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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Nastassia Vysotskaya Noah Maul Alessandra Fusco Souvik Hazra Jens Harnisch Tomás Arias-Vergara Andreas Maier Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Infineon Technologies AG Neubiberg Germany

Hypertension is considered to be one of the most critical risk factors for cardiovascular diseases. As such, continuous, accurate and non-invasive monitoring of blood pressure (BP) is of utmost importance and research on such approaches is gaining momentum. In this study, we propose a novel transformer-based model architecture that leverages historic pressure wave information for accurate blood pressure regression. We achieve remarkable results that satisfy both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) blood pressure monitoring standards, with 97% of errors less than 5mmHg and 1.02 ± 1.77mmHg (mean absolute error ± standard deviation) accuracy for systolic (SBP), and 93% and 1.57 ± 2.36mmHg for diastolic BP (DBP). To the best of our knowledge, this is the first approach that utilizes transformers for BP regression and the first radar approach to satisfy BP standards, demonstrating the predictive power of the proposed model and the suitability of radar for the task.

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Utilizing Deep Incomplete Classifiers to Implement Semantic Clustering for Killer Whale Photo Identification Data 27th

Utilizing Deep Incomplete Classifiers to Implement Semantic ...

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27th International Conference on pattern recognition, ICPR 2024

作者： Barnhill, Alexander Towers, Jared R. Nöth, Elmar Maier, Andreas Bergler, Christian Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Bay Cetology 257 Fir Street Alert BayBCV0N 1A0 Canada Department of Electrical Engineering Media and Computer Science Technical University of Applied Sciences Amberg-Weiden Amberg Germany

ISBN: (纸本)9783031781063

Machine-assisted photo identification processes require significant amounts of data for each member of a population of interest but offer the possibility to alleviate a significant amount of manual effort. Gathering such data is time consuming and opportunistic, leading to imbalanced datasets ill-suited for traditional machine (deep) learning efforts. Incomplete classifiers, trained on a subset of classes in a population, can be initially useful to identify the most commonly seen individuals. This study investigates the use of incomplete classifiers trained on a subset of often-observed individual killer whales to generate latent space representations of the larger population containing unseen individuals. These semantically relevant representations are subsequently clustered to investigate the efficacy of this method as a secondary identification mechanism. This method proves to be robust to a significant amount of noise while being able to isolate individuals unknown to the classifier when applying limited expert knowledge to the approximate size of the population. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： labeled data

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Intelligent Model for Predicting the Quality of Services Violation

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computers, Materials & Continua 2022年第5期71卷 3607-3619页

作者： Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs *** service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for *** users’major concerns;the factors for service reliability are based on response time,accessibility,availability,and *** this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and *** paper also compares Simple-PSO and Parallel *** simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

关键词： Accountability particle swarm optimization mutant particle swarm optimization quality of service service level agreement

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Mutual Information-Guided GA for Bayesian Network Structure Learning (Extended Abstract)

Mutual Information-Guided GA for Bayesian Network Structure ...

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International Conference on Data Engineering

作者： Kefei Yan Wei Fang Hengyang Lu Xin Zhang Jun Sun Xiaojun Wu Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.

关键词： Accuracy NP-hard problem Sociology Search problems Bayes methods Statistics Optimization

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Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

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computers, Materials & Continua 2022年第6期71卷 5603-5618页

作者： Shahid Mehmood Imran Ahmad Muhammad Adnan Khan Faheem Khan T.Whangbo Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Education sector has witnessed several changes in the recent *** changes have forced private universities into fierce competition with each other to get more students *** competition has resulted in the adoption of marketing practices by private universities similar to commercial *** get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their *** study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the *** techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore *** results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local *** on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

关键词： Social media higher education sentiment analysis content analysis competitive analysis text mining

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Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge

Ensemble and Personalized Transformer Models for Subject Ide...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Salvatore Calcagno Raffaele Mineo Daniela Giordano Concetto Spampinato Department of Electrical Electronics and Computer Engineering Pattern Recognition and Computer Vision Laboratory (PeRCeiVe Lab) University of Catania Italy

In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a CNN model - for the identification of subjects from wearable devices, while a personalized - one for each subject - scheme is used for relapse detection in psychotic disorder. Our final submitted solutions yield top performance on both tracks of the challenge: we ranked 2 nd on the subject identification task (with an accuracy of 93.85%) and 1 st on the relapse detection task (with a ROC-AUC and PR-AUC of about 0.65). Code and details are available at https://***/perceivelab/e-prevention-icassp-2023.

关键词： Performance evaluation Codes Wearable computers Signal processing Transformers Acoustics Object recognition

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Autonomous Drone Swarm Navigation in Complex Environments 19

Autonomous Drone Swarm Navigation in Complex Environments

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19th International Bhurban Conference on Applied sciences and Technology, IBCAST 2022

作者： Qamar, Suleman Qamar, Maryam Shahbaz, Muhammad Arif Arshad, Muhammad Saher Shah, Najmus Khan, Asifullah Pattern Recognition Lab DCIS Pakistan Institute of Engineering and Applied Sciences Islamabad Pakistan CS & IT University of Azad Jammu and Kashmir Pakistan Department of Computer Science and Engineering Kyung Hee University Korea Republic of

