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Decision Support System for Spontaneous Volunteers During Emergency Response

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Decision Support System for Spontaneous Volunteers During Em...

International Conference on computing and Networking Technology (ICCNT)

作者： Sruthy Anand Sethuraman N Rao A.D. Dev Nandan B.Madan Gopal Madhav A Das Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Disasters affect a large number of people yearly and recurrently at many locations. Emergencies compel the local community to participate in disaster response; they are mostly the first responders during any disaster. Hence, it is necessary to devise solutions to support the local community and enhance their resilience. This work proposes a comprehensive platform for the victims, administrators, and volunteers during a catastrophe. It encourages the participation of the local community in managing local resources, requesting help, and getting acknowledged for the service they lend. The platform helps recognize the local community’s strengths and provides a method to receive information from disaster managers. The platform will also bring more situational awareness to disaster managers and volunteers. It also helps disaster managers assess the disaster’s potential impact and vulnerability levels of the community at an early stage. The platform envisions providing a decision support system for the spontaneous volunteers to co-work with disaster managers in a regulated fashion by eliminating fake volunteers and messages. The research also focuses on the characteristics of technology adoption for the widespread acceptance of the solution.

关键词： Decision support systems Social networking (online) Disasters Urban areas Prototypes Disaster management Emergency services Real-time systems Mobile applications Resilience

LEARNING SURROGATE EQUATIONS FOR THE ANALYSIS OF AN AGENT-BASED CANCER MODEL

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arXiv 2025年

作者： Burrage, Kevin Burrage, Pamela Kreikemeyer, Justin N. Uhrmacher, Adelinde M. Weerasinghe, Hasitha N. School of Mathematical Sciences Queensland University of Technology BrisbaneQLD Australia Department of Computer Science University of Oxford United Kingdom Modelling and Simulation Institute for Visual and Analytic Computing Faculty of Informatics and Electrical Engineering University of Rostock Rostock Germany

In this paper, we adapt a two species agent-based cancer model that describes the interaction between cancer cells and healthy cells on a uniform grid to include the interaction with a third species – namely immune cells. We run six different scenarios to explore the competition between cancer and immune cells and the initial concentration of the immune cells on cancer dynamics. We then use coupled equation learning to construct a population-based reaction model for each scenario. We show how they can be unified into a single surrogate population-based reaction model, whose underlying three coupled ordinary differential equations are much easier to analyse than the original agent-based model. As an example, by finding the single steady state of the cancer concentration, we are able to find a linear relationship between this concentration and the initial concentration of the immune cells. This then enables us to estimate suitable values for the competition and initial concentration to reduce the cancer substantially without performing additional complex and expensive simulations from an agent-based stochastic model. The work shows the importance of performing equation learning from agent-based stochastic data for gaining key insights about the behaviour of complex cellular dynamics. © 2025, CC BY.

关键词： Lung cancer

Incomplete Gamma Kernels: Generalizing Locally Optimal Projection Operators

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arXiv 2022年

作者： Stotko, Patrick Weinmann, Michael Klein, Reinhard Institute of Computer Science II – Visual Computing University of Bonn Germany Department of Intelligent Systems Delft University of Technology Netherlands

We present incomplete gamma kernels, a generalization of Locally Optimal Projection (LOP) operators. In particular, we reveal the relation of the classical localized L1 estimator, used in the LOP operator for point cloud denoising, to the common Mean Shift framework via a novel kernel. Furthermore, we generalize this result to a whole family of kernels that are built upon the incomplete gamma function and each represents a localized Lp estimator. By deriving various properties of the kernel family concerning distributional, Mean Shift induced, and other aspects such as strict positive definiteness, we obtain a deeper understanding of the operator’s projection behavior. From these theoretical insights, we illustrate several applications ranging from an improved Weighted LOP (WLOP) density weighting scheme and a more accurate Continuous LOP (CLOP) kernel approximation to the definition of a novel set of robust loss functions. These incomplete gamma losses include the Gaussian and LOP loss as special cases and can be applied to various tasks including normal filtering. Furthermore, we show that the novel kernels can be included as priors into neural networks. We demonstrate the effects of each application in a range of quantitative and qualitative experiments that highlight the benefits induced by our modifications. Copyright © 2022, The Authors. All rights reserved.

