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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Patil, Sonal Ramagundam, Shashishekhar Sukumar, P. Khan, Mohiuddin Ali Patil, Nisha Deepak Nishant, Neerav G H Raisoni Institute of Engineering and Business Management HoD of CSE-IT Department Jalgaon India Software and Dev Engg4- SDET/ IT COMCAST MonroviaMD United States Department of Computer Science and Engineering Tamilnadu Erode India Jazan University Department of Computer & Network Engineering Jazan Saudi Arabia GCOERC Computer Engineering Nashik India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

Being able to see is fundamental to almost every aspect of our everyday lives, thus those who are visually impaired confront enormous obstacles. Thanks to recent developments in computer vision and computing, a system that can provide auditory input to visually impaired individuals regarding objects and their environments has been designed. Sequencing is crucial for preprocessing, feature selection, and model training. Although extremely accurate data input is still required for object detection, preparation is getting computationally cheaper. We were partially successful in reducing the image size by employing Gaussian Blur, which imparts a smoother appearance to the photos. We use color descriptors and Fourier transforms for feature extraction. Deep RBM training necessitates precise control over the attributes. In comparison to this method, current RBM and DNN algorithms look antiquated. The results demonstrated a notable enhancement in accuracy, reaching 98.31%. © 2024 IEEE.

关键词： Object detection

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INDICVOICES-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS 38

INDICVOICES-R: Unlocking a Massive Multilingual Multi-speake...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Sankar, Ashwin Anand, Srija Varadhan, Praveen Srinivasa Thomas, Sherry Singal, Mehak Kumar, Shridhar Mehendale, Deovrat Krishana, Aditi Raju, Giri Khapra, Mitesh AI4Bharat Department of Computer Science & Engineering Indian Institute of Technology Madras Tamil Nadu Chennai600036 India

Recent advancements in text-to-speech (TTS) synthesis show that large-scale models trained with extensive web data produce highly natural-sounding output. However, such data is scarce for Indian languages due to the lack of high-quality, manually subtitled data on platforms like LibriVox or YouTube. To address this gap, we enhance existing large-scale ASR datasets containing natural conversations collected in low-quality environments to generate high-quality TTS training data. Our pipeline leverages the cross-lingual generalization of denoising and speech enhancement models trained on English and applied to Indian languages. This results in IndicVoices-R (IV-R), the largest multilingual Indian TTS dataset derived from an ASR dataset, with 1,704 hours of high-quality speech from 10,496 speakers across 22 Indian languages. IV-R matches the quality of gold-standard TTS datasets like LJSpeech, LibriTTS, and IndicTTS. We also introduce the IV-R Benchmark, the first to assess zero-shot, few-shot, and many-shot speaker generalization capabilities of TTS models on Indian voices, ensuring diversity in age, gender, and style. We demonstrate that fine-tuning an English pre-trained model on a combined dataset of high-quality IndicTTS and our IV-R dataset results in better zero-shot speaker generalization compared to fine-tuning on the IndicTTS dataset alone. Further, our evaluation reveals limited zero-shot generalization for Indian voices in TTS models trained on prior datasets, which we improve by fine-tuning the model on our data containing diverse set of speakers across language families. We open-source code and data3 for all 22 official Indian languages. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient Path and Action Planning Method for Multi-Agent Pickup and Delivery Tasks under Environmental Constraints

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SN computer science 2023年第1期4卷 83页

作者： Yamauchi, Tomoki Miyashita, Yuki Sugawara, Toshiharu Department of Computer Science and Communications Engineering Waseda University 3-4-1 Okubo Tokyo Shinjuku-ku 1698555 Japan

We propose a method called path and action planning with orientation (PAPO) that efficiently generates collision-free paths to satisfy environmental constraints, such as restricted path width and node size, for the multi-agent pickup and delivery in non-uniform environment (N-MAPD) problem. The MAPD problem, wherein multiple agents repeatedly pick up and carry materials without collisions, has attracted considerable attention;however, conventional MAPD algorithms assume a specially designed environment and thus use simple, uniform models with few environmental constraints. Such conventional algorithms cannot be applied to realistic applications where agents need to move in more complex and restricted environments. For example, the actions and orientations of agents are strictly restricted by the sizes of agents and carrying materials and the width of the passages at a construction site and a disaster area. In our N-MAPD formulation, which is an extension of the MAPD problem to apply to non-uniform environments with constraints, PAPO considers not only the path to the destination but also the agents’ direction, orientation, and timing of rotation. It is costly to consider all these factors, especially when the number of nodes is large. Our method can efficiently generate acceptable plans by exploring the search space via path planning, action planning, and conflict resolution in a phased manner. We experimentally evaluated the performance of PAPO by comparing it with our previous method, which is the preliminary version of PAPO, the baseline method in a centralized approach, and fundamental meta-heuristic algorithms. Finally, we demonstrate that PAPO can efficiently generate sub-optimal paths for N-MAPD instances. © 2022, The Author(s).

