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Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus 25

Transformer-based Text Classification on Unified Bangla Mult...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Sourav, Md Sakib Ullah Mahmud, Mohammad Sultan Zheng, Hua Aljaidi, Mohammad Talukder, Md. Simul Hasan Bin Sulaiman, Rejwan Nur, Abdullah Hafez Al-Qerem, Ahmad Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shenzhen University College of Computer Science and Software Engineering Senzhen China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou510990 China Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Dhaka University of Engineering and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Northumbria University Newcastle Tayne School of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Due to its importance in studying people's thoughts on various Web 2.0 services, emotion classification is a critical undertaking. Most existing research is focused on the English language, with little work on low-resource languages. Though sentiment analysis, particularly emotion classification in English, has received increasing attention in recent years, little study has been done in the context of Bangla, one of the world's most widely spoken languages. In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes, i.e., anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labelled datasets of Bangla comments on six emotion classes with fresh manually labelled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available. © 2024 IEEE.

关键词： Classification (of information)

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A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method 19th

A Novel Deep Federated Learning-Based and Profit-Driven Ser...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Ouyang, Zhaobin Xia, Yunni Peng, Qinglan Li, Yin Chen, Peng Wang, Xu College of Computer Science Chongqing University Chongqing China School of Artificial Intelligence Henan University Zhengzhou China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Software Engineering Xihua University Chengdu China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (纸本)9783031545306

Service caching is an emerging solution to addressing massive service request in a distributed environment for supporting rapidly growing services and applications. With the explosive increases in global mobile data traffic, service caching over the edge computing architecture, Mobile edge computing (MEC), emerges for alleviating traffic congestion as well as for optimizing the efficiency of task processing. In this manuscript, we propose a novel profit-driven service caching method based on a federated learning model for service prediction and a deep reinforcement learning mode for yielding caching decisions (FPDRD) in an edge environment. The proposed method is temporal service popularity and user preference-aware. It aims to ensure quality of service (QoS) of delivery of cached service while maximizing the profits of network service providers. Experimental results clearly demonstrate that the FPDRD method outperforms traditional methods in multiple aspects. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Traffic congestion

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Education and Teaching Research of IoT Embedded Subject under the National Vocational and Technical Skills Standards 6

Education and Teaching Research of IoT Embedded Subject unde...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Yanglu Wang, Xingchen Yadav, Amit Shah, Abhishek Pratap College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Darwin Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to teach), faculty team (who teaches), and teaching evaluation (how well the teaching is) under the current status of embedded teaching on the Internet of Things (IoT) engineering discipline. Guided by the latest National Occupational Technical Skill Standards for IoT engineering professionals and in accordance with the educational philosophy of Chengdu Neusoft university, here we propose a design for teaching system for IoT embedded talent courses, clarified the key elements and formulated new ideas for the current development of talents in this direction to assist improvement and perfection of related curriculum systems, and to promote the establishment and operation of practical and technological activity platforms. It aims to provide practical references for the construction of professionalism in education and teaching standards of IoT. © 2025 IEEE.

关键词： Curricula

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A Transformer-based method for non-contact heart rate estimation from facial videos recorded in realistic environment 3

A Transformer-based method for non-contact heart rate estima...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Hu, Yi Liu, Xuenan Rao, Xinlong Li, Bojing School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China School of Software Hefei University of Technology Anhui Hefei230601 China

The Video Photoplethysmography (VPPG) technique, while increasingly popular due to its convenience and cost-effectiveness, faces challenges in handling continuous head movements and vigorous motion interferences encountered in real-life scenarios. In this paper, we present a Transformer-based approach aimed at enhancing the robustness of heart rate estimation from facial videos. Leveraging the selfattention mechanism inherent in Transformers, our method adeptly captures both temporal dependencies and spatial information, thereby elevating the accuracy and resilience of heart rate estimation, even in challenging conditions. Through extensive experiments conducted on real-world face video datasets, we illustrate the effectiveness of our approach. Our results demonstrate significant improvements over existing methods in mitigating motion artifacts and enhancing the reliability of non-contact heart rate estimation in practical environments. © 2024 Copyright for this paper by its authors.

