Reviewerly/exHarmony huggingface dataset:
KNN Non-Parametric Kernel Density Estimation Method for Motion Foreground Detection Based on Gaussian Filtering

Background subtraction is an important method in moving target detection. Its difficulty lies in the establishment of dynamic background model. All kinds of non-parametric density estimation methods are effective to solve this problem, but there are obvious shadow on the foreground image and can not clearly outline the contour of the target when the target moves forward and backward, left and right in a short distance. In order to solve this problem, K-Nearest Neighbor non-parametric kernel density estimation method based on Gaussian filter is proposed for target detection. K-Nearest Neighbor non-parametric kernel density estimation is used to analyze the probability density of pixels in the image and extract the dynamic target. Meanwhile, Gaussian filter is used to conduct high-pass filtering on the sampled video frame in the frequency domain. The resulting image is then fused with the foreground image in bit level to obtain the final target image. Experiments show that this method has certain generality, obvious effect of eliminating shadow, clear target contour, and can detect dynamic target in complex environment.
Design of a Broadband Electromagnetic Signal Monitoring and Evaluation System

Abstract Communication signal reconnaissance and evaluation is the basis for the implementation of communication anti-interference. The design of broadband electromagnetic signal monitoring and evaluation system realizes the function of communication signal reconnaissance and evaluation through the monitoring, collection, analysis and storage of complex electromagnetic signals, providing a basis for the effective implementation of communication anti-interference.The wideband electromagnetic signal monitoring and evaluation system can monitor, collect, analyze and store complex electromagnetic signals, and has the function of communication signal reconnaissance and evaluation.
GIS and User Experience in Decision Support for Retail Type Organizations

In recent years, spatial data analysis has become a transcendental strategy for companies, because these organizations can be more competitive in the market. For this, companies need to be linked in the field of Business Intelligence and be able to create a beneficial environment to obtain a better exploitation and visualization of their information. With the proper management of the information they can generate an appropriate knowledge so that the activities related to the company improve and generate an economic benefit. In the case of companies of the retail type, the analysis of spatial data allows them to discover valuable information, on occasions this information is only detectable by observing and analyzing through maps. Today, there are a lot of tools to perform this type of analysis. However, many tools are focused on technical level users and not employees. This research paper presents an analysis of the Handytec Company and how it manages its information and how a web solution improves the visualization and analysis of spatial data and helps in making decisions for its employees. The application allows the end user to visualize spatial information through maps and graphs, execute analysis tasks without having technical knowledge with the objective of supporting decision making.
Sepsis Detection Using Missingness Information

Sepsis is one of the leading causes of death in hospital. An early detection is crucial to reduce its consequences and mortality. The challenge of Computing in Cardiology 2019 is addressing this issue by providing about 40,000 records from intensive care unit patients. As clinical measurements are collected at irregular frequencies, this dataset is missing many observations. Simply discarding missing values is counterproductive. Indeed, it has been observed that missing data patterns hold relevant information regarding the patient health state. To take advantage of this information, we propose a sepsis detection model incorporating representations of missingness information. This model is a recurrent neural network network composed of two gated recurrent unit (GRU) layers to capture long-term dependencies and a sigmoid layer to output a probability of sepsis. First, the model is trained by simply imputing missing values in the dataset. Then, the dataset is extended with the pattern of missing values. Finally, the model with a modified GRU cell taking into account missing data is evaluated. Our best model achieves an utility score of 0.00 on the final test set.
A Path Planning Method of Unmanned Aerial Vehicle

In the process of unmanned aerial vehicle (UAV) flight path planning, it is sometimes necessary to limit the flight altitude of a UAV. However, some existing path planning methods are not applicable for this restricted condition. In this paper, for the condition that we require the UAV to fly only in a straight line, by analyzing the UAV' flight conditions and environmental constraints, we propose a path planning method based on related knowledges of spatial analytic geometry. We determine the planning space as two-dimension or three-dimension according to the UAV' flying altitude. Then we analyze, formalize these constraint conditions, and set the goal function by analyzing the goal conditions. Finally, we compute the goal function and limiting conditions according to the different goals and restriction. Based on the related knowledges of spatial analytic geometry, we give a optimal path planning method of UAV.
Comparison of Lipid Fractions of Icteric Sample by Using Three Devices