关键词： Environmental constraints Multi-agent path finding Multi-agent pickup and delivery problem Multi-agent planning Robot planning

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Very fast algorithms for implied barriers and moving-barrier options pricing

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Mathematics and computers in Simulation 2023年 205卷 251-271页

作者： Lu, Yu-Ming Lyuu, Yuh-Dauh Department of Computer Science & Information Engineering National Taiwan University No.30 Sec.3 Xinhai Road Taipei106032 Taiwan Department of Finance and Department of Computer Science & Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei10617 Taiwan

Two closely related O(nlogn)-time tree algorithms under the Black–Scholes model are presented, where n denotes the tree's number of time steps. The first finds the implied step barrier that matches the barrier-hitting probabilities exactly. In the constant-barrier case, the implied barrier is surprisingly accurate even for small ns;indeed, n=1 gives good results in typical situations. The second prices options with a time-dependent barrier (i.e., moving-barrier options). In practice, both algorithms are one to three orders faster than the standard algorithms even when n is moderate. As a consequence, large portfolios or datasets can finally be studied in a timely manner. Both algorithms can be easily tailored to handle barriers that are continuously monitored, discretely monitored, a mixture of both, or even when the model parameters are all time varying. © 2022 International Association for Mathematics and computers in Simulation (IMACS)

关键词： Fast Fourier transforms

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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ClassLIE: Structure- and Illumination-Adaptive Classification for Low-Light Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4765-4775页

作者： Wei, Zixiang Wang, Yiting Sun, Lichao Vasilakos, Athanasios V. Wang, Lin University of Warwick National Automotive Innovation Centre Iv Sensor Group Wmg CoventryCV4 7AL United Kingdom Lehigh University Department of Computer Science and Engineering BethlehemPA18015 United States Grimstad4879 Norway Lab of Artificial Intelligence Thrust Guangzhou511457 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong SAR Hong Kong

Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a nontrivial task. Some endeavors have been made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illumination levels at the local regions of an image. Consequently, the enhanced results are affected by unexpected artifacts, such as unbalanced exposure, blur, and color bias. This article proposes a novel framework, called ClassLIE, that combines the potential of CNNs and transformers. It classifies and adaptively learns the structural and illumination information from the low-light images in a holistic and regional manner, thus showing better enhancement performance. Our framework first employs a structure and illumination classification (SIC) module to learn the degradation information adaptively. In SIC, we decompose an input image into an illumination map and a reflectance map. A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error (MSE) on the illumination map. As such, each input image can be divided into patches with three enhancement difficulty levels. Then, a feature learning and fusion (FLF) module is proposed to adaptively learn the feature information with CNNs for different enhancement difficulty levels while learning the long-range dependencies for the patches in a holistic manner. Experiments on five benchmark datasets consistently show our ClassLIE achieves new state-of-the-art performance, with 25.74 peak signal-to-noise ratio (PSNR) and 0.92 structural similarity (SSIM) on the LOw-Light (LOL) dataset. © 2020 IEEE.

关键词： Image enhancement

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Secure Energy Efficiency for ARIS Networks with Deep Learning: Active Beamforming and Position Optimization

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5282-5296页

作者： Wang, Dawei Wang, Zijun Zhao, Hongbo Zhou, Fuhui Alfarraj, Osama Yang, Weichao Mumtaz, Shahid Leung, Victor C. M. Northwestern Polytechnical University School of Electronics and Information Shaanxi Xi'an710072 China Beihang University School of Electronic and Information Engineering Beijing100191 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing210000 China King Saud University Community College Computer Science Department Riyadh11437 Saudi Arabia Nottingham Trent University Department of Computer Science United Kingdom Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Incorporating an active reconfigurable intelligent surface on an unmanned aerial vehicle (UAV), denoted as an aerial reconfigurable intelligent surface (ARIS), introduces a novel dimension for secure transmissions. Given the constraint of limited battery capacity in UAVs, energy management emerges as a key challenge within UAV networks. In response, we propose a secure energy efficiency (SEE) transmission scheme for ARIS networks, where active ARIS is strategically deployed to enhance information security. In addition, a SEE optimal problem is formulated by considering the imperfect wiretap channel state information to optimize the active beamforming vector and the ARIS position. For this non-convex problem, we first reformulate the fractional SEE objective into an equivalent form and subsequently decompose it into two distinct subproblems: optimizing the UAV's position and designing the active beamforming. For the UAV's position optimization, we propose a sophisticated deep deterministic policy gradient algorithm that enables the UAV to autonomously determine the optimal ARIS position through a self-learning strategy. Regarding beamforming design, we transform this aspect into a quadratic constrained quadratic programming problem and design an alternating direction multiplier method to optimize the reflection coefficient. Subsequently, an alternating optimization algorithm is proposed to synergistically solve these subproblems. Empirical simulations validate our proposed scheme, indicating an improvement in SEE of up to 47.2%. This significant improvement underscores the efficacy of the proposed ARIS-assisted secure transmission scheme in enhancing both security and energy efficiency in UAV networks. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Temporal Type-2 Fuzzy System for Time-Dependent Explainable Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 573-586页

