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The effect of visual advanced driver assistance systems on a following human driver in a mixed-Traffic condition 7

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The effect of visual advanced driver assistance systems on a...

7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Ariansyah, Dedy Pardamean, Bens Caruso, Giandomenico Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Department of Mechanical Engineering Politecnico di Milano Milan 20156 Italy

Rapid development in vehicular technology has caused more automated vehicle control to increase on the roads. Studies showed that driving in mixed traffic with an autonomous vehicle (AV) had a negative impact on the time headway (THW) of conventional vehicles (CVs) (i.e., driven by humans). To address this issue, there is a need to equip CV with visual advanced driver assistance systems (ADASs) that helps the driver maintain safe headway when driving near AVs. This study examines the perception of drivers using visual ADAS and their associated risk while driving behind the AV at constant and varying speeds. The preliminary results showed that while visual ADAS could help drivers keep the safe THW, it could affect drivers' ability to react to emergencies. This implies that visual modality alone might not be sufficient and therefore requires some other feedback or intelligent transport systems to help drivers maintain safe driving in a mixed-Traffic condition. © 2022 Elsevier B.V.. All rights reserved.

关键词： Advanced driver assistance systems

Covariate Adjustment in Randomized Experiments Motivated by Higher-Order Influence Functions

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arXiv 2024年

作者： Zhao, Sihui Wang, Xinbo Liu, Lin Zhang, Xin School of Mathematical Sciences CMA-Shanghai Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University China Department of Bioinformatics and Biostatistics School of Life Sciences Shanghai Jiao Tong University China SJTU-Yale Joint Center for Biostatistics and Data Science Shanghai Jiao Tong University China Global Biometrics & Data Management Pfizer Inc

Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, is a fundamental theoretical device for constructing rate-optimal causal-effect estimators from observational studies. However, the value of HOIF for analyzing well-conducted randomized controlled trials (RCTs) has not been explicitly explored. In the recent U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines on the practice of covariate adjustment in analyzing RCTs, in addition to the simple, unadjusted difference-in-mean estimator, it was also recommended to report the estimator adjusting for baseline covariates via a simple parametric working model, such as a linear model. In this paper, we show that a HOIF-motivated estimator for the treatment-specific mean has significantly improved statistical properties compared to popular adjusted estimators in practice when the number of baseline covariates p is relatively large compared to the sample size n. We also characterize the conditions under which the HOIF-motivated estimator improves upon the unadjusted one. Furthermore, we demonstrate that a novel debiased adjusted estimator proposed recently by Lu et al. is, in fact, another HOIF-motivated estimator in disguise. Numerical and empirical studies are conducted to corroborate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Controlled drug delivery

Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

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data Intelligence 2022年第2期4卷 342-357页

作者： Stian Soiland-Reyes Genis Bayarri Pau Andrio Robin Long Douglas Lowe Ania Niewielska Adam Hospital Paul Groth Department of Computer Science The University of ManchesterManchester M139PLUK Informatics Institute University of AmsterdamAmsterdam 1098 XHThe Netherlands Institute for Research in Biomedicine(IRB Barcelona) The Barcelona Institute of Science and Technology(BIST)Barcelona O8028Spain The Spanish National Bioinformatics Institute(INB) Barcelona Supercomputing Center(BSC)Barcelona 08034Spain Data Science Institute Lancaster UniversityLancasterLancashire LA14YWUK Research IT IT ServicesThe University of ManchesterManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeshire CB101SDUK

We introduce the concept of Canonical Workflow Building Blocks(CWBB),a methodology of describing and wrapping computational tools,in order for them to be utilised in a reproducible manner from multiple workflow languages and execution *** concept is implemented and demonstrated with the BioExcel Building Blocks library(BioBB),a collection of tool wrappers in the field of computational biomolecular *** across different workflow languages is showcased through a protein Molecular Dynamics setup transversal workflow,built using this library and run with 5 different Workflow Manager Systems(WfMS).We argue such practice is a necessary requirement for FAIR Computational Workflows and an element of Canonical Workflow Frameworks for Research(CWFR)in order to improve widespread adoption and reuse of computational methods across workflow language barriers.

关键词： Scientific workflows Interoperable FAIR Computational tools Containers Software packaging FAIR digital object(FDO),BioExcel Building Blocks library(BioBB),Canonical Workflow Frameworks for Research(CWFR)

A review: data pre-processing techniques used for diabetes prediction

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Procedia Computer Science 2024年 245卷 667-676页

作者： Mahmud Isnan Gregorius Natanael Elwirehardja Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cmputer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification.

