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TechRxiv 2021年

作者： Moniruzzaman, Md. Yin, Zhaozheng Hossain, Md Sanzid Bin Guo, Zhishan Choi, Hwan Department of Computer Science Stony Brook University United States Department of Computer Science Department of Biomedical Informatics Stony Brook University United States Department of Electrical and Computer Engineering University of Central FL United States Department of Electrical and Computer Engineering University of Central FL United States Department of Mechanical and Aerospace Engineering University of Central FL United States

Identifying 3D human walking poses in unconstrained environments has many applications such as enabling prosthetists and clinicians to access the amputees’ walking functions outside clinics and helping amputees obtain an optimal walking condition with predictive control. Thus, we propose the wearable motion capture problem of reconstructing and predicting 3D human poses from the wearable IMU sensors and wearable cameras. To solve this challenging problem, we introduce a novel Attention-Oriented Recurrent Neural Network (AttRNet) that contains a sensor-wise attention-oriented recurrent encoder, a reconstruction module, and a dynamic temporal attention-oriented recurrent decoder, to reconstruct the current pose and predict the future poses. To evaluate our approach, we collected a new WearableMotionCapture dataset using wearable IMUs and wearable video cameras, along with the musculoskeletal joint angle ground truth. The proposed AttRNet shows high accuracy on the WearableMotionCapture dataset, and it also outperforms the current best methods on two public pose prediction datasets with IMU-only data: DIP-IMU and TotalCaputre. The source codes and the new dataset will be publicly available on https://***/MoniruzzamanMd/Wearable-Motion-Capture. © 2021, CC BY.

关键词： Recurrent neural networks

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA

An Exploratory Investigation into Sustaining Cybersecurity P...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Abdullah Mahfuzul Huda Mohammed Brayyich Noor Hanoon Haroon Abdul Azeem Khan Rajan Verma University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh KSA College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Department of Computer Technical Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Negara Brunei Darussalam Division of Research and Development Lovely Professional University Phagwara KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services.

关键词： Training Surveys Radio frequency Analytical models Web services Standards organizations Information security computer security Protection Testing

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A Reliability-constrained Association Rule Mining Method for Explaining Machine Learning Predictions on Continuity of Asthma Care

A Reliability-constrained Association Rule Mining Method for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yao Tong Yongli Wang Qibin Zhang Zhenxiang Zhang Gang Chen Computer and artificial intelligence school Zhengzhou University Zhengzhou China Biomedical informatics and medical education department University of Washington Seattle USA School of computer science and engineering Nanjing University of Technology Nanjing China School of nursing and health Zhengzhou University Zhengzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Continuity of care has been shown to possess numerous health benefits for asthma. However, the use of methodology to predetermine the level of care continuity has been underinvestigated. We recently built a machine learning model to predict the continuity of care for diagnosed asthma patients at the University of Washington Medicine. As there is a consensus on the un-explainable featured nowadays by black-box machine learning, which impedes the clinical deployment of our model. To tackle this issue, we proposed a reliability-constrained association rule mining method, called RC-ARM, to automatically explain the predictions of any machine learning model. First, we introduced the belief function to build a reliability-constrained rule framework. Then, the two-step reliable association rule mining algorithms were developed to generate reliable explanation rules for the machine learning model’s predictions. At last, clinical intervention suggestions regarding each mined rule were embedded for further understanding. The results showed that the proposed method could explain all (110/110) predictions of our machine learning model for asthma patients with low levels of continuity of care. This semantic-fused method sheds light on black-box models and encourages clinical experts to embrace the benefits of machine learning without any prior concern about its lack of explainability.

关键词： Machine learning algorithms Evidence theory Closed box Machine learning Predictive models Solids Prediction algorithms

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MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTERING 12

MOST DISCRIMINATIVE STIMULI FOR FUNCTIONAL CELL TYPE CLUSTER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Burg, Max F. Zenkel, Thomas Vystrčilová, Michaela Oesterle, Jonathan Höfling, Larissa Willeke, Konstantin F. Lause, Jan Müller, Sarah Fahey, Paul G. Ding, Zhiwei Restivo, Kelli Sridhar, Shashwat Gollisch, Tim Berens, Philipp Tolias, Andreas S. Euler, Thomas Bethge, Matthias Ecker, Alexander S. International Max Planck Research School for Intelligent Systems Tübingen Germany Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Tübingen AI Center University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany Centre for Integrative Neuroscience University of Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany Hertie Institute for AI in Brain Health University of Tübingen Germany Department of Neuroscience Baylor College of Medicine HoustonTX United States Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States University Medical Center Göttingen Department of Ophthalmology Germany Bernstein Center for Computational Neuroscience Göttingen Germany University of Göttingen Germany Department of Electrical and Computer Engineering Rice University HoustonTX United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expert domain knowledge and biases the procedure towards previously known cell types. In the visual cortex, it is still unknown what functional types exist and how to identify them. Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed. Here we propose an optimization-based clustering approach using deep predictive models to obtain functional clusters of neurons using Most Discriminative Stimuli (MDS). Our approach alternates between stimulus optimization with cluster reassignment akin to an expectation-maximization algorithm. The algorithm recovers functional clusters in mouse retina, marmoset retina and macaque visual area V4. This demonstrates that our approach can successfully find discriminative stimuli across species, stages of the visual system and recording techniques. The resulting most discriminative stimuli can be used to assign functional cell types fast and on the fly, without the need to train complex predictive models or show a large natural scene dataset, paving the way for experiments that were previously limited by experimental time. Crucially, MDS are interpretable: they visualize the distinctive stimulus patterns that most unambiguously identify a specific type of neuron. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Maximum principle