作者： Kiani, Mehrin Andreu-Perez, Javier Hagras, Hani School of Computer and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Simbad2 Department of Computer Science University of Jaén Jaén23071 Spain

Explainable artificial intelligence (XAI) focuses on transparent AI models and decisions, which are easy to understand, analyze, and augment by a nontechnical audience. Fuzzy logic systems (FLS)-based XAI provides an explainable framework while also modeling uncertainties in real-world environments. However, most real-life processes are not characterized by high uncertainty alone;they are also inherently time dependent, i.e., the processes are time variant. In this work, we present a novel temporal type-2 FLS-based approach for time-dependent XAI (TXAI) systems, which can account for the likelihood of a sample occurrence in the time domain by its the frequency. In the proposed temporal type-2 fuzzy sets (TT2FSs), a 4-D time-dependent membership function integrates the universe of discourse, its membership, and its frequency of occurrence across time. The TXAI system manifested better classification prowess in cross-validation tests, with a mean recall of 95.40% than a standard XAI system (based on nontemporal general type-2 fuzzy sets) that had a mean recall of 87.04%. TXAI also performed significantly better than most nonexplainable AI systems, with between 3.95% and 19.04% improvement gain in mean recall. In addition, TXAI can also outline the most likely time-dependent trajectories using the frequency and time dimensions embedded in the TXAI model;viz. given a rule at a determined time interval, what will be the next most likely rule at a subsequent time interval. In this regard, the proposed TXAI system can have profound implications for delineating the evolution of real-life time-dependent processes, such as behavioral or biological processes. © 2020 IEEE.

关键词： Fuzzy logic

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End-to-end dynamic residual focal transformer network for multimodal medical image fusion

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Neural Computing and Applications 2024年第19期36卷 11579-11601页

作者： Zhang, Weihao Yu, Lei Wang, Huiqi Pedrycz, Witold College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Mathematics and Statistics Chongqing University Chongqing401331 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge or logic usually cannot match the feature representation perfectly, which results in partial information loss. Furthermore, most deep learning-based fusion methods depend on convolutional operations, which only focus on local features and have limited retention of context information. To address the above issues, we propose an end-to-end dynamic residual focal transformer network for multimodal medical image fusion, termed DRFT. The DRFT framework is an end-to-end network with no need to manually design fusion rules. Firstly, the context-gated convolution is introduced to construct the context dynamic extraction module (CDEM) to extract the key semantic information more accurately from multimodal medical images. Then, a new residual transformer fusion module (RTFM) is designed by incorporating the focal transformer into the residual mechanism, which can not only extract the deep semantic features, but also adaptively learn the optimal fusion scheme. Finally, the nest architecture is employed to extract multiscale features. In addition, a new objective function consisting of global detail loss and fusion enhancement loss is designed to enrich the modal information in the fused image. Notably, the proposed network does not require the two-stage training strategy as opposed to the traditional encoder–decoder fusion structure. Extensive experimental results on mainstream datasets show that, compared with the state-of-the-art methods, the proposed DRFT delivers better performance in both qualitative and quantitative evaluation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient-Boosting and Fuzzy Rule-Based Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5771-5785页

作者： Li, Jinbo Liu, Peng Chen, Long Pedrycz, Witold Ding, Weiping Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China China Unicom Research Institute Beijing100048 China Henan University International Business School Zhengzhou47500 China University of Macau Department of Computer and Information Science 999078 China University of Alberta Department of Electrical and Computer Engineering EdmontonAB T6R 2V4 Canada City University of Macau Faculty of Data Science 999078 China

The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-based models excel in interpretability and have seen widespread application across diverse fields. However, they face challenges such as complex design specifications and scalability issues with large datasets. The fusion of different techniques and strategies, particularly gradient boosting, with fuzzy rule-based models offers a robust solution to these challenges. This article proposes an integrated fusion framework that merges the strengths of both paradigms to enhance model performance and interpretability. At each iteration, a fuzzy rule-based model is constructed and controlled by a dynamic factor to optimize its contribution to the overall ensemble. This control factor serves multiple purposes: it prevents model dominance, encourages diversity, acts as a regularization parameter, and provides a mechanism for dynamic tuning based on model performance, thus mitigating the risk of overfitting. Additionally, the framework incorporates a sample-based correction mechanism that allows for adaptive adjustments based on feedback from a validation set. Experimental results substantiate the efficacy of the presented gradient-boosting framework for fuzzy rule-based models, demonstrating performance enhancement, especially in terms of mitigating overfitting and complexity typically associated with many rules. By leveraging an optimal factor to govern the contribution of each model, the framework improves performance, maintains interpretability, and simplifies the maintenance and update of the models. © 2024 IEEE.

关键词： Optimization

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