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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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Towards Lightweight Underwater Depth Estimation

Towards Lightweight Underwater Depth Estimation

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Artificial Intelligence (CAI), IEEE Conference on

作者： Keyu Zhou Jin Chen Shuangchun Gui Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Underwater depth estimation is crucial in the applications of marine robotics. It can provide environment information for target tracking, robot navigation, and 3D reconstruction of underwater terrain. Existing works transform underwater images into in-air conditions to adapt methods that are designed for natural images. However, this may result in expensive computational resources. To overcome this limitation, we propose a lightweight knowledge distillation framework for underwater depth estimation. We utilize a powerful model designed for underwater images as the teacher model and a lightweight CNN model as the student model. We distill global features to enable the student to acquire both local and global information, thereby improving estimation performance. Our framework includes a global transformation module for efficient global feature distillation and a global-local fusion module to combine local and global information for final estimation. Experimental results on the FLSea dataset demonstrate that our student model is lighter than the teacher model while outperforming lightweight in-air models. Our network is 60% lighter than the teacher model and achieves a 3.1% improvement in the δ 1 metric compared to the lightweight in-air model.

关键词： Measurement Adaptation models Three-dimensional displays Target tracking Navigation Computational modeling Estimation

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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Design of Digital Twin Circuit Principle Experiment system Based on NCSLab

Design of Digital Twin Circuit Principle Experiment System B...

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第42届中国控制会议

作者： Yu-Zhong Li Guo-Ping Liu Yi Yu School of Computer Science and Engineering Huizhou University School of System Design and Intelligence Manufacture Southern University of Science and Technology

This paper is concerned with the requirements of current distance learning of circuit principle experiment of automation specialty,automatic fault detection of experimental equipment,automatic fault isolation,uninterrupted experiment in case of fault,and maintaining the number of available ***,based on the networked control system laboratory(NCSLab) architecture,digital twin,remote control technology and circuit design,aiming at the circuit experiment principle course,the digital twin are realized by Simulink,and a digital twin circuit principle experiment system(DT-CPES) is designed and *** designed system shows that the system can realize the sharing of experimental equipment,the digital twin can predict,and can also verify with the actual measurement data of the circuit *** the digital twin fault isolation controller(DT-FIC),the fault diagnosis,fault isolation and replacement output of the experimental equipment can be ***,the developed system reduces the overall risk and cost of installation and maintenance of large-scale circuit experiments,and provides fault-tolerant redundancy for the experimental system with low computational burden and high reliability.

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Identification of a Distant Speaker and Its Robustness*

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Chinese Journal of Electronics 2023年第2期20卷 278-282页

作者： Ye Jiang Zhenmin Tang Longbiao Wang School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Department of System Engineering Shizuoka University Japan

Robust speaker identification is presented for speech recorded by distant microphones. Three compensation approaches are investigated to improve the robustness of speaker identification in such environments. The first approach applies spectral subtraction before feature extraction to reduce the late-reverberation effect. The second approach makes use of feature warping as feature compensation in distant speaker identification under mismatched training-testing conditions. The third approach employs a novel method of initializing Gaussian mixture model parameters: combined division and k-means clustering. The experiment results show that, relative to the baseline system based on CMN, the channel-average recognition rates for the compensated system were 11.4%, 15.4%, 17.0%, and 17.8% higher for the TIMIT database and 6.8%, 6.4%, 9.3%, and 14.0% higher for the JNAS database for four different environments. In addition, the results show that the combination of the three approaches has better performance than the use of a single compensation method.

关键词： Databases Feature extraction Robustness Gaussian mixture model Microphones

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Few-Shot Gearbox Fault Diagnosis Based on Semi-Supervised Prototypical Network and Contrastive Learning

Few-Shot Gearbox Fault Diagnosis Based on Semi-Supervised Pr...

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2023 China Automation Congress, CAC 2023

作者： Cheng, Shulie Liang, Yingjie Ping, Zuowei Liao, Xuechao Wang, Maolin Zhang, Yong School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China Naval University of Engineering National Key Laboratory of Science and Technology on Vessel Integrated Power System Wuhan China School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350303759

Due to the scarcity of labeled faulty data in industrial practice, fault diagnosis models often face challenges related to overfitting and limited accuracy. This article introduces a novel solution to tackle the problem - a fault diagnosis method that leverages a combination of a semi-supervised prototype network and contrastive learning. Firstly, a limited number of labeled samples is employed to construct pairs of positive and negative samples. And the training method of contrastive learning is used to provide appropriate initial parameters for the autoencoder. Secondly, the autoencoder is employed as a feature mapping function within the prototype network and obtains the prototype using a limited number of labeled samples. Finally, a prototype refinement method fine-tunes the prototype through unlabeled data, reducing the impact of anomalous data and obtaining a more stable and accurate prototype. The effectiveness of the proposed method is evaluated using gearbox data as a validation dataset. Through comparative analysis with other methods, the results demonstrate that the proposed approach achieves superior diagnostic accuracy. © 2023 IEEE.

关键词： Failure analysis

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Resilient Cooperative Control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Single Block Encoder-Decoder Transformer Model for Multi-Step Traffic Flow Forecasting 6

Single Block Encoder-Decoder Transformer Model for Multi-Ste...

