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The Relationships between Influencers and Followers Who Troll and Proactive-Reactive Trolling

Proceedings of the Association for Information Science and Technology 2024年第1期61卷 1093-1095页

作者： Sun, Honglei Fichman, Pnina School of Information Management Nanjing University China Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Little is known about the differences between trolling by influencers and followers, the difference between proactive and reactive trolling, and the relationship between them. Based on a content analysis of 1,386 comments on 160 posts of 19 influencers during a Sina Weibo trolling event, we found that influencers troll more frequently than their followers and they troll proactively more often than reactively, while followers troll reactively more often than proactively. Influencers generated less but more impactful content compared with their followers. In both proactive and reactive trolling, influencers derailed the discussion while followers provoked through their proactive trolling and insulted through their reactive trolling. These findings extend the research scope of trolling asymmetry and establish theoretical connections between social media roles and proactive-reactive trolling. 87 Annual Meeting of the Association for Information Science & Technology | Oct. 25 – 29, 2024 | Calgary, AB, Canada.

关键词： Proactive-Reactive trolling Sina Weibo Social media influencers

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Gradual Guarantee via Step-Indexed Logical Relations in Agda

Gradual Guarantee via Step-Indexed Logical Relations in Agda

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2024 A Second Soul: Celebrating the Many Languages of Programming - Festschrift in Honor of Peter Thiemann's Sixtieth Birthday, PT 2024

作者： Siek, Jeremy G. School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

The gradual guarantee is an important litmus test for gradually typed languages, that is, languages that enable a mixture of static and dynamic typing. The gradual guarantee states that changing the precision of a type annotation does not change the behavior of the program, except perhaps to trigger an error if the type annotation is incorrect. Siek et al. (2015) proved that the Gradually Typed Lambda Calculus (GTLC) satisfies the gradual guarantee using a simulation-based proof and mechanized their proof in Isabelle. In the following decade, researchers have proved the gradual guarantee for more sophisticated calculi, using step-indexed logical relations. However, given the complexity of that style of proof, there has not yet been a mechanized proof of the gradual guarantee using step-indexed logical relations. This paper reports on a mechanized proof of the gradual guarantee for the GTLC carried out in the Agda proof assistant. © Jeremy G. Siek.

关键词： Differentiation (calculus)

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Usability, Efficacy, and Acceptability of the U.S. Cyber Trust Mark 25

Usability, Efficacy, and Acceptability of the U.S. Cyber Tru...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Caven, Peter Gurjar, Ambarish Zhang, Zitao Ma, Xinyao Camp, LJean School of Informatics Computing and Engineering Indiana Unviersity Bloomington IN United States School of Informatics Computing and Engineering Bloomington IN United States

ISBN: (纸本)9798400713941

The U.S. Cyber Trust Mark is intended to empower consumers and enable security by demand. But is there such a demand? To explore this, we recruited 599 participants and asked them to select their desired smart device using a simulated online marketplace. Participants were informed they would receive their selected light bulbs, and they were divided into five experimental groups based on different versions of the Mark. After the product selections, we surveyed them about their priorities and preferences. We found no significant differences between the groups as a whole. However, the subset of consumers who identified cybersecurity as most important were significantly more likely to select labeled products, spending 16.5% more. We detail these differences and preferences, then argue that an awareness program is needed to assist consumers in better understanding the long-term economic benefits of the U.S. Cyber Trust Mark. © 2025 Copyright held by the owner/author(s).

关键词： icons interaction IoT labels privacy security trust

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of Computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Prediction of Myocardial Infarction Using Convolutional Neural Network Method on Electrocardiogram Signal 7

Prediction of Myocardial Infarction Using Convolutional Neur...

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7th International Conference on Data Science and Its Applications, ICoDSA 2024

作者： Alifa, Prinintya Nuri Mandala, Satria Informatics School of Computing Telkom University Bandung Indonesia Human Centric Engineering School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798350365351

Myocardial infarction or MI is a medical condition that occurs when the supply of oxygen-rich blood to the heart muscle is interrupted or stops suddenly. As a result, the part of the heart muscle that does not get enough blood supply begins to suffer damage or death. Research related to MI using ECG signals has started since the 1900s. As technology and computing advances, analysis of ECG signals with more sophisticated methods, such as digital image processing and signal processing techniques, has been developed to improve accuracy and reliability in detecting myocardial infarction. But it does not provide detailed information about the interaction between embedding features. This research provides a solution to the above problem by developing a CNN-based MI model that will be optimized by Hyperparameter-tuning. This technique is proven to produce a solid and accurate MI model. However, it is important to run a comprehensive evaluation and validation of these models, including testing on existing data sets and peers. The methods used in the development of MI models with CNNs are as follows. First, this study examines the performance of ECG data using CNN algorithms, then the results will be compared with the performance of ECG data using Hyperparameter-tuning. The result of this research is the MI model with CNN Non-Tuning method has an accuracy of 77% and for CNN Fine-Tuning method has an accuracy of 80%. © 2024 IEEE.

