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arXiv 2020年

作者： Cao, Yanjun Yang, Chenhao Li, Rui Knoll, Alois Beltrame, Giovanni Department of Computer and Software Engineering École Polytechnique de Montréal 2900 Boul Édouard MontpetitQC Canada Wilhelm-Schickard-Institute for Computer Science University of Tübingen Germany School of Automation Chongqing University Chongqing China Department of Informatics Technical University of Munich Germany Department of Informatics Technical University of Munich Germany

Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides another venue to accurately locate devices both indoors and outdoors. In this paper, we study a localization solution with a single UWB anchor, instead of the traditional multi-anchor setup. Besides the challenge of a single UWB ranging source, the only other sensor we require is a low-cost 9 DoF inertial measurement unit (IMU). Under such a configuration, we propose continuous monitoring of UWB range changes to estimate the robot speed when moving on a line. Combining speed estimation with orientation estimation from the IMU sensor, the system becomes temporally observable. We use an Extended Kalman Filter (EKF) to estimate the pose of a robot. With our solution, we can effectively correct the accumulated error and maintain accurate tracking of a moving robot. Copyright © 2020, The Authors. All rights reserved.

关键词： Ultra-wideband (UWB)

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Interval Markov decision processes with multiple objectives: From Robust strategies to Pareto curves

Interval Markov decision processes with multiple objectives:...

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作者： Hahn, Ernst Moritz Hashemi, Vahid Hermanns, Holger Lahijanian, Morteza Turrini, Andrea School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast United Kingdom State Key Laboratory of Computer Science Institute of Software CAS Beijing China Department of Information Technology Audi AG Ingolstadt Germany Saarland University Saarland Informatics Campus Saarbrücken Germany Institute of Intelligent Software Guangzhou Guangzhou China Department of Smead Aerospace Engineering and Sciences University of Colorado BoulderCO United States

Accurate Modelling of a real-world system with probabilistic behaviour is a difficult task. Sensor noise and statistical estimations, among other imprecisions, make the exact probability values impossible to obtain. In this article, we consider Interval Markov decision processes (IMDPs), which generalise classical MDPs by having interval-valued transition probabilities. They provide a powerful modelling tool for probabilistic systems with an additional variation or uncertainty that prevents the knowledge of the exact transition probabilities. We investigate the problem of robust multi-objective synthesis for IMDPs and Pareto curve analysis of multi-objective queries on IMDPs. We study how to find a robust (randomised) strategy that satisfies multiple objectives involving rewards, reachability, and more general ω-regular properties against all possible resolutions of the transition probability uncertainties, as well as to generate an approximate Pareto curve providing an explicit view of the trade-offs between multiple objectives. We show that the multi-objective synthesis problem is PSPACE-hard and provide a value iteration-based decision algorithm to approximate the Pareto set of achievable points. We finally demonstrate the practical effectiveness of our proposed approaches by applying them on several case studies using a prototype tool. © 2019 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks

arXiv

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arXiv 2022年

作者： Grabowski, Bartosz Glomb, Przemyslaw Masarczyk, Wojciech Plawiak, Pawel Yıldırım, Özal Acharya, U. Rajendra Tan, Ru-San Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Warsaw University of Technology Pl. Politechniki 1 Warsaw00-661 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Department of Software Engineering Firat University Elazig Turkey School of Science and Technology Singapore University of Social Sciences Singapore Faculty of Business Education Law and Arts University of Southern Queensland Australia School of Engineering Ngee Ann Polytechnic 535 Clementi Road 599489 Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan Kumamoto University Kumamoto Japan Department of Cardiology National Heart Centre Singapore169609 Singapore Duke-NUS Medical School 169857 Singapore

Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and classification tasks. Moreover, we combined the two approaches, using unlabeled and labeled data, to train the model. We tested the model on the MIT-BIH Arrhythmia database. Our method showed high effectiveness in detecting cardiac arrhythmia based on modified Lead II ECG records, as well as achieved high quality of ECG signal approximation. For the former, our method attained overall accuracy of 87.33% and balanced accuracy of 80.54%, on par with reference approaches. For the latter, application of self-supervised learning allowed for training without the need for expert labels. The regression model yielded satisfactory performance with fairly accurate prediction of QRS complexes. Transferring knowledge from regression to the classification task, our method attained higher overall accuracy of 87.78%. © 2022, CC BY.

关键词： Electrocardiography

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A Multilevel Intelligent Assistant for Multilevel Social Network Analysis

A Multilevel Intelligent Assistant for Multilevel Social Net...

