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arXiv 2023年

作者： Kathpalia, Aditi Nagaraj, Nithin Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Consciousness Studies Programme National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru560 012 India

Signal processing and Information theory are two disparate fields used for characterizing signals for various scientific and engineering applications. Spectral/Fourier analysis, a technique employed in signal processing, helps estimation of power at different frequency components present in the signal. Characterizing a time-series based on its average amount of information (Shannon entropy) is useful for estimating its complexity and compressibility (eg., for communication applications). Information theory doesn't deal with spectral content while signal processing doesn't directly consider the information content or compressibility of the signal. In this work, we attempt to bring the fields of signal processing and information theory together by using a lossless data compression algorithm to estimate the amount of information or 'compressibility' of time series at different scales. To this end, we employ the Effort-to-Compress (ETC) algorithm to obtain what we call as a Compression Spectrum. This new tool for signal analysis is demonstrated on synthetically generated periodic signals, a sinusoid, chaotic signals (weak and strong chaos) and uniform random noise. The Compression Spectrum is applied on heart interbeat intervals (RR) obtained from real-world normal young and elderly subjects. The compression spectrum of healthy young RR tachograms in the log-log scale shows behaviour similar to 1/f noise whereas the healthy old RR tachograms show a different behaviour. We envisage exciting possibilities and future applications of the Compression Spectrum. Copyright © 2023, The Authors. All rights reserved.

关键词： Spectrum analysis

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Multi-Modal Perception for Soft Robotic Interactions Using Generative Models

Multi-Modal Perception for Soft Robotic Interactions Using G...

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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Enrico Donato Egidio Falotico Thomas George Thuruthel The BioRobotics Institute Sant'Anna School of Advanced Studies Pontedera (PI) Italy Departement of Excellence in Robotics & AI Sant'Anna School of Advanced Studies Pisa Italy Department of Computer Science University College London London United Kingdom

Perception is essential for the active interaction of physical agents with the external environment. The integration of multiple sensory modalities, such as touch and vision, enhances this perceptual process, creating a more comprehensive and robust understanding of the world. Such fusion is particularly useful for highly deformable bodies such as soft robots. Developing a compact, yet comprehensive state representation from multi-sensory inputs can pave the way for the development of complex control strategies. This paper introduces a perception model that harmonizes data from diverse modalities to build a holistic state representation and assimilate essential information. The model relies on the causality between sensory input and robotic actions, employing a generative model to efficiently compress fused information and predict the next observation. We present, for the first time, a study on how touch can be predicted from vision and proprioception on soft robots, the importance of the cross-modal generation and why this is essential for soft robotic interactions in unstructured environments.

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Robust Domain Generalization for Multi-modal Object Recognition

arXiv

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arXiv 2024年

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Department of Computer Information Technology Northern Arizona University FlagstaffAZ United States Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology CambridgeMA United States Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States College of Computing Georgia Institute of Technology AtlantaGA United States

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual fusion. To address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

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computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

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Smart Contracts and Blockchain : An Analytical Approach 6

Smart Contracts and Blockchain : An Analytical Approach

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Sayal, Anu Vasundhara, Chetlur Gupta, Veethika Gupta, Ashulekha Maheshawri, Himani Memoria, Minakshi Taylor's University Department of Mathematics Malaysia Karnataka Bangalore India School of Social Sciences Doon University Uttarakhand Dehradun India Department of Management Studies Uttarakhand Dehradun India Graphic Era Hill University Department of Computing Uttarakhand Dehradun India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350304480

The conventional paper-based legal agreements have been replaced with smart contracts in the modern day. The way legal contracts are utilized to bind the parties to do business is changing as a result of this developing idea. SM-Smart contracts offer a solution to the issue of trust in the digital era. A smart contract is a computer program that is designed to automatically execute the terms of a contract between two or more parties. It operates on a blockchain network and contains the rules and conditions of the agreement. A software developer creates smart contracts by computer-programming the terms and conditions of the written legal agreement. In order for all parties to be aware of the conclusion right away, smart contracts are used to automate the execution of legal agreements. No third party is involved in the contract's execution. Numerous platforms and applications built using blockchain technology depend heavily on smart contracts. © 2023 IEEE.

