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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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Demystifying and Analysing Metaverse Towards Education 4.0

Demystifying and Analysing Metaverse Towards Education 4.0

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Arman Raj Vandana Sharma Seema Rani Tanya Singh Ankit Kumar Shanu Ahmed Alkhayyat School of Computing Science and Engineering Galgotias University Greater Noida India Amity Institute of Information Technology Amity University India Amity Institute of Information Technology Amity University AUUP Noida India Department of Mechanical Engineering University of California Riverside USA College of technical engineering The Islamic University Najaf Iraq

The word Metaverse has influenced many sectors such as healthcare, education, retail and manufacturing and few more industries are there which will be impacted by 2026 as per the research conducted by Gartner. The word “Metaverse” especially in education sector came into existence after the COVID-19 epidemic when the humanity were forced to think about the new methodology of educating and teaching. This ecosphere is the combination of technologies which enables multimodal interactions with artificial environment, electronic library and people such as Virtual Reality (VR) and Augmented Reality (AR). It is believed that metaverse will improve collaboration, training process will be enhanced and most importantly it will create a happier workplace. This is only the reason that many corporate giants like Nvidia, facebook, apple, epic Games and companies has shifted towards this pedagogical ecosystem. This technology has the potential which enables absolute incorporating user conversation in actual-time and compelling interactivity with digital artifact. In this paper, we are addressing metaverse in education along with a detailed framework of metaverse in education. It includes a comparative study of conventional education, online education and metaverse education based on parameters like place of learning, resources used, teaching methodology, learning experience, learning target and learning assessment. Competency based education, energize student and positive attitude towards learning. The various challenges of the metaverse in educational sector are also debated. This paper will help the researcher's fraternity to get a deeper insight along with a clear perception of this ecosystem in education.

关键词： Training Visualization Three-dimensional displays Metaverse Social networking (online) Education Ecosystems

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Focus, Distinguish, and Prompt: Unleashing CLIP for Efficient and Flexible Scene Text Retrieval

arXiv

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arXiv 2024年

作者： Zeng, Gangyan Zhang, Yuan Wei, Jin Yang, Dongbao Zhang, Peng Gao, Yiwen Qin, Xugong Zhou, Yu School of Cyber Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Media Convergence and Communication Communication University of China China Institute of Information Engineering Chinese Academy of Sciences China Laboratory for Advanced Computing and Intelligence Engineering China TMCC College of Computer Science Nankai University China

Scene text retrieval aims to find all images containing the query text from an image gallery. Current efforts tend to adopt an Optical Character Recognition (OCR) pipeline, which requires complicated text detection and/or recognition processes, resulting in inefficient and inflexible retrieval. Different from them, in this work we propose to explore the intrinsic potential of Contrastive Language-Image Pre-training (CLIP) for OCR-free scene text retrieval. Through empirical analysis, we observe that the main challenges of CLIP as a text retriever are: 1) limited text perceptual scale, and 2) entangled visual-semantic concepts. To this end, a novel model termed FDP (Focus, Distinguish, and Prompt) is developed. FDP first focuses on scene text via shifting the attention to the text area and probing the hidden text knowledge, and then divides the query text into content word and function word for processing, in which a semantic-aware prompting scheme and a distracted queries assistance module are utilized. Extensive experiments show that FDP significantly enhances the inference speed while achieving better or competitive retrieval accuracy compared to existing methods. Notably, on the IIIT-STR benchmark, FDP surpasses the state-of-the-art model by 4.37% with a 4 times faster speed. Furthermore, additional experiments under phrase-level and attribute-aware scene text retrieval settings validate FDP’s particular advantages in handling diverse forms of query text. The source code will be publicly available at https://***/Gyann-z/FDP. © 2024, CC BY-NC-SA.

关键词： Semantics

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Subband-based Generative Adversarial Network for Non-parallel Many-to-many Voice Conversion

arXiv

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arXiv 2022年

作者： Ma, Jian Zheng, Zhedong Fei, Hao Zheng, Feng Chua, Tat-Seng Yang, Yi Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computing National University of Singapore Singapore118404 Singapore School of Computer Science Zhejiang University Zhejiang310058 China

