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E-Health and Bioengineering Conference (EHB)

作者： Shahin Amiriparian Maurice Gerczuk Justina Lutz Wolfgang Strube Irina Papazova Alkomiet Hasan Alexander Kathan Björn W. Schuller Chair of Health Informatics TUM University Hospital Munich Germany Munich Center for Machine Learning (MCML) Germany Department of Psychiatry Psychotherapy and Psychosomatics Faculty of Medicine University of Augsburg Germany German Center for Mental Health Munich Germany Munich Data Science Institute (MDSI) Germany

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

The delayed access to specialized psychiatric assessments and care for patients at risk of suicidal tendencies in emergency departments creates a notable gap in timely intervention, hindering the provision of adequate mental health support during critical situations. To address this, we present a non-invasive, speech-based approach for automatic suicide risk assessment. For our study, we collected a novel speech recording dataset from 20 patients. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. We proceed by conducting a binary classification to assess suicide risk in a leave-one-subject-out fashion. Our most effective speech model achieves a balanced accuracy of 66.2%. Moreover, we show that integrating our speech model with a series of patients' metadata, such as the history of suicide attempts or access to firearms, improves the overall result. The metadata integration yields a balanced accuracy of 94.4%, marking an absolute improvement of 28.2 %, demonstrating the efficacy of our proposed approaches for automatic suicide risk assessment in emergency medicine.

关键词： Accuracy Speech analysis Emergency medicine Mental health Metadata Predictive models Feature extraction Recording Risk management History

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Ensemble Methods for Collective Intelligence: Combining Ubiquitous ML Models in IoT

Ensemble Methods for Collective Intelligence: Combining Ubiq...

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IEEE International Conference on Big data

作者： Bharath Sudharsan Piyush Yadav Duc-Duy Nguyen Jefkine Kafunah John G. Breslin ARM Machine Learning Infrastructure Galway Ireland Data Science Institute NUI Galway Ireland

ISBN: (纸本)9781665445993

The concept of ML model aggregation rather than data aggregation has gained much attention as it boosts pre- diction performance while maintaining stability and preserving privacy. In a non-ideal scenario, there are chances for a base model trained on a single device to make independent but complementary errors. To handle such cases, in this paper, we implement and release the code of 8 robust ML model combining methods that achieves reliable prediction results by combining numerous base models (trained on many devices) to form a central model that effectively limits errors, built-in randomness and uncertainties. We extensively test the model combining performance by performing 15 heterogeneous devices and 3 datasets based experiments that exemplifies how a complicated collective intelligence can be derived from numerous elementary intelligence learned by distributed, ubiquitous IoT devices.

关键词： Performance evaluation data privacy Uncertainty Buildings Predictive models Collective intelligence Big data

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Beyond Words: Exploring Emotion Detection in Speech Using Sound

Beyond Words: Exploring Emotion Detection in Speech Using So...

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Emerging Technology (INCET), International Conference for

作者： Ruchika Vaidya Sarthak Gaurkhede Rahul Dattangire K.T.V Redddy Utkarsha Pachraney Divya Biradar Artificial intelligence And Machine Learning Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Data Engineering Publicis Sapient Houston Texas USA Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer Science University of Texas at Arlington Arlignton Texas USA

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Speech emotion recognition (SER) has the possible to revolutionize human-computer interaction and numerous other fields. This paper explores the application of deep learning, mostly Long Short-Term Memory (LSTM) networks, for SER by the Toronto Speech Emotion Set (TESS) dataset. TESS data undergoes rigorous pre-processing, including normalization and feature extraction, to prepare it for the LSTM model. This model choice leverages its ability to capture essential long-term dependencies inside the audio data. Through careful selection and optimization of hyperparameters, the LSTM network architecture is fine-tuned to achieve optimal performance. The training process employs optimizers and a loss function to guide the model in learning the complicated relationships between extracted features and conforming emotions. To comprehensively measure the model's efficiency, various metrics are used, together with accuracy, precision, recall, and F1-score. This research underwrites to the advancement of SER by representing the efficiency of deep learning, specifically LSTMs, on the TESS dataset. The findings not only offer valuable insights into model selection, data pre-processing, and performance calculation but also cover the technique for further exploration of deep learning in SER applications.

