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Corrigendum to “Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system” [Alex. Eng. J. 104 (2024) 504–514]

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Alexandria Engineering Journal 2025年 110卷 376-376页

作者： Adnan Hasan Bdair AIghuraibawi Selvakumar Manickam Zaid Abdi Alkareem Alyasseri Rosni Abdullah Ayman Khallel Riyadh Rahef Nuiaa Al Ogaili Fahd N. Al-Wesabi Abdulsamad Ebrahim Yahya National Advanced IPv6 Centre (NAv6) Universiti Sains Malaysia Penang 11800 Malaysia Baghdad College of Economic Sciences University Baghdad Iraq Information Technology Research and Development Center University of Kufa Najaf Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq School of Computer Sciences Universiti Sains Malays Penang 11800 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Department of Cybersecurity Engineering Technology Al Hikma University College Baghdad Iraq Department of Computer Science/ College of Computer Science and Information Technology Wasit University Kut Wasit 52001 Iraq Department of Computer Science College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Information Technology College of Computing and Information Technology Northern Border University Arar Saudi Arabia

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Quantum Gate for a Kerr Nonlinear Parametric Oscillator Using Effective Excited States

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Physical Review Applied 2022年第1期18卷 014019-014019页

作者： Taro Kanao Shumpei Masuda Shiro Kawabata Hayato Goto Frontier Research Laboratory Corporate Research & Development Center Toshiba Corporation 1 Komukai-Toshiba-cho Saiwai-ku Kawasaki 212-8582 Japan Research Center for Emerging Computing Technologies (RCECT) National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan

A Kerr nonlinear parametric oscillator (KPO) can stabilize a quantum superposition of two coherent states with opposite phases, which can be used as a qubit. In a universal gate set for quantum computation with KPOs, an Rx gate, which interchanges the two coherent states, is relatively hard to perform owing to the stability of the two states. We propose a method for a high-fidelity Rx gate by exciting the KPO outside the qubit space with parity-selective transitions, which can be implemented by only adding a driving field. In this method, the utilization of higher effective excited states leads to a faster Rx gate, rather than states near the qubit space. The proposed method can realize a continuous Rx gate and thus is expected to be useful for, e.g., recently proposed variational quantum algorithms.

关键词： Bifurcations Josephson effect Kerr effect Quantum gates Quantum information with solid state qubits Superconducting quantum optics Optical parametric oscillators & amplifiers Superconducting qubits

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Effects of Sodium Hydroxide Treatment on LLDPE/DS Composites: Tensile Properties and Morphology 1

Effects of Sodium Hydroxide Treatment on LLDPE/DS Composites...

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1st International Conference on Green and Sustainable computing (ICoGeS)

作者： Alnaid, Abduati Noriman, N. Z. Dahham, Omar S. Hamzah, R. Sudin, S. Idrus, S. Z. Syed Shayfull, Z. Mohammed, M. Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Faculty of Engineering Technology (FETech) Universiti Malaysia Perlis (UniMAP) UniCITI Alam Campus Sungai Chucuh Padang Besar Perlis 02100 Malaysia School of Computer and Communication Engineering (SCCE) Green Advanced Computing and Technology (GREAT) Research Group-CEGeoGTech Universiti Malaysia Perlis (UniMAP) Malaysia School of Human Development and Techno-communication (IKOM) Green Advanced Computing and Technology (GREAT) Research Group-CEGeoGTech Universiti Malaysia Perlis (UniMAP) Malaysia School of Manufacturing Engineering Universiti Malaysia Perlis Arau 02600 Malaysia Green Design and Manufacture Research Group Center of Excellence Geopolymer and Green Technology (CEGeoGTech) Universiti Malaysia Perlis Kangar 0100 Malaysia Center of Excellence Geopolymer and Green Technology (CEGeoGTech) School of Materials Engineering Universiti Malaysia Perlis (UniMAP) Kompleks Pengajian Jejawi 2 Arau 02600 Malaysia

In this research, the effect of different sodium hydroxide (NaOH) concentration (3, 6 and 9 wt.%) on the tensile properties of linear low density polyethylene / date seeds (LLDPE/DS) composites were investigated. The size of DS particles was measured at (125 - 250 m) and the ratio of LLDPE over DS was fixed at 90/10 wt.%. The samples were prepared using extrusion process (screw speed at 60 rpm and barrel and die temperature at 160 C and 165 C respectively) followed by injection moulding process (from feed to exit die zone temperature at 160, 165, 170 and 175 C respectively). Results suggest that the addition of NaOH particularly 3 wt% has improved the tensile strength. Additionally, the elongation at break has improved as well. However, the high concentration of NaOH, particularly 9 wt.% effects negatively on these properties. In contrast, the Modulus shows different trend, which increased as NaOH concentration increased in the composites. The SEM section shows the improvement on DS surface as low NaOH concentration used (3 wt.%) and deterioration on the DS surface as high NaOH concentration used (9 wt.%).

