-B observation data. The radar system errors are reflected by a translation, rather than a rotation, of the observation data. Therefore, in the SICP algorithm, a unit matrix first replaces the rotation matrix in the iterative closest point (ICP) algorithm. Then, the translation matrix is iteratively calculated, and finally, the cumulant of the translation matrix is calculated as the radar system error. The proposed algorithm is advantageous because it does not require the temporal registration of radar and ADS-B observation data when temporal and spatial uncertainties are present (e.g., when 2D radar observation data have low accuracy and contain many outliers). Additionally, the SICP algorithm can effectively reduce the dependence on sensor data accuracy. The experimental results obtained based on simulated and measured data demonstrate that compared to conventional registration algorithms, the proposed algorithm can rapidly and accurately estimate radar system errors and has higher registration accuracy.
Single Fog Image dehazing via fast Multi-scale Image Fusion

In this paper, we present a new image dehazing method via fast multi-scale image fusion. It is designed based on a fusion strategy. Instead of estimating the exact global atmospheric and the transmission separately as most previous methods did, our method directly constructs initial dehazing images with different exposure through the histogram analysis and L0 gradient minimization with adaptive boundary constraint to improve the visual dehazing effect. Experimental results show that this method outperforms state-of-the-art haze removal methods in terms of both efficiency and the dehazing visual effect.
K-SVD Based Point Cloud Coding for RGB-D Video Compression Using 3D Super-Point Clustering

In this paper, we present a novel 3D structure-awareness RGB-D video compression scheme, which applies the proposed 3D super-point clustering to partition the super-points in a colored point cloud, generated from an RGB-D image, into a centroid and a non-centroid super-point datasets. A super-point is a set of 3D points which are characterized with similar feature vectors. Input an RGB-D frame to the proposed scheme, the camera parameters are first used to generate a colored point cloud, which is segmented into multiple super-points using our multiple principal plane analysis (MPPA). These super-points are then grouped into multiple clusters, each of them characterized by a centroid super-point. Next, the median feature vectors of super-points are represented by the K singular value decomposition (K-SVD) based sparse codes. Given a super-point cluster, the sparse codes of the median feature vectors are very similar and thus the redundant information among them are easy to remove by the successive entropy coding. For each super-point, the residual super-point is computed by subtracting the feature vectors inside from the reconstructed median feature vector. These residual feature vectors are also collected and coded using the K-SVD based sparse coding to enhance the quality of the compressed point cloud. This process results in a multiple description coding scheme for 3D point cloud compression. Finally, the compressed point cloud is projected to the 2D image space to obtain the compressed RGB-D image. Experiments demonstrate the effectiveness of our approach which attains better performance than the current state-of-the-art point cloud compression methods.
Multi-retinal Images Stitching Based on the Maximum Fusion and Correction Ratio of Gray Average

In this paper, a novel method is proposed for stitching retinal images. Different from most existing methods which can only stitch two images together, our proposed method can stitch multiple (more than two) images all at once. First, all floating images are aligned to the reference one simultaneously upon performing image registration among them. Second, the maximum fusion algorithm is used to obtain a preliminary mosaic image from which the stitching seam is detected with a novel seam detection method. Finally, an innovative gray mean correction ratio algorithm is used to eliminate the stitching seams from the preliminary mosaic image. Experimental results show that our method not only can perform better in dealing with the abrupt changes near the splicing seams and in preserving the texture information but also can be implemented faster than existing algorithm.
An Integrated Classification Model for Massive Short Texts with Few Words

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models.
Predicates and Directives for a Parametric-associative Matching Mechanism for Shapes and Shape Grammars

Predicates and directives are proposed to extend the versatility and expressivity of a shape rule specification `language'.Specifically, a predicate serves to express a condition on the application of a parametric-associative shape rule that cannot simply be explicated within the left-hand side shape.A directive, on the other hand, is a value specification that is required when applying a parametric-associative shape rule, where this value specification cannot be derived from or expressed within the right-hand-side shape.We discuss the benefits of a set of predicates and directives to address seemingly simple requests that might actually be hard to express otherwise.Specifically, we elaborate on the motivation for introducing predicates and directives, and demonstrate the various predicates and directives in more detail, including their implementation.
A Study on the Performance of Base-m Polynomial Selection Algorithm Using GPU

Factorization of large integers has been being considered as a challenging problem in computer science and engineering since the earliest times of the computer technology. Despite the comprehensive efforts, there is still no reported deterministic polynomial-time algorithm; however, its complexity class is in fact not yet decided. A fast and robust polynomial-time algorithm for this problem is required to increase the processing capabilities of current systems. Yet, there are also hesitations at the same time within the community, due to the potential security threats that may appear in such a case. The (asymptotically) fastest algorithm ever found so far to factor large integers is the general number field sieve. Its performance depends on selection of “good” polynomials, which requires a specific procedure for such a selection. Another significant performance factor surely is the power of the processing hardware and their peripherals. This article unveils and discusses the impacts of heterogeneous computing using a graphics processor units (GPU) instead of a central processing unit (CPU) on the performance of polynomial selection and so of factoring large integers. Further, the article presents implementation details and a comparative performance evaluation of the Base-m polynomial selection method to select “good” polynomials. Accordingly, the GPU is found to be more effective over larger numbers with more roots, while the CPU appeared more effective over smaller numbers with less roots, possibly due to the excessive overheads in the GPU processing procedures.
Historical Document Text Binarization using Atrous Convolution and Multi-Scale Feature Decoder

