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arXiv 2024年

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of Computing Science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Germany Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag: Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym – the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

arXiv

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arXiv 2024年

作者： Bassi, Pedro R.A.S. Li, Wenxuan Tang, Yucheng Isensee, Fabian Wang, Zifu Chen, Jieneng Chou, Yu-Cheng Roy, Saikat Kirchhoff, Yannick Rokuss, Maximilian Huang, Ziyan Ye, Jin He, Junjun Wald, Tassilo Ulrich, Constantin Baumgartner, Michael Maier-Hein, Klaus H. Jaeger, Paul Ye, Yiwen Xie, Yutong Zhang, Jianpeng Chen, Ziyang Xia, Yong Xing, Zhaohu Zhu, Lei Sadegheih, Yousef Bozorgpour, Afshin Kumari, Pratibha Azad, Reza Merhof, Dorit Shi, Pengcheng Ma, Ting Du, Yuxin Bai, Fan Huang, Tiejun Zhao, Bo Wang, Haonan Li, Xiaomeng Gu, Hanxue Dong, Haoyu Yang, Jichen Mazurowski, Maciej A. Gupta, Saumya Wu, Linshan Zhuang, Jiaxin Chen, Hao Roth, Holger Xu, Daguang Blaschko, Matthew B. Decherchi, Sergio Cavalli, Andrea Yuille, Alan L. Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Pharmacy and Biotechnology University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy NVIDIA United States Germany Germany ESAT-PSI KU Leuven Belgium Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Germany Shanghai Jiao Tong University China Shanghai Artificial Intelligence Laboratory China Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany DKFZ Germany School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong Faculty of Informatics and Data Science University of Regensburg Germany Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Fraunhofer Institute for Digital Medicine MEVIS Germany Electronic & Information Engineering School Harbin Institute of Technology Shenzhen China China The Chinese University of Hong Kong Hong Kong Peking University China Department of Electrical and Computer Engineering Duke University United States Stony Brook University United States Department of Computer Science and Engineering Department of Chemical and Biological Engineering Division of Life Science Hong Kong University of Science and Technology Hong Kong Data Science and Computation Facility Fondazione Istituto Italiano di Tecnologia Italy Ecole Polytechnique Fédérale de Lausanne Switzerland

How can we test AI performance? This question seems trivial, but it isn’t. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms. In addition, we also evaluated pre-existing AI frameworks—which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain. Copyright © 2024, The Authors. All rights reserved.

关键词： Open systems

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Blockchain-Enabled Reliable Osmotic Computing for Cloud of Things: Applications and Challenges

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IEEE Internet of Things Magazine 2020年第2期3卷 63-67页

作者： Rasool, Saqib Saleem, Afshan Iqbal, Muddessar Dagiuklas, Tasos Bashir, Ali Kashif Mumtaz, Shahid Otaibi, Sattam Al Gujrat Pakistan Nexthon Technologies Gujrat Pakistan Division of Computer Science and Informatics School of Engineering London South Bank University United Kingdom SuITE Research Group London South Bank University United Kingdom Department of Computing and Mathematics Manchester Metropolitan University United Kingdom Instituto de Telecomunicações Pólo de Aveiro Portugal Innovation and Entrepreneurship Center Taif University Taif Saudi Arabia

Cloud of Things (CoT) refers to an IoT solution consuming the cloud services of a single cloud vendor. In this article, we have introduced the concept of a MultiCoT (http://***) solution which refers to the collaborative execution of an IoT solution by multiple cloud vendors. Cloudlets and ad-hoc clouds are the extensions of centralized cloud services, closer to the user, in the form of fog and edge computing layers respectively and the Osmotic Computing (OC) serves as a glue by accomplishing the seamless compute sharing across these layers. The OC can also be integrated within a MultiCoT solution for extending it across three computational layers of cloud, fog and edge. However, this can only be achieved after establishing enough trust among all the vendors that are working in collaboration to simultaneously serve a particular MultiCoT solution. Blockchain has been already proven for establishing trust and supporting reliable interactions among independently operating entities. Hence, it can be used for establishing trust among the multiple cloud vendors serving a single MultiCoT solution. In this article, we have presented the importance of using the proactive Blockchain-enabled Osmotic Manager (B-OM) for improving the reliability of OC. We have also highlighted the blockchain features that can improve the reliability of OC by establishing trust among the independently operating vendors of a MultiCoT solution, followed by the challenges associated with the integration of blockchain and OC along with the future research directions for achieving the proposed integration. © 2018 IEEE.