关键词： Blockchain Coding Execute Vending Machine

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Gauged Skyrme analogue of Chern-Pontryagin

arXiv

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arXiv 2024年

作者： Tchrakian, D.H. School of Theoretical Physics Dublin Institute for Advanced Studies Burlington Road Dublin 4 Ireland Department of Computer Science National University of Ireland Maynooth Maynooth Ireland

An analogue of the Chern-Pontryagin density for SO(D) gauged O(D + 1) Skyrme systems, referred to as Skyrme–Chern-Pontryagin (SCP) densities is known for dimensions D = 2, 3, 4. Since these are defined only through a prescription, it is necessary to extend the realisation to higher D, which is carried out here for D = 5. The construction of SCP densities in D = 2, 3, 4, 5 is presented here in a unified pattern with the aim of pointing out to the possible extrapolation to all dimensions. © 2024, CC BY.

关键词： Miocene

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An Analysis on the Power Consumption of Nodes in Ad hoc IEEE 802.11 Networks 3

An Analysis on the Power Consumption of Nodes in Ad hoc IEEE...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Moyeenudin, H.M. Narender, M. Mukunthan, M.A. Raman, Kanimozhi Balasubramanian, D. Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai600117 India Tkr College of Engineering &technology Hyderabad India School of Computing Veltech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai600062 India Kcg College of Technology Department of Mathematics Karapakkam Chennai600 097 India Jaya Engineering College Tamilnadu Thiruninravur602024 India

ISBN: (纸本)9798350322842

This article analyzes the power consumption of nodes in ad hoc IEEE 802.11 networks. The main objective of this analysis is to discover theoretic restrictions for the gains obtained in node lifetime through different energy conservation techniques found in the literature. The evaluation of energy consumption considers the characteristics of the medium access method and the interaction of the nodes in the packet forwarding process. The basic idea is to regulate the lifetime of a node based on its average consumption. Consumption is estimated based on the amount of time the node remnants transmitting, receiving or idle. The potential gains obtained by different techniques for saving or balancing energy consumption are then analysed. It is shown that the flow balancing technique can achieve a maximum gain for the node lifetime between 11% and 30%, depending on the network configuration. © 2023 IEEE.

关键词： Electric power utilization

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Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet

Machine Fault Diagnosis Using EMD-Gammatone Texture Represen...

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International Conference on Big Data and Smart computing (BIGCOMP)

作者： Mahe Zabin Anika Nahian Binte Kabir Muhammad Khubayeeb Kabir Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science & Technology KAIST Daejeon South Korea Department of Computer Science and Engineering Brac University Dhaka Bangladesh Artificial Intelligence and Big Data Department Woosong University Daejeon South Korea

Machine fault diagnosis involves the intricate process of detecting and isolating faults, which is particularly challenging due to various sources of noise and the complex nature of the faults. In automatic fault diagnosis, feature engineering becomes daunting because of the time-varying characteristics of the fault signals. As artificial intelligence continues to advance, deep learning models have been increasingly employed for machine fault classification. However, a significant limitation of state-of-the-art models is their high computational complexity, making them unsuitable for deployment on portable devices. In this paper, a lightweight fault diagnosis model is proposed that consists of a self-attention SqueezeN et architecture along with a hybrid texture representation technique using empirical mode decomposition (EMD) and gammatone-spectrogram (GS) filter. In the model, initially, the dominant signal is extracted from the ID audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD and then the dominant signals are converted to 2D texture maps applying the GS filter. Then, the generated texture maps are fed to the modified self-attention SqueezeNet classifier, with reduced model width and depth as input for training and validation. In the experimental evaluation, two public benchmark datasets- ToyADMOS and MIMII are used to validate the model. Finally, the model classifies the machine audio faults using the trained features. The experimental results demonstrated that the hybrid EMD-Gammatone texture imaging outperforms the other state-of-the-art methods like MFCC, Gammatone, and Hilbert Huang Transform with a self-attention (SA) based SqueezeNet architecture. The EMD-Gammatone spectrum-based feature extraction accurately detected the faults by exhibiting an accuracy of 89.32% and 96.46% for MIMII and ToyADMOS datasets, respectively. In addition, with the EMD-Gammatone Spectrogram images, the proposed model outperforms the conventional

