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Optimizing accuracy and efficiency in real-time people counting with cascaded object detection

International Journal of Information technology (Singapore) 2024年 1-14页

作者： Holla, M. Raviraja Suma, D. Holla, M. Darshan Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India

Growing concerns about public safety have driven the demand for real-time surveillance, particularly in monitoring systems like people counters. Traditional methods heavily reliant on facial detection face challenges due to the complex nature of facial features. This paper presents an innovative people counting system known for its robustness, utilizing holistic bodily characteristics for improved detection and tallying. This system achieves exceptional performance through advanced computer vision techniques, with a flawless accuracy and precision rate of 100% under ideal conditions. Even in challenging visual conditions, it maintains an impressive overall accuracy of 98.42% and a precision of 97.51%. Comprehensive analyses, including violin plot and heatmaps, support this outstanding performance. Additionally, by assessing accuracy and execution time concerning the number of cascading stages, we highlight the significant advantages of our approach. Experimentation with the TUD-Pedestrian dataset demonstrates an accuracy of 94.2%. Evaluation using the UCFCC dataset further proves the effectiveness of our approach in handling diverse scenarios, showcasing its robustness in real-world crowd counting applications. Compared to benchmark approaches, our proposed system demonstrates real-time precision and efficiency. © The Author(s) 2024.

关键词： Cascaded detector computer vision Object detection People counting Real-time

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A GPU scheme for multi-secret visual sharing with varied secret dimensions and contrast enhancement using blind super-resolution

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International Journal of Information technology (Singapore) 2024年第3期16卷 1801-1814页

作者： Holla, M. Raviraja Suma, D. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Karnataka Manipal 576104 India

Multi-secret visual sharing schemes are essential for secure and efficient sharing of sensitive visual information. However, existing schemes often overlook important considerations such as varied secret dimensions, contrast enhancement, and computational efficiency. This research addresses these challenges by proposing a comprehensive approach that addresses these limitations. Firstly, the scheme takes into account the varied dimensions of secret images encountered in real-world scenarios, allowing flexibility in sharing and reconstructing images of different sizes and aspect ratios. Secondly, the research integrates contrast enhancement techniques, such as blind super-resolution, to improve the visual quality and visibility of shared secret images affected by factors like noise, compression, or low lighting conditions. Lastly, to enhance the computational efficiency, the scheme leverages the power of Graphics Processing Units (GPUs) for parallel computing, enabling faster processing of large-scale image operations. The proposed scheme achieves outstanding results, with a high PSNR of 98.264, a strong NCC value of 0.965, an exceptionally low NAE value of 0.046, and an impressive SSIM value of 0.983. Furthermore, the GPU implementation provides a remarkable overall speedup of 112 × for 256 × 256 color images. © 2024, The Author(s).

关键词： Contrast GPU Multi-secret Real-time Security

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Explainable Deep Learning for Dermatology: Psoriasis vs. Eczema with Grad-CAM 2

Explainable Deep Learning for Dermatology: Psoriasis vs. Ecz...

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2nd IEEE International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2024

作者： Castelino, Glanet Jeshma Chadaga, Krishnaraj Sampathila, Niranjana Manipal Academy of Higher Education Manipal Institute of Technology Department of Biomedical Engineering Karnataka Manipal576104 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798350354461

Eczema and Psoriasis are two of the most prevalent inflammatory dermatological conditions with distinctive clinical features. However, their features may overlay with each other's symptoms often leading to erroneous assessment. Therefore, precise identification between the two classes is quite challenging and paramount. In recent years, there has been tremendous success in implementing deep learning tools in dermatology for evaluating skin disease variants, however the reliability of AI tools still remains questionable. In this research, we explore and compare 5 pre-trained DL models: ResNeXt-50 32x4d, Shufflenetv2-x1.0, EfficientNet-B2, MobileNetV2, EfficientNet-B4 using transfer learning to precisely categorize the two dermatological conditions i.e. Eczema and Psoriasis. Out of all, ResNeXt-50 32x4d model attained superior classification performance with 98.39% train accuracy rate and 88.41% test accuracy rate. For better comprehension and interpretability of the model's acquired layer-wise features and the yielded outcome, Explainable AI i.e. GradCAM is opted here. The outcomes of this study are highly significant towards enhanced skin prognosis and showcases that incorporating Grad-CAM may aid in accurately addressing these skin conditions severity at early stages for improved quality of life for afflicted individuals. © 2024 IEEE.

