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Proceedings of the 5th International Conference on Future Networks and Distributed Systems

作者： Arfin Shagufta Mohammad Tarique Hesham Sarfaraz Masood Ahmed Abd El-latif Department of Applied Sc. and Humanities Jamia Millia Islamia India Department of Computer Engineering Jamia Millia Islamia India Department of Mathematics and Computer Science Menoufia University Egypt

ISBN: (纸本)9781450387347

Based on the rising incidences of crime and violence, it has become a matter of general importance that technology may be developed to automatically detect the presence of violence in the surveillance footage. For law enforcement, the detection of violent incidents can play an important role in urban safety. The efficiency of event detectors is usually measured in terms of detection speed, precision, and generality over many types of video inputs in a distinct format. However, various recent studies in this area have either focused on the correctness of the model, its response speed, or both, but have missed on considering the multiple data sources for effective model analysis. The main objective of the work is to propose a real-time violence event identifier based on state-of-the-art deep learning methods. Primarily the proposed model is based on the ViT (Vision Transformer) architecture, while other models like ConvLSTM and VGG16 with LSTM were also explored in this work. Two benchmark datasets viz. the RLVS (Real Life Violence Situations) and the Hockey fight datasets were used in this work for robust training and test analysis of the proposed model. For evaluating the model’s performance various metrics including accuracy, f1-score, precision, and recall were evaluated. The results show that the Vision Transformer-based model outperformed all the explored models with an overall accuracy of 98% and 97% on the RLVS dataset and the Hockey datasets respectively, which are also significantly higher than the recent solutions proposed on these datasets.

关键词： Hockey Fight Dataset

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A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation

A data-driven Sensor Model for LIDAR Range Measurements used...

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IEEE International Conference on Robotic Computing (IRC)

作者： Florian Spiess Norbert Strobel Tobias Kaupp Samuel Kounev Faculty of Electrical Engineering University of Applied Sciences Wuerzburg-Schweinfurt Institute of Medical Engineering University of Applied Sciences Wuerzburg-Schweinfurt Institute Digital Engineering (IDEE) University of Applied Sciences Wuerzburg-Schweinfurt Faculty of Mathematics and Computer Science Julius-Maximilians-University of Wuerzburg

ISBN: (纸本)9781665472616

In this paper, an analysis of the precision of LIDAR range measurements is presented. LIDAR data from two different sensors (HLS-LFCD-LDS and SICK TIM561) were analyzed regarding the influence of range, incident angle to the surface, and material. Based on the results, a data-driven model for LIDAR precision behavior was developed, and a comparison with standard deviation models based on the vendor-provided specifications was presented. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.

关键词： Laser radar Navigation Computational modeling Robot sensing systems Sensors Behavioral sciences Mobile robots

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Prediction of Passenger Car Friction Brake Application Based on Longitudinal Dynamics Using On-Road Vehicle Driving Data

SSRN

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SSRN 2023年

作者： Jensen, Kenneth M. Santos, Ilmar F. Clemmensen, Line Katrine Harder Theodorsen, Søren Corstens, Harry J.P. Department of Civil and Mechanical Engineering Technical University of Denmark Kgs. Lyngby Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark ILIAS Solutions Brussels Belgium

This work considers the problem of predicting the braking state of vehicles, i.e. when the friction brakes are being applied. The work is aimed at providing the foundation for the prediction of brake pad wear from vehicle usage data. The braking state is predicted from vehicle longitudinal dynamics, considering 6-axis inertial measurement unit (IMU), global navigation satellite system (GNSS), and vehicle on-board diagnostics (OBD-II) data, along with sensor fusion for more accurate vehicle state estimation. The proposed method aims at providing broad applicability to different vehicles, by using only standard OBD-II data instead of relying on manufacturer specific data. Rolling resistance coefficient and product of aerodynamic drag coefficient and frontal area are estimated using linear regression on data from vehicle coast-down tests with the drivetrain disengaged. Engine mass moment of inertia along with engine braking torque is estimated from similar tests with the drivetrain engaged in different gears. The engine braking torque is approximated using a polynomial expression in the engine speed. Braking state predictions are made by comparing measured longitudinal decelerations with estimated, assuming that if the measured exceeds the estimated, then the difference is caused by the application of the friction brakes. Experimental validation was carried out using two modern passenger cars. Comparisons of measured and estimated decelerations from coast-down tests with fitted parameters show good agreements when the drivetrain is disengaged, as well as engaged. For one vehicle labels for the braking state are obtained indirectly using a camera pointed at the brake pedal, using object tracking to determine if the brakes are being applied. For the other vehicle, the braking states are obtained more directly trough access to the brake pressure. Overall prediction performance is good with accuracy >94% and precision around 99%, while recall is around 90% across the two v

