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arXiv 2023年

作者： Röseler, Leonore Scholtes, Ingo Gote, Christoph Data Analytics Group University of Zurich Zurich Switzerland Social Computing Group University of Zurich Zurich Switzerland Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany Systems Design ETH Zurich Zurich Switzerland

Background: Despite a growing body of literature on the impact of software bots on open source software development teams, their effects on team communication, coordination, and collaboration practices are not well understood. Bots can have negative consequences, such as producing information overload or reducing interactions between developers. Objective: The objective of this study is to investigate the effect of specific GitHub Actions on the collaboration networks of Open Source Software teams, using a network-analytic approach. The study will focus on Code Review bots, one of the most frequently implemented types of bots. Method: Fine-grained, time-stamped data of co-editing networks, developer-file contribution networks, as well as workflow runs and git commit logs will be obtained from a large sample of GitHub repositories. This will allow us to study how bots affect the collaboration networks of developers over time. By using a more representative sample of GitHub repositories than previous studies, which includes projects whose sizes span the whole range of Open Source communities, this study will provide generalizable results and updated findings on the general usage and distribution of GitHub Actions. With this study, we aim to contribute to advancing our knowledge of human-bot interaction and the effects of support tools on software engineering teams. Copyright © 2023, The Authors. All rights reserved.

关键词： Open source software

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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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BoTier: Multi-Objective Bayesian Optimization with Tiered Composite Objectives

arXiv

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arXiv 2025年

作者： Haddadnia, Mohammad Grashoff, Leonie Strieth-Kalthoff, Felix Harvard University Department of Biological Chemistry & Molecular Pharmacology BostonMA United States Harvard University Dana-Farber Cancer Institute Department of Cancer Biology BostonMA United States University of Wuppertal School of Mathematics and Natural Sciences Wuppertal Germany University of Wuppertal Interdisciplinary Center for Machine Learning and Data Analytics Wuppertal Germany

Scientific optimization problems are usually concerned with balancing multiple competing objectives, which come as preferences over both the outcomes of an experiment (e.g. maximize the reaction yield) and the corresponding input parameters (e.g. minimize the use of an expensive reagent). Typically, practical and economic considerations define a hierarchy over these objectives, which must be reflected in algorithms for sample-efficient experiment planning. Herein, we introduce BoTier, a composite objective that can flexibly represent a hierarchy of preferences over both experiment outcomes and input parameters. We provide systematic benchmarks on synthetic and real-life surfaces, demonstrating the robust applicability of BoTier across a number of use cases. Importantly, BoTier is implemented in an auto-differentiable fashion, enabling seamless integration with the BoTorch library, thereby facilitating adoption by the scientific community. © 2025, CC BY.

关键词： Bot (Internet)

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Thai Conversational Chatbot Classification Using BiLSTM and data Augmentation 1

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (数字)9789819979691

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19

arXiv

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arXiv 2022年

作者： Zhou, Helen Cheng, Cheng Shields, Kelly J. Kochhar, Gursimran Cheema, Tariq Lipton, Zachary C. Weiss, Jeremy C. Machine Learning Department Heinz College Carnegie Mellon University United States Highmark Health Enterprise Data & Analytics Datasicence R&D United States Allegheny Health Network United States

With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features;and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844–0.872 vs. 0.598–0.810). Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Development of an Application for Audio-Visual-Tactile Brainwave Entrainment in Patients with Affective and Psychosomatic Disorders

Development of an Application for Audio-Visual-Tactile Brain...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Vadim A. Korshunov Grigory R. Khazankin Dmitry S. Ivanishkin Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia

Brainwave entrainment is used in different types of treatment, one of which is treating affective and psychosomatic disorders. The better result is achieved if individual alpha-peak frequency is taken into account during entrainment and when it includes audio visual and tactile stimulation. The program which allows running stimulation scenarios is required to be available on different platforms because patients need to continue the procedures even after finishing them in hospital. So they need to be able to download and install this control program on their personal computers. The required application was developed as a multiplatform one using. NET Core framework in order to reduce costs of further maintenance.

关键词： Headphones Visualization Neuroscience Hospitals Operating systems Linux Microcomputers

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Automated Long-Term Monitoring of Stereotypical Movement in Polar Bears Under Human Care Using machine learning

SSRN

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SSRN 2024年

作者： Zuerl, Matthias Stoll, Philip Brehm, Ingrid Sueskind, Jonas Raab, René Petermann, Jan Zanca, Dario Simon, Ralph von Fersen, Lorenzo Eskofier, Bjoern Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91052 Germany Animal Physiology Department of Biology Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Behavioral Ecology and Conservation Lab Nuremberg Zoo Nuremberg90480 Germany

The welfare of captive animals is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant *** behaviour monitoring methods, however, are labour-intensive and subject to observer *** study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in captive animals, exemplified by a longitudinal study of two polar *** designed an animal tracking framework to localise and identify individual animals in the *** determining their position on the enclosure map via homographic transformation, we cleaned the resulting trajectories using a particle ***, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %.The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological *** work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated monitoring of stereotypical behaviour, paving the way for its application across different species and settings. © 2024, The Authors. All rights reserved.

