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Impersonated Human Speech Chatbot with Adaptive Frequency Spectrum 3

Impersonated Human Speech Chatbot with Adaptive Frequency Sp...

引用

3rd international conference on intelligent Communication and Computational Techniques, ICCT 2023

作者： Chettiar, Gautam Shukla, Amogh Nalwaya, Preet Sethi, Karman Prakash, Surya Vellore Institute of Technology Dept. of Communication Engineering Vellore India Department of Software Systems Vellore Institute of Technology Vellore India Indian Institute of Information Technology Allahabad Department of Electronics and Communication Engineering Prayagraj India

ISBN: (纸本)9781665453578

Recent trends in artificial intelligence and natural language processing models have led to the generation of highly efficient and versatile intelligent chatbot models., which have the potential to supplant human speech to the level of conversational proficiency. The proposed method creates a chatbot model that trains itself on open conversation datasets and aims to impersonate without compromising the emotional sentiments in the voice. These datasets extract from the applications such as WhatsApp., Telegram., Messenger., or any other chatting platform. Datasets convert to a machine-readable format., which is dynamically updated in real-time during the conversation., and then using speech conversion algorithms convert the reply into the desired individual's voice. The proposed model's conversational ability depends on the amount of conversation data., which gives the output in the person's voice frequency. By using an NLP-based chatbot trained on personalized data using KNN., and handling misses by pipelining the chatbot inputs to the GPT-2 model., the model can generate human-like replies even if there is data insufficiency. The natural replies are complemented with matching human voice and tone characteristics by using the vocoder model., which matches the spectral characteristics of the target voice onto the required voice. This opens a plethora of commercial and therapeutic applications that provide excellent insights into implementing natural communication models for humanoid and robotics innovations. © 2023 ieee.

关键词： Sentiment analysis

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A Statistically Motivated Likelihood for Track-Before-Detect

A Statistically Motivated Likelihood for Track-Before-Detect

引用

ieee international conference on Multisensor Fusion and Integration for intelligent systems (MFI)

作者： Bosser, Daniel Hendeby, Gustaf Nordenvaad, Magnus Lundberg Skog, Isaac Linkoping Univ Dept Elect Engn Linkoping Sweden Swedish Def Res Agcy FOI Div Underwater Technol Kista Sweden

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

A theoretically sound likelihood function for passive sonar surveillance using a hydrophone array is presented. The likelihood is derived from first order principles along with the assumption that the source signal can be approximated as white Gaussian noise within the considered frequency band. The resulting likelihood is a nonlinear function of the delay-and-sum beamformer response and signal-to-noise ratio (SNR). Evaluation of the proposed likelihood function is done by using it in a Bernoulli filter based track-before-detect (TkBD) framework. As a reference, the same TkBD framework, but with another beamforming response based likelihood, is used. Results from Monte-Carlo simulations of two bearings-only tracking scenarios are presented. The results show that the TkBD framework with the proposed likelihood yields an approx. 10 seconds faster target detection for a target at an SNR of -27 dB, and a lower bearing tracking error. Compared to a classical detect-and-track target tracker, the TkBD framework with the proposed likelihood yields 4 dB to 5 dB detection gain.

关键词： Target tracking Monte Carlo methods Array signal processing Surveillance Gaussian noise Sonar equipment Object detection

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A Clearer Image: Improving Object Detection in Real Rainy Conditions with Two-Stage processing

A Clearer Image: Improving Object Detection in Real Rainy Co...

引用

2023 ieee international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Yang, Mingxuan Han, Xiyu Ping, Xianyao Li, Zipeng Xiao, Jing School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Computer Science Wuhan University China

ISBN: (纸本)9798350313154

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in social life, such as intelligent security, autonomous driving, and so on. However, the performance of object detection could degrade rapidly under challenging weather scenarios including rainy conditions. Despite existing technologies having made impressive advancements, they are almost under synthetic rainy-clean pairs, which have a huge domain gap between synthetic and real rain. In this work, we propose an unsupervised deraining method under real rain and utilize a non-local contrastive learning for better decoupling the rain layer from the clean image. Furthermore, we introduce a semi-supervised detector during detection module to reduce the inconsistency of pseudo labels for better facilitating contrastive learning. Extensive experiments with different deraining and object detection methods demonstrate that our two-stage method obtains competitive performance in complex weather scenarios. © 2023 ieee.

关键词： Rain

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Physical Layer Secret Key Generation for Spatially Correlated Channels Based on Multi-Task Autoencoder 7

Physical Layer Secret Key Generation for Spatially Correlate...

