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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Hamilton, Kyle Longo, Luca Božić, Bojan School of Computer Science and SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland Artificial Intelligence and Cognitive Load Research Lab School of Computer Science SFI Centre for Research Training in Machine Learning Technological University Dublin Dublin Ireland

ISBN: (纸本)9798400701726

While the use of machine learning for the detection of propaganda techniques in text has garnered considerable attention, most approaches focus on "black-box" solutions with opaque inner workings. Interpretable approaches provide a solution, however, they depend on careful feature engineering and costly expert annotated data. Additionally, language features specific to propagandistic text are generally the focus of rhetoricians or linguists, and there is no data set labeled with such features suitable for machine learning. This study codifies 22 rhetorical and linguistic features identified in literature related to the language of persuasion for the purpose of annotating an existing data set labeled with propaganda techniques. To help human experts annotate natural language sentences with these features, RhetAnn, a web application, was specifically designed to minimize an otherwise considerable mental effort. Finally, a small set of annotated data was used to fine-tune GPT-3.5, a generative large language model (LLM), to annotate the remaining data while optimizing for financial cost and classification accuracy. This study demonstrates how combining a small number of human annotated examples with GPT can be an effective strategy for scaling the annotation process at a fraction of the cost of traditional annotation relying solely on human experts. The results are on par with the best performing model at the time of writing, namely GPT-4, at 10x less the cost. Our contribution is a set of features, their properties, definitions, and examples in a machine-readable format, along with the code for RhetAnn and the GPT prompts and fine-tuning procedures for advancing state-of-the-art interpretable propaganda technique detection. © 2024 Copyright held by the owner/author(s).

关键词： Machine learning

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Rethinking Few-shot 3D Point Cloud Semantic Segmentation

arXiv

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arXiv 2024年

作者： An, Zhaochong Sun, Guolei Liu, Yun Liu, Fayao Wu, Zongwei Wang, Dan Van Gool, Luc Belongie, Serge Computer Vision Laboratory ETH Zurich Switzerland Pioneer Centre for Artificial Intelligence University of Copenhagen Denmark Institute for Infocomm Research A*STAR Singapore Computer Vision Lab CAIDAS IFI University of Wurzburg Germany

This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. The former arises from non-uniform point sampling, allowing models to distinguish the density disparities between foreground and background for easier segmentation. The latter results from sampling only 2,048 points, limiting semantic information and deviating from the real-world practice. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built. Moreover, we propose a novel FS-PCS model. While previous methods are based on feature optimization by mainly refining support features to enhance prototypes, our method is based on correlation optimization, referred to as Correlation Optimization Segmentation (COSeg). Specifically, we compute Class-specific Multi-prototypical Correlation (CMC) for each query point, representing its correlations to category prototypes. Then, we propose the Hyper Correlation Augmentation (HCA) module to enhance CMC. Furthermore, tackling the inherent property of few-shot training to incur base susceptibility for models, we propose to learn non-parametric prototypes for the base classes during training. The learned base prototypes are used to calibrate correlations for the background class through a Base Prototypes Calibration (BPC) module. Experiments on popular datasets demonstrate the superiority of COSeg over existing methods. The code is available at ***/ZhaochongAn/COSeg. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Mental Health Analysis: ML And Explainable AI Predict Depression Among Bangladeshi University Students 24

Mental Health Analysis: ML And Explainable AI Predict Depres...

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Proceedings of the 3rd International Conference on Computing Advancements

作者： Sarzila Sahrin Jisha Shahriar Ayon Sharia Arfin Tanim Fahim Abdullah Md. Raeid Tahmid Enam Shrestha Md Sadi Al Huda Md. Asraf Ali Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Artificial Intelligence Research and Innovation Lab Dhaka Bangladesh Artificial Intelligence Research & Innovation Lab Dhaka Bangladesh

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4290-4302页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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A Brain-computer Interface for Controlling IoT Devices using EEG Signals 5

A Brain-Computer Interface for Controlling IoT Devices using...

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5th IEEE Ecuador Technical Chapters Meeting, ETCM 2021

作者： Chicaiza, Kelvin Ortiz Benalcázar, Marco E. Artificial Intelligence and Computer Vision Research Lab Department of Computer Science and Informatics Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9781665441414

Motor disability is the loss of the ability to move a limb of the body. Motor disabilities make difficult the interaction between a disabled person and her/his environment. Recent research has focused on developing innovative technologies that could be used by disabled people to improve their life quality. In this paper, a brain-computer interface for controlling IoT devices is proposed. This system is based on the use of the Muse-Headband sensor which captures EEG signals when a person blinks. This sensor is placed on the forehead of the user of the system. The EEGs are preprocessed and then classified into short and long blinks by computing their signal envelopes and using the k-NN algorithm. The classified blinks are then used to form control commands that are sent to a mosquitto server hosted in the cloud. This server is responsible for sending the control action to the connected IoT devices. The accuracy of the classifier designed in this work is 99.53%. Usability tests show that the probability of a user sending a wrong command, with the proposed system, is 4.83%. However, this probability decreases when the time of use of the system proposed in this work increases. © 2021 IEEE.