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The timer compression of data and information 3

The timer compression of data and information

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3rd IEEE International Conference on Data Stream Mining and Processing, DSMP 2020

作者： Skuratovskii, Ruslan Osadchyy, Yevgen Osadchyy, Volodymyr NTU KPI Named by I. Sikorskii and IAPM. Department of Informatics and Computer Science Kiev Ukraine National University Named by Taras Shevchenko Kiev Ukraine Igor Sikorsky Kyiv Polytechnic Institute Faculty of Informatics and Computer Engineering Kyiv Ukraine

ISBN: (纸本)9781728132143

The ability to find short representations, i.e. to compress data, is crucial for many intelligent systems. This paper is devoted to data compression and a transform-based quantitative data compression technique involving quick enumeration in a unary-binary time-based numeral system (NS). The symbols comprising the alphabets of human-computer interaction languages (HCIL), which are used in an informational message (IM), are collected in primary code tables, such as the ASCII table. The statistical-oriented data compression method using unconventional timer encryption and encoding information are proposed by us. It was constructed probability - discrete model of the set of character sequences and characterized some probabilistic algorithms associated with the recovery of text by its public key and its cipher. We find the possibility of parallel implementation of this method by building a block of timer tags. The necessary estimations of complexity are obtained. The method can be used to compress SMS messages. Probabilistic-statistical analysis and evaluation of their effectiveness are obtained. © 2020 IEEE.

关键词： Data compression

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Deep Learning of First-Order Nonlinear Hyperbolic Conservation Law Solvers

SSRN

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SSRN 2023年

作者： Morand, Victor Müller, Nils Weightman, Ryan Keimer, Alexander Piccoli, Benedetto Bayen, Alexandre M. Ecole Polytechnique Palaiseau France Department of Electrical Engineering and Computer Science University of California BerkeleyCA United States Max Planck Institute for Software Systems Saarland Informatics Campus Germany Max Planck Institute for Intelligent Systems Germany Department of Mathematics Rutgers University NJ United States Department of Mathematics FAU Erlangen-Nürnberg Germany

In this contribution, we study the numerical approximation of scalar conservation laws by computational optimization of the numerical flux function in a first-order finite volume method. The cell-averaging inherent to finite volume schemes presents a challenge in the design of such numerical flux functions toward achieving high solution accuracy. Dense neural networks, as well as (piecewise) polynomials, serve as function classes for the numerical flux. Using a parametrization of such a function class, we optimize the numerical flux with respect to its solution accuracy on a set of Riemann problems for which we have closed-form solutions. By design, our resulting first-order finite volume schemes are *** show empirically that the proposed schemes can achieve higher accuracy than the Godunov and EnquistOsher method on a wide range of *** proposed method can be applied to the inverse problem, i.e., the data-driven synthesis of macroscopic physical laws for real-world flux phenomena. © 2023, The Authors. All rights reserved.

关键词： Finite volume method

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ProgArch: Modeling the Adoption of Enterprise Architecture to Manage Development Program 7

ProgArch: Modeling the Adoption of Enterprise Architecture t...

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Mahatma, Kodrat Risnanto, Slamet Atika, Prima Dina Waseso, Bayu Team for Acceleration of Stunting Prevention Jakarta Indonesia Universitas Sangga Buana Faculty of Engineering Informatics Department Bandung Indonesia Universitas Bhayangkara Department of Informatics Jakarta Raya Indonesia Universitas Mercubuana Faculty of Computer Science Jakarta Indonesia

ISBN: (纸本)9781665404228

Although various frameworks, methods, and tools have been used in a development program, a comprehensive framework that can integrate all aspects and needs of a complex development program is not available. Simultaneously, in the IT/IS context, Enterprise Architecture (EA) concept has existed and becomes the organization's blueprint for aligning its vision and mission, business processes, and information *** to model an architectural framework that can adopt EA's capabilities and advantages and apply it in a complex development program context with significant IT, applications, and data components is key questions that lead this research. In this paper, ArchiMate 3.0, an EA framework from Open Group, was adopted to represent the development program management structure. We use observation from the stunting prevention acceleration program in Indonesia to explore EA's strength in representing the description, analysis, and visualization of multiple aspects of a complex case. It shows that the model we built can adopt important EA framework features like artifact integration, searchability, traceability, and multi-dimensional view capability. © 2020 IEEE.

