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Influence of land-sea breeze on PM _2.5 prediction in central and southern Taiwan using composite neural network

Scientific Reports 2023年第1期13卷 1-9页

作者： Kibirige, George William Huang, Chiao Cheng Liu, Chao Lin Chen, Meng Chang Institute of Information Science Academia Sinica New Taipei Taiwan Social Networks and Human Centered Computing Program Taiwan International Graduate Program New Taipei Taiwan National Chengchi University New Taipei Taiwan

PM2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning me...

PM_2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central Taiwan in the months when land-sea breeze dominates the accumulation of PM_2.5. © 2023, The Author(s).

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Beyond fair pay: Ethical implications of NLP crowdsourcing

arXiv

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arXiv 2021年

作者： Shmueli, Boaz Fell, Jan Ray, Soumya Ku, Lun-Wei Social Networks and Human-Centered Computing Tigp Academia Sinica Taiwan Institute of Service Science National Tsing Hua University Taiwan Institute of Information Science Academia Sinica Taiwan

The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair pay. We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production. We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and practice of human-subjects ethics in NLP research. We enumerate common scenarios where crowdworkers performing NLP tasks are at risk of harm. We thus recommend that researchers evaluate these risks by considering the three ethical principles set up by the Belmont Report. We also clarify some common misconceptions regarding the Institutional Review Board (IRB) application. We hope this paper will serve to reopen the discussion within our community regarding the ethical use of crowdworkers. Copyright © 2021, The Authors. All rights reserved.

关键词： Crowdsourcing

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DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection

DeepGuard: Deep Generative User-behavior Analytics for Ranso...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Gaddisa Olani Ganfure Chun-Feng Wu Yuan-Hao Chang Wei-Kuan Shih Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan National Tsing Hua University Hsinchu City Taiwan

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

关键词： Training Training data Organizations Tools Ransomware Security Informatics

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Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning

arXiv

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arXiv 2024年

作者： Khan, Sarwar Chen, Jun-Cheng Liao, Wen-Hung Chen, Chu-Song Research Center for Information Technology Innovation Academia Sinica Taiwan Social Networks Human-Centered Computing TIGP Academia Sinica Taiwan National Chengchi University Taiwan National Taiwan University Taiwan

Deepfake technology has raised concerns about the authenticity of digital content, necessitating the development of effective detection methods. However, the widespread availability of deepfakes has given rise to a new challenge in the form of adversarial attacks. Adversaries can manipulate deepfake videos with small, imperceptible perturbations that can deceive the detection models into producing incorrect outputs. To tackle this critical issue, we introduce Adversarial Feature Similarity Learning (AFSL), which integrates three fundamental deep feature learning paradigms. By optimizing the similarity between samples and weight vectors, our approach aims to distinguish between real and fake instances. Additionally, we aim to maximize the similarity between both adversarially perturbed examples and unperturbed examples, regardless of their real or fake nature. Moreover, we introduce a regularization technique that maximizes the dissimilarity between real and fake samples, ensuring a clear separation between these two categories. With extensive experiments on popular deepfake datasets, including FaceForensics++, FaceShifter, and DeeperForensics, the proposed method outperforms other standard adversarial training-based defense methods significantly. This further demonstrates the effectiveness of our approach to protecting deepfake detectors from adversarial attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Boosting Patch Correctness Checking via Combining Code-Edit Embeddings as Additional What-to-Change Information

Boosting Patch Correctness Checking via Combining Code-Edit ...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Chontipan Plengvittaya Zee Hen Tang Mi-Yen Yeh Chih-Ya Shen Social Networks and Human Centered Computing Taiwan International Graduate Program Taiwan Institute of Information Science Academia Sinica Taiwan Institute of Information Systems and Applications National Tsing Hua University Taiwan Data Science Degree Program National Taiwan University and Academia Sinica Taiwan

Patch Correctness Checking is the task of detecting automated-program-repair-generated patches that can pass the test suite but are still buggy. In this study, we investigated a new feature to improve the classification performance for automated patch correctness checking. Our approach involved leveraging code-change information at a finer granular level by embedding the code edits and combining them with existing code-change vectors. Through extensive experiments, the results revealed gains up to 3.4% and 3.2% on the Fl-score and AUC.

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Influence Value: Quantifying Topic Influence on social Media

Influence Value: Quantifying Topic Influence on Social Media

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Fernando H. Calderon Yi-Shin Chen Taiwan International Graduate Program Social Networks and Human-Centered Computing Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9781509021802

social Media specifically Micro-blogging sites have become very rich data repositories. However the generated data is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life experiences, discussions or events have a direct impact on the behaviors reflected in social networks. It is therefore of great importance to asses the impact these interactions are having over a social group. An alternative to answer this is determining how influential a topic is according to the behavior presented on a social network over time. It is then necessary to find and develop methods that can contribute towards this task. Having identified a topic in social media we can first classify it as time specific or long term, then posts relevant to the topic are collected and each one is assigned an emotion label. We then propose an Influence Value score which will be given to each topic based on its lifespan, emotion transition and reach. This lays ground to quantify how influential a topic is over a social group, specifically from events detected on twitter.