This paper presents a segmentation-based binarization model to extract text information from the historical document using convolutional neural networks. The proposed method uses atrous convolution feature extraction to learn useful text pattern from the document without making a significant reduction on the spatial size of the image. The model then combines the extracted feature using a multi-scale decoder to construct a binary image that contains only text information from the document. We train our model using a series of DIBCO competition datasets and compare the results with the existing text binarization methods as well as a state-of-the-art object segmentation model. The experiment results on the H-DIBCO 2016 dataset show that our method has an excellent performance on the pseudo F-Score metric that surpasses the result of various existing methods.
Semantic Location Privacy Protection Based on Privacy Preference for Road Network

In recent years, with the popularization of mobile intelligent terminals, location-based services (LBS) have been widely used. When users enjoy the convenience of LBS, they also face with the risk of leakage of location privacy. Therefore, it is very important to provide effective privacy protection service during the use of LBS. Previous methods of location privacy protection cannot meet the requirements of users for location privacy protection and service qualities. For this reason, an adjustable semantic location privacy protection scheme is presented in this paper. According to the road network, this scheme introduces the privacy tolerance and the deficient number to select adjacent road segment to satisfy users' requirements. Experimental results show that the proposed scheme supports users' privacy preference for location privacy protection and the required quality of service, and fully considers the user's personalized privacy requirements.
Utilization of Waste Corncobs as Mushroom Cultivication Media in The Pataan Village, Lamongan District

Background: Waste is part of environmental problems because the increase in volume of waste correlates with population growth and efforts to reduce waste are still limited. Pataan Village which is one of the villages in Sambeng Subdistrict is also inseparable from the garbage problem. Most of the household waste will be collected in the yard and then burned, both organic and inorganic waste. In Pataan Village, there is already a 3R Waste Disposal Site (TPS) which functions as a place to collect garbage and a place to sort waste, unfortunately it is not functioned optimally due to various obstacles. Purpose: Solve the problem of managing organic waste especially corncobs by maximizing it as a mushroom planting media. Method: Socialization, training and assistance of mushroom cultivation using corncobs media to youth groups who are members of the Karang Taruna Organizations in Pataan Village. Results: The results of training and assistance for 25 days, it can be noted that the optimization of corncobs as a medium for mushroom cultivation can be an effective solution in handling the waste problem in Pataan Village and has the potential to bring up new commodities in Pataan Village in the form of janggel mushrooms. Conclusion:Partners get increased knowledge and skills in utilizing corncobs as a medium for cultivating janggel mushrooms. So that in the next period can be given about how the processing of the janggel mushroom in order to have added value.
Wear the Right Head: Comparing Strategies for Encoding Sentences for Aspect Extraction

In this work we investigate the impact of encoding mechanisms used in neural aspect extraction models on the quality of the resulting aspects. We concentrate on the neural attention-based aspect extraction (ABAE) model and evaluate five different types of encoding mechanisms: simple averaging, self-attention with and without positional encoding, recurrent, and convolutional architectures. Our experiments on four datasets of user reviews demonstrate that, in the family of ABAE-like architectures, all models with different encoding mechanisms show the similar results in terms of standard coherence metrics for English and Russian data. Our qualitative study shows that all models yield interpretable aspects as well, and the difference in quality is often very minor.
Two-level atom and quantum system entanglement and squeezing with and without classical field and damping effects

In this article, we study the entropy squeezing and the nonlocal correlation between a nonlinear quantum system initially in the BGS (Barut-Girardello state) and 2LA (two-level atom). The numerical solution is used for solving the differential equations to obtain the density matrix. The relation be tween the atomic inversion, nonlocal correlation measured by the negativity and entropy are discussed. The effective of classical field and damping on the dynamical properties of the entanglement and entropy squeezing is examined. The results are shown that a high amount of classical field due to the entanglement reached to minimum values. Finally the proposed quantum system being more resistant to the damping effect in the absence of classical field than the presence of classical field.
A Clustering Algorithm for Multi-density Datasets

DBSCAN algorithm discovers clusters of various shapes and sizes. But it fails to discover clusters of different density. This is due to its dependency on global value for Eps. This paper introduces an idea to deal with this problem. The offered method estimates local density for a point as the sum of distances to its k-nearest items, arranges items in ascending order according to their local density. The clustering process is started from the highest density point by adding un-clustered points that have similar density as first point in cluster. Also, the point is assigned to current cluster if the sum of distances to its Minpts-nearest neighbors is less than or equal to the density of first point (core point condition in DBSCAN). Experimental results display the efficiency of the proposed method in discovering varied density clusters from data.
Peasant Farms and Industrial Development

The authors propose a general formulation of the economic and mathematical model of the problem of optimizing the size of the newly created peasant farms and Industrial Development, taking into account the chosen specialization of activity, as well as determining the optimal parameters for the already-known size of farms. The developed mathematical model differs from the classical one by the presence of additional blocks, which prescribe the sales channels of manufactured products and determine the necessary financial resources. The proposed methodological approach should be used for planning the development of regional economies, taking into account the existing specifics.