关键词： Sodium hydroxide

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Adaptive NMS: Refining pedestrian detection in a crowd

arXiv

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arXiv 2019年

作者： Liu, Songtao Huang, Di Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel NonMaximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density;(2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors;and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Leveraging Graph Neural Network with LSTM For Traffic Speed Prediction

Leveraging Graph Neural Network with LSTM For Traffic Speed ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Zhilong Lu Weifeng Lv Zhipu Xie Bowen Du Runhe Huang State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Hosei University Tokyo Japan

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Accurate traffic forecasting plays an important role in the smart city and is of great significance for urban traffic planning, management, and traffic control. However, road speed prediction is a challenge task due to the complex topological structure of road networks and stochastic traffic patterns. For capturing spatial and temporal dependencies simultaneously, in this paper we propose a novel graph neural network based traffic speed forecasting model, the graph Long short term Memory (GLSTM) model which consists Graph neural network (GNN) and Long short term Memory. To the best of our knowledge, this is the first time to combine LSTM and GNN to feed graph-structured data as input for train models in traffic speed prediction. More specifically, at first we construct a unweighted directed graph from road network and feed LSTM cell with graph-structured data. After that, we train the whole model based on encoder-decoder architecture and message-passing mechanism of GNN. Experiments show that our proposed method is able to utilize road structure to capture spatial-temporal dependencies based on GNN while capture long-term dependencies based on LSTM. The result of real world dataset shows that proposed method outperform state-of-the-art baseline methods.

关键词： Roads Forecasting Predictive models Recurrent neural networks Machine learning Accidents

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Targeting druggable proteins hisB, aroC, and trpD in the co-diphtheria infection caused by Corynebacterium ulcerans with Traditional Chinese Medicine

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Heliyon 2025年

作者： Sania Kanwal Ayesha Wisal Saman Sohail Paulo VS. Daltron Muhammad Shafique Muhammad Imran Ihosvany Camps Syed S. Hassan Department of Chemistry Islamia College Peshawar Peshawar-KP Pakistan 25000 Centre for Technological Development in Health (CDTS) Fiocruz Rio de Janeiro RJ 21040-361 Brazil Department of Pharmaceutical Sciences College of Pharmacy Shaqra University Shaqra Saudi Arabia Research Center for Advanced Materials Science (RCAMS) Department of Chemistry Faculty of Science King Khalid University Abha Aseer 61413 P.O. Box 9004 Saudi Arabia High Performance & Quantum Computing Labs Waterloo Canada Laboratório de Modelagem Computacional—LaModel Instituto de Ciências Exatas—ICEx. Universidade Federal de Alfenas—UNIFAL-MG Alfenas Minas Gerais Brazil JRC Genome Research International Center for Chemical and Biological Sciences (PCMD-ICCBS) University of Karachi Pakistan 75270

Corynebacterium ulcerans is a rod-shaped emerging zoonotic pathogen responsible for diphtheria-like infections in humans, posing a significant health threat to public due to its toxigenic potential and ability to affect multiple hosts. This study employed a subtractive genomics / proteomics approach to identify novel therapeutic targets. A total of 900 conserved non-host homologous proteins were identified across 20 C. ulcerans strains, of which 49 essential proteins were shortlisted based on their essential role in bacterial survival. Among these, hisB , aroC , and trpD were prioritized as potential drug targets due to their gene essentiality, PPI, critical roles in metabolic pathways, virulence, cellular localization, and druggability. These targets were subjected to molecular docking studies against a Traditional Chinese Medicine (TCM) compound library of 36,043 molecules. Lead compounds displayed strong binding affinities and favorable interactions with the active sites of the target proteins. Molecular dynamics simulations validated the stability of the protein-ligand complexes over 200 ns, further confirming the favorable interaction of the therapeutic targets. Functional pathways analysis highlighted the essential role in histidine, aromatic amino acid, and tryptophan biosynthesis, critical for bacterial viability. These findings provide valuable insights into the design of novel antimicrobial agents targeting C. ulcerans and pave the way for experimental validation of the identified drug candidates.

