scholarly journals Complexitor: An Educational Tool for Learning Algorithm Time Complexity in Practical Manner

2017 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Elvina Elvina ◽  
Oscar Karnalim
Keyword(s):  
Time Complexity ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Source Code ◽  
Test Cases ◽  
Educational Tool ◽  
Execution Sequence ◽  
Algorithm Implementation

Based on the informal survey, learning algorithm time complexity in a theoretical manner can be rather difficult to understand. Therefore, this research proposed Complexitor, an educational tool for learning algorithm time complexity in a practical manner. Students could learn how to determine algorithm time complexity through the actual execution of algorithm implementation. They were only required to provide algorithm implementation (i.e. source code written on a particularprogramming language) and test cases to learn time complexity. After input was given, Complexitor generated execution sequence based on test cases and determine its time complexity through Pearson correlation. An algorithm time complexity with the highest correlation value toward execution sequence was assigned as its result. Based on the evaluation, it can be concluded this mechanism is quite effective for determining time complexity as long as the distribution of given input set is balanced.

scholarly journals Introducing a Practical Educational Tool for Correlating Algorithm Time Complexity with Real Program Execution

2018 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
Gisela Kurniawati ◽  
Oscar Karnalim
Keyword(s):  
Time Complexity ◽  
Learning Algorithm ◽  
Promising Result ◽  
Pearson Correlation ◽  
Source Code ◽  
Program Execution ◽  
Educational Tool ◽  
Real Environment ◽  
Informal Observation ◽  
Three Components

Algorithm time complexity is an important topic to be learned for programmer; it could define whether an algorithm is practical to be used on real environment or not. However, learning such material is not a trivial task. Based on our informal observation regarding students’ test, most of them could not correlate Big-Oh equation to real program execution. This paper proposes JCEL, an educational tool that acts as a supportive tool for learning algorithm time complexity. Using this tool, user could learn how to correlate Big-Oh equation with real program execution by providing three components: a Java source code, source code input set, and time complexity equations. According to our evaluation, students feel that JCEL is helpful for learning the correlation between Big-Oh equation and real program execution. Further, the use of Pearson correlation in JCEL shows a promising result.

scholarly journals Interfacing Complexitor: An Empirical-based Educational Tool for Learning Time Complexity

2017 ◽  
Author(s):  
Oscar Karnalim ◽  
Elvina
Keyword(s):  
Undergraduate Students ◽  
Time Complexity ◽  
Learning Algorithm ◽  
Qualitative Evaluation ◽  
Empirical Approach ◽  
Educational Tool ◽  
Learning Time ◽  
Input And Output ◽  
Standard Application ◽  
Algorithm Implementation

Since learning algorithm time complexity in theoretical manner is rather difficult, an educational tool, which is named Complexitor, tried to incorporate empirical approach for teaching such material. Students can learn how to determine a time complexity for given algorithm based on the actual execution. Students are only required to provide algorithm implementation and input set. This paper extends the work of Complexitor by providing a stable interface and qualitative evaluation. The interface is developed based on input and output characteristic of Complexitor whereas the evaluation is represented as a survey toward 20 undergraduate students. Based on student’s perspective, Complexitor features, at some extent, may help students to learn algorithm time complexity. Moreover, they also state that our tool has fulfilled standard application aspects. In other words, our tool is eligible to be used for learning algorithm time complexity.

The smallest extraction problem

2021 ◽  
Vol 14 (11) ◽  
pp. 2445-2458
Author(s):  
Valerio Cetorelli ◽  
Paolo Atzeni ◽  
Valter Crescenzi ◽  
Franco Milicchio
Keyword(s):  
Unsupervised Learning ◽  
Optimization Problem ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Data Extraction ◽  
Source Code ◽  
Web Data ◽  
Web Data Extraction ◽  
New Family ◽  
Context Free

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.

scholarly journals Using Portfolios: Integrating Learning and Promoting for Social Work Students

10.18060/59 ◽  
2004 ◽  
Vol 5 (1) ◽  
pp. 105-123 ◽  
Author(s):  
Mona Schatz
Keyword(s):  
Social Work ◽  
Graduate Students ◽  
Pearson Correlation ◽  
Social Work Students ◽  
Educational Tool ◽  
Second Year ◽  
Graduate Social Work

