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International Journal of Mechatronics and Applied Mechanics 2024年第17期2024卷 114-124页

作者： Le, Chi An Le, Chi Hieu Nguyen, Van Duy Zlatov, Nikolay Le, Tan Hung Nguyen, Tien Anh Chu, Anh My Mahmud, Jamaluddin Le, Van Dang Nguyen, Ho Quang Ramesh, Dharavath Mengistu, Samueal Behera, Amar Packianather, Michael S. Faculty of Science and Engineering University of Greenwich Kent United Kingdom Institute of Biotechnology and Environment Nha Trang University Nha Trang Viet Nam Institute of Mechanics Bulgarian Academy of Sciences Sofia Bulgaria SOICT Hanoi University of Science and Technology Hanoi Viet Nam Le Quy Don Technical University Hanoi Viet Nam College of Engineering Universiti Teknologi MARA Selangor Malaysia ARTC Agency for Science Technology and Research Singapore Institute of Engineering and Technology Thu Dau Mot University Binh Duong Viet Nam Computer Science and Engineering Indian Institute of Technology Dhanbad India Department of Design Indian Institute of Technology Kanpur India School of Engineering Cardiff University Cardiff United Kingdom

There has been significant effort and a growing need to develop innovative and cost-effective solutions for real-time monitoring and operation of value chains and supply chains, especially to enhance the predictability and optimisation of complex production systems for a better adaptation to disruptions and market fluctuations as well as improved sustainability. This is particularly important when taking into account the impacts and emerging advancements of smart agriculture, smart manufacturing, Digital Twins, and Industry 5.0, where data-driven solutions and AI-enabled decision-making play an important role for improving real-time monitoring, quality control and management, and operational efficiency. This study presents a conceptual framework for integrating Digital Twins into a smart agriculture platform, focusing on the real-time monitoring and operation of the coffee value chain and supply chain, to demonstrate the potential of Digital Twins in advancing smart agriculture and digital supply chains. © 2024, Cefin Publishing House. All rights reserved.

关键词： Smart manufacturing

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Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning 38

Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Le...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Fei Wang, Peng Zhang, Lei Chen, Zhenghua Wei, Wei Ding, Chen Lin, Guosheng Zhang, Yanning School of Computer Science Northwestern Polytechnical University China School of Computer Science and Engineering University of Electronic Science and Technology of China China Institute for Infocomm Research Centre for Frontier AI Research A*STAR Singapore School of Computer Science Xi'an University of Posts & Telecommunications China School of Computer Science and Engineering Nanyang Technological University Singapore

Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain. Yet, in practical scenarios where the target task diverges from that in the source domain, meta-learning based method is susceptible to over-fitting. To overcome this, we introduce a novel framework, Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning, which is crafted to comprehensively exploit the cross-domain transferable image prior that each image can be decomposed into complementary low-frequency content details and high-frequency robust structural characteristics. Motivated by this insight, we propose to decompose each query image into its high-frequency and low-frequency components, and parallel incorporate them into the feature embedding network to enhance the final category prediction. More importantly, we introduce a feature reconstruction prior and a prediction consistency prior to separately encourage the consistency of the intermediate feature as well as the final category prediction between the original query image and its decomposed frequency components. This allows for collectively guiding the network's meta-learning process with the aim of learning generalizable image feature embeddings, while not introducing any extra computational cost in the inference phase. Our framework establishes new state-of-the-art results on multiple cross-domain few-shot learning benchmarks. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Semantic-Guided Voting with Segmentation for Visual Localization 6

Semantic-Guided Voting with Segmentation for Visual Localiza...