Voice conversion is to generate a new speech with the source content and a target voice style. In this paper, we focus on one general setting, i.e., non-parallel many-to-many voice conversion, which is close to the real-world scenario. As the name implies, non-parallel many-to-many voice conversion does not require the paired source and reference speeches and can be applied to arbitrary voice transfer. In recent years, Generative Adversarial Networks (GANs) and other techniques such as Conditional Variational Autoencoders (CVAEs) have made considerable progress in this field. However, due to the sophistication of voice conversion, the style similarity of the converted speech is still unsatisfactory. Inspired by the inherent structure of mel-spectrogram, we propose a new voice conversion framework, i.e., Subband-based Generative Adversarial Network for Voice Conversion (SGAN-VC). SGAN-VC converts each subband content of the source speech separately by explicitly utilizing the spatial characteristics between different subbands. SGAN-VC contains one style encoder, one content encoder, and one decoder. In particular, the style encoder network is designed to learn style codes for different subbands of the target speaker. The content encoder network can capture the content information on the source speech. Finally, the decoder generates particular subband content. In addition, we propose a pitch-shift module to fine-tune the pitch of the source speaker, making the converted tone more accurate and explainable. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performance on VCTK Corpus and AISHELL3 datasets both qualitatively and quantitatively, whether on seen or unseen data. Furthermore, the content intelligibility of SGAN-VC on unseen data even exceeds that of StarGANv2-VC with ASR network assistance. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism

SSRN

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SSRN 2023年

作者： Jiang, Xuetao Jiang, Meiyu Luo, Lihui Shen, Jun Zhou, Qingguo Zhou, Rui School of Information Science and Engineering Lanzhou University Tianshui South Road No.222 Gansu Lanzhou730000 China Gansu Provincial Industry Technology Center of Intelligent Equipment & Big Data for Disaster Prevention Northwest Institute of Eco-Environment and Resources Chinese Academy of Science Gansu Lanzhou730000 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Accurate photovoltaic (PV) generation forecasts can reduce power redeploy from the grid, thus increasing the supplier's profit in the day-ahead electricity market. However, the PV process is affected differently by various factors under different weather conditions, resulting in significantly different energy output curves. In this context, this paper proposes a day-ahead PV power forecasting method with weather conditioned attention mechanism. We propose a multi-stream attention fusion network (MSAFN) which utilizes an algorithm to derive the optimal decomposition algorithm for different weather conditions. The proposed conditional decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. The MSAFN incorporates multiple attention modules to learn the energy output patterns under various weather conditions. Notably, the attention modules adeptly learn patterns under diverse conditions, while simultaneously, the sharing of weights among the remaining components of the model effectively enhances prediction accuracy and facilitates a reduction in training time. We compare the state-of-the-art decomposition algorithms (VMD, EEMD and MSTL, etc.) and prediction models (BPN, LSTM, XGBoost and transformer, etc.) commonly used in PV prediction. The results show that the MSAFN model is more accurate than the models above, which has a noticeable improvement compared to other recent day-ahead PV predictions on DKASC. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Steel surface defect detection based on lightweight model

Steel surface defect detection based on lightweight model

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Le He Jun Huang Xue Li Bolun Guan School of Computer Science and Computing Anhui University of Technology Ma’Anshan China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Agricultural Economy and Information Research InstituteAnhui Academy of Agricultural Sciences Hefei China

Steel surface defect detection, with the goal of enhancing the quality of steel within the industry, is a vital process in steel production. Over the years, a number of techniques have been developed to meet the challenge of object detection. Nonetheless, designing lightweight models that can detect defects quickly and accurately remains challenging due to a significant amount of environmental interference and limited computational resources of the edge devices used in steel factories. In this paper, In this paper, we propose an improved Lightweight Detection Network(LDN) for steel surface defect detection. First, to extract features, the lightweight MobileNetV2 model is employed as the backbone. Second, to improve the detection accuracy, a feedback mechanism from the feature pyramid is added to the backbone network to integrate the features from shallow and deep layers. Additionally, the Efficient Intersection over Union(EIoU) loss is utilized to make the regression more accurate. The experimental results indicate that the method’s accuracy in recognition surpasses that of previous models, and the parameters and computational requirements have decreased significantly, leading to a rapid and precise detection of steel surface defects.

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Containerization in Multi-Cloud Environment: Roles, Strategies, Challenges, and Solutions for Effective Implementation

arXiv

引用

arXiv 2024年

作者： Waseem, Muhammad Ahmad, Aakash Liang, Peng Akbar, Muhammad Azeem Khan, Arif Ali Ahmad, Iftikhar Setälä, Manu Mikkonen, Tommi Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland School of Computing and Communications Lancaster University Leipzig Leipzig Germany School of Computer Science Wuhan University Wuhan China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland TietoEVRY Oy Tampere Finland Solita Oy Tampere Finland Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