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International IEEE Conference on Intelligent Systems, IS

作者： Man Tianxing Nataly Zhukova Georgi Tsochev ITMO University St. Petersburg Russia Laboratory of Computer-Information Systems and Software Engineering St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Russia Laboratory of Telematics Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

In recent years, social network data analysis is an emerging field. Multilevel social network analysis could help researchers to figure out comprehensive influencing factors. But the raw data extracted from life is complicated and inoperable so that the entire analysis process is multi-step and changeable. The characteristics of social network datasets at different levels are diverse. There is no general data analysis algorithms for each level. The characteristics of the data and the requirements of the task are important basis for choosing suitable analysis methods. But it is hard for non-expert researchers. This paper proposes an intelligent multilevel assistant for multilevel social network analysis and implements it based on ontology technology. Algorithms at different levels process the corresponding data forms until the appropriate output model is generated. The use of ontology technology makes this framework extensible and understandable. Such a framework is significant for non-experts.

关键词： Social network services Ontologies Machine learning algorithms Data mining Data analysis Task analysis

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Quantifying uncertainty in high-throughput density functional theory: a comparison of AFLOW, Materials Project, and OQMD

arXiv

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arXiv 2020年

作者： Hegde, Vinay I. Borg, Christopher K.H. del Rosario, Zachary Kim, Yoolhee Hutchinson, Maxwell Antono, Erin Ling, Julia Saxe, Paul Saal, James E. Meredig, Bryce Citrine Informatics 2629 Broadway Redwood CityCA94063 United States Olin College of Engineering 1000 Olin Way NeedhamMA02492 United States Molecular Sciences Software Institute Virginia Tech BlacksburgVA24061 United States

A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW), the Materials Project (MP), and the Open Quantum Materials Database (OQMD), and compare reported properties across each pair of databases for materials calculated using the same initial crystal structure. We find that HT-DFT formation energies and volumes are generally more reproducible than band gaps and total magnetizations;for instance, a notable fraction of records disagree on whether a material is metallic (up to 7%) or magnetic (up to 15%). The variance between calculated properties is as high as 0.105 eV/atom (median relative absolute difference, or MRAD, of 6%) for formation energy, 0.65 Å3/atom (MRAD of 4%) for volume, 0.21 eV (MRAD of 9%) for band gap, and 0.15 µB/formula unit (MRAD of 8%) for total magnetization, comparable to the differences between DFT and experiment. We trace some of the larger discrepancies to choices involving pseudopotentials, the DFT+U formalism, and elemental reference states, and argue that further standardization of HT-DFT would be beneficial to reproducibility. Copyright © 2020, The Authors. All rights reserved.

关键词： Density functional theory

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Deception and cloud integration: A multi-layered approach for DDoS detection, mitigation, and attack surface minimization in SD-IoT networks

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Computers and Electrical engineering 2025年 126卷

作者： Ameer El-Sayed Ahmed A. Toony Amr Tolba Fayez Alqahtani Yasser Alginahi Wael Said Department of Information Technology Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt Department of Business Information Systems High Institute for Commercial Sciences (Aboukir High Institutes) Ministry of Higher Education and Scientific Research Alexandria Egypt Computer Science Department Community College King Saud University Riyadh 11437 Saudi Arabia Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia Department of Computer Science Adrian College Michigan USA Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig 44511 Egypt

Detecting Distributed Denial of Service (DDoS) attacks in software-Defined Internet of Things (SD-IoT) networks is challenging due to vulnerabilities in single-controller architectures, the limitations of the OpenFlow protocol, evolving DDoS strategies, and resource constraints. This research proposes a multi-layered security framework that integrates deception-based security, cloud-integrated machine learning (ML), a new hierarchically distributed multi-controller (HDMC) architecture, P4-enabled real-time traffic monitoring, and adaptive mitigation. The framework includes dynamic time-based windowing for enhanced detection, a decoy network to divert attackers, and a cloud-based multi-task ML model (MT-EDD) for attack classification. It also features a synchronized multi-control design for secure communication and coordinated actions among multiple controllers and a dynamic monitoring algorithm for real-time traffic analysis. P4 switches extract features from network traffic and send them to a cloud-based server for preprocessing and analysis by a pre-trained ensemble learning model (MT-EDD), which predicts attack states and communicates results to the central controller for mitigation. The controller then enforces appropriate mitigation actions on P4 switches. This approach offloads computationally intensive tasks to the cloud, improving scalability and detection accuracy. Evaluations show the framework achieves an average accuracy of 98.42%, precision of 96.17%, recall of 94.72%, F1-score of 95.39%, and specificity of 98.22%. The proposed P4-enabled solution consumes 30% less bandwidth and 25% less CPU, reduces detection times by 54.3%, and improves detection accuracy by 5.2% compared to the OpenFlow-enabled method. The HDMC architecture, evaluated against a single-controller setup, demonstrated 40% higher throughput and 32% lower latency, confirming its superior performance across multiple metrics.