关键词： Blueprints

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Federated Learning with Quantum Secure Aggregation

arXiv

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arXiv 2022年

作者： Zhang, Yichi Zhang, Chao Zhang, Cai Fan, Lixin Zeng, Bei Yang, Qiang Department of Physics The Hong Kong University of Science and Technology Hong Kong College of Mathematics and Informatics South China Agricultural University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong WeBank China

This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that is designed to provide highly secure and efficient aggregation of local model parameters for federated learning. The scheme is secure in protecting private model parameters from being disclosed to semi-honest attackers by utilizing quantum bits i.e. qubits to represent model parameters. The proposed security mechanism ensures that any attempts to eavesdrop private model parameters can be immediately detected and stopped. The scheme is also efficient in terms of the low computational complexity of transmitting and aggregating model parameters through entangled qubits. Benefits of the proposed QSA scheme are showcased in a horizontal federated learning setting in which both a centralized and decentralized architectures are taken into account. It was empirically demonstrated that the proposed QSA can be readily applied to aggregate different types of local models including logistic regression (LR), convolutional neural networks (CNN) as well as quantum neural network (QNN), indicating the versatility of the QSA scheme. Performances of global models are improved to various extents with respect to local models obtained by individual participants, while no private model parameters are disclosed to semi-honest adversaries. Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits

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Taming the beast:a revised classification of Cortinariaceae based on genomic data

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Fungal Diversity 2022年第1期112卷 89-170页

作者： Kare Liimatainen Jan T.Kim Lisa Pokorny Paul M.Kirk Bryn Dentinger Tuula Niskanen Jodrell Laboratory Royal Botanic GardensKewSurrey TW93ABUK Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldHertfordshire AL109ABUK Institut Botànic de Barcelona(IBB CSIC-Ajuntament de Barcelona)08038 BarcelonaCataloniaSpain Biodiversity Informatics and Spatial Analysis Jodrell LaboratoryRoyal Botanic GardensKewSurrey TW93ABUK Natural History Museum of Utah and School of Biological Sciences University of UtahSalt Lake CityUTUSA Botanical Museum University of HelsinkiP.O.Box 700014 HelsinkiFinland

Family Cortinariaceae currently includes only one genus,Cortinarius,which is the largest Agaricales genus,with thousands of species *** species are important ectomycorrhizal fungi and form associations with many vascular plant gen-era from tropicals to arctic *** Cortinarius contains a lot of morphological variation,and its complexity has led many taxonomists to specialize in particular on infrageneric *** previous attempts to divide Cortinarius have been shown to be unnatural and the phylogenetic studies done to date have not been able to resolve the higher-level classification of the group above section *** approaches have revolutionized our view on fungal relationships and provide a way to tackle difficult *** used both targeted capture sequencing and shallow whole genome sequencing to produce data and to perform phylogenomic analyses of 75 single-copy genes from 19 *** addition,a wider 5-locus analysis of 245 species,from the Northern and Southern Hemispheres,was also *** on our results,a classification of the family Cortinariaceae into ten genera-Cortinarius,Phlegmacium,Thaxterogaster,Calonarius,Aureonarius,Cystinarius,Volvanarius,Hygronarius,Mystinarius,and Austrocortinarius-is *** genera,10 subgenera,and four sec-tions are described as new to science and five subgenera are introduced as new combinations in a new *** addition,41 section names and 514 species names are combined in new genera and four lecto-and epitypes *** position of Stephanopus in suborder Agaricineae remains to be *** capture sequencing is used for the first time in fungal taxonomy in *** provides a cost-efficient way to produce-omics data in species-rich ***-omics data was produced from fungarium specimens up to 21 years old,demonstrating the value of museum specimens in the study of the fungal tree of *** study is the first family revision in Agaricales based on genomi

关键词： Agaricales Fungariomics Fungi HybPiper Museomics Targeted capture sequencing Whole genome sequencing

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Parameterized complexity of minimum membership dominating set

arXiv

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arXiv 2021年

作者： Agrawal, Akanksha Choudhary, Pratibha Narayanaswamy, N.S. Nisha, K.K. Ramamoorthi, Vijayaragunathan Department of Computer Science and Engineering IIT Madras Chennai India Faculty of Informatics Czech Technical University in Prague Czech Republic

Given a graph G = (V, E) and an integer k, the Minimum Membership Dominating Set (MMDS) problem seeks to find a dominating set S ⊆ V of G such that for each v ∈ V, |N[v] ∩S| is at most k. We investigate the parameterized complexity of the problem and obtain the following results about MMDS: 1. W[1]-hardness of the problem parameterized by the pathwidth (and thus, treewidth) of the input graph. 2. W[1]-hardness parameterized by k on split graphs. 3. An algorithm running in time 2O(vc)|V |O(1), where vc is the size of a minimum-sized vertex cover of the input graph. 4. An ETH-based lower bound showing that the algorithm mentioned in the previous item is optimal. © 2021, CC BY.

关键词： Parameterization

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