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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Yin, Wen Liu, Cencen Xu, Yi Wahla, Ahmad Yiting, Huang Zheng, Dezhang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Laboratory of Intelligent Collaborative Computing Chengdu611731 China Bashu Middle School Chongqing400013 China

ISBN: (纸本)9782493814104

Although there have been some studies uses prompt learning for the Aspect-based Sentiment Analysis(ABSA) tasks, their methods of prompt-tuning are simple and crude. Compared with vanilla fine-tuning methods, prompt learning intuitively bridges the objective form gap between pre-training and fine-tuning. Concretely, simply constructing prompt related to aspect words fails to fully exploit the potential of Pre-trained Language Models, and conducting more robust and professional prompt engineering for downstream tasks is a challenging problem that needs to be solved urgently. Therefore, in this paper, we propose a novel syntax-aware enhanced prompt method (SynPrompt), which sufficiently mines the key syntactic information related to aspect words from the syntactic dependency tree. Additionally, to effectively harness the domain-specific knowledge embedded within PLMs for the ABSA tasks, we constructed two adaptive prompt frameworks to enhance the perception ability of the above method. After conducting extensive experiments on three benchmark datasets, we have found that our method consistently achieves favorable results. These findings not only demonstrate the effectiveness and rationality of our proposed methods but also provide a powerful alternative to traditional prompt-tuning. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation 26

Graph Contrastive Learning with Adaptive Augmentation for Kn...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Mei Ding, Zhaoyuan Yu, Jian Zhang, Wenbin Yang, Ming Zhao, Mankun College of Intelligence and Computing Tianjin University Tianjin China Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University MariettaGA United States

ISBN: (纸本)9798350331684

Knowledge concept recommendation is a kind of fine-grained recommendation in massive open online courses (MOOCs) scenario, user interaction data has the characteristics of strong collaborative signals and imbalanced interactions. This leads to a single recommendation and reduced accuracy. Recently, the ability of contrastive learning (CL) in mitigating interaction imbalance in recommender systems has received widespread attention. CL requires the use of augmentation methods to generate different views. Existing augmentation methods (1) augment only at the topology or feature level ignoring semantic or structural information, and (2) undifferentiated augmentation tends to lose the critical information. In this paper, we propose Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation (GCARec). Specifically, (1) topology level adaptive augmentation based on degree centrality captures critical structural information, and then (2) feature level adaptive augmentation based on degree centrality captures critical semantic information. Comprehensive experiments show that our proposed approach can outperform other baselines. Our implementations are available at https://***/DingZhaoyuan/GCARec. © 2023 IEEE.

关键词： Topology

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Enhancing Global Security: A Robust CNN Model for Deepfake Video Detection

Enhancing Global Security: A Robust CNN Model for Deepfake V...

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International Conference on Information and Computer Technologies (ICICT)

作者： Md Solaiman Md Shohel Rana Software Engineering Daffodil International University Dhaka Bangladesh Computing and Software Engineering Florida Gulf Coast University Fort Myers United States

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

In recent years, the rapid advancements in machine learning (ML), artificial intelligence (AI), and deep learning (DL) have ushered in a new era of sophistication in image and video manipulation techniques. Notably, the emergence of Deepfake technology, driven by AI, has garnered substantial attention. Deepfakes involve training DL models on extensive datasets of similar faces and subsequently mapping one person's expressions onto another's face, resulting in deceptively realistic fake videos that can be virtually indistinguishable from authentic ones. The proliferation of Deepfakes poses various threats, including the potential to incite political turmoil, coerce individuals, or fabricate false terrorist incidents. This trend undermines privacy, consent, and the trustworthiness of digital media. Consequently, there is a pressing need to continually advance Deepfake detection and prevention methodologies to safeguard against their malevolent use and uphold the integrity of digital content. This paper introduces a Convolutional Neural Network (CNN) based DL model specifically developed for the classification of Deepfake video frames. Our model exhibits impressive performance, as validated by a thorough analysis on the VDFD dataset, where it achieves an outstanding average precision, recall, and F1-score of 95%, 94%, and 94%, respectively. Moreover, our model showcases its efficacy across various widely recognized Deepfake datasets, including FF++, Celeb-DF, and DFDC, with frame detection average rates for precision, recall, and F1-score ranging from 80% to 85%. These compelling results signify that our proposed CNN-based frame detection technique is a powerful tool for Deepfake detection, emphasizing the critical significance of automated Deepfake detection with an exceptionally high detection rate. This technology represents a pivotal step toward protecting against the potential misuse of Deepfakes, reinforcing the security and integrity of digital content in our mo

关键词： Training Deep learning Deepfakes Privacy Social networking (online) Terrorism Pressing

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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A Multiobjective Particle Swarm Optimizer based Localized Feature Selection for Imbalanced Fault Diagnosis 13

A Multiobjective Particle Swarm Optimizer based Localized Fe...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Gao, Lin Zhou, Yu Guo, Hainan Wu Kwong, Sam Tak College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China College of Management Shenzhen University Guangdong Shenzhen China Lingnan University Department of Computing and Decision Sciences Hong Kong

ISBN: (纸本)9798350308365

Modern fault diagnosis faces an imbalance issue at both the sample and feature levels. A common approach is to combine balancing strategies with feature selection algorithms to address this problem. However, this may result in feature subsets that cannot accurately represent the true data distribution. This paper proposes an imbalanced fault diagnosis method called RPLFS-MOBPSO, which combines region purity (RP) with local feature selection to achieve class balance implicitly by partitioning local regions. RP is used as a new objective in the optimization process to achieve accurate fault detection. The adaptive reference point method from Non-Dominated Sorting Genetic Algorithm III is employed to maintain a diverse and evenly distributed external archive. On both the simulated Tennessee Eastma (TE) benchmark datasets and the real bearing experimental bench datasets, the proposed method achieves the best results compared to two LFS methods and four imbalanced ensemble algorithms based on different balancing strategies. Our source codes are available at: https://***/EMRGSZU/papers-code/tree/main/RPLFS-MOBPSO. © 2024 IEEE.

