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arXiv 2023年

作者： Abu-Affash, A. Karim Carmi, Paz Luwisch, Ori Mitchell, Joseph S.B. Department of Software Engineering Shamoon College of Engineering Israel Computer Science Department Ben-Gurion University Israel Department of Applied Mathematics and Statistics Stony Brook University United States

The Wiener index of a network, introduced by the chemist Harry Wiener [30], is the sum of distances between all pairs of nodes in the network. This index, originally used in chemical graph representations of the non-hydrogen atoms of a molecule, is considered to be a fundamental and useful network descriptor. We study the problem of constructing geometric networks on point sets in Euclidean space that minimize the Wiener index: given a set P of n points in d, the goal is to construct a network, spanning P and satisfying certain constraints, that minimizes the Wiener index among the allowable class of spanning networks. In this work, we focus mainly on spanning networks that are trees and we focus on problems in the plane (d = 2). We show that any spanning tree that minimizes the Wiener index has non-crossing edges in the plane. Then, we use this fact to devise an O(n4)-time algorithm that constructs a spanning tree of minimum Wiener index for points in convex position. We also prove that the problem of computing a spanning tree on P whose Wiener index is at most W, while having total (Euclidean) weight at most B, is NP-hard. Computing a tree that minimizes the Wiener index has been studied in the area of communication networks, where it is known as the optimum communication spanning tree problem. © 2023, CC0.

关键词： Atoms

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PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-based Vocabulary

PV-Cap: 3D Dynamic Scene Understanding Through Open Physics-...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Hidetomo Sakaino Thao Nguyen Phuong Vinh Nguyen Duy Data Solution Dept. AI-Image Group (FCJ.ABC) FPT Consulting Japan FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, large Vision Language (VL) models, i.e., CLIP, have demonstrated impressive capabilities in training solely on internet-scale image-language pairs. Moreover, almost all VL models have tackled indoor objects under controlled illumination and camera views. However, outdoor 3D environments are time-varying uncontrolled scenes under natural phenomena. Therefore, captions from such unseen scenes and objects are hard to obtain in a state-of-the-art (SOTA) one-shot algorithm, resulting in insufficient captions. This paper proposes PV-Cap (Physics-based Vocabulary for Caption) for enhancing 3D scene understanding through enriched captions. Since many tasks in understanding 3D dynamic scenes are hard to deal with, PVCap aims to disentangle such complexities through multiple grouped Deep Learning and Vision Language models step-wisely. Proposed i-VQA (iterative VQA) and 3D-CPP (3D Contrastive Physical-Scale Pretraining) extended from SOTA 2D-CLIP also contribute to generating physical and 3D-based captions. Using many images with 3D dynamic events, i.e., road scenes with traffic flow and accidents, experiments have demonstrated the usability and effectiveness of proposed PV-Cap over SOTA models in terms of segmentation and captions.

关键词： Deep learning Training Solid modeling Vocabulary Three-dimensional displays Roads Cameras

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Robust Feature engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems

arXiv

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arXiv 2024年

作者： Gardezi, Syed Saim Jawed, Soyiba Khan, Mahnoor Bukhari, Muneeba Khan, Rizwan Ahmed Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science School of Mathematics and Computer Science Institute of Business Administration Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naïve Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provides an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2024, CC BY-NC-ND.

关键词： Support vector machines

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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3D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunities

arXiv

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arXiv 2024年

作者： Bao, Yanqi Ding, Tianyu Huo, Jing Liu, Yaoli Li, Yuxin Li, Wenbin Gao, Yang Luo, Jiebo State Key Laboratory for Novel Software Technology Nanjing University 210023 China Applied Sciences Group Microsoft Corporation Redmond United States Department of Computer Science University of Rochester United States

3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to become a mainstream method for 3D representations. It can effectively transform multiview images into explicit 3D Gaussian through efficient training, and achieve real-time rendering of novel views. This survey aims to analyze existing 3DGS-related works from multiple intersecting perspectives, including related tasks, technologies, challenges, and opportunities. The primary objective is to provide newcomers with a rapid understanding of the field and to assist researchers in methodically organizing existing technologies and challenges. Specifically, we delve into the optimization, application, and extension of 3DGS, categorizing them based on their focuses or motivations. Additionally, we summarize and classify nine types of technical modules and corresponding improvements identified in existing works. Based on these analyses, we further examine the common challenges and technologies across various tasks, proposing potential research opportunities. © 2024, CC BY.

