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Trend analysis in competitive programming using topic modeling

Procedia computer science 2024年 245卷 591-600页

作者： Albert Yulius Ramahalim Christoffer Edbert Karuniawan Eko Setyo Purwanto Muhamad Keenan Ario Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving and mathematics as well as study algorithms and data structures to perform well in CP. This study aims to provide an original way to perform a trend analysis in CP, distinguishing topics frequently used in CP contests. To fulfill our goal, we create topic models based on previous topic modeling works to do natural language processing tasks using Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM). For our dataset, we constructed a corpus from Codeforces blog posts, a popular website for competitive programmers, by extracting its content and user comments. Our results indicate that BTM is powerful enough to do trend analysis in CP. The trend analysis recognized that dynamic programming and complexity analysis have been the most prominent topics for the last ten years. data structures and string algorithms are runners-up that may have potential trends in the future. This study opens up further research on other methods to perform trend analysis using better topic models and corpora.

关键词： competitive programming topic modeling trend analysis

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Statistical analysis and compression of DNA using weighted probability approach and modified run-length encoding

Statistical analysis and compression of DNA using weighted p...

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3rd Indian International Conference on Artificial Intelligence, IICAI 2007

作者： Srinivasa, K.G. Dhar, Sagnik Venugopal, K.R. Patnaik, L.M. Data Mining Laboratory M S Ramaiah Institute of Technology India Department of Computer Science University Visvesvaraya College of Engineering India Microprocessor Application Laboratory Indian Institute of Science Bangalore India

ISBN: (纸本)9780972741224

Rapid advancements in research in the field of DNA sequence discovery has led to a vast range of compression algorithms. The number of bits required for storing four bases of any DNA sequence is two, but efficient algorithms have pushed this limit lower. With the constant decrease in prices of memory and communication channel bandwidth, one often doubts the need of such compression algorithms. The algorithm discussed in this paper hence not only compresses the DNA sequence, but also allows one to generate finite length sequences, which can be used to find approximate pattern matches. DNA sequences are mainly of two types, Repetitive and Non-Repetitive. The compression technique used is meant for the non-repetitive parts of the sequence, where we make use of the fact that a DNA sequence consists of only 4 characters. The algorithm achieves bit/base ratio of 1.3 - 1.4(dependant on the database), but more importantly one of the stages of the algorithm can be used for efficient discovery of approximate patterns. Copyright © 2007 IICAI.

关键词： DNA

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Systematic Literature Review on Decision-Making of Requirement Engineering from Agile Software Development

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Procedia computer science 2019年 157卷 274-281页

作者： Reginald Putra Ghozali Herry Saputra M. Apriadin Nuriawan Suharjito Ditdit Nugeraha Utama Ariadi Nugroho Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This paper presents a systematic literature review of agile software development at decision making method for requirement engineering. Presently, agile software development method is operated to cope with requirements that changes dynamically. This study seeks to find out and discuss what types of method that have been exploited for decision making on managing feasible requirements and challenges of decision making in agile software development. Papers reviewed in this study are published from 2017 to present. Resulting 8 papers that have been identified of presenting decision making methods. Using these papers, 11 methods and 7 challenges of decision making identified. This study contributes a review of requirement management and engineering by providing decision making methods on agile software development and the challenges of decision making for requirement engineering.

关键词： Systematic Literature Review Agile Software Development Decision Making Requirement Engineering

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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia

Time-series Analysis of Correlation between Climatic Paramet...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Joko Pebrianto Trinugroho Digdo Sudigyo Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia.

关键词： Epidemics Temperature distribution Rain Meteorological factors Multimedia systems Time series analysis Weather forecasting

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An Improving Genetic Algorithm with Local Search for Solving Capacitated Vehicle Routing Problem 9

An Improving Genetic Algorithm with Local Search for Solving...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Poonpanit, Maythar Punkong, Narong Ratanavilisagul, Chiabwoot Kosolsombat, Somkiat Department of Computer and Information Sciences Bangkok Thailand Digital Technology for Business Faculty of Management Science Kanchanaburi Thailand Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

ISBN: (纸本)9798350352214

Capacitated Vehicle Routing Problem (CVRP) is the vehicle routing problem in which vehicles with limited space have to drop off items at different places. This item consists of certain amounts, weight or volume, and the vehicles can carry a limited load. Many researchers try to solve the CVRP by the meta-heuristic algorithms. The Genetic Algorithm (GA) is a classical method to solve the CVRP. To enhance performance of GA for solving CVRP, the recently proposed GA combines simulated annealing (SA) or a hybrid genetic and simulated annealing algorithm (HGSA). However, both GA and SA enhance population diversity and lead to over distribution of population. If the distribution is too much population, it prevents convergence of population to search for answers. It affects the search that obtains a bad answer. This paper proposed an Improving Genetic Algorithm with Local Search or GALS. The proposed method uses GA for decreasing or solving trapping into local optimum from searching and improving solution by local search techniques. The proposed method does not interrupt the population convergence, so it obtains a good answer. Experiments conducted on twenty-seven maps dataset have shown that the proposed method achieves encouraging performance compared to a hybrid genetic and simulated annealing algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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Analysis of delay factors on drilling projects on PT China Oilfields Services Limited 2

