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Enhancing the Efficiency of Convolutional Neural Networks through Quantization 4

Enhancing the Efficiency of Convolutional Neural Networks th...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Kansal, Vipashi Shnain, Ammar Hameed Pushkarna, Gaurav Manjunatha Dixit, Krishna Kant Rajkumar, K. Varada Graphic Era Deemed to be University Department of Computer Science & Engg Dehradun India College of Technical Engineering The Islamic University Department of Computers Techniques Engg Najaf Iraq Lovely Professional University Phagwara India New Horizon College of Engg Department of Mechanical Engg Bangalore India Gla University Department of Electrical Engg Mathura India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331518981

A new kind of image categorisation technology, Convolutional Neural Networks (CNNs) have shown themselves capable of astounding accuracy across a range of uses. Problems arise, however, when dealing with real-time applications in settings where resources are limited due to their computational complexity and resource needs. An extensive investigation on how to make convolutional neural networks (CNNs) better at picture classification is detailed in this work. We explore several optimization techniques, including network pruning, quantization, and the deployment of lightweight architectures such as MobileNet and SqueezeNet. Additionally, we investigate the impact of advanced training strategies like transfer learning and data augmentation on model performance. We show, by means of comprehensive tests, that our suggested approaches considerably reduce the memory footprint and computational cost of CNNs while preserving or even enhancing classification accuracy. Our findings provide valuable insights for deploying efficient CNN models in practical scenarios, paving the way for more accessible and scalable image classification solutions. © 2024 IEEE.

关键词： Convolutional neural networks

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MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials science

MELT: Materials-aware Continued Pre-training for Language Mo...

引用

2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Kim, Junho Kim, Yeachan Park, Jun-Hyung Oh, Yerim Kim, Suho Lee, SangKeun Department of Artificial Intelligence Korea University Seoul Korea Republic of Division of Language & AI Hankuk University of Foreign Studies Seoul Korea Republic of Department of Computer Science and Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798891761681

We introduce a novel continued pre-training method, MELT (Materials-aware continued pretraining), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science. Unlike previous adaptation strategies that solely focus on constructing domain-specific corpus, MELT comprehensively considers both the corpus and the training strategy, given that materials science corpus has distinct characteristics from other domains. To this end, we first construct a comprehensive materials knowledge base from the scientific corpus by building semantic graphs. Leveraging this extracted knowledge, we integrate a curriculum into the adaptation process that begins with familiar and generalized concepts and progressively moves toward more specialized terms. We conduct extensive experiments across diverse benchmarks to verify the effectiveness and generality of MELT. A comprehensive evaluation convincingly supports the strength of MELT, demonstrating superior performance compared to existing continued pretraining methods. In-depth analysis of MELT also shows that MELT enables PLMs to effectively represent materials entities compared to the existing adaptation methods, thereby highlighting its broad applicability across a wide spectrum of materials science. © 2024 Association for Computational Linguistics.

关键词： Semantics

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Robust Audio-Visual ASR with Unified Cross-Modal Attention 48

Robust Audio-Visual ASR with Unified Cross-Modal Attention

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Jiahong Li, Chenda Wu, Yifei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Audio-visual speech recognition (AVSR) takes advantage of noise-invariant visual information to improve the robustness of automatic speech recognition (ASR) systems. While previous works mainly focused on the clean condition, we believe the visual modality is more effective in noisy environments. The challenges arise from the difficulty of adaptive fusion of audio-visual information and the possible interferences inside the training data. In this paper, we present a new audio-visual speech recognition model with a unified cross-modal attention mechanism. In particular, the auxiliary visual evidence is combined with the acoustic feature along the temporal dimension in the unified space before the deep encoding network. This method provides a flexible cross-modal context and requires no forced alignment such that the model can learn to leverage the audio-visual information in relevant frames. In experiments, the proposed model is demonstrated to be robust to the potential absence of the visual modality or misalignment in audio-visual frames. On the large-scale audio-visual dataset LRS3, our new model further reduces the state-of-The-Art WER for clean utterances and significantly improves the performance under noisy conditions. © 2023 IEEE.