关键词： 3D modeling

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Envisaging an Intelligent Blockchain Network by Intelligence Sharing

Envisaging an Intelligent Blockchain Network by Intelligence...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Nayak, Arijit De, Sourav Bhattacharyya, Siddhartha Mukhopadhyay, Debarka Muhammad, Khan Gorbachev, Sergey Alumnus Software Limited Kolkata India Cooch Behar Govt. Engineering College Dept. of Computer Science Engineering Cooch Behar India Rajnagar Mahavidyalaya Birbhum India Department of Computer Science and Engineering Bangalore India Sungkyunkwan University Department of Applied Artificial Intelligence Seoul Korea Republic of National Research Tomsk State University Department of Innovative Technologies Tomsk Russia

ISBN: (纸本)9781665425773

Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network. © 2022 IEEE.

关键词： Blockchain

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IsoGAN: A Gan Based Method for Isometric View Images Generation From Three Orthographic Views Contour Drawings

IsoGAN: A Gan Based Method for Isometric View Images Generat...

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Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Thao Nguyen Phuong Hidetomo Sakaino Vinh Nguyen Duy Data Solution Dept. (FCJ.ABC) FPT Consulting Japan AI-Image Group FPT Software Department of Computer Science and Communications Engineering Waseda University

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

Reconstructing the 2D or 3D shape of an object from several 2D drawings is a crucial problem in computer-aided design (CAD). Despite the advancement of deep neural networks, automatic isometric image generation from three orthographic views line drawings using deep learning remains unresolved. Existing image-to-image translation techniques often generate images from just one input image. In this paper, we propose a novel method for the above task using a GAN-based model, namely IsoGAN. This method takes three images of object’s front, side, and top view as input, then analyzes the spatial and geometrical relations between each view and finally generates the corresponding isometric view image of the object. Extensive experiments on SPARE3D dataset show promising results of IsoGAN on isometric view generation task, demonstrating the effectiveness of the proposed IsoGAN.

关键词： Deep learning Solid modeling Design automation Three-dimensional displays Image synthesis Shape Transforms Vectors Generators Image reconstruction

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A Novel Deep Learning Approach for Capturing Time Series Dependencies and Improving Short-term Weather Forecasting 7

A Novel Deep Learning Approach for Capturing Time Series Dep...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Johri, Shiva Divyajyothi, M.G. Anitha, S. Rani, M. Sandya Murari, Thejovathi Shirisha, N. Oriental College of Management Oriental Group of Institutes Department of Management Madhya Pradesh Bhopal462022 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Vels Institute of Science Technology And Advanced Studies Department of Computer Science Engineering Pallavaram Krishnapuram Tamil Nadu Chennai India St. Martin's Engineering College Secunderabad Telangana Dhulapally500100 India Acharya Nagarjuna University Department of Computer Science And Engineering Andhra Pradesh Guntur India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

Weather forecasting is a critical and challenging task that requires accurate predictions based on historical data and intricate dependencies between time series. Traditional neural networks, such as Back Propagation through Time (BPTT) trained RNNs, struggle to effectively capture these dependencies, leading to suboptimal performance in weather forecasting. In this study, we suggest a novel Integrated-Stack based Bi-LSTM Model, which associations the strengths of LSTM and Bi-LSTM networks, to address these limitations and enhance the accuracy of short-term weather predictions. The primary objective of this research is to develop a versatile deep learning model capable of maintaining long-term dependances and reading any length of sequence, while simultaneously providing real-time short-term weather forecasts. To achieve this, the Integrated-Stack based Bi-LSTM Model is designed to exploit the versatility and non-linear adaptive processing ability of neural networks. The proposed model's architecture involves the integration of LSTM and Bi-LSTM layers. By incorporating both forward and reverse direction processing, the model gains a comprehensive understanding of complex patterns present in time-series data. This bidirectional approach enables the model to capture dependencies effectively, which is essential for accurate weather predictions. To estimate the presentation of the Integrated-Stack based Bi-LSTM Model, we conducted experiments using real-world weather data. We compared the model's predictive capabilities against traditional LSTM models and other state-of-the-art weather forecasting methods. The assessment metrics included accuracy, root mean square error (RMSE), and mean absolute error (MAE). © 2023 IEEE.

