Automated elicitation of functional dependencies from source codes of database transactions

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

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Source codes for the output of a delta modulator operating on speech

10.31274/rtd-180813-929 ◽

1971 ◽

Author(s):

David Cole Nicholas

Keyword(s):

Source Codes

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Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽

10.3390/computers10040047 ◽

2021 ◽

Vol 10 (4) ◽

pp. 47

Author(s):

Fariha Iffath ◽

A. S. M. Kayes ◽

Md. Tahsin Rahman ◽

Jannatul Ferdows ◽

Mohammad Shamsul Arefin ◽

...

Keyword(s):

Source Code ◽

Large Data ◽

Large Data Sets ◽

Detection Technique ◽

Data Sets ◽

Plagiarism Detection ◽

Source Codes ◽

Efficient Detection ◽

Mathematical Problems ◽

Automatic Scoring

A programming contest generally involves the host presenting a set of logical and mathematical problems to the contestants. The contestants are required to write computer programs that are capable of solving these problems. An online judge system is used to automate the judging procedure of the programs that are submitted by the users. Online judges are systems designed for the reliable evaluation of the source codes submitted by the users. Traditional online judging platforms are not ideally suitable for programming labs, as they do not support partial scoring and efficient detection of plagiarized codes. When considering this fact, in this paper, we present an online judging framework that is capable of automatic scoring of codes by detecting plagiarized contents and the level of accuracy of codes efficiently. Our system performs the detection of plagiarism by detecting fingerprints of programs and using the fingerprints to compare them instead of using the whole file. We used winnowing to select fingerprints among k-gram hash values of a source code, which was generated by the Rabin–Karp Algorithm. The proposed system is compared with the existing online judging platforms to show the superiority in terms of time efficiency, correctness, and feature availability. In addition, we evaluated our system by using large data sets and comparing the run time with MOSS, which is the widely used plagiarism detection technique.

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CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies

Genome Biology ◽

10.1186/s13059-021-02266-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rebecca Davies ◽

Ling Liu ◽

Sheng Taotao ◽

Natasha Tuano ◽

Richa Chaturvedi ◽

...

Keyword(s):

Gastric Cancer ◽

Functional Dependencies ◽

Loss Of Function ◽

Genetic Screens ◽

Transcript Isoforms ◽

Specific Loss ◽

Current Loss ◽

Specific Transcript ◽

Multiple Promoters ◽

Poor Patient

Abstract Introduction Genes contain multiple promoters that can drive the expression of various transcript isoforms. Although transcript isoforms from the same gene could have diverse and non-overlapping functions, current loss-of-function methodologies are not able to differentiate between isoform-specific phenotypes. Results Here, we show that CRISPR interference (CRISPRi) can be adopted for targeting specific promoters within a gene, enabling isoform-specific loss-of-function genetic screens. We use this strategy to test functional dependencies of 820 transcript isoforms that are gained in gastric cancer (GC). We identify a subset of GC-gained transcript isoform dependencies, and of these, we validate CIT kinase as a novel GC dependency. We further show that some genes express isoforms with opposite functions. Specifically, we find that the tumour suppressor ZFHX3 expresses an isoform that has a paradoxical oncogenic role that correlates with poor patient outcome. Conclusions Our work finds isoform-specific phenotypes that would not be identified using current loss-of-function approaches that are not designed to target specific transcript isoforms.

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Algorithms for the discovery of embedded functional dependencies

The VLDB Journal ◽

10.1007/s00778-021-00684-3 ◽

2021 ◽

Author(s):

Ziheng Wei ◽

Sven Hartmann ◽

Sebastian Link

Keyword(s):

Functional Dependencies

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Simulation of Diffusion Processes in Chemical and Thermal Processing of Machine Parts

Processes ◽

10.3390/pr9040698 ◽

2021 ◽

Vol 9 (4) ◽

pp. 698

Author(s):

Kateryna Kostyk ◽

Michal Hatala ◽

Viktoriia Kostyk ◽

Vitalii Ivanov ◽

Ivan Pavlenko ◽

...

Keyword(s):

Mathematical Modeling ◽

Temperature Distribution ◽

Thermal Treatment ◽

Diffusion Processes ◽

Nitrogen Concentration ◽

The Body ◽

Functional Dependencies ◽

Technological Parameters ◽

Gas Nitriding ◽

Temperature State

To solve a number of technological issues, it is advisable to use mathematical modeling, which will allow us to obtain the dependences of the influence of the technological parameters of chemical and thermal treatment processes on forming the depth of the diffusion layers of steels and alloys. The paper presents mathematical modeling of diffusion processes based on the existing chemical and thermal treatment of steel parts. Mathematical modeling is considered on the example of 38Cr2MoAl steel after gas nitriding. The gas nitriding technology was carried out at different temperatures for a duration of 20, 50, and 80 h in the SSHAM-12.12/7 electric furnace. When modeling the diffusion processes of surface hardening of parts in general, providing a specifically given distribution of nitrogen concentration over the diffusion layer’s depth from the product’s surface was solved. The model of the diffusion stage is used under the following assumptions: The diffusion coefficient of the saturating element primarily depends on temperature changes; the metal surface is instantly saturated to equilibrium concentrations with the saturating atmosphere; the surface layer and the entire product are heated unevenly, that is, the product temperature is a function of time and coordinates. Having satisfied the limit, initial, and boundary conditions, the temperature distribution equations over the diffusion layer’s depth were obtained. The final determination of the temperature was solved by an iterative method. Mathematical modeling allowed us to get functional dependencies for calculating the temperature distribution over the depth of the layer and studying the influence of various factors on the body’s temperature state of the body.

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Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans

Scientific Reports ◽

10.1038/s41598-021-93658-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Rohit Kundu ◽

Hritam Basak ◽

Pawan Kumar Singh ◽

Ali Ahmadian ◽

Massimiliano Ferrara ◽

...

Keyword(s):

Ct Scan ◽

Detection System ◽

Network Models ◽

Test Cases ◽

Gompertz Function ◽

Neural Network Models ◽

Source Codes ◽

Ensemble Strategy ◽

The World ◽

Long Time

AbstractCOVID-19 has crippled the world’s healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are being tested and supplied around the world, it will take a long time to reach every human being, more so with new variants of the virus emerging, enforcing a lockdown-like situation on parts of the world. Thus, there is a dire need for early and accurate detection of COVID-19 to prevent the spread of the disease, even more. The current gold-standard RT-PCR test is only 71% sensitive and is a laborious test to perform, leading to the incapability of conducting the population-wide screening. To this end, in this paper, we propose an automated COVID-19 detection system that uses CT-scan images of the lungs for classifying the same into COVID and Non-COVID cases. The proposed method applies an ensemble strategy that generates fuzzy ranks of the base classification models using the Gompertz function and fuses the decision scores of the base models adaptively to make the final predictions on the test cases. Three transfer learning-based convolutional neural network models are used, namely VGG-11, Wide ResNet-50-2, and Inception v3, to generate the decision scores to be fused by the proposed ensemble model. The framework has been evaluated on two publicly available chest CT scan datasets achieving state-of-the-art performance, justifying the reliability of the model. The relevant source codes related to the present work is available in: GitHub.

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