关键词： Corynebacterium ulcerans Diphtheria co-infection Subtractive proteomics Drug Discovery MD simulations

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Adaptive unimodal cost volume filtering for deep stereo matching

arXiv

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arXiv 2019年

作者： Zhang, Youmin Chen, Yimin Bai, Xiao Yu, Suihanjin Yu, Kun Li, Zhiwei Yang, Kuiyuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China DeepMotion

State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones. However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained. In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities. In addition, variances of the unimodal distributions for each pixel are estimated to explicitly model matching uncertainty under different contexts. The proposed architecture achieves state-of-the-art performance on Scene Flow and two KITTI stereo benchmarks. In particular, our method ranked the 1st place of KITTI 2012 evaluation and the 4th place of KITTI 2015 evaluation (recorded on 2019.8.20). The codes of AcfNet are available at: https://***/DeepMotionAIResearch/ DenseMatchingBenchmark. Copyright © 2019, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation

Cross-domain Object Detection through Coarse-to-Fine Feature...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yangtao Zheng Di Huang Songtao Liu Yunhong Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach.

关键词： Feature extraction Subspace constraints Object detection Detectors Task analysis Semantics Prototypes

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Embedding Evolutionary Multiobjective Optimization into Fuzzy Linguistic Combination Method for Fuzzy Rule-Based Classifier Ensembles

Embedding Evolutionary Multiobjective Optimization into Fuzz...

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IEEE International Conference on Fuzzy Systems

作者： Krzysztof Trawinski Oscar Cordon Arnaud Quirin European Centre for Soft Computing 33600 Mieres Spain European Centre for Soft Computing Mieres Spain Department of Computer Science and Artificial Intelligence (DECSAI) University of Granada Granada Spain Galician Research and Development Center in Advanced Telecommunications (GRADIANT) University of Vigo Vigo Spain

In a preceding contribution, we proposed a novel combination method by means of a fuzzy linguistic rule-based classification system. The fuzzy linguistic combination method was based on a genetic fuzzy system in order to learn its parameters from data. By doing so the resulting classifier ensemble was able to show a hierarchical structure and the operation of the latter component was transparent to the user. In addition, for the specific case of fuzzy classifier ensembles, the new approach allowed fuzzy classifiers to deal with high dimensional classification problems avoiding the curse of dimensionality. However, this approach strongly depended on one parameter defining the complexity of the final classifier ensemble and in consequence affecting the final accuracy. To avoid this tedious problem, we propose to automatically derive this parameter. For this purpose, we use the most common evolutionary multiobjective algorithm, namely NSGA-II, in order to optimize two criteria, complexity and accuracy. We carry out comprehensive experiments considering 20 UCI datasets with different dimensionality, showing the good performance of the proposed approach.

关键词： Training Pragmatics Complexity theory Accuracy Sparse matrices Bagging Proposals

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WorkflowHub: a registry for computational workflows

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Scientific Data 2025年第1期12卷 1-19页

作者： Ove Johan Ragnar Gustafsson Sean R. Wilkinson Finn Bacall Stian Soiland-Reyes Stuart Owen Nick Juty Carole Goble Simone Leo Luca Pireddu José M. Fernández Salvador Capella-Gutierrez Tom Brown Hervé Ménager Björn Grüning Frederik Coppens Australian BioCommons University of Melbourne Melbourne Victoria Australia Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak Ridge Tennessee USA Department of Computer Science The University of Manchester Manchester UK Informatics Institute University of Amsterdam Amsterdam The Netherlands Center for Advanced Studies Research and Development in Sardinia (CRS4) Pula Cagliari Italy Barcelona Supercomputing Center (BSC) Barcelona Spain Spanish National Bioinformatics Institute (INB) Barcelona Spain Leibniz Institute for Zoo- and Wildlife Research Berlin Germany Institut Pasteur Université Paris Cité Bioinformatics of Biostatistics Hub Paris 75015 France CNRS UMS 3601 Institut Français de Bioinformatique Evry France Albert-Ludwigs-Universität Freiburg Freiburg Germany VIB Data Core VIB Technologies Ghent Belgium

The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 800 workflows registered.

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