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

1971 ◽  
Author(s):  
David Cole Nicholas
Keyword(s):  
Source Codes

scholarly journals Online Judging Platform Utilizing Dynamic Plagiarism Detection Facilities

Computers ◽  
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.

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

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.

Descriptive Geometry Problems Visualization by Wolfram Mathematica

2021 ◽  
Vol 8 (4) ◽  
pp. 74-84
Author(s):  
S. Ignat'ev ◽  
A. Folomkin ◽  
E. Muratbakeev
Keyword(s):  
Teaching Practice ◽  
Educational Process ◽  
Ability To Work ◽  
Source Codes ◽  
Traditional Course ◽  
Wolfram Mathematica ◽  
Educational Institute ◽  
Software Product ◽  
Space Construction ◽  
High Educational

The possibilities of Wolfram Mathematica (WM), which is a package of symbolic mathematics, are endless. In this paper are investigated the possibilities of WM software product contextualization in the process of geometric-graphic teaching of students; is considered the experience for using a set of WM computational algorithms in teaching practice. The source codes and output data of our own WOLFRAM projects are offered, which are illustrative, in the form of interactive graphs, visualization of the solution for one of DG course’s basic problems on construction of points A, B, C and D, given by coordinates in space; construction a plane of general position passing through points A, B and C; determining positions of these points and the constructed plane in space; and also on determining the point D belonging to this plane. Have been presented examples of several of the existing WOLFRAM projects in the form of interactive graphs used in teaching. The study of applied issues and the ability to translate a professional problem into mathematical language, working in WM, provides a student with an opportunity to assimilate the necessary competencies. Interactive visualization of solving DG problems, which can be carried out using WM, together with traditional problem solving, enriches the content of geometric education, introduces new opportunities in organization of the educational process, allows stimulate, maintain and increase students' interest in studying the discipline and further research in the area of DG. However, the ability to work in WM cannot replace the fundamental knowledge obtained by students of a technical high educational institute from the traditional course, but only complements the DG course by WM acquaintance.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

Cooperative Sequence Clustering and Decoding for DNA Storage System with Fountain Codes

2021 ◽  
Author(s):  
Jaeho Jeong ◽  
Seong-Joon Park ◽  
Jae-Won Kim ◽  
Jong-Seon No ◽  
Ha Hyeon Jeon ◽  
...  
Keyword(s):  
Hamming Distance ◽  
Storage Systems ◽  
Storage System ◽  
Data Retrieval ◽  
Illumina Miseq ◽  
Error Correcting Codes ◽  
Read Length ◽  
Sequence Coverage ◽  
Source Codes ◽  
Dna Storage

Abstract Motivation In DNA storage systems, there are tradeoffs between writing and reading costs. Increasing the code rate of error-correcting codes may save writing cost, but it will need more sequence reads for data retrieval. There is potentially a way to improve sequencing and decoding processes in such a way that the reading cost induced by this tradeoff is reduced without increasing the writing cost. In past researches, clustering, alignment, and decoding processes were considered as separate stages but we believe that using the information from all these processes together may improve decoding performance. Actual experiments of DNA synthesis and sequencing should be performed because simulations cannot be relied on to cover all error possibilities in practical circumstances. Results For DNA storage systems using fountain code and Reed-Solomon (RS) code, we introduce several techniques to improve the decoding performance. We designed the decoding process focusing on the cooperation of key components: Hamming-distance based clustering, discarding of abnormal sequence reads, RS error correction as well as detection, and quality score-based ordering of sequences. We synthesized 513.6KB data into DNA oligo pools and sequenced this data successfully with Illumina MiSeq instrument. Compared to Erlich’s research, the proposed decoding method additionally incorporates sequence reads with minor errors which had been discarded before, and thuswas able to make use of 10.6–11.9% more sequence reads from the same sequencing environment, this resulted in 6.5–8.9% reduction in the reading cost. Channel characteristics including sequence coverage and read-length distributions are provided as well. Availability The raw data files and the source codes of our experiments are available at: https://github.com/jhjeong0702/dna-storage.

scholarly journals Seq-SymRF: a random forest model predicts potential miRNA-disease associations based on information of sequences and clinical symptoms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinlong Li ◽  
Xingyu Chen ◽  
Qixing Huang ◽  
Yang Wang ◽  
Yun Xie ◽  
...  
Keyword(s):  
Random Forest ◽  
Breast Neoplasms ◽  
Clinical Symptoms ◽  
Characteristic Curve ◽  
Vital Role ◽  
Sequence Information ◽  
Source Codes ◽  
Drug Identification ◽  
Disease Associations ◽  
Precision Recall Curve

Abstract Increasing evidence indicates that miRNAs play a vital role in biological processes and are closely related to various human diseases. Research on miRNA-disease associations is helpful not only for disease prevention, diagnosis and treatment, but also for new drug identification and lead compound discovery. A novel sequence- and symptom-based random forest algorithm model (Seq-SymRF) was developed to identify potential associations between miRNA and disease. Features derived from sequence information and clinical symptoms were utilized to characterize miRNA and disease, respectively. Moreover, the clustering method by calculating the Euclidean distance was adopted to construct reliable negative samples. Based on the fivefold cross-validation, Seq-SymRF achieved the accuracy of 98.00%, specificity of 99.43%, sensitivity of 96.58%, precision of 99.40% and Matthews correlation coefficient of 0.9604, respectively. The areas under the receiver operating characteristic curve and precision recall curve were 0.9967 and 0.9975, respectively. Additionally, case studies were implemented with leukemia, breast neoplasms and hsa-mir-21. Most of the top-25 predicted disease-related miRNAs (19/25 for leukemia; 20/25 for breast neoplasms) and 15 of top-25 predicted miRNA-related diseases were verified by literature and dbDEMC database. It is anticipated that Seq-SymRF could be regarded as a powerful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Seq-SymRF.

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