Analysis of delay factors on drilling projects on PT China O...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Sardjono, W. Perdana, W.G. Ansori, R. Utama, D.N. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management BINA NUSANTARA University Jakarta11480 Indonesia Post Graduate Program School of Environmental Science University of Indonesia Jakarta Indonesia Graduate Program Master of Business Administration Faculty of Economic and Business University of Gadjahmada Yogyakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

In carrying out drilling projects at PT China Oilfields Services Limited ( COSL) Indo, especially Project 1 to Project 4, there were a mismatch between the initial plan of the project and the actualisation in the field because there were inhibiting factors in implementing the project. The purpose of the research was to look the factors that caused for the additional time in well drilling project at PT. COSL Indo, to build a relationship model of these factors, and formulate a strategy for the company to be able to overcome the occurrence of additional time in the future project. data analysis was conducted by using a factor analysis method and the location of the research was carried out in just one company with 102 valid respondents. The results of the research indicate that there are four factors that influence the time gap, namely Project Management Activities, Risk Analysis and Procurement, Manage Stairs, and Project Planning Development. By knowing the causes of the delay, companies can find the best solutions for future learning and the impact on environmental, social and economic problems can also be anticipated so that sustainable development occurs in the context of petroleum management. © Published under licence by IOP Publishing Ltd.

关键词： Risk analysis

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Mitochondrial Haplogroup Assignment for High-Throughput Sequencing data from Single Individual and Mixed DNA Samples 1

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16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Alqahtani, Fahad Măndoiu, Ion I. Computer Science and Engineering Department University of Connecticut StorrsCT United States National Center for Artificial Intelligence and Big Data Technology King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

The inference of mitochondrial haplogroups is an important step in forensic analysis of DNA samples collected at a crime scene. In this paper we introduced efficient inference algorithms based on Jaccard similarity between variants called from high-throughput sequencing data of such DNA samples and mutations collected in public databases such as PhyloTree. Experimental results on real and simulated datasets show that our mutation analysis methods have accuracy comparable to that of state-of-the-art methods based on haplogroup frequency estimation for both single-individual samples and two-individual mixtures, with a much lower running time. © 2020, Springer Nature Switzerland AG.

关键词： Inference engines

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A fine-tuned vision transformer-based on limited dataset for facial expression recognition

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Procedia computer science 2024年 245卷 574-582页

作者： Rio Febrian Ronald Richie Huang Nicholas Setiono Dimas Ramdhan Andry Chowanda Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

With the increase of interest in Facial Expression Recognition (FER) in the past few decades. Several challenges surfaced with the invention of many different FER models which are often based on Convolution Neural Network (CNN) architectures. Recently, an attention-based transformer model has been presented to address FER. One of the major issues with Transformers is the need for a large data quantity for training. Therefore, in this paper, we propose to learn how to fine-tune a vision transformer-based (ViT) model using a limited dataset. We will be using the JAFFE dataset, which consists of only 213 images containing seven different emotions. The proposed method is evaluated using several fine-tuning methods, such as adding dropout, data augmentation, and layer freezing. We compared the models implemented with 5% dropout regularization, augmented dataset (up to 5000 images), and freezing the initial model's layers, fine-tuning around a fourth of the last layers. The best model was achieved by fine-tuning ViT L-16 with 96.06% accuracy, trained with 5% dropout in the augmented dataset, and freezing the initial 21st layers. We also compared our model to the other previous work model and the results showed that our model reached the state-of-the art for the JAFFE dataset.

关键词： Facial Expression Recognition Vision Transformer Attention Deep Learning

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Application of Deep Neural Networks for Weed Detection and Classification 2

Application of Deep Neural Networks for Weed Detection and C...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ganesan, R. Manjula, G. Chandan, Madhavarapu Thamarai, I. Rajavelu, S. Bhatt, Upendra Mohan Sarkar, Swagata Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Thiruvallur601206 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522302 India Panimalar Engineering College Department of Computer Science and Engineering Chennai city campus Tamil Nadu 600029 India Department of Physics Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department OfArtificial Intelligence and Data Science Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechanical or chemical methods can be applied appropriately to contain the pests. This research presents the application and comparison of machine learning techniques, with the aim of automating the classification of images for agricultural challenges, such as the detection of defective seeds, and weeds and the category between these and native vegetation, while finally, the architecture of a convolutional neural network is presented. As a differential, the network's self-learning ability stands out, as images are captured in less than ideal conditions at varying heights and lighting levels in most cases. This research is expected to provide important information on artificial intelligence techniques that can be used in the classification of weed images, a factor that will contribute to the forestry and agricultural sector. © 2023 IEEE.

关键词： Classification (of information)

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BERT base model for toxic comment analysis on Indonesian social media

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Procedia computer science 2023年 216卷 714-721页

作者： Ghinaa Zain Nabiilah Simeon Yuda Prasetyo Zahra Nabila Izdihar Abba Suganda Girsang Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897.

关键词： Toxic Comment MBERT IndoBERT IndoRoBERTa Small Indonesian social media

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