Containerization in a multi-cloud environment facilitates workload portability and optimized resource utilization. Containerization in multi-cloud environments has received significant attention in recent years both from academic research and industrial development perspectives. However, there exists no effort to systematically investigate the state of research on this topic. The aim of this research is to systematically identify and categorize the multiple aspects of containerization in multi-cloud environment. We conducted the Systematic Mapping Study (SMS) on the literature published between January 2013 and July 2024. One hundred twenty one studies were selected and the key results are: (1) Four leading themes on containerization in multi-cloud environment are identified: ‘Scalability and High Availability’, ‘Performance and Optimization’, ‘Security and Privacy’, and ‘Multi-Cloud Container Monitoring and Adaptation’. (2) Ninety-eight patterns and strategies for containerization in multi-cloud environment were classified across 10 subcategories and 4 categories. (3) Ten quality attributes considered were identified with 47 associated tactics. (4) Four catalogs consisting of challenges and solutions related to security, automation, deployment, and monitoring were introduced. The results of this SMS will assist researchers and practitioners in pursuing further studies on containerization in multi-cloud environment and developing specialized solutions for containerization applications in multi-cloud environment. © 2024, CC BY.

关键词： Cloud platforms

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Halftone Image Steganography Based on Minimizing Distortion with Pixel Density Transition 7th

Halftone Image Steganography Based on Minimizing Distortion...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Yu, Mujian Luo, Junwei Xu, Bozhi Chen, Guoliang Lu, Wei School of Computer Science and Engineering Guangdong Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China Guangdong Science and Technology Innovation Monitoring and Research Center Guangzhou510033 China

ISBN: (纸本)9783030786113

Many advanced halftone steganographic schemes focus only on the distortion of human visual perception or the distortion according to statistics. In this paper, a halftone image steganography based on minimizing distortion with density transition is proposed which aims at utilizing the entropy model and pixel density to resist potential steganalysis. First, the entropy model is established on the image database to describe the texture content and transformed to the preliminary distortion score map. Because the form of texture presentation is distinct between halftone images and ordinary binary images, the feature of pixel density is introduced to represent the local intensity in images. Then the pixel density transition adjustment based on the entropy model is presented, which makes the distortion score more reliable. The final additive distortion map is generated by combining the entropy model and the strategy of density transition. To play the advantage of distortion measurement, syndrome-trellis code (STC) is applied with the distortion map to minimize the embedding distortions. Experimental results demonstrate that compared with other halftone steganographic schemes, the proposed method achieves high statistical security and great visual quality with considerable embedding capacity. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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ReconBoost: Boosting Can Achieve Modality Reconcilement

arXiv

引用

arXiv 2024年

作者： Hua, Cong Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of multi-modal learning tend to explore multi-modal features simultaneously. The resulting gradient prohibits further exploitation of the features in the weak modality, leading to modality competition, where the dominant modality overpowers the learning process. To address this issue, we study the modality-alternating learning paradigm to achieve reconcilement. Specifically, we propose a new method called ReconBoost to update a fixed modality each time. Herein, the learning objective is dynamically adjusted with a reconcilement regularization against competition with the historical models. By choosing a KL-based reconcilement, we show that the proposed method resembles Friedman's Gradient-Boosting (GB) algorithm, where the updated learner can correct errors made by others and help enhance the overall performance. The major difference with the classic GB is that we only preserve the newest model for each modality to avoid overfitting caused by ensembling strong learners. Furthermore, we propose a memory consolidation scheme and a global rectification scheme to make this strategy more effective. Experiments over six multi-modal benchmarks speak to the efficacy of the method. We release the code at https://***/huacong/ReconBoost. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Predictive Analysis of Consumer Behavior Using Supervised Learning Techniques

Predictive Analysis of Consumer Behavior Using Supervised Le...

引用

International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： K. Sashi Rekha T. Amutha R. Usharani S. Pushparani V.M. Sivagami Department of Computer Science and Engineering SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Information Technology Sri Venkateswara College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The study presented here consists of an entire framework for prediction of consumers behavior through the machine training of supervised learning methods. The strategy involves the following: data collection, preprocessing, exploration, feature selection, model selection, training, evaluation, interpretation and deployment. The systematic process is tailored to clear the pathway for combining data about customer taste, sophisticated habits, and purchasing trends. Primary components include data preprocessing, training of machine learning models, feature selection, and ethical issues regarding data privacy and transparency. The outcomes of the implementation are a high accuracy in consumers clustering, efficient classification of customers' behavior, as well as unveiling a bunch of crucial predictors that affect the customers mood and behavior. As a result, the report stresses the necessity of integrated monitoring and redirection strategies to realize the potential of the created models. The idea of predictive analysis as an evolving tool that enables marketers to tailor their campaigns more closely, and improve the overall customer experience therefore also receives attention. The proposed system methodology constitutes a solid process framework for business defined to aim at using predictive analytics to gain competitive edge in the current dynamic marketplace.

关键词： Training Adaptation models Consumer behavior Supervised learning Data preprocessing Reinforcement learning Feature extraction Data models Predictive analytics Stress

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