关键词： Enclosures

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Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using machine learning

Prediction of Stress Coping Capabilities from Nightly Heart ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Linda Vorberg Siri Pflueger Robert Richer Katharina M. Jaeger Arne Küderle Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Stress is related to short- and long-term alterations in stress systems, including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). While it is well established that stress experienced during the day can affect sleep quality, less is known about how it affects stress systems during the night. We assume that stress coping strategies can have an impact on how stress carries over into the night and that individuals with bad coping mechanisms show elevated activation of stress systems during sleep. For that reason, we recorded the heart rate (HR) and heart rate variability (HRV) of 21 healthy participants on two consecutive nights during sleep and the first hour after awakening and extracted cortisol and alpha-amylase from saliva samples collected in the first hour after awakening. Stress coping capabilities were assessed using self-reports. We performed backward stepwise multiple regression models to analyze the relationship between HR(V) and stress coping and trained different machine learning-based regression algorithms to predict positive (SVF Pos ) and negative (SVF Neg ) stress coping capabilities, respectively. Our results show that individuals with higher SVF Neg scores showed higher SNS activity during the night, whereas higher SVF Pos scores indicated lower SNS activity. SVF Pos was predicted with a mean absolute error (MAE) of 1.51 ± 0.73 and SVF Neg with an MAE of 2.79 ± 1.53. Our findings indicate an association between nightly HR(V) and the individual’s capability of coping with stress. This provides further information about how stress influences sleep and might be used for tailored intervention and feedback on successful stress coping.

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VoStress – Voice-based Detection of Acute Psychosocial Stress

VoStress – Voice-based Detection of Acute Psychosocial Stre...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Marie Oesten Robert Richer Luca Abel Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Current stress assessment methods include self-reports and biomarkers which are evaluated in often complex, laboratory procedures. Due to that investigating new indicators for acute stress is crucial for the development of automatic stress detection systems. A promising extension might be provided by investigating speech, which has been shown to be affected by negative emotions and threatening situations. For that reason, we extracted verbal acoustics from audio data collected during a study where N=21 participants underwent the Trier Social Stress Test (TSST), the gold standard for laboratory stress induction, and a stress-free control condition (friendly-TSST) while concurrently collecting cortisol via saliva samples to assess the biological response to stress. Our results show that acute stress leads to significant (p < 0.05) alterations of acoustic features. A stepwise backward multiple linear regression model explained 58.8 % of the variance of the maximum cortisol increase. In addition to that, we performed classification experiments that distinguished stress from non-stress situations with an accuracy of 80.0 ± 12.7 %. While further research is needed to validate our approach, we are convinced that the information extracted from speech can be a valuable indicator for automatic stress detection systems and can even predict the biological response to stress situations.

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Predicting Influential Higher-Order Patterns in Temporal Network data

Predicting Influential Higher-Order Patterns in Temporal Net...

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

作者： Christoph Gote Vincenzo Perri Ingo Scholtes Data Analytics Group University of Zurich Zurich Switzerland Chair of Systems Design ETH Zurich Switzerland Chair of Machine Learning for Complex Networks Julius-Maximilians-Universität Würzburg Würzburg Germany

Networks are frequently used to model complex systems comprised of interacting elements. While edges capture the topology of direct interactions, the true complexity of many systems originates from higher-order patterns in paths by which nodes can indirectly influence each other. Path data, representing ordered sequences of consecutive direct interactions, can be used to model these patterns. On the one hand, to avoid overfitting, such models should only consider those higher-order patterns for which the data provide sufficient statistical evidence. On the other hand, we hypothesise that network models, which capture only direct interactions, underfit higher-order patterns present in data. Consequently, both approaches are likely to misidentify influential nodes in complex networks. We contribute to this issue by proposing five centrality measures based on MOGen, a multi-order generative model that accounts for all indirect influences up to a maximum distance but disregards influences at higher distances. We compare MOGen-based centralities to equivalent measures for network models and path data in a prediction experiment where we aim to identify influential nodes in out-of-sample data. Our results show strong evidence supporting our hypothesis. MOGen consistently outperforms both the network model and path-based prediction. We further show that the performance difference between MOGen and the path-based approach disappears if we have sufficient observations, confirming that the error is due to overfitting.

关键词： Social networking (online) Computational modeling Complex networks Predictive models Length measurement data models Topology

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