COMPARISON OF LIPID FRACTIONS OF ICTERIC SAMPLE BY USING THREE DEVICES Ummul Khair1, Asvin Nurulita2,3, Darwati Muhadi2,31 Specialist Doctoral Education Program of Clinical Pathology Science, Faculty of Medicine, Hasanuddin University/RSUP Dr. Wahidin Sudirohusodo, Makassar 2 Department of Clinical Pathology Science, Faculty of Medicine, Hasanuddin University/RSUP Dr. Wahidin Sudirohusodo, Makassar3RSUP Dr. Wahidin Sudirohusodo, Makassar ABSTRACTIntroductionLipid fraction assessment in laboratory includes cholesterol, HDL, LDL, Triglycerides. The icteric sample is characterized by elevated levels of bilirubin and darkness yellow sample color. This research was to determine the comparison of lipid fraction of icteric sample by using three devices.MethodCross-sectional study at Clinical Pathology Laboratory of Dr. Wahidin Sudirohusodo of Makassar. Comparison of lipid fraction of icteric samples against 3 (three) devices (Pentra, Biomajesty, Conelab) in period April - June 2018. Statistical analysis using SPSS program.Result and Discussions Total sample of 50 indicated that cholesterol level by Biomajesty was lower than Pentra400 (p &lt; 0.001), while the cholesterol level by Conelab was significantly lower than Pentra400 (p &gt; 0.001), but not significantly different with Biomajesty (p &lt; 0.05). HDL level by Biomajesty did not differ significantly with Pentra400 (p &gt; 0.05). HDL level by Conelab were significantly lower than Pentra400 (p &lt; 0.001) and Biomajesty (p &lt; 0.01). HDL level by Biomajesty was smallest than Pentra and Conelab. LDL level by Biomajesty did not differ significantly with Pentra400 (p &gt; 0.05) while LDL level by Conelab was significantly lower than Pentra400 (p &lt; 0.001) and Biomajesty (p &lt; 0.001). It can be seen that the range of TG by Biomajesty is smallest (more accurate) than Pentra400 and Conelab. TG level by Biomajesty was significantly lower than Pentra400 (p &lt; 0.01) while TG level by Conelab was not significantly different than Pentra400 (p &gt; 0.05), but it was significantly higher than Biomajesty (p &lt; 0.001).Conclusion and RecommendationThe research shows that Biomajesty is an accurate device for the measurements of HDL, LDL, and TG of icteric samples, whereas Conelab is an accurate device for cholesterol measurement of icteric samples.KeywordsLipid fraction, icteric sample, Pentra, Biomajesty, Conelab
REAL TIME CCTV DETEKSI MANUSIA DENGAN SISTEM VIRTUAL LINE

Abstrak Real Time CCTV Deteksi Manusia Dengan Sistem Virtual Line adalah sistem pengawas suatu tempat yang dapat mendeteksi manusia saat melintasi garis virtual tersebut. Metode ini dapat digunakan untuk keamanan suatu tempat, bisnis, dan lainnya. Ketika adanya kejahatan di suatu tempat CCTV pada umumnya hanya dapat merekam kejadian tersebut untuk bukti. Maka dibutuhkan CCTV yang mampu mendeteksi manusia saat akan melakukan kejahatan dan CCTV ini dapat melakukan pekerjaan tersebut. Pengolahan citra mampu memberikan pengenalan terhadap objek diruang lingkup CCTV tersebut, sistem garis virtual yang diterapkan pada kamera CCTV dan metode Haar Cascade untuk mendeteksi manusia saat melintasi garis virtual secara Real Time. Sistem ini dapat mendeteksi manusia saat melintasi garis virtual tersebut. Abstract Real Time CCTV Smart Virtual Line the Supervisor System is where you can locate moving objects and people. This method can be used for security, location, work and more. When There is a crime somewhere in the CCTV in general only the incident can be recorded for evidence. Then the CCTV takes the ability to detect the moving objects that are transported by humans and it can locate the job. Image processing is able to supply an Introduction to the objects in the room of the range of the CCTV system, the virtual line applied to CCTV camera and method Her Waterfall to Locate a Moving object Real time. This system can detect moving objects-people. Keywords: Real Time, Virtual Line, Image Processing, Haar Cascade
A Novel Fusion Method for 3D-TV View Synthesis Using Temporal and Disparity Correlations

View synthesis like depth-image-based rendering (DIBR) plays a significant role in content creation for three-dimensional television. However, perceptual errors introduced by current view synthesis often result in severe distortions in synthesized images. In this paper, we propose a novel view synthesis fusion (VSF) method that adaptively exploits temporal and disparity correlations to improve the quality of the synthesized picture. The proposed VSF method defines a robust correlation assessment metric for fusing several pre-created virtual-view candidates. Unlike conventional methods, the proposed fusion algorithm is applied to both hole and non-hole areas. Experimental results show the proposed method significantly outperforms in peak signal-to-noise ratio (PSNR) and subjective visual quality, compared to other conventional methods.
Cluster Ensemble Kernel for Kernel-based Classification