关键词： Brain computer interface

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Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf 38

Learning to Discuss Strategically: A Case Study on One Night...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Xuanfa Wang, Ziyan Du, Yali Fang, Meng Zhang, Haifeng Wang, Jun Institute of Automation Chinese Academy of Sciences China Cooperative AI Lab Department of Informatics King's College London United Kingdom School of Artificial Intelligence University of Chinese Academy of Sciences China University of Liverpool United Kingdom Nanjing Artificial Intelligence Research of IA China AI Centre Department of Computer Science UCL United Kingdom

Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs), recent agents built with these often neglect the control over discussion tactics, which are essential in communication scenarios and games. As a variant of the famous communication game Werewolf, One Night Ultimate Werewolf (ONUW) requires players to develop strategic discussion policies due to the potential role changes that increase the uncertainty and complexity of the game. In this work, we first present the existence of the Perfect Bayesian Equilibria (PBEs) in two scenarios of the ONUW game: one with discussion and one without. The results showcase that the discussion greatly changes players' utilities by affecting their beliefs, emphasizing the significance of discussion tactics. Based on the insights obtained from the analyses, we propose an RL-instructed language agent framework, where a discussion policy trained by reinforcement learning (RL) is employed to determine appropriate discussion tactics to adopt. Our experimental results on several ONUW game settings demonstrate the effectiveness and generalizability of our proposed framework. The project page of our paper: ***. © 2024 Neural information processing systems foundation. All rights reserved.

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A Global Post hoc XAI Method for Interpreting LSTM Using Deterministic Finite State Automata 32

A Global Post hoc XAI Method for Interpreting LSTM Using Det...

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32nd Irish Conference on artificial intelligence and Cognitive Science, AICS 2024

作者： Gupta, Gargi Qureshi, M. Atif Longo, Luca School of Computer Science Technological University Dublin Ireland eXplainable Analytics Group Faculty of Bussiness Technological University Dublin Ireland Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Dublin Ireland

We propose a global post-hoc XAI method to interpret Long Short-Term Memory (LSTM) models for univariate time series classification. Our approach integrates Symbolic Aggregate approXimation (SAX) to convert continuous time series into symbolic representations during preprocessing. We then apply k-means clustering to the activated hidden states of the LSTM, from which we extract Deterministic Finite Automata (DFA), which provides a transparent and interpretable explanation of the model’s decision-making process. Experiments on synthetic and real-world datasets demonstrate high fidelity between DFA and LSTM, with enhanced interpretability for high-stakes domains like healthcare and power demand forecasting. © 2024 Copyright for this paper by its authors.

关键词： Continuous time systems

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Autoencoder-Assisted Feature-Type Distributed Clustering for Clinical and Biomedical Data Analysis

Autoencoder-Assisted Feature-Type Distributed Clustering for...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Lenat Thomas Madhu S. Nair Saptarshi Bej Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi India Department of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The Uniform Manifold Projection and Approximation (UMAP) assisted Feature-type Distributed Clustering (FDC) workflow, contrary to the traditional UMAP algorithm, was found to be more informative for dimensionality reduction in tabular Clinical and Biomedical Routine Data (CBRD) due to the presence of diverse feature-types in such datasets. However, the prospect of using powerful neural network models for the Feature-type distributed dimensionality reduction task is yet to be explored. Considering the broader applicative potential of neural networks, we compared some autoencoder-based neural networks as an alternative to the UMAP algorithm for the FDC workflow. The study uses standard objective measures such as Silhouette Score and DB-Index to compare the quality of clusters and embeddings generated by the Autoencoder-assisted FDC (FDC-AE) approaches with the established UMAP-assisted FDC (FDC-UMAP) workflow. The evaluation involved two datasets in each size category from the medical field. The results indicate that for a dimensionality reduction and cluster identification task, the FDC-AE can be more effective compared to the FDC-UMAP, especially on larger datasets. Our research opens up the possibility of feature-distributed multi-label supervised dimensionality reduction, as well as the usage of pre-trained networks for such complex dimensionality reduction tasks.

关键词： Dimensionality reduction Manifolds Biological system modeling Neural networks Clustering algorithms Distributed databases Biomedical measurement Communications technology Bioinformatics Standards

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label Few Classify Many: N-Shot GAN for Abnormality Screening Using Chest X-Rays

Label Few Classify Many: N-Shot GAN for Abnormality Screeni...

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6th International Conference on Computational intelligence in Pattern Recognition, CIPR 2024

作者： Pandey, Priyam Singh, Satish Kumar Santosh, K.C. Applied Artificial Intelligence Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad Prayagraj211015 India

ISBN: (纸本)9789819780921

Typical machine learning models typically demand a substantial volume of data for effective training, ensuring optimal performance during testing. However, these models often fail to specify the extent of data required—how big data is big enough to begin with? The "label few, classify many" paradigm is interesting in such a scenario where data is limited, and this holds true for healthcare informatics like others. To address this challenge, we explore the application of n-shot GANs in generating synthetic images, particularly in the context of chest X-rays for detecting COVID-19. Starting with one-shot GAN, our findings reveal that training on just 160 healthy chest X-rays achieves outstanding results, with maximum AUCs of 97.2 and 97.4 from ROC and PR curves on a test dataset comprising approximately 3600 chest X-rays. These results demonstrate comparable performance with state-of-the-art approaches in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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