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7th International Conference on computer science and Artificial Intelligence, CSAI 2023

作者： Yang, Chenrushi Mei, Ruidong Cheng, Hui School of System Science and Engineering Sun Yat-sen University Guangdong Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China

ISBN: (纸本)9798400708688

This paper presents a discussion on the challenges encountered in localizing mobile robots in large-scale indoor environments, particularly the issues of kidnapping and localization failure, which are prevalent in factories and malls. Visual place recognition (VPR) methods are often used to alleviate these issues;however, in large-scale indoor settings, obtaining ground truth through precise 3D reconstruction using cameras can be time-consuming and challenging due to their limited field of view and varying illumination conditions. LiDAR is more suitable for large-scale mapping due to its wider field of view and its robustness to environmental changes and sensor drifts;however, the cost of LiDAR systems can be prohibitive for indoor applications. RGB-D cameras are lighter and less expensive, making them more suitable for a wide range of applications on multi-robot platforms. Therefore, it is important to investigate the use of RGB-D camera data for place recognition, especially in indoor contexts where only LiDAR maps can be used for place recognition. This paper presents a rigorous comparative study based on existing techniques to identify the key factors in addressing indoor place recognition challenges. © 2023 ACM.

关键词： Cameras

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Pasha, Syed Tangim Islam, Ashraful Rahman, Mohammed Masudur Ahmed, Eshtiak Ahmed Foysal, Md. Ferdouse Zahangir Alam, Md Daffodil International University Department of Computing and Information System Bangladesh Independent University Center for Computational and Data Sciences Bangladesh Independent University Department of Computer Science and Engineering Bangladesh International Standard University Department of Computer Science and Engineering Bangladesh Daffodil International University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350337617

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%. © 2023 IEEE.

关键词： Semantics

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Video matting based on local-global features fusion 4

Video matting based on local-global features fusion

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Dong, Niuniu Liang, Yihui Zou, Kun Li, Wensheng Feng, Fujian School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Video matting aims at accurately separating foreground from videos. Recent video matting researches pursue to eliminate auxiliary inputs. However, due to the limited ability of extracting global correlation features, these methods suffer from performance degradation when dealing with complex scenes or natural background videos. To address this challenge, we propose a video matting method called Video Matting Based on Local-Global Features Fusion (VMBLGFF) which can extract both comprehensive global correlation features and local subtle features. VMBLGFF contains two closely connected networks: a transformer network that utilizes window and global attention mechanisms to obtain global correlation features within and cross windows, and a fusion network that integrates local subtle features into the global correlation features to supplement the local detail information which may be overlooked by the attention mechanisms. VMBLGFF alleviates the issue of limiting global correlation features and has been benchmarked on both synthetic and real datasets, and the results demonstrate that VMBLGFF improves the quality of video matting and exhibits good generalization performance. © 2024 SPIE.

关键词： computer vision

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Position Encoding Based Convolutional Neural Networks for Machine Remaining Useful Life Prediction

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1427-1439页

作者： Ruibing Jin Min Wu Keyu Wu Kaizhou Gao Zhenghua Chen Xiaoli Li Institute for Infocomm Research Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore School of Computer Science Liaocheng UniversityLiaocheng 252000 Macao Institute of System Engineering Macao University of Science and TechnologyTaipaMacao 999078China

Accurate remaining useful life(RUL)prediction is important in industrial *** prevents machines from working under failure conditions,and ensures that the industrial system works reliably and ***,many deep learning based methods have been proposed to predict *** these methods,recurrent neural network(RNN)based approaches show a strong capability of capturing sequential *** allows RNN based methods to perform better than convolutional neural network(CNN)based approaches on the RUL prediction *** this paper,we question this common paradigm and argue that existing CNN based approaches are not designed according to the classic principles of CNN,which reduces their ***,the capacity of capturing sequential information is highly affected by the receptive field of CNN,which is neglected by existing CNN based *** solve these problems,we propose a series of new CNNs,which show competitive results to RNN based *** with RNN,CNN processes the input signals in parallel so that the temporal sequence is not easily *** alleviate this issue,a position encoding scheme is developed to enhance the sequential information encoded by a ***,our proposed position encoding based CNN called PE-Net is further improved and even performs better than RNN based *** experiments are conducted on the C-MAPSS dataset,where our PE-Net shows state-of-the-art performance.

关键词： Convolutional neural network(CNN) deep learning position encoding remaining useful life prediction

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

Image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background Image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the Image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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Combination of CRITIC and PROMETHEE II Methods as An Early Detection of Potential Dropout Students 6

Combination of CRITIC and PROMETHEE II Methods as An Early D...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Yusa, Mochammad Coastera, Funny Farady Rahmadani, Deni Sari, Julia Purnama Kusrini, Kusrini Hartanto, Anggit Dwi University of Bengkulu Faculty of Engineering Department of Informatics Bengkulu Indonesia University of Bengkulu Faculty of Engineering Department of Information System Bengkulu Indonesia Universitas Amikom Yogyakarta Magister of Informatics Engineering Faculty of Computer Science Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Information System Yogyakarta Indonesia

ISBN: (纸本)9798350315639

Dropout (DO) has negative implications for individuals and educational institutions. The field of education data mining (EDM) offers valuable contributions to prevent dropout cases and improve student retention. This study focuses on implementing EDM to identify students at risk of dropping out by integrating academic activity criteria with real-time data sources. Previous studies have encountered limitations in classification and clustering techniques, with classification relying on labeled training data (supervised learning) and clustering constrained by attribute or criteria weight (unsupervised learning). This study aims to address these limitations by combining the PROMETHEE II ranking approach and the CRITIC weighting method, producing a list of students with dropout potential. The results can aid stakeholders in implementing preventive measures. The study involves several steps: 1) Collecting data through integration with academic portals;2) Filtering and selecting criteria for student academic activities;3) Transforming and normalizing the data;4) Applying automatic weighting using CRITIC;5) Calculating criteria rankings using the PROMETHEE II technique;and 6) Validating the ranking outcomes. © 2023 IEEE.