This paper presents a method for some semi-supervised and supervised classification problems based on properties of the averaged co-association matrix obtained with a cluster ensemble. The ensemble clustering is performed as a preliminary step of data processing. The main property states that the matrix is a valid kernel matrix, thus it can be used in different classification methods that use kernels such as Kernel Nearest Neighbor, SVM, Kernel Fisher Discriminant. Some properties of the suggested method connected with its convergence to optimal classifier are studied. Numerical experiments show that the accuracy of the proposed algorithms is often higher than other state-of-the-art methods, especially under the presence of complex data structures and noise distortions.
The Method of Vehicle Type Identification Based on Improved Corner Feature

Abstract This paper present a method of vehicle type identification using improved corner points as key points for feature description. The corner points can reflect the information of different type. Therefore, we adopt the features of corner points to recognize which type the vehicle belongs to. The method of Harris corner detection was improved, and the ratio of eigenvalues of M matrix in different directions was added to the corner response. The corner points were detected by improved corner response and results of test showed that the count of the detected corner points was increased. Considering rotation invariance and scale invariance, feature description is carried out around detected corner points. The vector of feature is determined by the Harr wavelet response of the pixels around the corner point, but the direction is only one of the eight values, and the length of the vector is 16. The matching rate of feature vectors between different types is used to determine which type the vehicle belongs to. The results of experiment confirmed the validity of the method.
Time Prediction Model of WeChat Official Account User Number Based on Nonlinear Autoregressive Neural Network

E-sign is an WeChat official account which also serves as a national-level university student innovation and entrepreneurship project focusing on WeChat sign-ins. Since launched, it has attracted the attention and praise of college teachers and students. This paper attempts to use the existing data of the WeChat official account to reveal the temporal growth law of the number of users, so as to predict the future development trend of it. In this paper, from the perspective of time series, applying the neural network, we constructed a prediction model. Through continuous data training, the ideal number of hidden neurons and delay days are obtained, which yields good predictive results.
RNS-To-Binary converters for New Three-moduli sets {2<sup>k</sup>, 2<sup>k</sup>-1, 2<sup>k</sup>(2<sup>k</sup>-1)+ 1} and {2<sup>k</sup>, 2<sup>k</sup>-1, 2<sup>k</sup>(2<sup>k</sup>-1)-1}

In this paper, Residue Number System (RNS)-to-binary converters for two new three-moduli sets {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> _ 1)+l} and {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> -1)-1} using Mixed Radix Conversion (MRC) technique are proposed. The architectures of the reverse converters are designed so as to facilitate implicit sign detection. The proposed designs are compared regarding hardware resources and computation time with three moduli reverse converters, sign detectors and reverse converters with sign detectors for the dynamic ranges of about 4k bits described in literature. The ASIC implementation results for the proposed designs are also provided using 180 nm technology.
Comfort-aware Cognitive Buildings Leveraging Deep Reinforcement Learning

This paper presents a novel approach for the management of buildings by leveraging cognitive technologies. The proposed approach exploits the Deep Reinforcement Learning paradigm to learn from both a physical and a simulated environment so as to optimize people comfort and energy consumption.
Three-player nim with podium rule

If a combinatorial game involves more than two players, the problem of coalitions arises. To avoid the problem, Shuo-Yen Robert Li analyzed three-player nim with the podium rule, that is, if a player cannot be last, he should try to be last but one. With that simplification, he proved that a disjunctive sum of nim piles is a $${\mathcal {P}}$$ -position if and only if the sum modulo 3 of the binary representations of the piles is equal to zero. In this paper, we extend the result in order to understand the complete characterization of the outcome classes, the possible reductions of the game forms, the equivalence classes under the equality of games and related canonical forms.
Sifat-Sifat Representasi Indekomposabel The Properties of Indecomposable Representations

A directed graph is also called as a quiver where is a finite set of vertices, is a set of arrows, and are two maps from to . A representation of a quiver is an assignment of a vector space to each vertex of and a linear mapping to each arrow. We denote by the direct sum of representasions and of a quiver . A representation is called indecomposable if is not ishomorphic to a direct sum of non-zero representations. This paper study about the properties of indecomposable representations. These properties will be used to investigate the necessary and sufficient condition of indecomposable representations.
Performance Comparison of Unsupervised Deep Hashing with Data-independent Hashing for Content-Based Audio Retrieval

