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A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

Multimedia Tools and Applications 2024年第15期83卷 45407-45422页

作者： Chan, Din Yuen Wang, Jhing-Fa Chin, Hsu-Ting Department of Computer Science and Information Engineering National Chiayi University Chiayi City Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

In AI pandemic applications, the online automatic AI recording apparatus for official councils such as court trials, business conferences and commercial meetings will become imperative because it could let the opinion identification and consensus of participants be synchronically available to implicitly diminish social costs such as follow-up disputes and controversies. Hence, in this study, an automatic on-line multi-dialogue recording system is completely constructed, where the unbounded interleaved-state recurrent neural networks (UIS-RNN) with proposed crux improvements is exploited to achieve confident speaker-diarization. For keeping the systematic robustness, a denoising spectral-LSTM, which is precisely modified from the dual-signal transformation LSTM (DTLN), can strengthen its subsequent crux-improved UIS-RNN and automatic speech recognition (ASR). Finally, the MacBERT model is set to rectify the possible wrong words in conversed sentences according to the learned rational context. For making our system being a practical software apparatus in the use of unmarked multi-person councils, we have also completed the convenient interfaces for the operations of ASR and speaker-diarization, which can exhibit on-line denoising efficacy and speaker-diarization results as well as offer real-time hand-crafted rectifications to common users. In extensive experiments, the proposed recording system can promise high accuracy rates of online speaker diarization and speech-separated ASR. Our proposed system had been examined by the cooperated law court staffs, who offered the noise-embedded speeches of practical court field to test our system. Since the tight recording burden had been indeed noticeably alleviated in their legal-action councils, the court staffs had endorsed that the proposed entire system could be a friendly labor-saving AI apparatus for on-line automatic multi-dialogue recording. © The Author(s), under exclusive licence to Springer science+Business Media,

关键词： Audio recordings

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Multiplayer Reach-Avoid Differential Games in 3D Space Inspired by Harris'Hawks'Cooperative Hunting Tactics

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Research 2024年第2期2023卷 41-54页

作者： Wanying Ruan Haibin Duan Yongbin Sun Wanmai Yuan Jie Xia State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina Peng Cheng Laboratory ShenzhenChina Information Science Academy of CETC BeijingChina

This paper investigates a multiplayer reach-avoid differential game in 3-dimensional(3D)space,which involves multiple pursuers,multiple evaders,and a designated target *** evaders aim to reach the target region,while the pursuers attempt to guard the target region by capturing the *** class of research holds significant practical ***,the complexity of the problem escalates substantially with the growing number of players,rendering its solution extremely *** this paper,the multiplayer game is divided into many subgames considering the cooperation among pursuers,reducing the computational burden,and obtaining numerically tractable strategies for ***,the Apollonius sphere,a fundamental geometric tool for analyzing the 3D differential game,is formulated,and its properties are *** on this,the optimal interception point for the pursuer to capture the evader is derived and the winning conditions for the pursuer and evader are ***,based on the Apollonius sphere,the optimal state feedback strategies of players are designed,and simultaneously,the optimal one-to-one pairings are ***,the Value function of the multiplayer reach-avoid differential game is explicitly given and is proved to satisfy Hamilton-Jacobi-Isaacs(HJI)***,the matching algorithm for the case with pursuers outnumbered evaders is provided through constructing a weighted bipartite graph,and the cooperative tactics for multiple pursuers are proposed,inspired by the Harris'Hawks intelligent cooperative hunting ***,numerical simulations are conducted to illustrate the effectiveness of the theoretical results for both cases where the number of adversary players is equal and unequal between the 2 groups.