关键词： Speech recognition

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An Adaptive Thyroid Health Monitoring for Women

An Adaptive Thyroid Health Monitoring for Women

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： P. Dhanalakshmi Narnavaram Indhuja Chejarla Sumanth Reddy Billakanti Mokshith Gudiyatham Bharath Kumar Chinthaparthi Prashanth Kumar Reddy (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Mohan Babu University Tirupati India Department of Computer Science and Engineering Sree Vidyanikethan Engineering College (Autonomous) Tirupati India Department of AI & ML Mohan Babu University Tirupati India

ISBN: (数字)9798331537012

ISBN: (纸本)9798331537029

Thyroid well being is essential to theoverall health of women since thyroidhormone imbalance may cause severe ill nesses like hypothyroidism, hyperthyroidism, and metabolic disorders. These conditions significantly impact reproductive health, leading to menstrual irregularities, infertility, and pregnancy complications. Early detection and continuous monitoring are essential for effective management. Traditional diagnostic approaches often suffer from limited sensitivity, specificity, and accuracy. This study proposes a hybrid machine learning algorithm combining AdaBoost and Q-learning for thyroid disease classification. The approach incorporates data preprocessing, feature selection, and dynamic classifier selection to enhance model performance. The proposed method was evaluated using a publicly available thyroid disease dataset. Experimental results demonstrate that our model achieves $99.41 \%$ accuracy, $98.13 \%$ precision, $98.13 \%$ recall, and a $98.13 \%$ F1-score, outperforming conventional models such as DSHM and other baseline *** hybrid Q-learning with Adaboost model effectively reduces classification errors and addresses challenges associated with imbalanced medical datasets. This improved sensitivity and specificity facilitate early and real-time diagnosis, supporting clinicians in delivering accurate and efficient treatment. However, challenges related to computational complexity and model fine-tuning highlight the need for further research.

关键词： Adaptation models Accuracy Q-learning Sensitivity Computational modeling Sensitivity and specificity Real-time systems Monitoring Thyroid Diseases

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Real-time Twist Rebar Detection System exploiting GAN-based Data Augmentation technique 1

Real-time Twist Rebar Detection System exploiting GAN-based ...

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1st International Workshop on Intelligent Software engineering, ISE 2022

作者： Park, Jong Chan Kim, Gun-Woo Department of AI Convergence Engineering Gyeongsang National University Jinju Korea Republic of School of Computer Science Department of AI Convergence Engineering Gyeongsang National University Jinju Korea Republic of

Currently, ai image analysis research is being conducted on automated cutting, bending, and loading systems, which are the main facilities of rebar processing factories. For automation, various datasets through machine vision cameras are required. However, environmental factors include difficult data collection or high production costs to collect datasets in the production process. To solve these problems, we propose a real-time twist rebar detection system based on GAN (Generative adversarial network), with real rebar datasets collected from 20 rebar videos. In this paper, we generated additional datasets from a deep image generation network and detected rebars' endpoints through YOLO (You Only Look Once) v4, a deep-learning object detection model. In experiments, we generated rebar images corresponding to normal and abnormal, the measured quality between real rebar dataset and generated synthetic rebar dataset by FID (Frechet Inception Distance). As a result, FID measurements showed the normal synthetic rebar dataset 79.363 and the abnormal synthetic rebar dataset 113.973. After that, as a result of training in YOLO v4 by combining the synthetic rebar dataset generated from GAN and the real rebar dataset, we obtained the mean Average Precision (mAP) of 100% and a misdetection rate of 5% compared to the real rebar dataset, the mAP increased by 0.6%, and decreased by 10%. Overall, our results demonstrate a strong effect on rebar twist detection accuracy and misdetection rate. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Object detection

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Lightweight privacy-aware secure authentication scheme for cyber-physical systems in the edge intelligence era

Lightweight privacy-aware secure authentication scheme for c...

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作者： Bakkiam Deebak, Deebak AL-Turjman, Fadi School of Computer Science and Engineering Vellore Institute of Technology Vellore India Artificial Intelligence Engineering Department Research Center for AI and IoT Near East University Mersin 10 Turkey

Internet of Things (IoT) and cyber-physical systems demand proper real-time data processing and analysis to fulfill the essential characteristics of seamless computing services such as less processing time, dynamic network management, and location awareness. With the evolution of mobile edge computing, numerous security and privacy issues have been addressed including policy management, authentication, access control, and confidentiality. The technologies such as big-data, Internet of Things, and cloud have improved the development of modern healthcare systems to improve the quality of medical services. It is nowadays integrating the computing methods and communication technologies such as wireless sensor networks, radio frequency identification, smartphones, and location awareness to collect and analyze the physiological data of the patient. The e-Health entities such as medical device, client, and server rely on analytical models to carry out early diagnosis and remote monitoring. As it is based on e-Health applications, a real-time healthcare system is preferred to integrate with mobile cloud computing. Since the medical data are hugely exposed to vulnerabilities, a lightweight privacy-aware secure authentication (LPASA) scheme is presented. To achieve better secure efficiencies, this article is preferable to use lightweight cryptographic operations, namely, one-way hashing function and bitwise exclusive-OR. Moreover, the formal and informal security analysis shows that the proposed LPASA can withstand the possible active and passive attacks such as replay, man-in-the-middle, privileged-insider, online-guessing, and so forth. The experimental analysis demonstrates that the proposed LPASA scheme achieves a better quality of services in terms of packet delivery ratio, energy consumption per data delivery bit, and percentage of packets with low latency to adopt in resource-constrained environments. © 2021 John Wiley & Sons, Ltd.