关键词： Weather forecasting

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An Overall Disease Analysis of Mango Using Neural Network with Hybrid Feature model

An Overall Disease Analysis of Mango Using Neural Network wi...

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2023 IEEE World Conference on applied Intelligence and Computing, AIC 2023

作者： Kaur, Jatinder Sharma, Ashwani K. Kaushal, Manudeep Badhoutiya, Arti Reddy, Nirmala Alkhayyat, Ahmed Chandigarh Engineering College Department of Ece Jhanjeri Mohali140307 India Chandigarh Engineering College Department of Applied Sciences Jhanjeri Mohali140307 India Gulzar Group of Institutes Dean Professional Studies and Dean Corporate Affairs Punjab Khanna141401 India Gla University Department of Electrical Engineering Mathura281406 India Ies College of Technology Bhopal Ies University Department of Computer Science and Engineering Bhopal India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9798350310061

Mango is a highly valued fruit crop, with arable land covering about 36% of the total fruit area. With roughly 23% of the total area under mango, Uttar Pradesh and Andhra Pradesh lead the way, followed by Tamil Nadu, Karnataka, Gujarat, and Bihar. Global food security is affected a lot due to crop plant diseases. These diseases directly affect the quality of food, fruits, etc., resulting in a fall in agricultural productivity. Farmers must assess the leaf, fruit, or tree through visual interpretation to determine whether any fruit crop part is contaminated or blooming properly. But this traditional technique has its limitations;it is inconsistent, unreliable, and prone to mistakes. Researchers have proposed a variety of techniques for resolving the above-stated issues. Most of them preferred the modelling of convolutional neural network models according to problem areas by giving low-resolution images as input, resulting in low disease detection accuracy. In this work, we use an artificial neural network (ANN) technique with the objective of early detection of disease in any part of the fruit crop with tiny disease blobs that can only be seen with better resolution images. All the contaminated blobs are segmented for the entire dataset after a pre-processing phase using a contrast enhancement approach. © 2023 IEEE.

关键词： Fruits

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Interactive and Explainable Optimized Learning for DDoS Detection in Consumer IoT Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Saiyed, Makhduma F Al-Anbagi, Irfan Hossain, M. Shamim University of Regina Faculty of Engineering and Applied Science ReginaSKS4S 0A2 Canada King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

The integration of Internet of Things (IoT) in consumer environments enhances convenience and security while increasing Human-computer Interaction (HCI). However,this increased interactivity has also increased the vulnerability of Consumer IoT (CIoT) networks to cyber threats,mainly Distributed Denial of Service (DDoS) attacks. The DDoS attacks,which vary in volume,present substantial challenges to these networks' operational integrity and customer trust. This paper introduces the Artificial Intelligence (AI)-driven (ADEPT) system that utilizes explainable and optimized deep-ensemble learning with pruning for DDoS detection. The system uses attention-based ensemble DL for DDoS detection,combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. To address the resource constraints of edge devices in CIoT networks,the system uses Differential Evolution (DE)-based pruning and quantization techniques,optimizing the model for efficient deployment on edge nodes while preserving high performance. An HCI interface is designed to allow network administrators and researchers to engage with the system through dynamic visualizations,facilitating complex data interpretation and empowering administrators to refine detection strategies. The interface,integrating SHapley Additive exPlanations (SHAP) and risk assessment,enhances model transparency and interpretability,highlighting the synergy of HCI and AI. The ADEPT system is evaluated using an experimental testbed and CIoT datasets and has demonstrated over 90% accuracy in detecting high-and low-volume DDoS attacks. © 1975-2011 IEEE.

关键词： Risk assessment

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