关键词： holds satisfy rendering

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Data hiding with thumbnail-preserving encryption for cloud medical images

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Multimedia Tools and Applications 2024年第36期83卷 84161-84177页

作者： Xu, Shuying Chang, Chin-Chen Horng, Ji-Hwei Department of Information Engineering and Computer Science Feng Chia University Taichung407 Taiwan Department of Electrical Engineering National Quemoy University Kinmen892 Taiwan

To serve a convenient healthcare network, storing medical images and diagnosis records in the cloud is a straightforward solution. Encrypting the medical images before uploading them to the cloud is a trivial strategy to prevent data breaches. However, the availability of images is also reduced. Since all images look noisy, one cannot obtain the intended image by a quick viewing of the file folder. In this paper, we propose a total solution to these considerations. A reversible data hiding scheme based on thumbnail-preserving image encryption is presented. By leveraging a multi-scale Haar wavelet transform and extended run-length coding, we can produce thumbnail-preserving encrypted medical images that provide a sufficient vacating room for embedding electronic patient records. The experimental results demonstrate that our scheme effectively balances the confidentiality and availability of encrypted medical images. It provides over 1 bit per pixel of capacity for embedding electronic patient records, making it suitable for cloud-based medical data management. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cryptography

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Class-conditional domain adaptation for semantic segmentation

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Computational Visual Media 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li James H.Elder Runmin Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.

关键词： domain adaptation generative adversarial networks semantic segmentation cityscapes

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uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

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IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

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Deep Learning-Based Enhanced Object Detection for Humanoid Robots 1

Deep Learning-Based Enhanced Object Detection for Humanoid R...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Negi, Akshit Patel, Harshita Vellore Institute of Technology School of Computer Science Engineering & Information Systems Vellore India

ISBN: (纸本)9798331520540

In the contemporary world, humanoid robots are likely to play a key role in various fields, including health care, domestic service, hospitality, business, and military and security activities. The robots are employed to assist people in accomplishing specific missions or sometimes substitute human beings in hazardous situations. To accomplish these missions effectively, humanoid robots should be very good at recognition and subsequent processing of objects. The main goal of this research is to enhance the object recognition capabilities Leverage the advancements in deep learning to provide the control of Robotis-Op3 humanoid robots. In this work, the researchers analyzed popular deep neural models, among which are VGG16 and ResNet, highly promoted for their robust object recognition ability, as well as the more recent CNN architectures such as SSD MobileNetV3. This research improves object recognition capabilities on hu-manoid robots as such to enhance performance. in different domains, allowing them to better recognize and interact with the objects around them. This development will then allow more efficient and effective utilization of humanoid robots, extending their application and contribution in society. © 2025 IEEE.

关键词： Anthropomorphic robots

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Recommendation System using Emotion Detection 3

Recommendation System using Emotion Detection

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Susindra, S. Sweta, B. Keerthikha Shri, R. Brindha, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331527549

The need for a personalized user experience brought recommendation systems to the forefront of digital innovation. However, traditional approaches tend to often forget human emotions, which represent a critical driver in decision making. The paper proposes a new framework in which the detection of emotions becomes part of the recommendation system through advanced facial expression analysis and sentiment evaluation. The paper discusses a hybrid architecture combining emotion recognition via CNN and a collaborative filtering technique for dynamic recommendations. The study outcome demonstrated an enormous improvement in user engagement and satisfaction. It further underlines the transformative potential of emotion-aware systems. © 2025 IEEE.

关键词： Emotion Recognition

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Energy-Efficient Scheduling Based on Task Migration Policy Using DPM for Homogeneous MPSoCs

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computers, Materials & Continua 2023年第1期74卷 965-981页

作者： Hamayun Khan Irfan Ud din Arshad Ali Sami Alshmrany Department of Electrical Engineering Faculty of Engineering&Technology Superior University Lahore54000Pakistan Department of Computer Science Faculty of Computer Science&IT Superior University Lahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahAl Madinah Al Munawarah42351Saudi Arabia