关键词： Authentication

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Hypothesis testing the circuit hypothesis in LLMs 24

Hypothesis testing the circuit hypothesis in LLMs

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Claudia Shi Nicolas Beltran-Velez Achille Nazaret Carolina Zheng Adrià Garriga-Alonso Andrew Jesson Maggie Makar David M. Blei Department of Computer Science Columbia University New York FAR AI Computer Science and Engineering University of Michigan Ann Arbor

ISBN: (纸本)9798331314385

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented. One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. But how can we evaluate this hypothesis? In this paper, we formalize a set of criteria that a circuit is hypothesized to meet and develop a suite of hypothesis tests to evaluate how well circuits satisfy them. The criteria focus on the extent to which the LLM's behavior is preserved, the degree of localization of this behavior, and whether the circuit is minimal. We apply these tests to six circuits described in the research literature. We find that synthetic circuits - circuits that are hard-coded in the model - align with the idealized properties. Circuits discovered in Transformer models satisfy the criteria to varying degrees. To facilitate future empirical studies of circuits, we created the circuitry package, a wrapper around the TransformerLens library, which abstracts away lower-level manipulations of hooks and activations. The software is available at https://***/blei-lab/circuitry.

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DiffVoice: Text-to-Speech with Latent Diffusion 48

DiffVoice: Text-to-Speech with Latent Diffusion

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Zhijun Guo, Yiwei Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-Lance Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then jointly model the duration and the latent representation with a diffusion model. Subjective evaluations on LJSpeech and LibriTTS datasets demonstrate that our method beats the best publicly available systems in naturalness. By adopting recent generative inverse problem solving algorithms for diffusion models, DiffVoice achieves the state-of-the-art performance in text-based speech editing, and zero-shot adaptation. © 2023 IEEE.

关键词： diffusion probabilistic model speech editing speech synthesis variational autoencoder zero-shot adaptation

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Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Compliant

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Comp...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Munilla, J. Burmester, M. Caballero-Gil, P. University of Málaga Dpt. of Telecommunication Engineering Málaga29071 Spain Florida State Unviersity Department of Computer Science TallahasseeFL32304 United States University of La Laguna Dept. of Computer Science and Ai Tenerife La Laguna Spain

ISBN: (纸本)9798350364910

Trust and security are critical deployment require-ments for Industrial Internet of Things (IIoT) networks. A recent protocol, called TRUTH, integrates security mechanisms for authentication and privacy alongside a Dempster-Shafer based trust model, to assess the trustworthiness of collected data. We analyze this protocol and show that it does not comply with the implementation requirements of the 5G standard, and that flaws in the identification of involved parties hinder its adoption by 5G devices. We show how to fix this protocol and tailor it for the specific 5G implementation constraints, by enhancing its computational and communication performance. Finally, we show how to prove the security of this fixed protocol (TRUTH +) using the Tamarin automated verification tool. © 2024 IEEE.

关键词： Authentication Protocol

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Mern (Mongodb, Express-Js, React-Js, Node-Js) Stack Web-Based Streaming and Broadcasting Application

Mern (Mongodb, Express-Js, React-Js, Node-Js) Stack Web-Base...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Vineet Kumar Singh Priyank Awasthi Lakshman Tripathi Sahil Thakur Priyanka Singh Department Of Computer Science & Engineering (AI) ABES Institute of Technology Ghaziabad India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

In this paper, authors present a comprehensive approach to develop a web-based streaming and broadcasting application, centering on the integration of WebRTC (Web Real-Time Communication) and RTMP (Real-Time Messaging Protocol) technologies. Using the advantages of both traditions while addressing their unique limitations, the system aims to provide a consistent level of client *** engineering comprises of a WebRTC server for real-time communication between peers, encouraging the capture and transmission of audio and video streams. In order to increase compatibility and flexibility, WebRTC streams are converted to RTMP format using the Simple RTMP Server (SRS). More widespread availability across platforms and devices that facilitate RTMP-based playback is enabled by this *** integration of WebRTC and RTMP advancements within the streaming application, users can benefit from instantaneous communication features while ensuring compatibility with a wider variety of playback devices. This method provides an adaptable setup suitable for many streaming and broadcasting situations, including live events, video conferences, and online multimedia content delivery.

关键词： Digital multimedia broadcasting computers Protocols Streaming media Peer-to-peer computing Multimedia communication Servers Streams WebRTC Videoconferences

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