关键词： Parameter estimation

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CellMirror: Deciphering Cell Populations from Spatial Transcriptomics Data by Interpretable Contrastive Learning 1

CellMirror: Deciphering Cell Populations from Spatial Transc...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xia, Junjie Cui, Jiangnan Huang, Zhenyu Zhang, Shuangquan Yao, Fang Zhang, Yijian Zuo, Chunman Institute of Artificial Intelligence Donghua University Department of Applied Mathematics Shanghai China College of Computer Science and Technology Jilin University Changchun China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Maternal and Child Medicine Southern Medical University Affiliated Shenzhen Maternity and Child Healthcare Hospital Shenzhen China Xinhua Hospital Affiliated Shanghai Jiao Tong University of Medicine Department of General Surgery Shanghai China Institute of Artificial Institute Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial transcriptomics (ST) has enabled us elucidating tumor microenvironments, however, the technological limitations without single-cell resolution severely hinder its application. Here, we propose CellMirror, an interpretable contrastive learning model to decipher heterogeneous cell populations in ST data by single-cell RNA-sequencing (scRNA-seq) data. Specifically, CellMirror learns the disentangled shared (representing biological variations in both data) and salient features (specific to ST data) by two contrastive variational encoders, while constructing the relations between genes and features by a shared linear decoder. In various cancer samples, CellMirror outperforms other tools in learning common features for label transfer, and interpretation of features. Particularly, in breast cancer studies, CellMirror detects finer domains in ST data missed by other methods, and is robust to dissect cell populations in ST data using independent scRNA-seq data. These results demonstrate applications of CellMirror in interpreting the complex tumor structure in ST data by integrating scRNA-seq data. © 2023 IEEE.

关键词： Data integration

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Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning

Identifying Cell Types in Single-Cell Multimodal Omics Data ...

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International Conference on Knowledge and Systems engineering (KSE)

作者： Van Hoan Do Stefan Canzar Center for Applied Mathematics and Informatics Le Quy Don Technical University Hanoi Vietnam Department of Computer Science and Engineering Huck Institutes of the Life Sciences The Pennsylvania State University University Park PA United States

Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods.

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On Turán-type problems and the abstract chromatic number

arXiv

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arXiv 2024年

作者： Gerbner, Dániel Karim, Hilal Hama Kucheriya, Gaurav HUN-REN Alfréd Rényi Institute of Mathematics Hungary Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. BudapestH-1111 Hungary Department of Applied Mathematics Charles University Prague Czech Republic

In 2020, Coregliano and Razborov introduced a general framework to study limits of combinatorial objects, using logic and model theory. They introduced the abstract chromatic number and proved/reproved multiple Erdős-Stone-Simonovits-type theorems in different settings. In 2022, Coregliano extended this by showing that similar results hold when we count copies of Kt instead of edges. Our aim is threefold. First, we provide a purely combinatorial approach. Second, we extend their results by showing several other graph parameters and other settings where Erdős-Stone-Simonovits-type theorems follow. Third, we go beyond determining asymptotics and obtain corresponding stability, supersaturation, and sometimes even exact *** Codes 05C35 © 2024, CC BY.

关键词： Number theory

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Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set 10

Classification Models and Variable Selection for SME Credit ...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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Explicitly Solvable Continuous-time Inference for Partially Observed Markov Processes

arXiv

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arXiv 2023年

作者： Chen, Daniel Strang, Alexander G. Eckford, Andrew W. Thomas, Peter J. The Department of Mathematics Applied Mathematics and Statistics Department of Electrical Control and Systems Engineering Department of Computer and Data Science Department of Biology Case Western Reserve University ClevelandOH44106 United States Department of Statistics University of Chicago ChicagoIL60637 United States Department of Electrical Engineering and Computer Science York University TorontoONM3J 1P3 Canada

Many natural and engineered systems can be modeled as discrete state Markov processes. Often, only a subset of states are directly observable. Inferring the conditional probability that a system occupies a particular hidden state, given the partial observation, is a problem with broad application. In this paper, we introduce a continuous-time formulation of the sum-product algorithm, which is a well-known discrete-time method for finding the hidden states' conditional probabilities, given a set of finite, discrete-time observations. From our new formulation, we can explicitly solve for the conditional probability of occupying any state, given the transition rates and observations within a finite time window. We apply our algorithm to a realistic model of the cystic fibrosis transmembrane conductance regulator (CFTR) protein for exact inference of the conditional occupancy probability, given a finite time series of partial observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Nonlinear Impulsive Control Design for Biologically Grounded NSM Tumor Growth Model Using Exact Linearized Mapping

