PeerJ Computer Science最新文献

{"title":"Automatic cassava disease recognition using object segmentation and progressive learning.","authors":"Chang Che, Nian Xue, Zhen Li, Yilin Zhao, Xin Huang","doi":"10.7717/peerj-cs.2721","DOIUrl":"10.7717/peerj-cs.2721","url":null,"abstract":"<p><p>Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environments, making them unsuitable for large-scale agricultural use. This study aims to develop a deep learning framework that enables early, accurate, and efficient detection of cassava diseases in real-world conditions. We propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings. Our approach achieves superior performance on the Cassava Leaf Disease Classification (CLDC) dataset from the Kaggle competition, with an accuracy of 91.43%, outperforming all other participants. The proposed method offers a practical and efficient solution for cassava disease detection, demonstrating the potential for large-scale, real-world application in agriculture.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2721"},"PeriodicalIF":3.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DE-RALBA: dynamic enhanced resource aware load balancing algorithm for cloud computing.","authors":"Altaf Hussain, Muhammad Aleem, Atiq Ur Rehman, Umer Arshad","doi":"10.7717/peerj-cs.2739","DOIUrl":"https://doi.org/10.7717/peerj-cs.2739","url":null,"abstract":"<p><p>Cloud computing provides an opportunity to gain access to the large-scale and high-speed resources without establishing your own computing infrastructure for executing the high-performance computing (HPC) applications. Cloud has the computing resources (<i>i.e</i>., computation power, storage, operating system, network, and database <i>etc</i>.) as a public utility and provides services to the end users on a pay-as-you-go model. From past several years, the efficient utilization of resources on a compute cloud has become a prime interest for the scientific community. One of the key reasons behind inefficient resource utilization is the imbalance distribution of workload while executing the HPC applications in a heterogenous computing environment. The static scheduling technique usually produces lower resource utilization and higher makespan, while the dynamic scheduling achieves better resource utilization and load-balancing by incorporating a dynamic resource pool. The dynamic techniques lead to increased overhead by requiring a continuous system monitoring, job requirement assessments and real-time allocation decisions. This additional load has the potential to impact the performance and responsiveness on computing system. In this article, a dynamic enhanced resource-aware load balancing algorithm (DE-RALBA) is proposed to mitigate the load-imbalance in job scheduling by considering the computing capabilities of all VMs in cloud computing. The empirical assessments are performed on CloudSim simulator using instances of two scientific benchmark datasets (<i>i.e</i>., heterogeneous computing scheduling problems (HCSP) instances and Google Cloud Jobs (GoCJ) dataset). The obtained results revealed that the DE-RALBA mitigates the load imbalance and provides a significant improvement in terms of makespan and resource utilization against existing algorithms, namely PSSLB, PSSELB, Dynamic MaxMin, and DRALBA. Using HCSP instances, the DE-RALBA algorithm achieves up to 52.35% improved resources utilization as compared to existing technique, while more superior resource utilization is achieved using the GoCJ dataset.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2739"},"PeriodicalIF":3.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum.","authors":"Ramneet Kaur, Mudita Uppal, Deepali Gupta, Sapna Juneja, Syed Yasser Arafat, Junaid Rashid, Jungeun Kim, Roobaea Alroobaea","doi":"10.7717/peerj-cs.2675","DOIUrl":"10.7717/peerj-cs.2675","url":null,"abstract":"<p><p>Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2675"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935774/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm.","authors":"Hussein Ridha Sayegh, Wang Dong, Bahaa Hussein Taher, Muhanad Mohammed Kadum, Ali Mansour Al-Madani","doi":"10.7717/peerj-cs.2745","DOIUrl":"10.7717/peerj-cs.2745","url":null,"abstract":"<p><p>As the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks to discern between regular network traffic and potential malicious attacks. This article proposes a new IDS based on a hybrid method of metaheuristic and deep learning techniques, namely, the flower pollination algorithm (FPA) and deep neural network (DNN), with an ensemble learning paradigm. To handle the problem of imbalance class distribution in intrusion datasets, a roughly-balanced (RB) Bagging strategy is utilized, where DNN models trained by FPA on a cost-sensitive fitness function are used as base learners. The RB Bagging strategy derives multiple RB training subsets from the original dataset and proper class weights are incorporated into the fitness function to attain unbiased DNN models. The performance of our IDS is evaluated using four commonly utilized public datasets, NSL-KDD, UNSW NB-15, CIC-IDS-2017, and BoT-IoT, in terms of different metrics, <i>i.e</i>., accuracy, precision, recall, and F1-score. The results demonstrate that our IDS outperforms existing ones in accurately detecting network intrusions with effective handling of class imbalance problem.