Portfolios are a valuable educational tool to aid in the integrative experience for graduate social work students. Forty-one graduate students were asked to evaluate their portfolio experience. A Pearson correlation shows that graduate students find the experience of developing a portfolio to be reflective of their second year MSW program (r=.511; p

Adapting Test-Driven Development to Build Robust Web Services

2013 ◽  
pp. 218-237
Author(s):  
Nuno Laranjeiro ◽  
Marco Vieira
Keyword(s):  
Web Services ◽  
Software Development ◽  
Source Code ◽  
Test Cases ◽  
Services Research ◽  
Test Driven Development ◽  
Robustness Testing ◽  
Open Source Code ◽  
Testing Approach ◽  
Software Services

Web services are increasingly being used in business critical environments as a mean to provide a service or integrate distinct software services. Research indicates that, in many cases, services are deployed with robustness issues (i.e., displaying unexpected behaviors when in presence of invalid input conditions). Recently, Test-Driven Development (TDD) emerged as software development technique based on test cases that are defined before development, as a way to validate functionalities. However, programmers typically disregard the verification of limit conditions, such as the ones targeted by robustness testing. Moreover, in TDD, tests are created before developing the functionality, conflicting with the typical robustness testing approach. This chapter discusses the integration of robustness testing in TDD for improving the robustness of web services during development. The authors requested three programmers to create a set of services based on open-source code and to implement different versions of the services specified by TPC-App, using both TDD and the approach presented in this chapter. Results indicate that TDD with robustness testing is an effective way to create more robust services.

scholarly journals Scoring functions for drug-effect similarity

2020 ◽  
Author(s):  
Stephan Struckmann ◽  
Mathias Ernst ◽  
Sarah Fischer ◽  
Nancy Mah ◽  
Georg Fuellen ◽  
...  
Keyword(s):  
Cell Line ◽  
Drug Effect ◽  
Pearson Correlation ◽  
Source Code ◽  
New Drugs ◽  
Supplementary Information ◽  
Disease Genes ◽  
Scoring Functions ◽  
Profile Changes

Abstract Motivation The difficulty to find new drugs and bring them to the market has led to an increased interest to find new applications for known compounds. Biological samples from many disease contexts have been extensively profiled by transcriptomics, and, intuitively, this motivates to search for compounds with a reversing effect on the expression of characteristic disease genes. However, disease effects may be cell line-specific and also depend on other factors, such as genetics and environment. Transcription profile changes between healthy and diseased cells relate in complex ways to profile changes gathered from cell lines upon stimulation with a drug. Despite these differences, we expect that there will be some similarity in the gene regulatory networks at play in both situations. The challenge is to match transcriptomes for both diseases and drugs alike, even though the exact molecular pathology/pharmacogenomics may not be known. Results We substitute the challenge to match a drug effect to a disease effect with the challenge to match a drug effect to the effect of the same drug at another concentration or in another cell line. This is welldefined, reproducible in vitro and in silico and extendable with external data. Based on the Connectivity Map (CMap) dataset, we combined 26 different similarity scores with six different heuristics to reduce the number of genes in the model. Such gene filters may also utilize external knowledge e.g. from biological networks. We found that no similarity score always outperforms all others for all drugs, but the Pearson correlation finds the same drug with the highest reliability. Results are improved by filtering for highly expressed genes and to a lesser degree for genes with large fold changes. Also a network-based reduction of contributing transcripts was beneficial, here implemented by the FocusHeuristics. We found no drop in prediction accuracy when reducing the whole transcriptome to the set of 1000 landmark genes of the CMap’s successor project Library of Integrated Network-based Cellular Signatures. All source code to re-analyze and extend the CMap data, the source code of heuristics, filters and their evaluation are available to propel the development of new methods for drug repurposing. Availability https://bitbucket.org/ibima/moldrugeffectsdb Contact [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

scholarly journals From source code to test cases: A comprehensive benchmark for resource leak detection in Android apps