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6th International Conference on Software engineering and computer science, CSECS 2023

作者： Wang, Chao Xie, Dong Cao, Qingjun Guo, Rui Zhu, Feng Nanjing Research Institute of Electronic Engineering Nanjing China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350306378

Scene coordinate regression has significantly enhanced visual localization by segmenting landmark patches and aggregating votes for landmark points within these patches. However, this approach neglects dynamic semantic objects and patch-level semantic inconsistencies, leading to inaccuracies in the voting process. In this paper, we present a novel visual localization framework guided by semantics. Our framework introduces two critical modules: the Pruning Semantically Dynamic Object module, which leverages semantic segmentation to reduce interference from dynamic semantic objects, and the Eliminating Semantic Label Inconsistencies module, aimed at mitigating patch-level semantic inconsistencies. Comprehensive tests on 7-Scenes and Cambridge datasets exhibit the superior performance of our approach. Across both datasets, our method outperforms the scene coordinate regression technique, validating the effectiveness of our proposed framework. © 2023 IEEE.

关键词： Semantics

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On the design of an AI-driven secure communication scheme for internet of medical things environment

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Digital Communications and Networks 2023年第5期9卷 1080-1089页

作者： Neha Garg Rajat Petwal Mohammad Wazid D.P.Singh Ashok Kumar Das Joel J.P.C.Rodrigues Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Center for Security Theory and Algorithmic ResearchInternational Institute of Information TechnologyHyderabad500032India College of Computer Science and Technology China University of Petroleum(East China)Qingdao266555China Instituto de Telecomunicaçoes 6201-001CovilhaPortugal

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the *** is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare *** other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare ***,this system is prone to securityand privacy-related attacks on healthcare ***,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is *** mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for *** discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to ***,we provide the security analysis of the proposed scheme to show its security against different possible ***,a comparative study of the proposed scheme with other similar schemes is *** results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality ***,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

关键词： Internet of Medical Things(IoMT) Security Authentication and key agreement Artificial Intelligence(AI) Big data analytics

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Syntax Analysis of Protocol Fields Based on Graph Learning 21

Syntax Analysis of Protocol Fields Based on Graph Learning

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Xinglong, Chen Kegang, Hao Xuehang, Li Jiacheng, Gong Yukun, Zhu Erfan, Zhao Southwest China Research Institute of Electronic Equipment Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331519254

In network trace-based protocol reverse engineering, multi-sequence alignment and sequence clustering algorithms are commonly used for message analysis. However, these approaches primarily focus on the characteristics and variation patterns of field values, making it challenging to conduct an in-depth semantic analysis of fields. Additionally, existing message analysis methods model messages solely through sequence models, neglecting the inter-field relationships resulting from the behaviors of both parties in message exchanges. To address these limitations, we propose a field semantic analysis method based on message interaction graphs. Specifically, we introduce a method for modeling field sequences using message interaction graphs, which leverage the graph's topological structure to represent correlations between fields induced by message interaction behaviors while preserving the inherent characteristics of field values through node features. Furthermore, we present GraphProtocol, a protocol field semantic analysis framework based on graph neural networks. The experiment results demonstrate that GraphProtocol achieves superior identification performance compared to traditional sequence-based approaches, highlighting its effectiveness in enhancing protocol security analysis and vulnerability detection. © 2024 IEEE.

关键词： Graph neural networks

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Numerical simulation of highly efficient double perovskite solar cell using SCAPS-1D

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Materials Today: Proceedings 2023年 73卷 584-589页

作者： Rana, Ashish D. Pharne, Indrajit D. Bhargava, Kshitij Department of Electrical and Computer Science Engineering Institute of Infrastructure Technology Research And Management Gujarat Ahmedabad 380026 India Department of Electrical Engineering Sanjay Ghodawat University Maharashtra Kolhapur 41610 India

This study focuses on designing of lead-free double perovskite solar cells (DPSCs). Lead-free organic–inorganic DPSCs have achieved very good efficiency within a short period of active research. Formamidinium based double perovskite solar cells ((FA)2BiCuI6) have emerged as a promising replacement of lead-based organic–inorganic PSCs. The electron and hole transport layers (ETLs and HTLs) play important role towards the performance of DPSCs. Herein, we investigate the effect of various combinations of ETLs and HTLs for achieving an optimized highly efficient (FA)2BiCuI6 based DPSCs using SCAPS-1D. The selected ETLs were SnO2, TiO2, PCBM and HTLs were SpiroOMeTAD, NiO, PEDOT: PSS. Total 09 cells were analyzed to achieve the maximum power conversion efficiency (PCE). The best efficiency of 15.49% was yielded by a cell having SnO2 as ETL and NiO as HTL. © 2022