关键词： Dermatology

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PMFRO: Personalized Men’s Fashion Recommendation Using Dynamic Ontological Models 22nd

PMFRO: Personalized Men’s Fashion Recommendation Using Dyna...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Arunkumar, S. Deepak, Gerard Priyadarshini, J. Sheeba Santhanavijayan, A. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Bengaluru India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

There is a thriving need for an expert intelligent system for recommending fashion especially focusing on men’s fashion. As it is an area which is neglected both in terms of fashion and modelling intelligent systems. So, in this paper the PMFRO framework for men’s recommendation has been put forth which indicates the semantic similarity schemes with auxiliary knowledge and machine intelligence in a very systematic manner. The framework intelligently creates mapping of the preprocessed preferences and the user records and clicks with that of the items in the profile. So, this model aggregates community user profiles and also maps the men’s fashion ontology using strategic semantic similarity schemes. Semantic similarity is evaluated using Lesk similarity and NPMI measures at several stages and instances with differential set thresholds and the dataset is classified using the feature control, machine learning bagging classifier which is an ensemble model in order to recommend the men’s fashion. The PMFRO framework is an intelligent amalgamation and integration of auxiliary knowledge, strategic knowledge, user profile preferences as well as machine learning paradigms and semantic similarity models for recommending men’s fashion and overall precision of 94.68% and FDR of 0.06 was achieved using the PMFRO model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

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Interplay between cerebral and muscular activations during motor tasks: An fNIRS-sEMG study 46

Interplay between cerebral and muscular activations during m...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Huang, Ruisen Shang, Wenze Lin, Yongji Liang, Fengyan Yin, Ming Lu, Xiao-Ping Wu, Xinyu Gao, Fei Chinese Academy of Sciences Institute of Advanced Integration Technology Shenzhen Institute of Advanced Technology Guangdong Shenzhen China Hainan University School of Biomedical Engineering Hainan China Macau University of Science and Technology School of Computer Science and Engineering China

ISBN: (纸本)9798350371499

Understanding the intricate relationship between brain activations and muscle activations during motor tasks is crucial for elucidating motor control mechanisms and designing effective rehabilitation strategies. In this study, we investigated this relationship using functional near-infrared spectroscopy (fNIRS) and surface electromyography (sEMG) technologies. Three healthy male subjects performed lower-limb motor tasks while their brain activations and muscle activations were recorded simultaneously. The fNIRS data were converted to optical densities and filtered by lowpass filter, detrending, and short-separation regression to remove artifacts. The filtered signals were fed to a general linear model with the desired hemodynamic responses (dHRFs) constructed with Balloon model and simulated physiological noises reconstructed with the parameters obtained from the spectrum analysis of baseline signals. Despite the extracted hemodynamic responses from fNIRS data, sEMG signals were lowpass and analyzed to detect muscle activations. Results revealed that cerebral activations during lateral leg flexion tasks were similar yet only partially consistent with those observed in stand-up tasks. Significant correlations between brain and muscle activations were also observed, highlighting the complex interplay between central and peripheral neural mechanisms during motor control. These findings provide valuable insights into the neural basis of motor control and have implications for developing personalized rehabilitation interventions and assistive technologies to enhance motor function in clinical populations. © 2024 IEEE.

关键词： Near infrared spectroscopy

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InputJump: Augmented reality-facilitated cross-device input fusion based on spatial and semantic information

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虚拟现实与智能硬件（中英文） 2024年第6期6卷 502-526页

作者： Xin ZENG Xiaoyu WANG Tengxiang ZHANG Yukang YAN Yiqiang CHEN Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China Goertek Inc. Bejing 261031China The Hong Kong University of Science and Technology Hong Kong 999077China Department of Computer Science Hajim School of Engineering and Applied SciencesUniversity of RochesterRochesterNY 14627US

The proliferation of computing devices requires seamless cross-device *** reality(AR)headsets can facilitate interactions with existing computers owing to their user-centered views and natural *** this study,we propose InputJump,a user-centered cross-device input fusion method that maps multi-modal cross-device inputs to interactive elements on graphical *** input jump calculates the spatial coordinates of the input target positions and the interactive elements within the coordinate system of the AR *** also extracts semantic descriptions of inputs and elements using large language models(LLMs).Two types of information from different inputs(e.g.,gaze,gesture,mouse,and keyboard)were fused to map onto an interactive *** proposed method is explained in detail and implemented on both an AR headset and a desktop *** then conducted a user study and extensive simulations to validate our proposed *** results showed that InputJump can accurately associate a fused input with the target interactive element,enabling a more natural and flexible interaction experience.