关键词： heart rate detection non-contact type Transformer Video Photoplethysmography

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Deep Residual Fourier and Self-Attention for Arbitrary Scale MRI Super-Resolution

Deep Residual Fourier and Self-Attention for Arbitrary Scale...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Xiaojie Xia, Jing Gao, Shanshan Hao, Xingwei Zhou, Yuanfeng School of Software Shandong University Jinan China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350337488

The excellent inherent contrast between biological tissues afforded by MR imaging is one of the foremost characteristics of this technique, but it depends on the scan time and hardware devices. Recent studies have shown significant progress in deep learning-based single-image super-resolution (SISR) algorithms based on MR images. Many researchers have employed implicit functions for super-resolution tasks, achieving arbitrary resolution upsampling. Nevertheless, challenges like loss of generated image texture and weak high-frequency features persist. We propose an arbitrary multiple super-resolution model based on deep residual Fourier transform and a selfattention mechanism to tackle these issues. We first use the deep Fourier residual block to construct the high and low-frequency difference of the image, compensating for the spectral bias of the multiple perceptron (MLP) network. Building upon the substantial similarity in medical image tissue structures, we add a vertical and horizontal self-attention mechanism to capture the internal correlation of features in the vertical and horizontal directions. Finally, we learn a continuous functional representation to execute super-resolution tasks at any scale. Experiment results show the effectiveness of our method on the T2 sequence of public dataset SIMON and T1, T2, and Proton Density (PD) weighted scan sequences of public dataset IXI. We conduct qualitative and quantitative comparisons of the three contrasts, illustrating the superiority of our approach. © 2023 IEEE.

关键词： self-attention single-image super-resolution the deep Fourier residual block

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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User and Item Sub-contexts Induced Fuzzy Concept Set for Recommendation 11th

User and Item Sub-contexts Induced Fuzzy Concept Set for R...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Liu, Zhonghui Tang, Pu Min, Fan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Institute for Artificial Intelligence Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9783031927430

Formal concept analysis (FCA) has been employed for recommendation systems. However, conditional FCA approaches are confined to binary user-item relationships, failing to incorporate explicit rating information and consequently diminishing recommendation accuracy. In this paper, we develop fuzzy sub-contexts with rating information and generate a fuzzy concept set for recommendation. For fuzzy sub-context, we devise dual (user/item) similarity metrics for sub-context generation from fuzzy formal context. For fuzzy concepts, we develop a construction algorithm using concept size as heuristic information, which generates a fuzzy concept set based on the fuzzy sub-context. For the recommendation, we design user weights to calculate recommendation confidence within the fuzzy concept to formulate personalised recommendations. Experiments have been undertaken on nine real datasets. Results show that our algorithm achieves superior recommendation quality compares to seven algorithms: collaborative filtering algorithms (kNN, IBCF, BMF, GreConD-kNN) and formal concept-based recommendation algorithms (GRHC, CSPR, CSBR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

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PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model 39

PointDGMamba: Domain Generalization of Point Cloud Classific...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yang, Hao Zhou, Qianyu Sun, Haijia Li, Xiangtai Liu, Fengqi Lu, Xuequan Ma, Lizhuang Yan, Shuicheng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Information Management Nanjing University Nanjing China Skywork AI Singapore Department of Computer Science and Software Engineering The University of Western Australia Australia Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification (PCC) models toward unseen domains. However, they often suffer from limited receptive fields or quadratic complexity due to using convolution neural networks or vision Transformers. In this paper, we present the first work that studies the generalizability of state space models (SSMs) in DG PCC and find that directly applying SSMs into DG PCC will encounter several challenges: the inherent topology of the point cloud tends to be disrupted and leads to noise accumulation during the serialization stage. Besides, the lack of designs in domain-agnostic feature learning and data scanning will introduce unanticipated domain-specific information into the 3D sequence data. To this end, we propose a novel framework, PointDGMamba, that excels in strong generalizability toward unseen domains and has the advantages of global receptive fields and efficient linear complexity. PointDGMamba consists of three innovative components: Masked Sequence Denoising (MSD), Sequence-wise Cross-domain Feature Aggregation (SCFA), and Dual-level Domain Scanning (DDS). In particular, MSD selectively masks out the noised point tokens of the point cloud sequences, SCFA introduces cross-domain but same-class point cloud features to encourage the model to learn how to extract more generalized features. DDS includes intra-domain scanning and cross-domain scanning to facilitate information exchange between features. In addition, we propose a new and more challenging benchmark PointDG-3to1 for multi-domain generalization. Extensive experiments demonstrate the effectiveness and state-of-the-art performance of PointDGMamba. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Convolutional neural networks

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Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition 41

Learning Label Shift Correction for Test-Agnostic Long-Taile...

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Tong Mao, Zhen Zhou, Zi-Hao Wan, Yuanyu Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Software Technology Zhejiang University Ningbo China

Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https://***/Stomach-ache/label-shift-correction. Copyright 2024 by the author(s)

关键词： Black-box testing

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Detecting and Corrupting Convolution-based Unlearnable Examples 39

Detecting and Corrupting Convolution-based Unlearnable Examp...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)157735897X

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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