关键词： Perovskite solar cells

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Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

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computers, Materials & Continua 2023年第1期74卷 1523-1540页

作者： Sandeep Kumar MohdAnul Haq Arpit Jain C.Andy Jason Nageswara Rao Moparthi Nitin Mittal Zamil S.Alzamil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAP522502India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Computer Science and Engineering Teerthanker Mahaveer UniversityMoradabadUttar Pradesh244001India Department of Electronics and Communication Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia

Day by day,biometric-based systems play a vital role in our daily *** paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert *** proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,*** proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and *** key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and *** proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and *** comparing with existing technologies,the proposed algorithm gave a better error rate and less *** rate and time is decreased by 19.79%,5.13 *** the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT *** proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

关键词： Speech emotion recognition classifier implementation feature extraction and selection smart assistance

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TSDDISCOVER: Discovering Data Dependency for Time Series Data 40

TSDDISCOVER: Discovering Data Dependency for Time Series Dat...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Yingze Wang, Hongzhi Wang, Chen Liu, Yida Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Research Center for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Intelligent devices often produce time series data that suffer from significant data quality issues. While the utilization of data dependency in error detection and data repair has been somewhat beneficial, it remains inadequate in accurately representing the data quality of time series datasets. In recognition of the obvious characteristics inherent in time series data, we introduce a novel data dependency, termed TSDD. It effectively captures the contextual relationships embedded within multivariate time series, thereby enhancing the semantic richness of data quality representations. We analyze the complexity of both implication and consistency problems for TSDD reasoning, and develop TSDD discovery algorithm TSDDISCOVER, which consists of functional structure discovery, allowable error bound determination, and validation of TSDD patterns. Experimental results on real-life datasets verify TSDDISCOVER efficiently discovers high-quality TSDD patterns. In comparing the performance of TSDD-based error detection with several leading data quality constraints, our findings reveal that the former achieves an average improvement of 12% in accuracy and 30% in the F1 score over other dependency-based detection methods. © 2024 IEEE.

关键词： Semantics

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Efficient Relaxed Functional Dependency Discovery with Minimal Set Cover 40

Efficient Relaxed Functional Dependency Discovery with Minim...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Liu, Yida Wang, Hongzhi Wang, Chen Song, Yichen Yang, Donghua Wang, Jianmin School of Computer Science and Technology Harbin Institute of Technology China National Engineering Research Center for Big Data Software EIRI Tsinghua University China

ISBN: (纸本)9798350317152

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data governance. However, with the diverse range of data sources and the exponential growth in data volume, exact FDs can sometimes be impractical for real-world applications. In contrast, relaxed functional dependencies (RFDs), which allows for some flexibility in attribute value comparisons, demonstrates greater adaptability and flexibility for big data scenarios. To address the efficient discovery of RFDs, this paper proposes a novel mining method to supplement the current research gaps. By establishing a difference table for tuples, we transform the problem into a specialized minimal set covering problem. Additionally, we introduce two optimization strategies: reducing the time complexity of enumerating the left-hand side of the base RFDs to 0 (1) and decreasing the search complexity for feasible LHS attributes and threshold candidates from O(2m-l) to O(1.5m-1). We rigorously proof that our mining approach guarantees the identification of validity and minimal RFDs. Experiments on nine real-world datasets reveal that our method significantly improves efficiency compared to existing techniques. Furthermore, it uncovers more concise and higher-quality RFDs. Importantly, the RFDs extracted through our methodology exhibit better performance in downstream cleaning tasks. © 2024 IEEE.

关键词： Quality management

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