关键词： Osmosis

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An Improved Wood Identification Accuracy Using Gaussian Pyramid and Laplacian Edge Detection Based on Android Smartphone

An Improved Wood Identification Accuracy Using Gaussian Pyra...

引用

International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Bambang Sugiarto Muhammad Rosyid Arifin Riffa Haviani Laluma Esa Prakasa Gunawansyah Ade Geovania Azwar Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Computer Vision Research Group Research Center for Informatics Indonesian Institute of Sciences (LIPI) Bandung Indonesia

ISBN: (数字)9781728175980

ISBN: (纸本)9781728175997

Several studies have been carried out for the rapid wood identification process without eye observation of the wood anatomists. computer vision is the first choice in this case so that the identification results are rapid and more accurate than the conventional method. Our previous research developed a method for wood identification using the Histogram of Oriented Gradient (HOG) feature extraction and Support Vector Machine (SVM) as a classifier on Android smartphones. This paper proposes an improved wood identification accuracy of the HOG method and SVM classifier by utilizing several methods on the image preprocessing i.e. the Gaussian pyramid and the Laplacian edge detection methods. The Gaussian pyramid is used to reduce the wood image into a smaller group of pixels to qualify size wood image in the extraction process without reducing the image quality. On the other hand, to clear and distinguish the pattern in the wood image, the Laplacian edge detection is used. In our experiments, wood images from five wood species were used i.e. Kembang Semangkok, Ketapang, Preparat Darat, Pinang, and Puspa. The result showed that each wood species have increased accuracy, precision, recall, and specificity. The lowest increment accuracy was for Pinang and Puspa species at 4.00% of accuracy and zero precision value is found in Puspa species. Furthermore, from five wood species, there was a significantly increased result so it is very useful for improving the result of identification using HOG descriptor and SVM Classifier.

关键词： Image edge detection Support vector machines Laplace equations Feature extraction Testing Training informatics

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Automated Essay Scoring Using Natural Language Processing And Text Mining Method

Automated Essay Scoring Using Natural Language Processing An...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Gunawansyah Riska Rahayu Nurwathi Bambang Sugiarto Gunawan Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Computer vision Research Group Research Center of Informatics Indonesian Institute of Sciences (LIPI) Bandung Indonesia

ISBN: (数字)9781728175980

ISBN: (纸本)9781728175997

The use of technology really helps to maximized the effectiveness and efficiency of work expecially in the education field. Elearning is the concept of education that has begun to be widely implemented at this covid-19 pandemic to avoid the spread of transmission through social distancing. One of elearning types is essay but for large participants, it need much effort for evaluate by human rater. The inconsistency of assessment by the rater due to fatigue can also affect the quality of the assessment. Developing a system that can learn and understand on its own without having to be repeatedly programmed by humans used machine learning and computational linguistics to study the interaction between computers and human natural language used natural language processing proposed in this research. Natural language processing and text mining methods are able to provide a good assessment which is influenced by several processes, namely tokenization, stopword, stemming and support with the number of keywords, and the synonym of more complex keywords. The automated essay scoring system is proven to provide consistent and objective assessments and is able to approach human raters assessments.

关键词： Electronic learning Tokenization Text mining informatics Education Machine learning Libraries

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SoK: Cross-border Criminal Investigations and Digital Evidence

arXiv

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arXiv 2022年

作者： Casino, Fran Pina, Claudia López-Aguilar, Pablo Batista, Edgar Solanas, Agusti Patsakis, Constantinos Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Spain Information Management Systems Institute Athena Research Center Artemidos 6 Marousi15125 Greece European Judicial Cybercrime Network Eurojust The Hague Netherlands Anti-Phishing Working Group-Europe Av. Diagonal 621-629 Barcelona08028 Spain Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece

Digital evidence underpin the majority of crimes as their analysis is an integral part of almost every criminal investigation. Even if we temporarily disregard the numerous challenges in the collection and analysis of digital evidence, the exchange of the evidence among the different stakeholders has many thorny issues. Of specific interest are cross-border criminal investigations as the complexity is significantly high due to the heterogeneity of legal frameworks which beyond time bottlenecks can also become prohibiting. The aim of this article is to analyse the current state of practice of cross-border investigations considering the efficacy of current collaboration protocols along with the challenges and drawbacks to be overcome. Further to performing a legally-oriented research treatise, we recall all the challenges raised in the literature and discuss them from a more practical yet global perspective. Thus, this article paves the way to enabling practitioners and stakeholders to leverage horizontal strategies to fill in the identified gaps timely and accurately. © 2022, CC BY-NC-SA.