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2745"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven flight path monitoring technique using recurrent neural network for the safety management of commercial aircraft.","authors":"Naeun Kim, Mohamed H Hamza, Bong-Hwan Koh","doi":"10.7717/peerj-cs.2753","DOIUrl":"10.7717/peerj-cs.2753","url":null,"abstract":"<p><p>Aviation spins, particularly at low altitudes, significantly contribute to fatalities due to limited recovery time. Standard recovery procedures typically only become eligible after a spin is fully developed, by which time multiple turns may have already resulted in substantial altitude loss. The primary challenge in upset prevention is heavy reliance on the pilot's situational awareness, which is only effective before the spin has been fully developed. To address this issue, this study proposes an early detection capability to significantly enhance immediate response actions, potentially mitigating altitude loss and enabling pilots to recognize the initial signs of upset conditions. This research introduces a real-time predictive tool based on a novel recurrent neural network (RNN) model that utilizes data from the NASA Generic Transport Model (GTM)-a research platform designed for experimental flight case studies-to predict nonlinear flight responses during the critical initial seconds of a spin. Rigorous validation against ground truth data demonstrates the RNN model's superior predictive capabilities in detecting incipient spin phase, offering an essential tool for proactive spin management and reducing the risk of ground collisions. This early detection capability empowers pilots to identify the initial signs of upset conditions and make informed operational decisions, ultimately improving aviation safety. This advancement underscores the potential of advanced machine learning technologies to transform safety protocols by enabling earlier and more effective intervention strategies, thereby preempting catastrophic events.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2753"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven balance evaluation: a comparative study between blind and non-blind individuals using the mini-BESTest.","authors":"Milagros Jaén-Vargas, Josué Pagán, Shiyang Li, María Fernanda Trujillo-Guerrero, Niloufar Kazemi, Alessio Sansò, Benito Codina-Casals, Roy Abi Zeid Daou, Jose Javier Serrano Olmedo","doi":"10.7717/peerj-cs.2695","DOIUrl":"10.7717/peerj-cs.2695","url":null,"abstract":"<p><p>There are 2.2 billion visually impaired individuals and 285 million blind people worldwide. The vestibular system plays a fundamental role in the balance of a person related to sight and hearing, and thus blind people require physical therapy to improve their balance. Several clinical tests have been developed to evaluate balance, such as the mini-BESTest. This test has been used to evaluate the balance of people with neurological diseases, but there have been no studies that evaluate the balance of blind individuals before. Furthermore, despite the scoring of these tests being not subjective, the performance of some activities are subject to the physiotherapist's bias. Tele-rehabilitation is a growing field that aims to provide physical therapy to people with disabilities. Among the technologies used in tele-rehabilitation are inertial measurement units that can be used to monitor the balance of individuals. The amount of data collected by these devices is large and the use of deep learning models can help in analyzing these data. Therefore, the objective of this study is to analyze for the first time the balance of blind individuals using the mini-BESTest and inertial measurement units and to identify the activities that best differentiate between blind and sighted individuals. We use the OpenSense RT monitoring device to collect data from the inertial measurement unit, and we develop machine learning and deep learning models to predict the score of the most relevant mini-BESTest activities. In this study 29 blind and sighted individuals participated. The one-legged stance is the activity that best differentiates between blind and sighted individuals. An analysis on the acceleration data suggests that the evaluation of physiotherapists is not completely adjusted to the test criterion. Cluster analysis suggests that inertial data are not able to distinguish between three levels of evaluation. However, the performance of our models shows an F1-score of 85.6% in predicting the score evaluated by the mini-BESTest in a binary classification problem. The results of this study can help physiotherapists have a more objective evaluation of the balance of their patients and to develop tele-rehabilitation systems for blind individuals.