关键词： Normal distribution

4x4 Census Transform 5

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4x4 Census Transform

5th IEEE International Conference on Signal and Image Processing, ICSIP 2020

作者： Rukundo, Olivier Norwegian Colour and Visual Computing Laboratory Department of Computer Science Gjovik Norway

ISBN: (纸本)9781728168968

This paper proposes a novel 4x4 census transform (4x4CT) to encourage further research in computer vision and visual computing. Unlike the traditional census transform (3x3CT) which only uses a nine pixels kernel or 3x3 window size, the proposed 4x4CT uses a sixteen pixels kernel or 4x4 window size with four overlapped groups of 3x3 kernel size. In each overlapping group, a reference input pixel profits from its nearest eight pixels to produce an eight bits binary string convertible to a grayscale decimal integer corresponding to the 4x4CT's output pixel. Preliminary subjective assessments were more promising with the 4x4CT transformed images than 3x3CT transformed images. © 2020 IEEE.

关键词： computer vision

SERI: High-Throughput Streaming Acceleration of Electron Repulsion Integral Computation in Quantum Chemistry using HBM-based FPGAs

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SERI: High-Throughput Streaming Acceleration of Electron Rep...

International Conference on Field Programmable Logic and Applications

作者： Philip Stachura Guanyu Li Xin Wu Christian Plessl Zhenman Fang Simon Fraser University Burnaby BC Canada Paderborn Center for Parallel Computing Department of Computer Science Paderborn University Warburger Str. 100 Paderborn Germany

ISBN: (数字)9798331530075

ISBN: (纸本)9798331530082

The computation of electron repulsion integrals (ERIs) is a key component for quantum chemical methods. The intensive computation and bandwidth demand for ERI evaluation presents a significant challenge for quantum-mechanics-based atomistic simulations with hybrid density functional theory: due to the tens of trillions of ERI computations in each time step, practical applications are usually limited to thousands of atoms. In this work, we propose SERI, a high-throughput streaming accelerator for ERI computation on HBM-based FPGAs. In contrast to prior buffer-based designs, SERI proposes a novel streaming architecture to address the on-chip buffer limitation and the floorplanning challenge, and leverages the high-bandwidth memory to overcome the bandwidth bottleneck in prior designs. Moreover, to meet the varying computation, bandwidth, and floorplanning requirements between the 55 canonical quartet classes in ERI calculation, we design an automation tool, together with an accurate performance model, to automatically customize the architecture and floorplanning strategy for each canonical quartet class to maximize their throughput. Our performance evaluation on the AMD/Xilinx Alveo U280 FPGA board shows that, SERI achieves an average speedup of 9.80 x over the previous best-performing FPGA design, a 3.21x speedup over a 64-core AMD EPYC 7713 CPU, and a 15.64x speedup over an Nvidia A40 GPU. It reaches a peak throughput of 23.8 GERIS ($10^{9}$ ERIs per second) on one Alveo U280 FPGA. SERI will be released soon at https://***/SFU-HiAccel/SERI.

关键词： Performance evaluation Computational modeling Quantum chemistry Quantum mechanics Bandwidth computer architecture Throughput System-on-chip Field programmable gate arrays Electrons

A Survey on Federated Learning Poisoning Attacks and Defenses

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arXiv 2023年

作者： Liang, Junchuan Wang, Rong Feng, Chaosheng Chang, Chin-Chen School of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China Department of Information Engineering and Computer Science Feng Chia University Taichung40724 Taiwan

As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized data collaboratively without explicitly aggregating the data. Due to its ability to break data silos, federated learning has received increasing attention in many fields, including finance, healthcare, and education. However, the invisibility of clients’ training data and the local training process result in some security issues. Recently, many works have been proposed to research the security attacks and defenses in federated learning, but there has been no special survey on poisoning attacks on federated learning and the corresponding defenses. In this paper, we investigate the most advanced schemes of federated learning poisoning attacks and defenses and point out the future directions in these areas. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

Pan-Cancer Tumor Infiltrating Lymphocyte Detection based on Federated Learning

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Pan-Cancer Tumor Infiltrating Lymphocyte Detection based on ...

