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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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Adaptive Loose Optimization for Robust Question Answering

arXiv

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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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On Aggregation in Ensembles of Multilabel Classifiers 23rd

On Aggregation in Ensembles of Multilabel Classifiers

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23rd International Conference on Discovery Science, DS 2020

作者： Nguyen, Vu-Linh Hüllermeier, Eyke Rapp, Michael Loza Mencía, Eneldo Fürnkranz, Johannes Heinz Nixdorf Institute and Department of Computer Science Paderborn University Paderborn Germany Knowledge Engineering Group TU Darmstadt Darmstadt Germany Computational Data Analytics Group JKU Linz Linz Austria

ISBN: (纸本)9783030615260

While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members of the ensemble has received little attention so far. In this paper, we introduce a formal framework of ensemble multilabel classification, in which we distinguish two principal approaches: "predict then combine" (PTC), where the ensemble members first make loss minimizing predictions which are subsequently combined, and "combine then predict" (CTP), which first aggregates information such as marginal label probabilities from the individual ensemble members, and then derives a prediction from this aggregation. While both approaches generalize voting techniques commonly used for multilabel ensembles, they allow to explicitly take the target performance measure into account. Therefore, concrete instantiations of CTP and PTC can be tailored to concrete loss functions. Experimentally, we show that standard voting techniques are indeed outperformed by suitable instantiations of CTP and PTC, and provide some evidence that CTP performs well for decomposable loss functions, whereas PTC is the better choice for non-decomposable losses. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Dynamics of Ventricular Electrophysiology Are Unmasked Through Noninvasive Electrocardiographic Imaging

Dynamics of Ventricular Electrophysiology Are Unmasked Throu...

引用

Computers in Cardiology (CinC)

作者： Job Stoks Bianca van Rees Uyen Chau Nguyen Ralf Peeters Paul GA Volders Matthijs Cluitmans Maastricht University Medical Center Maastricht the Netherlands Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands BIOMED Hasselt University Hasselt Belgium CARIM Maastricht The Netherlands

ISBN: (纸本)9781665467216

Dynamic variability of ventricular activation and recovery can be a physiological phenomenon, but is also known to increase susceptibility to arrhythmias. It has been extensively studied on the 12-lead electrocardiogram (ECG), but subtle (patho)physiological variations may be challenging to detect and localize due to the limited spatial resolution. Electrocardiographic imaging (ECGI) could be a useful noninvasive high-resolution mapping technique to investigate ventricular dynamics in more detail. Ventricular activation and recovery times (ATs and RTs) were examined using ECGI in 10 normal subjects. Zero-th order Tikhonov regularization was used in combination with a spatiotemporal estimation method to determine ATs and RTs. Dynamics were defined as standard deviations of ventricular ATs and RTs over three beats. Dynamics were higher for recovery than for activation during sinus rhythm, and significantly exaggerated after ventricular ectopy. Left ventricular areas were less dynamic than right ventricular areas. Since arrhythmias may arise due to an increase in ventricular dynamics in the diseased heart, these results provide an important basis for future research on ventricular dynamics and arrhythmias.

关键词： Heart Pathology Imaging Estimation Rhythm Physiology Spatiotemporal phenomena

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Gradient-based label binning in multi-label classification

arXiv

引用

arXiv 2021年

作者： Rapp, Michael Mencía, Eneldo Loza Fürnkranz, Johannes Hüllermeier, Eyke Knowledge Engineering Group TU Darmstadt Darmstadt Germany Computational Data Analysis Group JKU Linz Linz Austria Heinz Nixdorf Institute Paderborn University Paderborn Germany

In multi-label classification, where a single example may be associated with several class labels at the same time, the ability to model dependencies between labels is considered crucial to effectively optimize non-decomposable evaluation measures, such as the Subset 0/1 loss. The gradient boosting framework provides a well-studied foundation for learning models that are specifically tailored to such a loss function and recent research attests the ability to achieve high predictive accuracy in the multi-label setting. The utilization of second-order derivatives, as used by many recent boosting approaches, helps to guide the minimization of non-decomposable losses, due to the information about pairs of labels it incorporates into the optimization process. On the downside, this comes with high computational costs, even if the number of labels is small. In this work, we address the computational bottleneck of such approach | the need to solve a system of linear equations | by integrating a novel approximation technique into the boosting procedure. Based on the derivatives computed during training, we dynamically group the labels into a predefined number of bins to impose an upper bound on the dimensionality of the linear system. Our experiments, using an existing rule-based algorithm, suggest that this may boost the speed of training, without any significant loss in predictive performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