关键词： Decision support systems

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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CRAFT: Criticality-Aware Fault-Tolerance Enhancement Techniques for Emerging Memories-Based Deep Neural Networks

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IEEE Transactions on computer-Aided Design of Integrated Circuits and systems 2023年第10期42卷 3289-3300页

作者： Nguyen, Thai-Hoang Imran, Muhammad Choi, Jaehyuk Yang, Joon-Sung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical Engineering School of Electrical Engineering and Computer Science National University of Sciences and Technology Islamabad44000 Pakistan Department of Semiconductor Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electrical and Electronic Engineering Department of System Semiconductor Engineering Yonsei University Seoul03722 Korea Republic of

Deep neural networks (DNNs) have emerged as the most effective programming paradigm for computer vision and natural language processing applications. With the rapid development of DNNs, efficient hardware architectures for deploying DNN-based applications on edge devices have been extensively studied. Emerging nonvolatile memories (NVMs), with their better scalability, nonvolatility, and good read performance, are found to be promising candidates for deploying DNNs. However, despite the promise, emerging NVMs often suffer from reliability issues, such as stuck-at faults, which decrease the chip yield/memory lifetime and severely impact the accuracy of DNNs. A stuck-at cell can be read but not reprogrammed, thus, stuck-at faults in NVMs may or may not result in errors depending on the data to be stored. By reducing the number of errors caused by stuck-at faults, the reliability of a DNN-based system can be enhanced. This article proposes CRAFT, i.e., criticality-aware fault-tolerance enhancement techniques to enhance the reliability of NVM-based DNNs in the presence of stuck-at faults. A data block remapping technique is used to reduce the impact of stuck-at faults on DNNs accuracy. Additionally, by performing bit-level criticality analysis on various DNNs, the critical-bit positions in network parameters that can significantly impact the accuracy are identified. Based on this analysis, we propose an encoding method which effectively swaps the critical bit positions with that of noncritical bits when more errors (due to stuck-at faults) are present in the critical bits. Experiments of CRAFT architecture with various DNN models indicate that the robustness of a DNN against stuck-at faults can be enhanced by up to 105 times on the CIFAR-10 dataset and up to 29 times on ImageNet dataset with only a minimal amount of storage overhead, i.e., 1.17%. Being orthogonal, CRAFT can be integrated with existing fault-tolerance schemes to further enhance the robustness of DNNs aga

关键词： Fault tolerance

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PPDO:a privacy-preservation-aware delay optimization task-offloading algorithm for collaborative edge computing

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Frontiers of Information Technology & Electronic engineering 2025年第1期26卷 27-41页

作者： Chao JING Jianwu XU College of Computer Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China

Although collaborative edge computing(CEC)systems are beneficial in enhancing the performance of mobile edge computing(MEC),the issue of user privacy leakage becomes prominent during task *** address this issue,we design a privacy-preservation-aware delay optimization task-offloading algorithm(PPDO)in a CEC *** considering location and usage pattern privacy protection,we establish a privacy task model to interfere with the edge server and ensure user *** address the extra delay arising from privacy protection,we subsequently leverage a Markov decision processing(MDP)policy-iteration-based algorithm to minimize delays without compromising *** simultaneously accelerate the MDP operation,we develop an extension that improves the PPDO by optimizing the action ***,a comprehensive simulation was conducted using the edge user allocation(EUA)*** results demonstrated that PPDO achieves an optimal trade-off between privacy protection and delay with a minimum delay compared with existing ***,we examined the advantages and disadvantages of improving PPDO.

关键词： Collaborative edge computing Task offloading Privacy protection Markov decision process

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Prediction Analysis of Greeting Gestures based on Data Augmentation and LSTM 23

Prediction Analysis of Greeting Gestures based on Data Augme...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wibow, Angga Wahyu Sato-Shimokawara, Eri Kurnianingsih Graduate School of System Design Tokyo Metropolitan University Department of Computer Science Tokyo Japan Politeknik Negeri Semarang Department of Electrical Engineering Semarang Indonesia

ISBN: (纸本)9798331528041

The field of Human Activity Recognition (HAR) is growing significantly in several areas but little research focuses on cultural behavior. How machine learning can explain human activity as a promotional tool in understanding cultural differences in a region is very challenging. Studies using Recurrent Neural Network (RNN), especially Long Short-Term Memory (LSTM), require large data when used to research human activity recognition. Using small data is a challenge when using LSTM. This study aims to determine which data augmentation is most appropriate when used using LSTM to predict Japanese greeting gestures, namely eshaku, keirei, saikeirei, waving hands, and Indonesian greeting gestures, namely the movement of bringing both hands together in front of the chest with a slight bow of the head. This study proposes to compare several data augmentations, namely jittering, scaling, permutation, cropping, reverse, time shifting, frequency domain, and synthetic data generation. We evaluate model performance using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) metrics. The experimental results show that Time Shifting has the best MSE, RMSE, and MAE values compared to others and Frequency Domain has the best R2 values compared to others. This study concludes that Time Shifting and Frequency Domain are the best data augmentation methods when using LSTM in predicting greeting gestures using small data. These findings provide valuable insights into the effectiveness of various data augmentation methods in motion prediction tasks with limited data sets, as well as the importance of selecting appropriate data augmentation methods in human motion analysis. Future work could use data from two or more people using model explanations generated by machine learning and developing a new method for cultural behavior. © 2024 IEEE.

关键词： Mean square error

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