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

作者： Maja J. Hjuler Line H. Clemmensen Sneha Das CNRS Grenoble INP LIG University Grenoble Alpes Grenoble France School of Computer Science Queensland University of Technology Brisbane QLD Australia Dept. of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We introduce EmoLIME 1 , a version of local interpretable model-agnostic explanations (LIME) for black-box Speech Emotion Recognition (SER) models. To the best of our knowledge, this is the first attempt to apply LIME in SER. EmoLIME generates high-level interpretable explanations and identifies which specific frequency ranges are most influential in determining emotional states. The approach aids in interpreting complex, high-dimensional embeddings such as those generated by end-to-end speech models. We evaluate EmoLIME, qualitatively, quantitatively, and statistically, across three emotional speech datasets, using classifiers trained on both hand-crafted acoustic features and Wav2Vec 2.0 embeddings. We find that EmoLIME exhibits stronger robustness across different models than across datasets with distribution shifts, highlighting its potential for more consistent explanations in SER tasks within a dataset.

关键词： Emotion recognition Explainable AI Closed box Speech recognition Signal processing Predictive models Acoustics Robustness Speech processing

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Design and Surface Flashover Test of 10 kV SiC Power Module for DC Shipboard Application

Design and Surface Flashover Test of 10 kV SiC Power Module ...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Li, Xiaoling Liu, Yang Schwartz, Skyler Guo, Ning Karimi, Ebrahim Mantooth, H. Alan Graber, Lukas Cuzner, Robert University of Arkansas Department of Electrical Engineering Fayetteville United States Georgia Institute of Technology School of Electrical & Computer Engineering Atlanta United States University of Wisconsin-Milwaukee College of Engineering and Applied Science Milwaukee United States

ISBN: (纸本)9798350316445

Future all-electric ships will likely use a Medium Voltage DC (MVDC) power distribution system, which has several advantages over MVAC, such as higher power density and higher reliability. As key components for power distribution systems, 10 kV power modules need to meet the insulation requirements for various fault conditions. Grounding faults can cause extremely high electric stress between the module terminals and baseplate hence resulting in surface flashover breakdown. This paper aimed at the insulation challenges of 10 kV power module for the MVDC shipboard application. The maximum voltage stresses of four typical grounding schemes are compared at single line-to-ground faults. The surface flashover tolerance of a 10 kV SiC power module is verified under steady DC voltage and the worst-case transient fault overvoltage. The experimental results can provide a reference for the MV power module insulation design and the module baseplate grounding schemes. © 2023 IEEE.

关键词： 10 kV power module MVDC shipboard power system surface flashover

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The Impact of Data Augmentation on Sentiment Analysis of Translated Textual Data

The Impact of Data Augmentation on Sentiment Analysis of Tra...

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Omran, Thuraya Sharef, Baraa Grosan, Crina Li, Yongmin Brunel University London Department of Computer Science Uxbridge United Kingdom Ahlia University Department of Information Technology Manama Bahrain King's College London Division of Applied Technologies for Clinical Care Strand United Kingdom

ISBN: (纸本)9781665463720

Sentiment analysis is an application of natural language processing that requires an abundance of data that may not be achieved sometimes for some reason. Data augmentation is one technique that deals with the lack of data by creating synthetic training data without adding new ones. It boosts model performance, especially with deep learning ones. Despite its influential role in boosting the model performance, it attracted very little attention from the researchers of the Arabic NLP community, specifically with scarce language resources such as Arabic and its dialects. In this study, one of the augmentation techniques called random swap was applied with LSTM deep learning model to classify three parallel datasets. The three parallel datasets are Bahraini dialects, Modern Standard Arabic and English. The results show an improvement in the LSTM model by 14.06%, 12.57%, and 11.04% on Bahraini dialects, Modern Standard Arabic, and English datasets, respectively, when applying the augmentation technique over that of no application. © 2023 IEEE.

关键词： Sentiment analysis

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Method for evaluating the monetary added value of the usage of a digital twin for additive manufacturing 16

Method for evaluating the monetary added value of the usage ...

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16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Oettl, Fabio Hörbrand, Sebastian Wittmeir, Tobias Schilp, Johannes Chair of Digital Manufacturing Faculty of Applied Computer Science Augsburg University Am Technologiezentrum 8 Augsburg86159 Germany Fraunhofer Research Institution of Casting Composite and Processing Technology Am Technologiezentrum 10 Augsburg86159 Germany

By combining the additive manufacturing (AM)-process with digital concepts, such as the digital twin (DT) or the digital part file (DPF), the competitiveness of additive manufacturing is increased. A quantitative approach to evaluate the usage of a DPF in AM will be introduced within this paper. The focus is set on the production as an early lifecycle-phase, which means that the AM-production process gets analyzed regarding potential advantages of using a DPF. These advantages are transferred into a monetary value with our approach. By calculating the total costs of the DPF, a monetary overall value is obtained. © 2023 Elsevier B.V.. All rights reserved.