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Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning

引用

Measurement: Sensors 2022年 24卷

作者： Naidu, K Bapayya Ravi Prasad, B. Hassen, Samar Mansour Kaur, Chamandeep Al Ansari, Mohammed Saleh Vinod, R. Nivetha, M. Kiran Bala, B. Surampalem Jawaharlal Nehru Technological University Kakinada India CSE Dept Marri Laxman Reddy Institute of Technology and Management Hyderabad500043 India Computer Science JazanUnivertsity Jazan Saudi Arabia Jazan University Saudi Arabia Jazan Saudi Arabia College of Engineering Department of Chemical Engineering University of Bahrain Bahrain School of Contemporary Knowledge Systems Chinmaya Vishwavidyapeeth Cochin Kerala India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy India Head of the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Log files are a significant piece of records that once properly analyzed, can yield helpful information. The usage of cloud services for log analysis is almost inevitable due to the greater rate of production and quantity of hardware and sensors that produce logs. The idea is to adopt machine learning mechanisms to "understand" what such a test suite's "anticipated" behavior is. This implies the system needs to be able to learn the difference among a Hadoop log file that contains a threat that does not really, based on past patterns. Many programs that really can effectively monitor the Hadoop cluster have been developed. The bulk of these tools collect relevant information from every cluster node and send it to be processed. The majority of these diagnostic tools were post-execution techniques commonly. This paper gives an experimental evaluation of the various log analysts used in Hadoop for error monitoring and detection. MapReduce is configured using these files. The algorithms were tested on a parser set of labelled log files, and their efficiency was assessed by looking for abnormal events in the Hadoop log files and executing an experimentation using the methods. The algorithms used were Term Frequency Inverse Document Frequency, Random Forest, and Local Outlier Factor. Later, the clustering with K-Means and Principal component analysis is utilized to extract certain relevant details from the data, monitor groups of bits of data to find aberrant events. The Term Frequency Inverse Document Frequency strategy surpassed the other two techniques in predicting threat occurrences in data, according to the findings. The findings would help developers in locating abnormal occurrences without having to go through the Hadoop log file row by row individually. The framework generates events that behave in a different way than the rest of such events in the log and leads the mechanism to fail to work. © 2022 The Authors

关键词： Principal component analysis

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Exploring linguistic features and user engagement in Chinese online mental health counseling

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Heliyon 2024年第19期10卷 e38042页

作者： Zhang, Liyuan Liu, Dexi Li, Jing Wan, Changxuan Liu, Xiping School of Computer and Artificial Intelligence Jiangxi University of Finance and Economics JiangXi Nanchang 330013 China School of Mathematics and Computer YuZhang Normal College Jiangxi Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Jiangxi Nanchang 330013 China School of Electronic Management Science Fujian Jiangxia University Fujian Fuzhou 350108 China

With the popularity of online mental health platforms, more individuals are seeking help and receiving social support by openly discussing their problems. Therefore, it's crucial to gain a deeper understanding of which problem disclosures and social support on these platforms can attract more user attention and engagement. Previous research has primarily focused on social media forums. Our work concentrates on the professional mental health platform, intending to understand the linguistic features present in posts that promote user engagement and interaction. We employ text mining and deep learning techniques to analyze posts consisting of 22,250 questions from help-seekers and 78,328 answers providing social support extracted from the Chinese online mental health counseling platform. Initially, we analyze the high-frequency words and topics of the questions and answers to gain insights into the primary focal points and the range of topics covered in these posts. The results indicate that work-related issues are the most concerning and troublesome for help-seekers, and the topics that users follow are approximately 8 types, including growth, family, in-love, marriage, emotions, human-relations, behavioral-therapy and career. Subsequently, we analyze the language usage in question-and-answer posts with different engagement from three aspects: vocabulary categories, linguistic style matching, and language modeling, aiming to identify which linguistic features can attract more user attention and engagement. The results reveal that high-engagement answer posts exhibit a higher degree of linguistic style matching with the corresponding questions, and the use of vocabulary categories also influences the attention and engagement of the posts. By exploring the linguistic features and patterns displayed in posts with different levels of engagement on the professional online mental health platform, this study offers deep insights into user behavior and the factors that impact

关键词： Language modeling Linguistic style matching Online mental health counseling The topics of questions and answers Vocabulary categories

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Generative AI for Advanced UAV Networking

引用

IEEE Network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

arXiv

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arXiv 2024年

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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