关键词： Diabetes Classification data Pre-Processing Machine Learning Imbalanced dataset

HARD: Hard Augmentations for Robust Distillation

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arXiv 2023年

作者： Nix, Arne F. Burg, Max F. Sinz, Fabian H. Institute for Bioinformatics and Medical Informatics University of Tübingen Germany Institute for Computer Science Campus Institute Data Science University of Göttingen Germany Institute for Theoretical Physics University of Tübingen Germany

Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a few shortcomings: it has recently been shown that this method is unsuitable to transfer simple inductive biases like shift equivariance, struggles to transfer out of domain generalization, and optimization time is magnitudes longer compared to default non-KD model training. To improve these aspects of KD, we propose Hard Augmentations for Robust Distillation (HARD), a generally applicable data augmentation framework, that generates synthetic data points for which the teacher and the student disagree. We show in a simple toy example that our augmentation framework solves the problem of transferring simple equivariances with KD. We then apply our framework in real-world tasks for a variety of augmentation models, ranging from simple spatial transformations to unconstrained image manipulations with a pretrained variational autoencoder. We find that our learned augmentations significantly improve KD performance on in-domain and out-of-domain evaluation. Moreover, our method outperforms even state-of-the-art data augmentations and since the augmented training inputs can be visualized, they offer a qualitative insight into the properties that are transferred from the teacher to the student. Thus HARD represents a generally applicable, dynamically optimized data augmentation technique tailored to improve the generalization and convergence speed of models trained with KD. © 2023, CC BY.

关键词： Distillation

Genetic prediction of quantitative traits: a machine learner’s guide focused on height

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arXiv 2023年

作者： Bourguignon, Lucie Weis, Caroline Jutzeler, Catherine R. Adamer, Michael Biomedical Data Science Lab D-HEST ETH Zurich Switzerland Schulthess Klinik Switzerland Swiss Institute of Bioinformatics Switzerland Machine Learning and Computational Biology Lab D-BSSE ETH Zurich Switzerland

Machine learning and deep learning have been celebrating many successes in the application to biological problems, especially in the domain of protein folding. Another equally complex and important question has received relatively little attention by the machine learning community, namely the one of prediction of complex traits from genetics. Tackling this problem requires in-depth knowledge of the related genetics literature and awareness of various subtleties associated with genetic data. In this guide, we provide an overview for the machine learning community on current state of the art models and associated subtleties which need to be taken into consideration when developing new models for phenotype prediction. We use height as an example of a continuous-valued phenotype and provide an introduction to benchmark datasets, confounders, feature selection, and common metrics. © 2023, CC BY-NC-ND.

关键词： Forecasting

IoT for Water Quality Categorization 5

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IoT for Water Quality Categorization

5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

Deciphering the unique dynamic activation pathway in a G protein-coupled receptor enables unveiling biased signaling and identifying cryptic allosteric sites in conformational intermediates

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arXiv 2025年

作者： Fan, Jigang Zhu, Chunhao Lan, Xiaobing Zhuang, Haiming Li, Mingyu Zhang, Jian Lu, Shaoyong Medicinal Chemistry and Bioinformatics Center Shanghai Jiao Tong University School of Medicine Shanghai200025 China College of Pharmacy Ningxia Medical University Ningxia Hui Autonomous Region Yinchuan750004 China Center for Data Science Peking University Beijing100871 China

Neurotensin receptor 1 (NTSR1), a member of the Class A G protein-coupled receptor superfamily, plays an important role in modulating dopaminergic neuronal activity and eliciting opioid-independent analgesia. Recent studies suggest that promoting β-arrestin-biased signaling in NTSR1 may diminish drugs of abuse, such as psychostimulants, thereby offering a potential avenue for treating human addiction-related disorders. In this study, we utilized a novel computational and experimental approach that combined nudged elastic band-based molecular dynamics simulations, Markov state models, temporal communication network analysis, site-directed mutagenesis, and conformational biosensors, to explore the intricate mechanisms underlying NTSR1 activation and biased signaling. Our study reveals a dynamic stepwise transition mechanism and activated transmission network associated with NTSR1 activation. It also yields valuable insights into the complex interplay between the unique polar network, non-conserved ion locks, and aromatic clusters in NTSR1 signaling. Moreover, we identified a cryptic allosteric site located in the intracellular region of the receptor that exists in an intermediate state within the activation pathway. Collectively, these findings contribute to a more profound understanding of NTSR1 activation and biased signaling at the atomic level, thereby providing a potential strategy for the development of NTSR1 allosteric modulators in the realm of G protein-coupled receptor biology, biophysics, and medicine. © 2025, CC BY-NC-SA.

关键词： Markov processes

Implementation of Computer Vision of Jakarta Weather Image Categorization Using ResNet 8

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Implementation of Computer Vision of Jakarta Weather Image C...

8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Purwandari, Kartika Sigalingging, Join W.C. Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer vision data augmentation deep transfer learning residual network weather image categorization