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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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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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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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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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Hierarchical Attention Mechanism for Domain-invariant Spoken Language Identification via Inter-domain Alignment and Intra-domain Language Discrimination

Hierarchical Attention Mechanism for Domain-invariant Spoken...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Goswami, Urvashi Muralikrishna, H. Kumar, Sujeet Dileep, A.D. Thenkanidiyoor, Veena Indian Institute of Technology Mandi School of Computing and Electrical Engineering Mandi India Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal576104 India Indian Institute of Technology Dharwad Department of Computer Science and Engineering Dharwad India National Institute of Technology Goa Department of Computer Science and Engineering Goa India

ISBN: (纸本)9798331505776

Modern methods for spoken language identification (LID) have demonstrated promising results when trained on large datasets. However, their effectiveness is considerably impacted by the discrepancies between the training and testing distributions. Unsupervised domain adaptation (UDA) addresses the domain discrepancies by aligning feature distributions across source and target domains without using labeled target data, thereby ensuring the model performs consistently across both domains. This alignment, however, may come at the cost of reduced model discriminability. To enhance performance under domainmismatched conditions, a LID system should learn domaininvariant representations of speech while maintaining language discriminability at every level, from initial segment-level representations to final utterance-level representations. To address this issue, in this paper, we propose a hierarchical attention network that simultaneously enhances model domain invariance through inter-domain alignment and improves model's language discriminability using intra-domain contrastive discrimination. The performance on the target domain shows that the proposed method exceeds the effectiveness of the top-performing existing approaches. © 2024 IEEE.

关键词： Distribution transformers

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MahitiBandhu: A Solar-Powered Information Kiosk System for Rural Community Empowerment

MahitiBandhu: A Solar-Powered Information Kiosk System for R...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Arakeri, Megha P. Lakshmana Jain, Ayush Hemachandra, G. Mohan Gowda, V. Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Seamovations Labs Pvt. Ltd Bangalore India Ramaiah Institute of Technology Department of Information Science and Engineering Bangalore India Ramaiah Institute of Technology Department of Electrical and Electronics Engineering Bangalore India Gitam School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350382693

A major portion of Karnataka's population lives in rural areas. Most of the rural people are illiterate and poor due to lack of proper services and infrastructure. There are various rural development schemes run by the government of Karnataka/India for the benefit of villagers. However, most people in the villages of Karnataka are unaware of these schemes due to lack of education, poverty, inaccessibility, and lack of technological knowledge. Hence, the government is trying to render services and information to the public using ICT tools. While there are few examples of achievements of ICT in rural development, there are many failures due to lack of connectivity, bandwidth, language problems, and power supply. Also, Gram Panchayat of the village does not have proper facilities to give widespread publicity about government services and many people do not have smartphones to access information online. Thus, this paper focuses on the development of a solar-powered interactive touch-screen based kiosk system for the rural region. This single system can provide information about all rural schemes for education, agriculture, healthcare, and employment. It also gives information on other services like banking, vaccination drives, and fertilizers for farmers. This is an end-to-end user application with a text-free user interface and interacts based on audio, video, and image display irrespective of the user's literacy level. © 2024 IEEE.

关键词： Touch screens

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Single-shot single-beam coherent Raman scattering thermometry based on optically induced air lasing

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Light(science & Applications) 2024年第12期13卷 3253-3260页

作者： Xu Lu Yewei Chen Francesco Mazza Siyi He Zihan Li Shunlin Huang Quanjun Wang Ning Zhang Bo Shen Yuzhu Wu Jinping Yao Ya Cheng State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Faculty of Aerospace Engineering Delft University of TechnologyKluyverweg 12629 HS DelftThe Netherlands Institute of Modern Optics Nankai UniversityTianjin 300350China School of Microelectronics Shanghai UniversityShanghai 200444China

Thermometric techniques with high accuracy,fast response and ease of implementation are desirable for the study of dynamic combustion environments,transient reacting flows,and non-equilibrium ***,single-shot singlebeam coherent Raman scattering(SS-CRS)thermometry is developed,for the first time to our knowledge,by using air lasing as a *** show that the air-lasing-assisted CRS signal has a high signal-to-noise ratio enabling single-shot measurements at a 1 kHz repetition *** SS-CRS thermometry consistently exhibits precision of<2.3%at different temperatures,but the inaccuracy grows with the increase in *** high measurement repeatability,1 kHz acquisition rate and easy-to-implement single-beam scheme are achieved thanks to the unique temporal,spectral and spatial characteristics of air *** work opens a novel avenue for high-speed CRS thermometry,holding tremendous potential for fast diagnostics of transient reacting flows and plasmas.

关键词： scattering lasing beam

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