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6th IEEE Symposium on computers and Informatics, ISCI 2024

作者： Omar, Mas Yakub, Fitri Talip, Mohamad Sofian Abu Maslan, Mohd Nazmin Sinha, Vijay Kumar Muljono, Muljono Universiti Teknologi Malaysia Wee Laboratory Malaysia-Japan International Institute of Technology Kuala Lumpur Malaysia Universiti Teknologi Malaysia Malaysia-Japan International Institute of Technology Department of Electronic System Engineering Kuala Lumpur Malaysia University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur Malaysia Universiti Teknikal Malaysia Faculty of Industrial and Manufacturing Technology and Engineering Melaka Malaysia Chitkara University School of Engineering & Technology Department of Computer Science & Engineering Himachal Pradesh India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350353853

Accurate traffic flow forecasting is crucial for managing and planning urban transportation systems. Despite the widespread use of sequence modelling models like Long Short-Term Memory (LSTM) for this purpose, the potential of Transformer models remains underexplored. This is particularly true for the simplest form of a single block encoder-decoder Transformer model, which can be finely tuned through optimised hyperparameters. This paper examines the performance of a singular horizon-step forecasting method for multi-step traffic flow forecasting using a proposed Single Block Encoder-Decoder Transformer model optimised with a Grid Search algorithm. Results demonstrate that this model can enhance forecasting accuracy compared to the state-of-the-art LSTM model typically used for multi-step forecasting. The model effectively captures long-range temporal dependencies within a single road traffic flow dataset. It was tested on hourly traffic flow data to forecast the next 24 hours for the I5-North freeway in California, sourced from the Caltrans Performance Measurement system. The optimal configuration included an embedding dimension of 32, a feed-forward dimension of 128, and 8 attention heads. Results show a significant improvement, with a 4.7% reduction in Root Mean Squared Error compared to an LSTM model with two hidden layers of 100 neurons each, showcasing the potential of Single Block Encoder-Decoder Transformer models for real-world traffic prediction applications. © 2024 IEEE.

关键词： Mean square error

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Internet of Things for Agriculture Industry: A Bibliometric Analysis in the Last Five Years 11

Internet of Things for Agriculture Industry: A Bibliometric ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Maulana, Fairuz Iqbal Puspitasari, Chasandra Hamim, Miftahul Adi, Puput Dani Prasetyo Arifuddin, Rahman Widartha, Vandha Pradwiyasma School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Business School Bina Nusantara University Entrepreneurship Business Creation Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Pukyong National University Department of Information System Busan Korea Republic of

ISBN: (纸本)9798350389654

With the arrival of new technologies, the farm business is always changing. It is one of the most important in the world. The Internet of Things (loT) is one of the most exciting new technologies of the past few years. It could completely change the way we farm. We look at the literature about loT in agriculture over the last five years in this study. According to our findings, the use of loT in agriculture has been steadily increasing over the previous five years. This research provides a bibliometric evaluation of academic papers covering the years 2018-2022, with an emphasis on the Internet of Things (loT) in the agricultural sector. The analysis is performed using data obtained from the Scopus database. 120 documents were chosen and evaluated to acquire insights on the present state of research in this topic. Co-citation analysis identified wearable technology, farmers, loT devices, food, and wireless sensor networks (WSNs) as prominent research subjects in the agricultural industry's use of the Internet of Things (loT). The findings can provide valuable insights for scholars, practitioners, funding agencies, and policymakers in comprehending the patterns and focal points in this domain. The authors and institutes highlighted in this analysis are useful resources for collaborating and staying updated on the newest advances in the loT for Agriculture Industry. © 2024 IEEE.

关键词： Agricultural economics

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The Effect of Digital Marketing on Micro, Small and Medium Enterprise in Indonesia 1

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International Conference on Applied Informatics and Media Design, AIMD 2019

作者： Sasongko, Alvin Igo Widjaja, Gregorius Christian Theodore, Jason Afriliana, Nunik Matsuo, Tokuro Gaol, Ford Lumban School of Information System Bina Nusantara University Jakarta Indonesia Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9783031307690

ISBN: (纸本)9783031307683

Digital Marketing is a product marketing strategy using digital media and internet networks. Nowadays, digital marketing is a necessity for companies that thrive in the digital world. It greatly affects the growth of MSME (Micro, Small, Medium Enterprises) especially when it comes to increasing their sales performance by enhancing their brand awareness through various media or instruments such as a Facebook ad, Instagram advertising, Search Engine Optimization, and Search Engine Marketing. This research is aimed to measure how much impact of digital marketing utilization into Micro, Small, Medium enterprises. On the other hand, it also provides an understanding on what are the best digital marketing method to be utilized in the current situation. The method we used in this paper is quantitative. Data were collected through various questions constructed in an electronic questionnaire. This research provides a reference of digital marketing strategies for Micro, Small and Medium business owners. The result shows that digital marketing has a huge impact on the growth of Micro, Small and Medium Enterprise from increasing sales, increasing brand awareness, and gaining customers based on the data collected from both customer and Micro, Small, and Medium Enterprise players in Indonesia. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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