关键词： Convolutional neural networks

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Designing Everyday Family Technologies: Supporting Health and Wellbeing in Family-Food Interactions with Children 25

Designing Everyday Family Technologies: Supporting Health an...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Wu, Yuxing Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States

ISBN: (纸本)9798400711879

Technology is pervasive in everyday family life. However, creating meaningful sociotechnical systems that are useful for (and desired by) families remains a complex and evolving challenge. To design for families in the digital age, HCI research requires holistic understanding and in-depth investigation to unpack social dynamics and situated use beyond the individual. My dissertation applies a family-centered approach to examine how technologies might be better designed to support everyday social, health, and developmental goals and challenges. Focusing on family-food interactions, my work unpacks complexities in navigating various food-related experiences to inform design implications. © 2025 Copyright held by the owner/author(s).

关键词： Food ingredients

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Ultrafast Online Clutter Filtering for Ultrasound Microvascular Imaging

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2477-2491页

作者： Chen, Yinran Fang, Baohui Li, Huaying Huang, Lijie Luo, Jianwen Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Tsinghua University School of Biomedical Engineering Beijing100084 China

Spatiotemporal clutter filtering via robust principal component analysis (rPCA) has been widely used in ultrasound microvascular imaging. However, the performance of the rPCA clutter filtering highly relies on low-rank modeling for tissue signals and sparse modeling for blood flow signals. Moreover, current rPCA clutter filters are typically based on static processing and have to access a batch of beamformed frames for optimization. This prevents these filters from ultrafast realization. This paper adopts the iteratively reweighted least squares (IRLS) rPCA framework to model tissue and blood flow signals for improved clutter filtering. More importantly, the static IRLS-rPCA filter is upgraded to a spatiotemporal-constrained online method to instantaneously extract blood flow signals from the ongoing beamformed frame. Simulations and in-vivo experiments on a contrast-enhanced rat kidney and a contrast-free human liver demonstrated that the IRLS-rPCA clutter filter achieves higher sensitivity, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) than other rPCA methods. Particularly, the static IRLS-rPCA clutter filter obtains more than 2 dB improvements in CNR over the compared methods in the human liver dataset. The proposed online clutter filter achieves comparable image quality to the static version and processing time of 0.028 ± 0.004 seconds per frame. The corresponding acceleration factor of the online clutter filter over all the tested methods is more than 20. © 2025 IEEE.

关键词： Radar clutter

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the school of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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Exploring Users’ Perspectives of Mobile Health Privacy and Autonomy 17th

Exploring Users’ Perspectives of Mobile Health Privacy and ...

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17th EAI International Conference on Pervasive computing Technologies for Healthcare, PervasiveHealth 2023

作者： Starks, Thomas Patil, Kshitij Martin-Hammond, Aqueasha School of Informatics Computing and Engineering Indiana University – Indianapolis IndianapolisIN46202 United States

ISBN: (纸本)9783031597169

The increased use of mobile health (mHealth) applications and the corresponding exchange of sensitive data has underscored privacy concerns. Privacy notices are often unengaging or incomprehensible, leading to questions of informed consent and trust. While studies have focused on providing solutions aimed to simplify privacy language and reduce cognitive burden, often overlooked are the behavioral aspects of individual attitudes, norms, and perceived control that lead to dynamic intentions for engagement. In this paper, we use existing behavior models as a lens to understand users’ privacy experiences, behaviors, and perspectives toward mHealth data privacy policies. In 15 semi-structured interviews with adult users of mHealth applications, participants encountered persistent challenges when engaging and articulating the value of privacy. Participants do not understand how privacy notices are designed, which leads to superficial awareness and control that does not actually support their perceptions of autonomy and trust in mHealth. As a result, users felt sub-optimal autonomy when engaging in privacy interactions. We discuss design considerations for autonomy-supporting privacy notices that may help users feel a greater sense of agency when interacting with mHealth applications. © ICST Institute for Computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： mHealth

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