关键词： Mixed reality Spatial Semantic Large language models

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Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective 41

Rethinking Guidance Information to Utilize Unlabeled Samples...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples.A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their ***, EntMin emphasizes prediction discriminability while neglecting prediction *** alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled *** analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label *** by this finding, we propose a Label-Encoding Risk Minimization (LERM).It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label *** a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a ***, we analyze the relationships between LERM and ERM as well as ***, we verify the superiority of the LERM under several label insufficient *** codes are available at https://***/zhangyl660/LERM. Copyright 2024 by the author(s)

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KIASOntoRec: A Knowledge Infused Approach for Socially Aware Ontology Recommendation 13th

KIASOntoRec: A Knowledge Infused Approach for Socially Aware...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Valecha, Aastha Deepak, Gerard Surya, Deep ak Department of Mechanical Engineering Indian Institute of Technology Delhi India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274985

The World Wide Web is progressively adopting web 3.0 standards, and there is a demand for semantically compatible recommendation techniques in today’s world. This paper presents a framework to facilitate Knowledge infused automatic Ontology recommendation. User queries and user clicks data are preprocessed and Structural Topic Modelling is employed to identify the latent sematic structure found in the corpus and generate topics. RDF triplets are generated based on the topics extracted using OntoCollab. RDF data along with the Google’s Knowledge Base API used to obtain the subgraphs and relevant topics. Bagging algorithm based on Random Forest Classification Technique with Decision Trees Algorithm is used to classify the terms. A knowledge base is constructed by merging the subgraphs. Semantic similarity among entities of the knowledge graph and the top 25% of the classified results is computed using SemantoSim and ANOVA. The topic set is extracted and subjected to axiomatization. Finally, the most relevant Ontology to the user query is suggested to the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

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The internal circulations on internal mass transfer rate of a single drop in nonlinear uniaxial extensional flow

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Chinese Journal of Chemical engineering 2023年第7期59卷 51-60页

作者： Anjun Liu Jie Chen Meng Guo Chengmin Chen Meihong Yang Chao Yang Qilu University of Technology(Shandong Academy of Sciences) Shandong Computer Science Center(National Supercomputer Center in Jinan)Jinan 250101China CAS Key Laboratory of Green Process and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China Jinan Key Laboratory of High Performance Industrial Software Jinan Institute of Supercomputer TechnologyJinan 251013China

The internal flow of a droplet in the nonlinear extensional flow field will exhibit more than two internal circulations with the variation of nonlinear intensity(E).In this paper,the effect of positions and sizes of internal circulations on internal mass transfer rate of a single spherical droplet in a nonlinear extensional flow field is studied and compared with that in a linear extensional flow *** simulation results show that when E≥0,there are two symmetrical internal circulations in the droplet,which is the same with that in a linear extensional *** limit value of mass transfer rate Sh is 15,which is equal to that in a linear extensional flow,no matter how large E *** E≤-3/7,the number of internal flow circulation of a droplet increase to four and the transfer rate Sh *** E=-1,the maximum internal transfer rate Sh equals 30 which is twice of that in a linear extensional *** generation of new flow circulations in droplets and the circulation positions will enhance mass transfer when E≤-3/7,which provides a new idea for enhancing the internal mass transfer rate of droplets.

关键词： Internal mass transfer Nonlinear extensional flow field Spherical droplet Internal circulations

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A Neural Architecture Search Method with Multi-Dimensional Correlation Representation for Fault Diagnosis 9

A Neural Architecture Search Method with Multi-Dimensional C...

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9th IEEE Smart World Congress, SWC 2023

作者： Liu, Yumeng Wang, Tingqi Zheng, Xu Tian, Ling Wang, Hongan Institute of Software Chinese Academy of Sciences Beijing Key Laboratory of Human-Computer Interaction Beijing China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350319804

As deep learning has become more widely used for fault diagnosis, the shortcomings of model transferability and human model design costs are growing increasingly evident. The current work has tackled each of these two issues. Domain adaptation methods can provide transportability by mapping data to a common domain. For the consumption of manually constructed networks, Neural Architecture Search (NAS) is an intelligent solution that automatically select the ideal network structure. Unfortunately, there is little work to connect these two methodologies in fault diagnosis. Furthermore, these approaches are incapable in extracting internal correlations from instrumental signal data. In this paper, we propose a transferrable NAS fault diagnosis method, which maximizes savings in manual costs. We also integrate graph neural networks (GNN) in a stepwise manner into the NAS and domain adaptation to provide a multi-dimension correlation representation. Firstly, we structure the signal data into graph based on temporal relationships. Then, NAS designs automatically for selecting the optimal network structure and useful features. In NAS, graph encoder of GNN is used to establish internal relationships. Finally, domain adaptation provides transferability. At the same time, GNN's weights in domain adaptation help to further mine data internal associations. We tested our method on two publicly available datasets and performed comparisons. The experimental results show the proposed method has superior performance. © 2023 IEEE.

关键词： Domain Adaptation Fault Diagnosis Graph Neural Network Neural Architecture Search

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