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2695"},"PeriodicalIF":3.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative artificial intelligence and machine learning methods to screen social media content.","authors":"Kellen Sharp, Rachel R Ouellette, Rujula Singh Rajendra Singh, Elise E DeVito, Neil Kamdar, Amanda de la Noval, Dhiraj Murthy, Grace Kong","doi":"10.7717/peerj-cs.2710","DOIUrl":"10.7717/peerj-cs.2710","url":null,"abstract":"<p><strong>Background: </strong>Social media research is confronted by the expansive and constantly evolving nature of social media data. Hashtags and keywords are frequently used to identify content related to a specific topic, but these search strategies often result in large numbers of irrelevant results. Therefore, methods are needed to quickly screen social media content based on a specific research question. The primary objective of this article is to present generative artificial intelligence (AI; <i>e.g</i>., ChatGPT) and machine learning methods to screen content from social media platforms. As a proof of concept, we apply these methods to identify TikTok content related to e-cigarette use during pregnancy.</p><p><strong>Methods: </strong>We searched TikTok for pregnancy and vaping content using 70 hashtag pairs related to \"pregnancy\" and \"vaping\" (<i>e.g</i>., #pregnancytok and #ecigarette) to obtain 11,673 distinct posts. We extracted post videos, descriptions, and metadata using Zeeschuimer and PykTok library. To enhance textual analysis, we employed automatic speech recognition <i>via</i> the Whisper system to transcribe verbal content from each video. Next, we used the OpenCV library to extract frames from the videos, followed by object and text detection analysis using Oracle Cloud Vision. Finally, we merged all text data to create a consolidated dataset and entered this dataset into ChatGPT-4 to determine which posts are related to vaping and pregnancy. To refine the ChatGPT prompt used to screen for content, a human coder cross-checked ChatGPT-4's outputs for 10 out of every 100 metadata entries, with errors used to inform the final prompt. The final prompt was evaluated through human review, confirming for posts that contain \"pregnancy\" and \"vape\" content, comparing determinations to those made by ChatGPT.</p><p><strong>Results: </strong>Our results indicated ChatGPT-4 classified 44.86% of the videos as exclusively related to pregnancy, 36.91% to vaping, and 8.91% as containing both topics. A human reviewer confirmed for vaping and pregnancy content in 45.38% of the TikTok posts identified by ChatGPT as containing relevant content. Human review of 10% of the posts screened out by ChatGPT identified a 99.06% agreement rate for excluded posts.</p><p><strong>Conclusions: </strong>ChatGPT has mixed capacity to screen social media content that has been converted into text data using machine learning techniques such as object detection. ChatGPT's sensitivity was found to be lower than a human coder in the current case example but has demonstrated power for screening out irrelevant content and can be used as an initial pass at screening content. Future studies should explore ways to enhance ChatGPT's sensitivity.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2710"},"PeriodicalIF":3.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facial expression recognition using visible and IR by early fusion of deep learning with attention mechanism.","authors":"Muhammad Tahir Naseem, Chan-Su Lee, Tariq Shahzad, Muhammad Adnan Khan, Adnan M Abu-Mahfouz, Khmaies Ouahada","doi":"10.7717/peerj-cs.2676","DOIUrl":"10.7717/peerj-cs.2676","url":null,"abstract":"<p><p>Facial expression recognition (FER) has garnered significant attention due to advances in artificial intelligence, particularly in applications like driver monitoring, healthcare, and human-computer interaction, which benefit from deep learning techniques. The motivation of this research is to address the challenges of accurately recognizing emotions despite variations in expressions across emotions and similarities between different expressions. In