关键词： Deep learning Training Emotion recognition Accuracy Training data Speech recognition Feature extraction

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On the Mathematics of RNA Velocity II: Algorithmic Aspects

arXiv

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arXiv 2023年

作者： Li, Tiejun Wang, Yizhuo Yang, Guoguo Zhou, Peijie LMAM School of Mathematical Sciences Peking University Beijing100871 China Center for Machine Learning Research Peking University Beijing100871 China Center for Data Science Peking University Beijing100871 China Department of Mathematics University of California Irvine IrvineCA92697 United States

In a previous paper [CSIAM Trans. Appl. Math. 2 (2021), 1-55], the authors proposed a theoretical framework for the analysis of RNA velocity, which is a promising concept in scRNA-seq data analysis to reveal the cell state-transition dynamical processes underlying snapshot data. The current paper is devoted to the algorithmic study of some key components in RNA velocity workflow. Four important points are addressed in this paper: (1) We construct a rational time-scale fixation method which can determine the global gene-shared latent time for cells. (2) We present an uncertainty quantification strategy for the inferred parameters obtained through the EM algorithm. (3) We establish the optimal criterion for the choice of velocity kernel bandwidth with respect to the sample size in the downstream analysis and discuss its implications. (4) We propose a temporal distance estimation approach between two cell clusters along the cellular development path. Some illustrative numerical tests are also carried out to verify our analysis. These results are intended to provide tools and insights in further development of RNA velocity type methods in the future. © 2023, CC BY-NC-SA.

关键词： RNA

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization

BIM: Improving Graph Neural Networks with Balanced Influence...

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International Conference on data Engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

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Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation

arXiv

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arXiv 2023年

作者： Bender, Sidney Anders, Christopher J. Chormai, Pattarawat Marxfeld, Heike Herrmann, Jan Montavon, Grégoire Machine Learning Group Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Max Planck School of Cognition Leipzig Germany BASF SE Ludwigshafen am Rhein Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

This paper introduces a novel technique called counterfactual knowledge distillation (CFKD) to detect and remove reliance on confounders in deep learning models with the help of human expert feedback. Confounders are spurious features that models tend to rely on, which can result in unexpected errors in regulated or safety-critical domains. The paper highlights the benefit of CFKD in such domains and shows some advantages of counterfactual explanations over other types of explanations. We propose an experiment scheme to quantitatively evaluate the success of CFKD and different teachers that can give feedback to the model. We also introduce a new metric that is better correlated with true test performance than validation accuracy. The paper demonstrates the effectiveness of CFKD on synthetically augmented datasets and on real-world histopathological datasets. © 2023, CC0.

关键词： Distillation

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Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation

Towards Fixing Clever-Hans Predictors with Counterfactual Kn...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Sidney Bender Christopher J. Anders Pattarawat Chormai Heike Marxfeld Jan Herrmann Grégoire Montavon Machine Learning Group Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Max Planck School of Cognition Leipzig Germany BASF SE Ludwigshafen am Rhein Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

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Role of machine learning in Climate Change Prediction and Mitigation

Role of Machine Learning in Climate Change Prediction and Mi...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Nayan Jikar Yash Tale Abhay Tale Aditya Barhate Prateek Verma Aman Jikar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action.

关键词： Climate change Renewable energy sources Ethics Prevention and mitigation Scalability data integrity Collaboration data models Internet of Things Meteorology

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Optimal kernel choice for score function-based causal discovery 24

Optimal kernel choice for score function-based causal discov...

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Proceedings of the 41st International Conference on machine learning

作者： Wenjie Wang Biwei Huang Feng Liu Xinge You Tongliang Liu Kun Zhang Mingming Gong School of Mathematics and Statistics The University of Melbourne Australia and Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Halicioğlu Data Science Institute (HDSI) University of California San Diego School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates and Department of Philosophy Carnegie Mellon University

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

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Cost-Effective Communication in UDN in Indoor and Outdoor Environment via machine learning

Cost-Effective Communication in UDN in Indoor and Outdoor En...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Karman, K. Nattar Velmurugan, V. Raju, Kommisetti Murthy Sajana, T. Vijayalakshmi, V. Dhanraj, JoshuvaArockia Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Vel Tech Rangarajan and Dr.Sagunthala RandD Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Andhra Pradesh West Godavari India Department of Artificial Intelligence and Data Science KoneruLakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mechatronics Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350334357

In general, applications on a densely populated network are slower. When there are no opportunities to interact with the devices on the network, the user is forced to communicate at some cost. Thus, the inconsistency that exists there increases the cost over that dense network. The single way to evaluate prospect usage is to look at that have used in the earlier period. This paper proposes an upgraded approach to upgrading existing services within the Ultra Dense cloud Network (UDN). In this way the cost for the services available there is calculated in advance and a system is developed to manage it effectively. This proposed method clearly shows that the effective cost can be managed by combining high-costservices and low-costservices separately. This will help to manage the various communication processes to achieve high effectiveness in Ultra Dense cloud Network (UDN). The proposed method achieves 93.28% of Cost-effective data Management.49% of Application log failure, 42 % of system log failure, 98% of network log management, 96% of security log management, 99% cost effective management. © 2023 IEEE.

关键词： Cost effectiveness

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