关键词： Crime

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A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds

Artificial Intelligence Chemistry

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Artificial Intelligence Chemistry 2024年第2期2卷

作者： Muhamad Akrom Supriadi Rustad Hermawan Kresno Dipojono Ryo Maezono Study Program in Informatics Engineering Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R 2 ) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1) , methyl quinoxaline-6-carboxylate (Q2) , (2 E ,3 E )-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3) , (2 E ,3 E ) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4) , ( E )-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5) , and 2-(4-methoxyphenyl)-7-methylthieno[3,2- b ] quinoxaline (Q6) , show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

关键词： Quantum neural network QSPR Corrosion inhibition Quinoxaline

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Classification of Chicken Meat Freshness using Convolutional Neural Network Algorithms

Classification of Chicken Meat Freshness using Convolutional...

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International Conference on Innovation and Intelligence for informatics, Computing, and Technologies (3ICT)

作者： Calvin Ghiri Basuki Putra Esa Prakasa Departement of Electrical Engineering Universitas Bangka Belitung Pangkalpinang Indonesia Computer Vision Research Group Research Center of Informatics Indonesian Institute of Sciences (LIPI) Bandung Indonesia

ISBN: (数字)9781728196732

ISBN: (纸本)9781728196749

Broiler chicken meat is one of the most widely consumed meat types in Indonesia, this high level of consumption makes a lot of consumer demand in the market. However, there was a found seller who sells broiler chicken meat that are rotten. In this study, we develop chicken meat freshness identification using a convolutional neural network algorithm. This study used the image dataset of broiler chicken breasts. There are two categories of chicken meat used in the study, namely, fresh and rotten. The meat images were acquired by using a smartphone camera. For the process of cropping chicken meat images, we use thresholding with the Otsu method and conversion of RGB images to binary images to select the area of RGB images before cropping the images. The chicken meat images were cropped into three sizes and then used as a dataset in the study. The chicken meat image dataset was trained using a simple architecture that was self-made called Ayam6Net, we also used the AlexNet, VGGNet, and GoogLeNet architectures as a comparison. Ayam6Net has the highest accuracy of 92.9%. From the experiment results, we can conclude that using Ayam6Net architecture with dataset $400\times 400$ pixels has a better accuracy result compared with other architectures and other sizes image datasets.

关键词： computer architecture Training informatics Feature extraction Technological innovation Gas detectors Image color analysis

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SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

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Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Predicting Neurological Recovery from Coma After Cardiac Arr...

引用

computers in Cardiology (CinC)

作者： Matthew A Reyna Edilberto Amorim Reza Sameni James Weigle Andoni Elola Ali Bahrami Rad Salman Seyedi Hyeokhyen Kwon Wei-Long Zheng Mohammad M Ghassemi Michel JAM van Putten Jeannette Hofmeijer Nicolas Gaspard Adithya Sivaraju Susan T Herman Jong Woo Lee M Brandon Westover Gari D Clifford Department of Biomedical Informatics Emory University USA Department of Neurology Weill Institute for Neurosciences University of California San Francisco USA Department of Neurology Massachusetts General Hospital USA Department of Electronic Technology University of the Basque Country UPV/EHU Spain Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Michigan State University USA Clinical Neurophysiology Group University of Twente The Netherlands Department of Neurology and Clinical Neurophysiology Medisch Spectrum Twente The Netherlands Department of Neurology Rijnstate Hospital The Netherlands Department of Neurology Yale School of Medicine USA Service de Neurologie Hôpital Universitaire de Bruxelles and Université Libre de Bruxelles Belgium Department of Neurology Barrow Neurological Institute Comprehensive Epilepsy Center USA Department of Neurology Brigham and Women's Hospital USA Department of Neurology Beth Israel Deaconess Medical Center USA McCance Center for Brain Health Massachusetts General Hospital USA Harvard Medical School USA Department of Biomedical Engineering Georgia Institute of Technology USA

The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

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