检索结果-内蒙古大学图书馆

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

引用

computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

来源：评论

学校读者我要写书评

暂无评论

The Use of Long Short-Term Memory Models to Estimate Soybean Pricing: A Regional Climate Data Evaluation From Brazil

引用

IEEE Access 2025年 13卷 101869-101884页

作者： Oliva, Jefferson Tales Martelli, Otávio Pigozzo de Castro Barbosa, Marco Antônio Molin, Viviane Dal Casanova, Dalcimar Teixeira, Marcelo Federal University of Technology–Paraná Graduate Program in Electrical and Computer Engineering Pato Branco85503-390 Brazil Federal University of Technology–Paraná Department of Academic Informatics Pato Branco85503-390 Brazil Federal University of Technology–Paraná Graduate Program in Production and Systems Engineering Pato Branco85503-390 Brazil

This work uses machine learning methods to analyze the influence of the Brazilian climate on international soybean price variability. For this purpose, climatic data, historical series of the extended national consumer price index, dollar price historical series, soybean harvest historical series, and international soybean price historical series are collected from different data sources. The data is then preprocessed and grouped by state. Afterward, data from the five most productive Brazilian states, Mato Grosso (MT), Mato Grosso do Sul (MS), Goiás (GO), Paraná (PR), and Rio Grande do Sul (RS), which together correspond to 74.9% of total production, are chosen. Then, predictive models were built using the long short-term memory (LSTM) method, which is applied to each state dataset considering two different settings: with and without climatic data. Thus, two models were built for each Brazilian state, totaling ten predictors. These models are evaluated using 10-fold sequential split cross-validation, whose predictive results are used for performance comparison through statistical hypothesis testing. In the experimental evaluation, when trained with climatic data, the PR model achieved the lowest average values of root mean squared error (RMSE) and mean absolute error (MAE), which are 20.90 and 16.72, respectively. Regarding predictors trained without climate data, the RS and PR obtained the lowest averaged values of RMSE (20.84) and MAE (16.81), respectively. After performing statistical hypothesis tests, no significant differences were found between the models, stating, with certainty 95%, that the Brazilian climatic data did not influence soybean prices in our study. © 2013 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses

AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of O...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lu, Xiaotian Li, Jiyi Takeuchi, Koh Kashima, Hisashi Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (纸本)9798891761681

Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP). Answers to open-ended questions are highly diverse and difficult to quantify, and cannot be simply evaluated as correct or incorrect, unlike close-ended questions with definitive answers. While large language models (LLMs) have demonstrated strong capabilities across various tasks, they exhibit relatively weaker performance in evaluating answers to open-ended questions. In this study, we propose a method that leverages LLMs and the analytic hierarchy process (AHP) to assess answers to open-ended questions. We utilized LLMs to generate multiple evaluation criteria for a question. Subsequently, answers were subjected to pairwise comparisons under each criterion with LLMs, and scores for each answer were calculated in the AHP. We conducted experiments on four datasets using both ChatGPT-3.5-turbo and GPT-4. Our results indicate that our approach more closely aligns with human judgment compared to the four baselines. Additionally, we explored the impact of the number of criteria, variations in models, and differences in datasets on the results. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Metrics and Red Teaming in Cyber Resilience and Effectiveness: A Systematic Literature Review 29

Metrics and Red Teaming in Cyber Resilience and Effectivenes...

引用

29th International Conference on Telecommunications, ICT 2023

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350361100

Rising cyber risks have compelled organizations to adopt better cyber-protection measures. This study focused on discovering crucial security metrics and assessing the function of red teaming in enhancing cybersecurity defenses against novel cyber hazards. The PRISMA standard considered nine core research works issued between 2014 and 2023. The inclusion of red teaming best practices can significantly enhance cybersecurity architecture. Accurate simulations of cyber threats during red teaming exercises help identify vulnerabilities, and actively embracing red teaming can amplify an organization's capacity to repel future cyber assaults. Researchers and practitioners can utilize the study's insights to pioneer novel security solutions. Combining red teaming methodologies with relevant metrics is essential for enhancing cybersecurity posture. The study's discoveries grant companies a priceless benefit in navigating the rapidly changing cyber threat environment and reinforcing their cyber protection mechanisms. © 2023 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Visualization and Simulation of Any MPU Architecture Design with Meta-Instruction 23

Visualization and Simulation of Any MPU Architecture Design ...

引用

23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Morimoto, Tomoyuki Wang, Ziming Luo, Jiahui Ogawa, Sota Tsutsumi, Toshiyuki Meiji University Department of Computer Science School of Science and Technology Kanagawa Japan Graduate School of Science and Technology Meiji University Computer Science Program Kanagawa Japan

ISBN: (纸本)9798350353952

Our conventional architecture design tool converted instructions of microprocessor (MPU) into meta-instructions with both semantic and functional expressions, and it displayed a circuit diagram of the meta-instruction. The tool was able to perform dynamic simulation only on an educational processor called COMET. However, the meta-instruction must be able to express the arbitrary instruction of at least different significant existing MPUs in order to design arbitrary MPUs. Furthermore, it should be possible to visualize all instructions and then execute them. Therefore, we have improved the meta-instruction so that the architecture design tool applies to major MPUs: MIPS, ARM, and RISC-V in addition to COMET. The improved architecture design tool visualizes and simulates the circuit diagram of the improved meta-instruction. The result shows the possibility of the meta-instruction-based architecture design tool being able to design any MPU. © 2024 IEEE.