关键词：

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Design, Synthesis, Antidiabetic Activity and in Silico Studies of New Hydrazone Derivatives Derived from Acetohexamide

SSRN

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SSRN 2025年

作者： Kurşun Aktar, Bedriye Seda Sıcak, Yusuf Oruç-Emre, Emine Elçin Kılıç, Rabia Sağlam, Ebru Taşdemir, Demet Kaya, Süleyman Yılmaz, Gizem Tatar Yaglioglu, Ayse Sahin Department of Hair Care and Beauty Services Yeşilyurt Vocational School Malatya Turgut Özal University Malatya Turkey Department of Medicinal and Aromatic Plants Köyceğiz Vocational School Mugla Sitki Kocman University Köyceğiz Muğla Turkey Department of Chemistry Faculty of Arts and Sciences Gaziantep University Gaziantep Turkey Respiratory Diseases and Respiratory Surgery Research and Practice Center Gaziantep University Gaziantep Turkey Department of Medical Biochemistry Faculty of Medicine Gaziantep University Gaziantep Turkey Department of Biostatistics and Medical Informatics Faculty of Medicine Karadeniz Technical University Trabzon Turkey Karadeniz Technical University Institute of Health Sciences Department of Bioinformatics Trabzon61080 Turkey Yılmaz Bilişim R&D Consulting Software Engineering and Services Trade Limited Company Trabzon61081 Turkey Department of Chemistry and Chemical Process Technology Technical Sciences Vocational School Amasya University Amasya Turkey

Diabetes is a well-known endocrine system disorder whose incidence has increased recently. The has been shown that the synthesis of new more effective antidiabetic drugs is needed because the drugs used in the treatment of diabetes have many side effects and are used in combination. Since hydrazone derivatives are very active compounds with antidiabetic effects, this aim of this study was to synthesize a series of hydrazone derivatives showing potential antidiabetic activity. In the inhibition activity of α-amylase and α-glucosidase, the compounds that showed the best inhibitory effect against both enzymes were determined as compounds 8 (IC50= 30.21±0.16 µM for α-amylase;IC50= 38.06±0.80 µM for α-glucosidase) and 10 (IC50= 34.49±0.37 µM for α-amylase;IC50= 40.44±0.23 µM for α-glucosidase). All of the compounds were evaluated for their cytotoxic effect by the 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (MTT) test on the human embryonic kidney cell line (HEK293). Compounds 7 and 9 have been observed to kill half of the healthy HEK293 cells (IC50: 61.04 µM and IC50: 69.25 µM) respectively. Other compounds and the positive control acarbose showed no cytotoxic effects on healthy cells even at high concentrations. Additionally, the properties of molecules such as H-bond acceptors, H-bond donors, topological polar surface area (TPSA), Lipinski, iLogP, Gastrointestinal (GI) absorption, and Blood-Brain Barrier (BBB) permeability were also investigated with the SwissADME program used in drug design. Molecular docking simulations of the synthesized hydrazone derivatives, especially compounds 8 and 10, revealed strong interactions with α-glucosidase and α-amylase in their active sites. © 2025, The Authors. All rights reserved.

关键词： Amylases

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Accurate position tracking with a single UWB anchor

Accurate position tracking with a single UWB anchor

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yanjun Cao Chenhao Yang Rui Li Alois Knoll Giovanni Beltrame Department of Computer and Software Engineering École Polytechnique de Montréal Québec CA Wilhelm-Schickard-Institute for Computer Science University of Tübingen Germany School of Automation Chongqing University Chongqing China Department of Informatics Technical University of Munich Germany

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

关键词： Robot sensing systems Estimation Observability Velocity measurement Distance measurement Mobile robots

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Author Correction: The 4D Nucleome Data Portal as a resource for searching and visualizing curated nucleomics data

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Nature communications 2022年第1期13卷 6561页

作者： Sarah B Reiff Andrew J Schroeder Koray Kırlı Andrea Cosolo Clara Bakker Luisa Mercado Soohyun Lee Alexander D Veit Alexander K Balashov Carl Vitzthum William Ronchetti Kent M Pitman Jeremy Johnson Shannon R Ehmsen Peter Kerpedjiev Nezar Abdennur Maxim Imakaev Serkan Utku Öztürk Uğur Çamoğlu Leonid A Mirny Nils Gehlenborg Burak H Alver Peter J Park Department of Biomedical Informatics Harvard Medical School Boston MA 02115 USA. Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA. Karya SMD Software Solutions İzmir 35040 Turkey. Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA. Department of Biomedical Informatics Harvard Medical School Boston MA 02115 USA. peter_park@hms.harvard.edu.

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