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Swarm intelligence is the behavior exhibited by a group of organisms performing simple actions in unison which leads to complex global behavior. This learnt behavior promotes formation of artificial swarm systems for accomplishing complex tasks and in this paper, the behavior learnt by a swarm of drones is used to accomplish multiple tasks related to complex environment navigation, obstacle avoidance and single-target tracking. In the past, manual formulation of artificial swarm systems has been attempted. The swarms usually have to be flexible entities that need to adapt to changing environments but manually crafted swarms can't adapt to newer operating conditions thus they are not feasible. To tackle these challenges, an autonomous drone swarm navigation (ADSN) system is presented by introducing a customized architecture based on Truly Proximal Policy Optimization (TPPO) with the addition of memory cells. Furthermore, suitable 3D environments are designed for conductive learning with the addition of stability factors for drones to mimic real-life environments. Measures like Mean Cumulative Reward (MCR), Value Loss (VL), and Entropy are used to measure the performance of the presented ADSN system and other models with and without LSTM and found that incorporating memory does enhance the performance of the models although in some cases increase might not be that significant. TPPO with memory seems to have least loss and almost always performs best. Significant improvements were achieved compared to existing methodologies in convergence speed and enhanced stability of the model by customizing the architecture and hyper- parameter optimization. It is observed that Soft Actor Critic (SAC) is highly dependent on the values of the hyper-parameters and its results vary greatly with different values of hyper-parameters. The presented model has applications in maze navigation, target tracking, hover drones and other real-time scenarios. © 2022 IEEE.

关键词： Drones

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DITS: A New Domain Independent Text Spotter 27th

DITS: A New Domain Independent Text Spotter

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27th International Conference on pattern recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Shivakumara, Palaiahnakote Pal, Umapada Ghosal, Palash Wu, Xiao-Jun Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Baranagar India School of Science Engineering and Environment University of Salford Manchester United Kingdom Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India Jiangnan University Wuxi China

ISBN: (纸本)9783031784941

Text spotting in diverse domains, such as drone-captured images, underwater scenes, and natural scene images, presents unique challenges due to variations in image quality, contrast, text appearance, background complexity, and external factors like water surface reflections and weather conditions. While most existing approaches focus on text spotting in natural scene images, we propose a Domain-Independent Text Spotter (DITS) that effectively handles multiple domains. We innovatively combine the Real-ESRGAN, developed for regular image enhancement, with the DeepSolo, developed for scene text spotting, in an end-to-end fashion for text detection and spotting on images of different domains. The key idea behind our approach is that improving image quality and text-spotting accuracy are complementary goals. Real-ESRGAN enhances image quality, making the text more discernible, while DeepSolo, a state-of-the-art text spotting model, accurately localizes and recognizes text in the enhanced images. We validate the superiority of our proposed model by evaluating it on datasets from drone, underwater, and scene domains (ICDAR 2015, CTW1500, and Total-Text). Furthermore, we demonstrate the domain independence of our model through cross-domain validation, where we train on one domain and test on others. Our dataset and code will be publicly available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image quality

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On the Relation Between Internal Language Model and Sequence Discriminative Training for Neural Transducers

On the Relation Between Internal Language Model and Sequence...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zijian Yang Wei Zhou Ralf Schlüter Hermann Ney Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany AppTek GmbH Aachen Germany

Internal language model (ILM) subtraction has been widely applied to improve the performance of the RNN-Transducer with external language model (LM) fusion for speech recognition. In this work, we show that sequence discriminative training has a strong correlation with ILM subtraction from both theoretical and empirical points of view. Theoretically, we derive that the global optimum of maximum mutual information (MMI) training shares a similar formula as ILM subtraction. Empirically, we show that ILM subtraction and sequence discriminative training achieve similar effects across a wide range of experiments on Librispeech, including both MMI and minimum Bayes risk (MBR) criteria, as well as neural transducers and LMs of both full and limited context. The benefit of ILM subtraction also becomes much smaller after sequence discriminative training. We also provide an indepth study to show that sequence discriminative training has a minimal effect on the commonly used zero-encoder ILM estimation, but a joint effect on both encoder and prediction + joint network for posterior probability reshaping including both ILM and blank suppression.

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Automatic Modal Parameter Identification with Methods of Artificial Intelligence 38th

Automatic Modal Parameter Identification with Methods of Art...

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38th IMAC, A Conference and Exposition on Structural Dynamics, 2020

作者： Gollnick, Maik Herfert, Daniel Heimann, Jan Department of Structural Dynamics/Pattern Recognition Society for the Advancement of Applied Computer Science Berlin Germany

ISBN: (纸本)9783030477165

The increasing use of operational and experimental modal analysis provides experienced and inexperienced users always face new challenges. Automated evaluation, as a tool for distinguishing between physical and mathematical poles and for removing uncertainties in the accuracy of results, is becoming increasingly important (Jenny Lau, et al.: "Automatic modal analysis: reality or myth?"). This work deals specifically with the description of extreme values and their automated selection in indicator functions. The developed method is based on methods of pattern recognition and artificial intelligence and does not require any further parameters and or expert knowledge for later execution. The functional values of the indicator functions are represented by a simple feature vector with small dimensions and classified by a "Support Vector Machine" (SVM). Since the characteristics describe the function course, they are independent of the measurement parameters, such as sampling rate or frequency spacing. To increase robustness, existing methods from the field of "computer vision" were adapted and new ideas developed. Due to the versatile applicability of the method, an enormous database is required for the learning process. Therefore, a method for the creation of a training basis with synthetic data was developed. This covers a very wide range of applications and special cases, such as modes that are close together or strongly damped. In addition, the influence of inaccuracies, such as sensor noise, was modelled. © 2021, The Society for Experimental Mechanics, Inc.

关键词： Modal analysis

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