this work, we propose an early fusion approach that combines features from visible and infrared modalities using publicly accessible VIRI and NVIE databases. Initially, we developed single-modality models for visible and infrared datasets by incorporating an attention mechanism into the ResNet-18 architecture. We then extended this to a multi-modal early fusion approach using the same modified ResNet-18 with attention, achieving superior accuracy through the combination of convolutional neural network (CNN) and transfer learning (TL). Our multi-modal approach attained 84.44% accuracy on the VIRI database and 85.20% on the natural visible and infrared facial expression (NVIE) database, outperforming previous methods. These results demonstrate that our single-modal and multi-modal approaches achieve state-of-the-art performance in FER.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2676"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization study of intelligent accounting manager system modules in adaptive behavioral pattern learning and simulation.","authors":"Yifan Wang, Rongjie Qin, Musadaq Mansoor","doi":"10.7717/peerj-cs.2684","DOIUrl":"10.7717/peerj-cs.2684","url":null,"abstract":"<p><p>Within the ambit of the digital epoch, the advent of adaptive learning technologies heralds a paradigmatic shift in the realm of accounting management, garnering increasing scrutiny for augmenting learning outcomes <i>via</i> more sagacious educational methodologies and refining the accounting management protocols through the employment of sophisticated optimization techniques. This manuscript delineates an avant-garde health classification schema for accounting management, termed the A-CHMM-FD methodology, which amalgamates the merits of the Analytic Hierarchy Process (AHP) with the Coupled Hidden Markov Model (CHMM) to enhance the precision and efficacy of risk detection. Utilizing the AHP modality, we quantify diverse accounting metrics, subsequently subjected to independent scrutiny <i>via</i> the CHMM. This results in an exhaustive evaluation of entities as healthy, at-risk, or high-risk employing fuzzy delineations. Empirical validation on publicly available financial risk datasets and the pragmatic deployment of bespoke datasets affirm the superior efficiency and precision of the proposed framework. Applying this methodology within the health classification of accounting management emerges as efficacious, charting a novel technological trajectory for managing accounting risks and offering fresh perspectives on the nurturing of accounting understanding and the acquisition of knowledge.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2684"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A laboratory experiment on using different financial-incentivization schemes in software-engineering experimentation.","authors":"Dmitri Bershadskyy, Jacob Krüger, Gül Calıklı, Siegmar Otto, Sarah Zabel, Jannik Greif, Robert Heyer","doi":"10.7717/peerj-cs.2650","DOIUrl":"10.7717/peerj-cs.2650","url":null,"abstract":"<p><p>In software-engineering research, many empirical studies are conducted with open-source or industry developers. However, in contrast to other research communities like economics or psychology, only few experiments use financial incentives (<i>i.e</i>., paying money) as a strategy to motivate participants' behavior and reward their performance. The most recent version of the SIGSOFT Empirical Standards mentions payouts only for increasing participation in surveys, but not for mimicking real-world motivations and behavior in experiments. Within this article, we report a controlled experiment in which we tackled this gap by studying how different financial incentivization schemes impact developers. For this purpose, we first conducted a survey on financial incentives used in the real-world, based on which we designed three incentivization schemes: (1) a performance-dependent scheme that employees prefer, (2) a scheme that is performance-independent, and (3) a scheme that mimics open-source development. Then, using a between-subject experimental design, we explored how these three schemes impact participants' performance. Our findings indicate that the different schemes can impact participants' performance in software-engineering experiments. Our results are not statistically significant, possibly due to small sample sizes and the consequent lack of statistical power, but with some notable trends that may inspire future hypothesis generation. Our contributions help understand the impact of financial incentives on participants in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e2650"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11935764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}