关键词： Media social groups Twitter Wheels Measurement Big data

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Multimodal Forgery Detection Using Ensemble Learning

Multimodal Forgery Detection Using Ensemble Learning

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14th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Ahmad, Wasim Lin, Chia Wen Tsao, Yu Wang, Hsin-Min Acad Sinica Inst Informat Sci Social Networks & Human Centered Comp Program Taiwan Int Grad Program New Taipei Taiwan Natl Tsing Hua Univ Inst Informat Syst & Applicat Hsinchu Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan Natl Chengchi Univ Dept Comp Sci Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat New Taipei Taiwan

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AI-synthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data, so there is still room for further improvement. In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module. Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real. Experimental results on a recently released multimodal FakeAVCeleb dataset show that the proposed method achieves 89% accuracy, significantly outperforming existing models.

关键词： Deepfakes Visualization Information processing Transformers Forgery Ensemble learning Task analysis

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CRED: Credibility-Enabled social Network Based Q&A System for Assessing Answers Correctness

CRED: Credibility-Enabled Social Network Based Q&A System fo...

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IEEE Conference on Wireless Communications and Networking

作者： Imad Ali Ronald Y. Chang Cheng-Hsin Hsu Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

In a question & answer (Q& A) system, credible users provide answers of higher correctness. However, in a distributed social network based Q& A (SNQ& A) system, an asker does not know a k-hop answerer's credibility, thus making it difficult for the asker to assess the answer correctness. Therefore, a credibility-enabled distributed SNQ& A system is crucial for determining the correctness of the answers. To this end, we propose CRED, a credibility-enabled distributed SNQ& A system, which facilitates each user to assess the correctness of the provided answers. CRED utilizes subjective logic to build interestwise friend-to-friend credibility opinions under uncertainties. The developed opinions are then accumulated by CRED to get each user's aggregated credibility opinion, which may reflect the user's real credibility. CRED forwards a question to users with highest credibility beliefs in the question interest category. Our evaluation results show that, on average, CRED accomplishes higher success ratio, higher answer correctness, and lower answer uncertainty by 12.1%, 16.4%, and 22.2%, respectively, as compared to the best-performing baseline systems.

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LOCI: A Mobile Q&A System with Multimodal Motivation Scheme for Local Intent Questions in Dynamic social networks

LOCI: A Mobile Q&A System with Multimodal Motivation Scheme ...

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IEEE Conference on Vehicular Technology (VTC)

作者： Imad Ali Ronald Y. Chang Cheng-Hsin Hsu Chi-Han Lee Taiwan International Graduate Program in Social Networks and Human-Centered Computing Institute of Information Science Academia Sinica Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

Recent research has shown that people are frequently searching for information with local intent. The existing systems fail to answer this class of questions as they are not mainly designed to handle it. In this paper, we propose LOCI, a mobile question & answer system with a multimodal motivation scheme for local intent questions where users provide timely and accurate answers in a dynamic social network. LOCI mines users' shared and contextual information to identify the most relevant users for each question. The proposed multimodal motivation scheme exploits users' social ties and monetary rewards to raise their basic motivation levels. LOCI assigns questions to the best answerers via our three proposed algorithms: (i) LOCI-RT, which immediately assigns newly arrived questions/users to available users/questions, (ii) LOCI-LO, which uses short-time batches for locally optimal questions-users assignment, and (iii) LOCI-AD, which assigns short- and long-deadline questions immediately and via batching, respectively. We conduct: (i) a survey to support our assumptions regarding the proposed multimodal motivation scheme, and (ii) trace-driven experiments to evaluate the performance of the three proposed LOCI's algorithms.

关键词： social networking (online) Resource management Heuristic algorithms Real-time systems Computer science Search engines Servers

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Content-Based Echo Chamber Detection on social Media Platforms

Content-Based Echo Chamber Detection on Social Media Platfor...

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International Conference on Advances in social Network Analysis and Mining, ASONAM

作者： Fernando H. Calderón Li-Kai Cheng Ming-Jen Lin Yen-Hao Huang Yi-Shin Chen Social Networks and Human Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan National Taiwan University Taipei City Taiwan

ISBN: (数字)9781450368681

ISBN: (纸本)9781450368681

“Echo chamber” is a metaphorical description of a situation in which beliefs are amplified inside a closed network, and social media platforms provide an environment that is well-suited to this phenomenon. Depending on the scale of the echo chamber, a user's judgment of different opinions may be restricted. The current study focuses on detecting echoing interaction between a post and its related comments to then quantify the predominating degree of echo chamber behavior on Facebook pages. To enable such detection, two content-based features are designed; the first aids stance representation of comments on a particular discussion topic, and the second focuses on the type and intensity of emotion elicited by a subject. This work also introduces data-driven semi-supervised approaches to extract such features from social media data.

关键词： Feature extraction social network services Fans Urban areas Connectors Linguistics Computational modeling

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