引用

7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Zhou, Jingqi Zeng, Xin Tongji University Dept. of Automation Shanghai China Tongji University Dept. of Information and Communication Engineering Shanghai China

ISBN: (纸本)9781665478571

Physics layer key, as an emerging method to enhance information security, is generated based on the reciprocity and spatial decorrelation of wireless channels. However, due to the half-duplex mode of the communication systems, the channels detected by the legitimate sides are not completely reciprocal. Meanwhile, when the eavesdropper is extremely close to the legitimate side, the spatial correlation could be very strong and threaten the information security. This paper combines the autoencoder and the multi-task learning into a novel physical layer key generation scheme, i.e., Multi-Task Autoencoder (MTAE). MTAE extracts the reciprocal features from the weakly correlated channel estimates of the legitimate sides while reducing its correlation with eavesdropper. Simulation results show good randomness and consistency with the primary keys generated based on MTAE for the legitimate sides, while increasing the difference in the extracted features between eavesdropper and its adjacent node. The proposed method has great potential in scenarios requiring high security. © 2022 ieee.

关键词： Learning systems

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models 5

Evolutionary Multi-Objective Neural Architecture Search for ...

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5th international conference on Data-Driven Optimization of Complex systems, DOCS 2023

作者： Yang, Shangshang Zhen, Cheng Tian, Ye Ma, Haiping Liu, Yuanchao Zhang, Panpan Zhang, Xingyi School of Artificial Intelligence Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei China Automation for Process Industries Northeastern University State Key Laboratory of Synthetical Shenyang China

ISBN: (纸本)9798350393521

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures. © 2023 ieee.

关键词： Multiobjective optimization

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HePiLUT: Resource Efficient Heterogeneous Pipelined CNN Accelerator for FPGAs 5

HePiLUT: Resource Efficient Heterogeneous Pipelined CNN Acce...

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5th international conference on intelligent Computing, Communication, Networking and Services, ICCNS 2024

作者： Al Amin, Rashed Hossain, Md Shahi Amran Obermaisser, Roman Institute for Embedded Systems University of Siegen Siegen Germany

ISBN: (纸本)9798350354690

Field programmable gate arrays (FPGAs) have gained recognition as a suitable platform for implementing Convolutional Neural Network (CNN)-based algorithms due to their favorable attributes, such as energy efficiency and parallel computing capabilities. Nevertheless, deploying CNNs on resource-constrained devices poses challenges due to the substantial computational demands of CNNs and limited on-chip hardware resources. A majority of prior research predominantly relies on the limited block RAM (BRAM) for storing CNN parameters, and costly digital signal processing (DSP) blocks for executing multiply-accumulate (MAC) operations. To address these constraints, this paper introduces HePiLUt,a novel compiler designed to analyze the CNN structure and parameters, and automatically generates a heterogeneous pipelined CNN accelerator architecture by utilizing Look-Up Table (LUT). In addition, a dataflow controller in the pipelining architecture has been developed to foster a resource-efficient and low-latency CNN accelerator for FPGAs. HePiLUT leverages LUTs to store input image data and execute MAC operations instead of DSP blocks. The efficacy of the proposed CNN accelerator compiler has been evaluated on the Xilinx ZCU102 FPGA platform utilizing the Visual Geometry Group (VGG) architecture and CIFAR-10 dataset. The findings underscore HePiLUT's capability, showcasing a substantial reduction compared to state-of-the-art CNN accelerator design. However, these substantial savings come at a trade-off, as there is a 3% decrease in throughput performance. The proposed HePiLUtsignificantly reduces the FPGA resource utilization and automatic compiler solution in CNN implementation, providing considerable support for sustainable and energy-efficient computing systems. © 2024 ieee.

关键词： Table lookup

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Weighted Sum-Rate Maximization for RIS-Assisted MU-MIMO Communication systems with Low-Resolution ADCs/DACs 8

Weighted Sum-Rate Maximization for RIS-Assisted MU-MIMO Comm...

引用

8th international conference on Communication, Image and signal processing, CCISP 2023

作者： Dong, Yingli Li, Hui Dong, Limeng Liang, Rui Wang, Ruonan School of Electronics and Information Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350305838

This paper focuses on maximizing the weighted sum-rate (WSR) in downlink communication systems that uti-lize reconfigurable intelligent surfaces (RIS) to enhance MU-MIMO transmission. To reduce implementation costs and power consumption, low-resolution ADCs/DACs are configured at the BS. Unlike the traditional design that predicated on perfect channel state information (CSI), we establish a WSR optimization problem given imperfect estimated CSI. Specifically, based on imperfect statistical CSI, we propose a robust weighted minimum mean square error (WMMSE) algorithm to maximize the beam-forming matrix, and then use riemann conjugate gradient (RCG) algorithm to optimize the phase shift vector with the aim of maximizing the WSR for all users. Numerical examples indicate that the proposed scheme improves system's robustness as well as achieves optimal WSR performance using three quantization bits. © 2023 ieee.