2018 ◽  
Vol 49 (3) ◽  
pp. 540-548 ◽  
Author(s):  
Oliviero Riganelli ◽  
Daniela Micucci ◽  
Leonardo Mariani
Keyword(s):  
Source Code ◽  
Leak Detection ◽  
Test Cases ◽  
Android Apps ◽  
Resource Leak

scholarly journals Investigating Serum and Tissue Expression Identified a Cytokine/Chemokine Signature as a Highly Effective Melanoma Marker

Cancers ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3680
Author(s):  
Marco Cesati ◽  
Francesca Scatozza ◽  
Daniela D’Arcangelo ◽  
Gian Carlo Antonini-Cappellini ◽  
Stefania Rossi ◽  
...  
Keyword(s):  
Single Molecule ◽  
Learning Algorithm ◽  
Profile Analysis ◽  
Pearson Correlation ◽  
Tissue Expression ◽  
Support Vector ◽  
Expression Data ◽  
Serum Samples ◽  
Tissue Samples ◽  
Mortality And Morbidity

The identification of reliable and quantitative melanoma biomarkers may help an early diagnosis and may directly affect melanoma mortality and morbidity. The aim of the present study was to identify effective biomarkers by investigating the expression of 27 cytokines/chemokines in melanoma compared to healthy controls, both in serum and in tissue samples. Serum samples were from 232 patients recruited at the IDI-IRCCS hospital. Expression was quantified by xMAP technology, on 27 cytokines/chemokines, compared to the control sera. RNA expression data of the same 27 molecules were obtained from 511 melanoma- and healthy-tissue samples, from the GENT2 database. Statistical analysis involved a 3-step approach: analysis of the single-molecules by Mann–Whitney analysis; analysis of paired-molecules by Pearson correlation; and profile analysis by the machine learning algorithm Support Vector Machine (SVM). Single-molecule analysis of serum expression identified IL-1b, IL-6, IP-10, PDGF-BB, and RANTES differently expressed in melanoma (p < 0.05). Expression of IL-8, GM-CSF, MCP-1, and TNF-α was found to be significantly correlated with Breslow thickness. Eotaxin and MCP-1 were found differentially expressed in male vs. female patients. Tissue expression analysis identified very effective marker/predictor genes, namely, IL-1Ra, IL-7, MIP-1a, and MIP-1b, with individual AUC values of 0.88, 0.86, 0.93, 0.87, respectively. SVM analysis of the tissue expression data identified the combination of these four molecules as the most effective signature to discriminate melanoma patients (AUC = 0.98). Validation, using the GEPIA2 database on an additional 1019 independent samples, fully confirmed these observations. The present study demonstrates, for the first time, that the IL-1Ra, IL-7, MIP-1a, and MIP-1b gene signature discriminates melanoma from control tissues with extremely high efficacy. We therefore propose this 4-molecule combination as an effective melanoma marker.

scholarly journals MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Yang-Ming Lin ◽  
Ching-Tai Chen ◽  
Jia-Ming Chang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Tandem Mass ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Library Search

Abstract Background Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search. Results We propose MS2CNN, a non-linear regression model based on deep convolutional neural networks, a deep learning algorithm. The features for our model are amino acid composition, predicted secondary structure, and physical-chemical features such as isoelectric point, aromaticity, helicity, hydrophobicity, and basicity. MS2CNN was trained with five-fold cross validation on a three-way data split on the large-scale human HCD MS2 dataset of Orbitrap LC-MS/MS downloaded from the National Institute of Standards and Technology. It was then evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiments. On average, our model shows better cosine similarity and Pearson correlation coefficient (0.690 and 0.632) than MS2PIP (0.647 and 0.601) and is comparable with pDeep (0.692 and 0.642). Notably, for the more complex MS2 spectra of 3+ peptides, MS2PIP is significantly better than both MS2PIP and pDeep. Conclusions We showed that MS2CNN outperforms MS2PIP for 2+ and 3+ peptides and pDeep for 3+ peptides. This implies that MS2CNN, the proposed convolutional neural network model, generates highly accurate MS2 spectra for LC-MS/MS experiments using Orbitrap machines, which can be of great help in protein and peptide identifications. The results suggest that incorporating more data for deep learning model may improve performance.

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