Increasing the life span and efficiency of Multiprocessor System on Chip(MPSoC)by reducing power and energy utilization has become a critical chip design challenge for multiprocessor *** the advancement of technology,the performance management of central processing unit(CPU)is *** densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip *** energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor(CMOS)circuits and reduces the speed by 10%–15%because excessive on-chip temperature shortens the chip’s life *** this paper,we address the scheduling&energy utilization problem by introducing and evaluating an optimal energy-aware earliest deadline first scheduling(EA-EDF)based technique formultiprocessor environments with task migration that enhances the performance and efficiency in multiprocessor systemon-chip while lowering energy and power *** selection of core andmigration of tasks prevents the system from reaching itsmaximumenergy utilization while effectively using the dynamic power management(DPM)*** in the execution of tasks the temperature and utilization factor(u_(i))on-chip increases that dissipate more *** proposed approach migrates such tasks to the core that produces less heat and consumes less power by distributing the load on other cores to lower the temperature and optimizes the duration of idle and sleep times across multiple *** performance of the EA-EDF algorithm was evaluated by an extensive set of experiments,where excellent results were reported when compared to other current techniques,the efficacy of the proposed methodology reduces the power and energy consumption by 4.3%–4.7%on a utilization of 6%,36%&46%at 520&624 MHz operating frequency when particularly in comparison to other energy-aware methods for *** are running and accurately scheduled to make an energy-efficient

关键词： Dynamic power management dynamic voltage&frequency scaling dynamic thermal management multiprocessor system on chip complementary metal oxide semiconductor reliability

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Spectral analysis of bone-conducted speech using modified linear prediction

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International Journal of Speech Technology 2024年第4期27卷 1039-1053页

作者： Ohidujjaman Hasan, Mahmudul Zhang, Shiming Huda, Mohammad Nurul Uddin, Mohammad Shorif Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh Computer Science and Engineering Comilla University Comilla3506 Bangladesh School of Electrical and Information Northeast Agricultural University Harbin150030 China Computer Science and Engineering United International University Dhaka1212 Bangladesh Computer Science and Engineering Green University of Bangladesh Kanchon1460 Bangladesh Computer Science and Engineering Jahangirnagar University Savar1342 Bangladesh

This paper improves the performance of linear prediction (LP) in precise spectral estimation of bone-conducted (BC) speech. Inherently, BC speech contains a wide spectral dynamic range that causes ill conditioning in the autocorrelation (ACR) method and its variants, where the Levinson–Durbin (L–D) algorithm is commonly implemented. Instead of the conventional LP-based spectral estimation methods, we utilize the covariance-based method, specifically the modified covariance (MC) method, where the orthogonal decomposition algorithm is deployed. In this paper, we derive the MC method from the least squares (LS) technique for BC speech analysis. The MC method reduces the eigenvalue expansion that compresses the spectral dynamic range of the BC speech signal. The effect of spectral dynamic range compression declines the ill-conditioned properties of LP. Through the proposed method using synthetic BC speech, the resulting power spectrum provides more accurate peaks than the conventional methods. The validity of the proposed method is also analyzed by inspecting real BC speech. This study reveals the utmost use of BC speech in speech processing systems. The experimental results demonstrate that the proposed method provides more accurate spectral estimation for synthetic and real BC speeches compared with conventional spectral estimation methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Autocorrelation

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Risk Prediction of Heart Disease using Deep SHAP Techniques 2

Risk Prediction of Heart Disease using Deep SHAP Techniques

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Saranya, A. Narayan, Subhashini VIT University Vellore School of Computer Science Engineering and Information systems India

ISBN: (纸本)9798350371314

The automatic identification o f h eart d iseases is a challenging problem in the medical field, a nd i t constitutes the primary cause of death. Heart failure prediction, one of the symptoms of cardiovascular disease, has gained importance among physicians. In addition to increasing feature ranking and clinical prediction, deep learning also helps with output interpretation for medical practitioners. Therefore, the idea behind Explainable Artificial Intelligence (XAI) is to address the problem of deep learning algorithms in the healthcare domain not being explainable, in this case, and give the user knowledge about the model's inner working process in an understandable format. In our proposed work, convolutional neural networks are used to predict the disease, and the Deep SHAP model is used to visualize the model predictions with an accuracy of 0.90, sensitivity of 0.97, and F1-score of 0.86 for class 1. The results section includes metrics computed for two classes, such as recall, precision, and F1-score. © 2024 IEEE.

关键词： Health care

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