2024 IEEE International Conference on Big Data, BigData 2024

作者： Baid, Ujjwal Pati, Sarthak Kurc, Tahsin M. Gupta, Rajarsi Bremer, Erich Abousamra, Shahira Thakur, Siddhesh P. Saltz, Joel H. Bakas, Spyridon Division of Computational Pathology Department of Pathology & Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Radiology & Imaging Sciences Indiana University School of Medicine IndianapolisIN United States Indiana University Melvin and Bren Simon Comprehensive Cancer Center IndianapolisIN United States Department of Informatics Technical University of Munich Munich Germany Department of Biomedical Informatics Stony Brook University Stony BrookNY United States Department of Computer Science Stony Brook University Stony BrookNY United States Department of Computer Science Luddy School of Informatics Computing and Engeineering Indiana University IndianapolisIN United States

ISBN: (纸本)9798350362480

Advances in deep learning (DL) have shown great promise in revolutionizing healthcare, notwithstanding their success hinging on the availability of centralized large and diverse data. Such centralization is challenging because of numerous concerns relating to privacy, data-ownership, intellectual property, and compliance with varying regulatory policies. Federated learning (FL), offers a new decentralized paradigm to train DL models in healthcare. In this study, we evaluate the effect of FL in developing DL models for the analysis of digitized tissue sections, specifically whole slide images (WSIs). A classification application was considered as the example use case, to quantify the distribution of Tumor Infiltrating Lymphocytes (TILs), which are a critical biomarker in cancer research, providing valuable insights into patient outcomes. We trained a VGG classification model using 50 × 50 micron patches extracted from the WSIs with their associated TIL/nonTIL label. We simulated a FL environment, where different cancer types are included across each collaborating node. Our results show that the model trained with the federated training approach achieves similar performance, both quantitatively and qualitatively, to that of a model trained with all the training data pooled at a centralized location. Our study shows that FL has tremendous potential for enabling the development of more robust and accurate models for histopathology image analysis without having to collect large and diverse training data at a single location. Particularly for TILs, our FL approach yields a single DL model trained across numerous anatomical sites and able to robustly generalize to unseen cancer types. © 2024 IEEE.

关键词： Federated learning

Geometric structure and transversal logic of quantum Reed–Muller codes

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arXiv 2024年

作者： Barg, Alexander Coble, Nolan J. Hangleiter, Dominik Kang, Christopher Joint Center for Quantum Information and Computer Science University of Maryland & NIST United States Institute for Systems Research Department of Electrical & Computer Engineering University of Maryland United States Department of Computer Science University of Maryland College Park United States Simons Institute for the Theory of Computing University of California at Berkeley United States Department of Computer Science University of Chicago United States

Designing efficient and noise-tolerant quantum computation protocols generally begins with an understanding of quantum error-correcting codes and their native logical operations. The simplest class of native operations are transversal gates, which are naturally fault-tolerant. In this paper, we aim to characterize the transversal gates of quantum Reed–Muller (RM) codes by exploiting the well-studied properties of their classical counterparts. We start our work by establishing a new geometric characterization of quantum RM codes via the Boolean hypercube and its associated subcube complex. More specifically, a set of stabilizer generators for a quantum RM code can be described via transversal X and Z operators acting on subcubes of particular dimensions. This characterization leads us to define subcube operators composed of single-qubit π/2k Z-rotations that act on subcubes of given dimensions. We first characterize the action of subcube operators on the code space: depending on the dimension of the subcube, these operators either (1) act as a logical identity on the code space, (2) implement non-trivial logic, or (3) rotate a state away from the code space. Second, and more remarkably, we uncover that the logic implemented by these operators corresponds to circuits of multi-controlled-Z gates that have an explicit and simple combinatorial description. Overall, this suite of results yields a comprehensive understanding of a class of natural transversal operators for quantum RM codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

Enhancing Urban Home Security: Deep Learning Enabled Vehicle Identification

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Enhancing Urban Home Security: Deep Learning Enabled Vehicle...

Emerging Technology (INCET), International Conference for

作者： V S N Lokesh Yarramallu Sanjit Teja Tummalapalli Yatham Nitish Reddy Sruthy Anand Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The growing rate of crimes in cities is a genuine concern these days. Preventing crimes from happening is a need for the city residents and the administrator community. Various technology-enabled solution approaches can be integrated to prevent crimes in cities. In this work, we explore how the amalgamation of Deep Learning and IoT can prevent crimes and provide enhanced security to citizens. Crimes happen for several reasons, and the right technology-enabled solutions can help prevent crimes by alerting the intended stakeholders on time. This paper focuses on the importance of identifying vehicles and the importance of detecting non-resident vehicles to alert the residents and related security personnel about them. We detailed the steps used to identify the vehicle using YOLOv8. The work also explored various OCR tools to find the best one for number plate identification.

关键词： Deep learning Image color analysis Urban areas Smart homes Safety Planning Security