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Fine-grained Background Representation for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Yin, Xu Im, Woobin Min, Dongbo Huo, Yuchi Pan, Fei Yoon, Sung-Eui The School of Computing Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Faculty of the Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of The State Key Lab of CAD and CG Zhejiang University China Zhejiang Lab 310058 China The School of Computer Science Engineering University of Michigan United States The Faculty of School of Computing Korea Advanced Institute of Science and Technology Deajeon34141 Korea Republic of

Generating reliable pseudo masks from image-level labels is challenging in the weakly supervised semantic segmentation (WSSS) task due to the lack of spatial information. Prevalent class activation map (CAM)-based solutions are challenged to discriminate the foreground (FG) objects from the suspicious background (BG) pixels (a.k.a. co-occurring) and learn the integral object regions. This paper proposes a simple fine-grained background representation (FBR) method to discover and represent diverse BG semantics and address the co-occurring problems. We abandon using the class prototype or pixel-level features for BG representation. Instead, we develop a novel primitive, negative region of interest (NROI), to capture the fine-grained BG semantic information and conduct the pixel-to-NROI contrast to distinguish the confusing BG pixels. We also present an active sampling strategy to mine the FG negatives on-the-fly, enabling efficient pixel-to-pixel intra-foreground contrastive learning to activate the entire object region. Thanks to the simplicity of design and convenience in use, our proposed method can be seamlessly plugged into various models, yielding new state-of-the-art results under various WSSS settings across benchmarks. Leveraging solely image-level (I) labels as supervision, our method achieves 73.2 mIoU and 45.6 mIoU segmentation results on Pascal Voc and MS COCO test sets, respectively. Furthermore, by incorporating saliency maps as an additional supervision signal (I+S), we attain 74.9 mIoU on Pascal Voc test set. Concurrently, our FBR approach demonstrates meaningful performance gains in weakly-supervised instance segmentation (WSIS) tasks, showcasing its robustness and strong generalization capabilities across diverse domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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advanced FFT architecture based on cordic method for brain signal encryption system 2

Advanced FFT architecture based on cordic method for brain s...

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2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021

作者： Pal, Souvik Suseendran, G. Akila, D. Jayakarthik, R. Jabeen, T. Nusrat Global Institute of Management and Technology Department of Computer Science and Engineering India Vels Institute of Science Technology Advanced Studies Department of Information Technology Chennai India Anna Adarsh College for Women Department of Computer Science Chennai India

ISBN: (纸本)9781728194912

The human brain usually generates brain wave signals used for medical research to study the state of the human body. Most common diseases like seizures, insomnia, or other diseases such as brain tumors can be diagnosed using brain wave signals captured with a device's help. Apart from these, nowadays, many devices are invented that operates with brain signals for people with disabilities. And now, in this paper, we will use brain signals for authenticating high-security devices using a network since secondary damage cannot be caused in brain wave signals like generated in a fingerprint, iris, face, etc. Brain wave serves as high-security biometric data. However, brain signals can also be hacked once captured by a malicious personality [14]. Here we will encrypt Brain wave signal with an advanced FFT architecture that incorporates the Cordic system in it. His method enhances high-security transmission of Brain signals over the network for authenticating a high-security device. © 2021 IEEE.

关键词： Fast Fourier transforms

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