关键词： Channel state information

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An intelligent Software Tool for Audio signal-based Hinglish to English Language Translation 6

An Intelligent Software Tool for Audio Signal-based Hinglish...

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6th ieee Pune Section international conference, PuneCon 2023

作者： Dhavale, Aditi Vikrant Dhavale, Amit Vikrant Dhavale, Sunita Vikrant Symbiosis Institute of Technology Department of Artificial Intelligence & Machine Learning Pune India Indian Institute of Technology Department of Mechanical Engineering Indore India Defence Institute of Advanced Technology SoCE & Ms Pune India

ISBN: (纸本)9798350324204

Most of the current Indian generation speaks/writes using Hinglish on various social media platforms. It is difficult to understand and interpret Hinglish using existing computational linguistic tools mainly based on English. Hence, there is a need to provide automated Hinglish to English Language Translation tools to integrate with existing NLP-based applications like hate speech detection. Currently, such complex language translations are manually intensive and require domain experts. Also, there is a scarcity of standard datasets in this domain for carrying out helpful research. In this research, we propose an intelligent AI-based translator for translating audio data signals from Hinglish to English, which can be integrated into various NLP-based applications in the Indian Context. We also created a large-scale customized annotated Hinglish to English dataset containing 27135 entries of size 4.121MB. After extensive experimentation, we received the highest BLEU (Bi-Lingual Evaluation Understudy) score of 92.20 on our customized Hinglish to English dataset, while on the original PHINC dataset, we found a BLEU score of 70 approx. We also developed a Python TKinter-based user-friendly GUI application that takes audio signal/voice as input and provides English-translated text as output. © 2023 ieee.

关键词： Natural language processing systems

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The Implementation of Supervised Learning Recommender System to Enhance Reading Interest of Visitors in Regional Libraries 9

The Implementation of Supervised Learning Recommender System...

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9th international conference on signal processing and intelligent systems, ICSPIS 2023

作者： Darminta, Atep Sumitra, Irfan Dwiguna Universitas Komputer Indonesia Master of Information Systems Bandung Indonesia

ISBN: (纸本)9798350369922

In the era of digitization, regional libraries face challenges in sustaining the reading interest of their visitors. One solution that could be implemented to enhance the reading interest of visitors in regional libraries is by applying the supervised learning recommender system method based on the profiling of previous book borrowers. This study aims to implement this method on the borrowing history of books by visitors to regional libraries in order to recommend books that align with their reading interests, thereby increasing the number of borrowed books, facilitating visitors in finding the books they need, and providing the best service such as user experience to the visitors. The method used in this research is item-based collaborative filtering with the K-Nearest Neighbor (K-NN) algorithm and cosine similarity distance to find similar book titles based on the titles previously borrowed by book borrowers. The visitor's borrowing history data in this study consists of Bibliographic Data from book borrowers in 2022 at regional libraries. The data underwent preprocessing to enhance accuracy, resulting in quantitative data comprising attributes such as borrower ID, book ID, and result. The model evaluation method used a confusion matrix on the training data compared to the testing data to determine the accuracy, precision, recall, and f-measure values generated by the created K-Nearest Neighbor (K-NN) model. The result of this study is a recommender system model that shows the top 10 book titles based on the borrowing history of previous book titles entered by the visitor. The performance measurements of the recommendation model yield the following results: the model's accuracy stands at 68.53%, signifying its ability to accurately classify all samples. Precision is recorded at 99.79%, indicating that the model tends to have minimal false positives (FP). The recall rate is 68.61%, suggesting that the model excels at detecting positive samples compared to negative

关键词： Recommender systems

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On Runtime Reduction in 3D Extended Object Tracking by Measurement Downsampling

On Runtime Reduction in 3D Extended Object Tracking by Measu...

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2023 ieee Symposium Sensor Data Fusion and international conference on Multisensor Fusion and Integration, SDF-MFI 2023

作者： Baur, Tim Reuter, Johannes Hanebeck, Uwe D. Konstanz Germany Karlsruhe Germany

ISBN: (纸本)9798350382587

In 3D extended object tracking (EOT), well-established models exist for tracking the object extent using various shape priors. A single update, however, has to be performed for every measurement using these models leading to a high computational runtime for high-resolution sensors. In this paper, we address this problem by using various model-independent downsampling schemes based on distance heuristics and random sampling as pre-processing before the update. We investigate the methods in a simulated and real-world tracking scenario using two different measurement models with measurements gathered from a LiDAR sensor. We found that there is a huge potential for speeding up 3D EOT by dropping up to 95% of the measurements in our investigated scenarios when using random sampling. Since random sampling, however, can also result in a subset that does not represent the total set very well, leading to a poor tracking performance, there is still a high demand for further research. © 2023 ieee.

关键词： signal sampling

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