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IFAC-PapersOnLine 2023年第2期56卷 7228-7233页

作者： Zehor Belkhatir Soulaimane Berkane Larry Norton Allen Tannenbaum School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester LE1 9BH UK Department of Computer Science and Engineering University of Quebec in Outaouais Gatineau QC Canada Department of Medicine Memorial Sloan Kettering Cancer Center NY 10065 USA Department of Computer Science and Department of Applied Mathematics and Statistics Stony Brook University Stony Brook NY 11794 USA

This paper investigates the problem of personalized chemotherapy scheduling using a system-modeling approach based on a nonlinear impulsive control technique. In particular, this work deals with the so-called Norton-Simon-Massagué (NSM) model, a minimal biologically grounded model for controlled tumor growth, which has been accepted in clinical oncology for breast cancer treatment. In this paper, we continue this line of research, to advance the concept of closed-loop control a step further towards widespread clinical use. In the present work, the problem of chemotherapy drug scheduling is approached using an impulsive control strategy combined with a state-space observer. The technical challenges of the nonlinearity of the controlled system and the impulsive nature of control input are handled via the utilization of a novel mapping that transforms the control problem into a “simplified” linear discrete-time control problem. Theoretical guarantees regarding the sign and invertibility of the proposed transformation are demonstrated. Then, an observer-based state feedback controller is proposed for chemotherapy drug scheduling as a proof-of-concept control strategy. Simulation results showing the performance of the designed closed-loop controlled chemotherapy schedules in reducing and eradicating tumor volume are provided using model's parameters derived using real tumor data and Doxirubicin drug.

关键词： Controlled tumor growth Nonlinear impulsive control Norton-Simon-Massagué (NSM) model Exact linearized transformation observer-based state feedback

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Nanoparticle aggregation effect on nonlinear convective nanofluid flow over a stretched surface with linear and exponential heat source/sink

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International Journal of Thermofluids 2023年 19卷

作者： Rawat, Sawan Kumar Yaseen, Moh Shafiq, Anum Kumar, Manoj Al-Mdallal, Qasem M. Department of Mathematics Graphic Era Deemed to be University Uttarakhand Dehradun248 002 India Department of Applied Science Meerut Institute of Engineering and Technology Uttar Pradesh Meerut250 005 India School of Mathematics and Statistics Nanjing University of Information Science and Technology Nanjing China Center for Applied Mathematics of Jiangsu Province Nanjing University of Information Science and Technology Nanjing210044 China Department of Mathematics Statistics and Computer Science G. B. Pant University of Agriculture and Technology Uttarakhand Pantnagar263 145 India Department of Mathematical Sciences College of Science UAE University Al-AinP.O. Box 17551 United Arab Emirates

A nanofluid flow (TiO2/Ethylene Glycol) over a stretched surface caused by the considerable nonlinear convection is investigated with the nanoparticle aggregation effect in the presence of quadratic thermal radiation. The modified Maxwell model and Bruggeman model are utilized, which depict the nanoparticle aggregation effect. In addition, the system employs two separate variable heat sources i.e., linear heat source and exponential heat source. The similarity transformations are utilized to convert the basic governing PDEs to ODEs. The "bvp4c function" in MATLAB is used to numerically handle the resultant nonlinear system. The impact of the involved parameters is explored on the non-dimensional fields of velocity boundary layer patterns, velocity, streamlines, temperature, and heat transfer rate. Graphs compare and display the control of pertinent parameters on the distinct flow properties. A comparison of the behavior of the solution profiles with and without the influence of nanoparticle aggregation is illustrated. The nanoparticle aggregates significantly enhance the velocity and heat transmission mechanism. Furthermore, the influence of varied heat sources (linear and exponential) aids in growing the thermal boundary layer. Nanofluid flow with nanoparticle aggregation has a 5.36% higher Nusselt number value in comparison to flow without aggregation effect when the volume fraction of nanoparticles is varied from 0 to 0.1. The results of the study will be helpful in the fields where applications of a continuously stretching/shrinking surface are used like in many engineering processes and industries, glass-fiber production, aerodynamic extrusion of plastic sheets, and manufacturing of paper and rubber sheets. © 2023 The Author(s)

关键词： Titanium dioxide

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