关键词： Microprocessor chips

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

引用

HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

来源：评论

学校读者我要写书评

暂无评论

Rice Commodity Supply Chain Issues in Indonesia: Exploring Blockchain Technology as an Alternative Solution 5

Rice Commodity Supply Chain Issues in Indonesia: Exploring B...

引用

5th Technology Innovation Management and Engineering science International Conference, TIMES-iCON 2024

作者： Taufiq, Rohmat Warnars, Harco Leslie Hendric Spits Soeparno, Haryono Oktavia, Tanty Computer Science Department - BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System Management Department BINUS Graduate Program Master of IS Management Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350362602

—Agriculture has long been recognized as pivotal to economic development, offering avenues to alleviate poverty, foster prosperity, and sustainably meet the food demands of a burgeoning global population projected to reach 9.7 billion by 2050. However, persistent inefficiencies within agricultural supply chains, particularly in sectors like rice production, pose formidable challenges. Addressing these inefficiencies is imperative to avert a surge in hunger and malnutrition. Failure to do so could condemn 815 million people to hunger and leave 3 out of 10 individuals malnourished. Thus, investigating solutions to enhance the resilience of rice supply chains is paramount. This research endeavors to identify and analyze challenges inherent in tracing the rice supply chain, offering a conceptual model proposal as a potential remedy. Employing a methodology centered on scholarly discourse and extensive literature review, our study reveals various impediments facing rice commodity supply chains in Indonesia. Furthermore, this study leverages blockchain technology, specifically the Sawtooth Hyperledger platform, to streamline the tracing process for rice commodities and rationalize the supply chain by reducing intermediary actors. From the proposed model, there is great hope that it can provide implications for the rice supply chain to run more efficiently. ©2024 IEEE.

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Fasttext Using Bayesian Optimization for Movie Review Sentiment Analysis 1

Fine-Tuning Fasttext Using Bayesian Optimization for Movie R...

引用

1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Halim, Rico Girsang, Abba Suganda Riftiarrasyid, Mohammad Faisal Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350360554

The advancements in technology during the 20th century resulted in the onset of the digital computer era. This study investigates the relative importance of earlier algorithms in comparison to more recent ones. Various research projects suggest improving vectorization by combining conventional and modern techniques, while others suggest optimizing it through algorithmic methods. This study primarily focuses on employing Bayesian optimization to optimize hyperparameters, hence improving the performance evaluation of TF-IDF FastText sentiment classification models. This study presented four models: the initial model utilized FastText with a preprocessing step that involved removing stopwords, the second model employed Fast-Text without any optimization using Bayesian optimization, the third model utilized FastText with optimization using Bayesian optimization, and the fourth model combined FastText with TF-IDF and was further optimized using Bayesian optimization. The Support Vector Machine (SVM) technique will be employed to evaluate all of the models. The findings suggest that the model's performance stays unchanged when stopwords are eliminated (Precision, Recall, F1: 0.9007). The model demonstrates a slight enhancement through the utilization of Bayesian optimization, resulting in a 0.02% increase, reaching a final accuracy of 0.9019%. Bayesian optimization is frequently employed to improve performance. The performance of Bayesian optimization is affected by the magnitude of the learning rate, and models that do not utilize Bayesian optimization demonstrate inferior performance. Both the TF-IDF FasText model and FastText model yielded comparable outcomes, attaining an F1 score of 0.9019 after being tuned by Bayesian optimization. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Abstractive Indonesian News Summarization Using BERT2GPT 7

Abstractive Indonesian News Summarization Using BERT2GPT

引用

7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023

作者： Nasari, Maulin Maulina, Ayu Girsang, Abba Suganda Computer Science Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350382266

This study examines the necessity of employing BERT2GPT for single-document summarization in the current age of escalating digital data. The primary focus of this work is on the abstractive technique, which tries to generate versatile summaries that surpass the primary sentences of the original content. This study employs two models, namely BERT and GPT -2, for the purpose of the summarization system. The paper introduces the BERT2GPT model, which merges the bidirectional characteristics of BERT with the generative powers of GPT. The findings indicate that the BERT2GPT method successfully catches significant information and linguistic nuances, hence enhancing the quality of the generated summaries. The corresponding average values for Rl, R2, and RL are 0.62, 0.56, and 0.60, respectively. © 2023 IEEE.

关键词： Abstractive Summarization BERT2GPT single-document summarization

来源：评论

学校读者我要写书评

暂无评论

Object Size Recognition as Intra-class Variations using Transfer Learning

Object Size Recognition as Intra-class Variations using Tran...

引用

2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Rahmania, Rissa Hendric Spits Warnars, Harco Leslie Soewito, Benfano Gaol, Ford Lumban Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The ability to differentiate various products in the retail store plays an essential role to provide effectiveness to customers and reduce or even eliminate long queues. However, traditional machine learning algorithms are incapable of recognizing many subordinate categories in various retail product. This paper aims to recognize the retail product recognition algorithm based on the YOLOv7 model in terms of intra-class variations, with the sub-categories of brand and size. We used two schemes of the dataset to compare recognition performance between them. Firstly, the YOLOv7 is applied in the two schemes of the dataset that is annotated with the subordinate category to detect the brand as meta category. Secondly, the proposed method is applied by adding the object size classification into the YOLOv7 model where the square area of the bounding box was calculated to classify the product according to size. Confidence score and square area are used to verify the object and to obtain the product size, which represents sub-category of the product. The experimental results show that our proposed method achieves higher recall compared to baseline object detection. © 2023 IEEE.

关键词： Object recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：