Many hashing methods for content-based audio retrieval have been proposed recently. The early exploration of hashing focuses on data-independent methods where modular arithmetic, dedicated hash functions or simple random projections are used for constructing hash functions. In this way, the hash codes do not necessarily preserve the accurate semantic similarities and may degrade the performance of audio retrieval. In this paper, we propose a novel unsupervised deep hashing method for content-based audio retrieval. Compared with data-independent hashing methods, our method defines the hash functions as a deep neural network which the binary hash codes are produced at one layer. For the codes to be efficient, the independence, balance, and similarity preserving properties are included and optimized in the objective function. Experimental results on ballroom datasets show that our proposed method outperforms state-of-the-art data-independent hashing methods in both effectiveness and efficiency.
Automatic Generation of Structured Text Documents Based on the Ontological Modeling Approach

Business documentation and regulations are the integral part of our work life, since it structures, determines and manages the human activity. That is why keeping the documentation updated is essentially important for business success. However, this process could be highly resource-investive. In order to reduce these expenses, the researchers are trying to introduce an automation in this procedure. The current work is dedicated to the development of the structured business text documents generation. This study utilizes the ontological approach of a domain representation in order to capture real world knowledge concepts and relations between them.
Is Turkey a Favorable Environment for FDIs?

The aim of this chapter is to measure the impact of institutional variables such as economic freedom and democracy on foreign direct investment from the European Union to Turkey. In this framework, the author models institutional factors affecting foreign direct investments. The model includes economic freedom index, political rights, civil liberties of Turkey, and Gross Domestic Products of selected European countries. In theoretical part, the perspective of institutional economics on economic freedoms and democracy is discussed. A brief literature about the relationship between foreign direct investments and economic freedoms and democracy is revealed. Stochastic frontier analysis was used in the implementation to show the relationship between variables and efficiency of European countries' foreign direct investments to Turkey. Proposals for policy implication have been made in the conclusion part. This chapter shows civil liberties and economic freedom index have positive effect on foreign direct investments that supports institutionalists.
Using History of Belief States for Adaptive Dialogue Management

This paper applies the theory of information space for an inquiry into dialogue management based upon the partially observable Markov decision process (POMDP) and proposes to maintain the history of belief states for the analysis of trend changes. The establishment of relationship between the number of changing trends in belief states and level of knowledge among users then directs a modification of POMDP-based dialogue manager to assist users achieving their goals by adaptively choosing different sets of policies according to the type of users. Results of experiment demonstrate that this modification allows embodied agents to reduce the length of dialogue while increasing the accuracy of intention discovery.
SEGATE: Unveiling Semantic Inconsistencies between Code and Specification of String Inputs

Automated testing techniques are often assessed on coverage based metrics. However, despite giving good coverage, the test cases may miss the gap between functional specification and the code implementation. This gap may be subtle in nature, arising due to the absence of logical checks, either in the implementation or in the specification, resulting in inconsistencies in the input definition. The inconsistencies may be prevalent especially for structured inputs, commonly specified using string-based data types. Our study on defects reported over popular libraries reveals that such gaps may not be limited to input validation checks. We propose a test generation technique for structured string inputs where we infer inconsistencies in input definition to expose semantic gaps in the method under test and the method specification. We assess this technique using our tool SEGATE, Semantic Gap Tester. SEGATE uses static analysis and automaton modeling to infer the gap and generate test cases. On our benchmark dataset, comprising of defects reported in 15 popular open-source libraries, written in Java, SEGATE was able to generate tests to expose 80% of the defects.
Variant-Kudu: An Efficient Tool kit Leveraging Distributed Bitmap Index for Analysis of Massive Genetic Variation Datasets

The storage and analysis of massive genetic variation datasets in variant call format (VCF) become a great challenge with the rapid growth of genetic variation data in recent years. Traditional single process based tool kits become increasingly inefficient when analyzing massive genetic variation data. While emerging distributed storage technology such as Apache Kudu offers attractive solution, it is demanded to develop distributed storage tool kit for VCF dataset. In this article, we present Variant-Kudu, an efficient genome tool kit for storing and analyzing massive genetic variation datasets. Based on a new distributed scheme, the genetic variation data would be segmented and stored in Kudu on multinode. With this scheme, data can be randomly accessed at low latency and scanned efficiently. Aiming at reducing the queries' execution time, a strategy of distributed bitmap index is proposed and a parallel query method is designed, which expedite analyses of massive genetic variation data. Variant-Kudu is a scalable tool kit to analyze massive genetic variation datasets, and our experiments demonstrate that Variant-Kudu achieves high performance on a multinode cluster.