关键词： 3D printing

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Forecasting land use changes in crop classification and drought using remote sensing

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Journal of Arid Land 2025年第5期17卷 575-589页

作者： Mashael MAASHI Nada ALZABEN Noha NEGM Venkatesan VEERAMANI Sabarunisha Sheik BEGUM Geetha PALANIAPPAN Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science Applied College at MahayilKing Khalid UniversityAbha 61421Saudi Arabia Department of Civil Engineering University College of EngineeringAnna UniversityAriyalur 621731India Department of Biotechnology P.S.R.Engineering CollegeSivakasi 626140India Department of Electronics and Communication Engineering School of Engineering and TechnologyDhanalakshmi Srinivasan UniversitySamayapuram 621112India

Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t

关键词： land use and land cover(LULC) crop attributes drought vulnerability machine learning models remote sensing

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Automating the Selection of Proxy Variables of Unmeasured Confounders 41

Automating the Selection of Proxy Variables of Unmeasured Co...

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41st International Conference on Machine Learning, ICML 2024

作者： Xie, Feng Chen, Zhengming Luo, Shanshan Miao, Wang Cai, Ruichu Geng, Zhi Department of Applied Statistics Beijing Technology and Business University Beijing China School of Computer Science Guangdong University of Technology Guangzhou510006 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Probability and Statistics Peking University Beijing China

Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational data. One difficulty inhibiting the practical use is finding valid proxy variables of unobserved confounding to a target causal effect of interest. These proxy variables are typically justified by background knowledge. In this paper, we investigate the estimation of causal effects among multiple treatments and a single outcome, all of which are affected by unmeasured confounders, within a linear causal model, without prior knowledge of the validity of proxy variables. To be more specific, we first extend the existing proxy variable estimator, originally addressing a single unmeasured confounder, to accommodate scenarios where multiple unmeasured confounders exist between the treatments and the outcome. Subsequently, we present two different sets of precise identifiability conditions for selecting valid proxy variables of unmeasured confounders, based on the second-order statistics and higher-order statistics of the data, respectively. Moreover, we propose two data-driven methods for the selection of proxy variables and for the unbiased estimation of causal effects. Theoretical analysis demonstrates the correctness of our proposed algorithms. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach. Copyright 2024 by the author(s)

关键词： Higher order statistics

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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P4-MTAGG - a Framework for Multi-Tenant P4 Network Devices

P4-MTAGG - a Framework for Multi-Tenant P4 Network Devices

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International Conference on Network and Service Management

作者： Fabian Brisch Andreas Kassler Sándor Laki Peter Hudoba Institute of Applied Computer Science Deggendorf Institute of Technology Deggendorf Germany Computer Science Department Karlstads Universitet Karlstad Sweden Faculty of Informatics ELTE Eötvös Loránd University Budapest Hungary

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

The current P4 programmability model assumes that a P4 programmable device is owned and controlled by a single tenant. However, in typical NFV scenarios, support for multiple tenants is desirable. When each tenant may want to deploy their own P4 pipeline offering different network functions (NF), supporting multiple co-existing tenant pipelines on a single platform is difficult because it requires pipeline merging, control plane support, and resource management of the platform. In this paper, we present P4-MTAGG, a novel framework for flexibly deploying multiple P4 programmable NFs on a programmable match-action pipeline while supporting multiple tenants. P4-MTAGG consists of i) novel compiler-add-ons for automatic merging multiple P4-pipelines, ii) p4runtime-proxy to allow for control plane access of the aggregated pipelines together with policy-based resource management for the P4 target, and iii) orchestrator to automate the provisioning of a network node utilizing aggregation either in a simulated or real hardware environment. In this demo, we show how P4-MTAGG aggregates multiple NFs of varying complexity in Mininet. The user can orchestrate the aggregation process through a GUI. The per-tenant traffic is routed through the set of NFs using segment routing. Through the GUI, the user can instruct the p4runtime-proxy to enforce per-tenant bandwidth limits, which configure the per-tenant available resources in the data plane.

关键词： Aggregates Pipelines Merging Bandwidth Routing Hardware Complexity theory Resource management Noise measurement Graphical user interfaces

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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