关键词： Genetic algorithms

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A Blockchain-Enabled Decentralized Federated Learning System with Transparent and Open Incentive and Audit Contracts 5th

A Blockchain-Enabled Decentralized Federated Learning System...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Cai, Zeju Chen, Jianguo Diao, Zulong Hua, Xiang School of Software Engineering Sun Yat-Sen University Zhuhai519000 China Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Purple Mountain Laboratories Nanjing210000 China

ISBN: (纸本)9789819981007

Federated learning is an innovative and secure artificial intelligence model that ensures distributed privacy protection. However, FL faces serious challenges such as potential single point failures, lack of transparency in audits, and insufficient incentives for participants. In this paper, to address the above challenges, we propose a Blockchain-enabled Decentralized Federated Learning (BC-DFL) system with transparent and open incentive and audit mechanisms. We design a two-stage model training contract that enables a transparent and decentralized FL process. In the first stage, local models are collected, and after reaching the predetermined conditions, the winning nodes of blockchain mining perform model aggregation in the second stage. In addition, to reduce the block size, we leverage the Inter-Planetary File System to convert local models into hashes and record them on the blockchain. Moreover, to incentivize desirable behavior and deter malicious actions, we further propose a rewarding formula and corresponding incentive contract. Our smart contracts incorporate a mutual evaluation mechanism for nodes, which enables fair rewards for benign nodes while punishing those with nefarious intent. The experimental results reveal that the proposed system achieves a high anti-single point of failure value and high incentive goal while maintaining similar accuracy to the original FL system. The source code is available on GitHub. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Smart Children Management Using Data Analytics, Machine Learning and IoT 1st

Smart Children Management Using Data Analytics, Machine Lear...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Faruk, Md Jobair Hossain Adnan, Muhamad Hariz Muhamad College of Computing and Software Engineering Kennesaw State University Marietta United States Computing Department Faculty of Art Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia

ISBN: (纸本)9789811621826

One of the biggest challenges faced by parents is managing the difficult or defiant behavior of children of the twenty-first century. Technology can help to provide support in managing children’s activities and helping parents to be in control. Recent advances in IoT have sparked the interest of researchers to develop a smart management system for children. This paper presents a review of the smart management of children using the internet of things (IoT), machine learning, and Data Analytics. The objective is to identify potential approaches regarding smart management for children based on IoT for future researchers. The reviews were conducted from secondary sources, where conference proceedings and journals have been analyzed from Scopus and other databases. The finding indicates that IoT-based smart system has robust accuracy and performance. Thus, exploiting IoT for the development of a smart management system for children will provide significant benefits to the future generation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem 30

Benchmarking Hallucination in Large Language Models based on...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Sun, Yuhong Yin, Zhangyue Guo, Qipeng Wu, Jiawen Qiu, Xipeng Zhao, Hui Software Engineering Institute East China Normal University China School of Computer Science Fudan University China Shanghai AI Laboratory China Shanghai Key Laboratory of Trustworthy Computing Shanghai China

ISBN: (纸本)9782493814104

Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering (QA) based on the unanswerable math word problem (MWP). To support this approach, we innovatively develop a dataset called Unanswerable Math Word Problem (UMWP) which comprises 5200 questions across five categories. We developed an evaluation methodology combining text similarity and mathematical expression detection to determine whether LLM considers the question unanswerable. The results of extensive experiments conducted on 31 LLMs, including GPT-3, InstructGPT, LLaMA, and Claude, demonstrate that in-context learning and reinforcement learning with human feedback (RLHF) training significantly enhance the model's ability to avoid hallucination. We show that utilizing MWP is a reliable and effective approach to assess hallucination. Our code and data are available at https://***/Yuki-Asuuna/UMWP. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Reinforcement learning

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ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smoke Detection

ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smo...

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

作者： Jiang, Yuhan Wang, Zhongyuan Hua, Jie Liang, Jinbi Xu, Zengmin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350368741

In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air conditioner recycling. It contains a total of 10, 724 images covering three common scenarios of air conditioner refrigerant leak: loading port, refrigerant extraction, and disassembly. All images are sourced from 656 video segments of real air conditioner recycling scenarios, captured by surveillance cameras deployed on an environmental company's recycling production line. The annotations for the refrigerant leak smoke in the images are performed by industry experts to ensure high accuracy and consistency. To the best of our knowledge, ACRL-10K is the first dataset specifically designed for refrigerant leak smoke detection. Based on the ACRL-10K dataset, we present the performance of mainstream object detectors, including the YOLO series, as a baseline to conduct the following work: 1) preliminarily summarize the challenges of using the dataset for refrigerant leak smoke detection;2) show the detection results of benchmark methods;and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the ACRL-10K dataset would hopefully advance research and applications in refrigerant leak early warning during air conditioner dismantling and recycling processes. © 2025 IEEE.

关键词： Air Conditioner Dataset Object Detection Refrigerant Leak Smoke Detection

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