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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Kuang Fengfei Lin Yaojm Yu Xiehua Chen Meilong Key Laboratory of Big Data and Artificial Intelligence in University of Fujian Province MinNan Science and Technology University Fujian Quanzhou China School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province MinNan Science and Technology University Quanzhou China

Wireless communication network plays an important role in various industries. One of the key concern in wireless communication is network security. This paper proposes an optimal transmission lines assignment with maximal reliabilities in the wireless communication. Firstly, a reliability evaluation metric with a transmission line assignment is proposed for calculating the reliability service under some constraints. Secondly, based on the reliability, non-dominated sorting Genetic algorithm is used for working out the optimal solution via considering the network security. Some simulated experiments are conducted that the proposed approach is able to obtain the reasonable solution under the time constraints.

关键词： Wireless communication Power transmission lines Computer network reliability Computational modeling Network security Reliability engineering Time factors

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Efficient FPGA-based graph processing with hybrid pull-push computational model

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Frontiers of Computer science 2020年第4期14卷 13-28页

作者： Chengbo YANG Long ZHENG Chuangyi GUI Hai JIN National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China

Hybrid pull-push computational model can provide compelling results over either of single one for processing real-world *** and pipeline parallelism of FPGAs make it potential to process different stages of graph ***,considering the limited on-chip resources and streamline pipeline computation,the efficiency of hybrid model on FPGAs often suffers due to well-known random access feature of graph *** this paper,we present a hybrid graph processing system on FPGAs,which can achieve the best of both *** approach on FPGAs is unique and novel as ***,we propose to use edge block(consisting of edges with the same destination vertex set),which allows to sequentially access edges at block granularity for locality while still preserving the *** to the independence of blocks in the sense that all edges in an inactive block are associated with inactive vertices,this also enables to skip invalid blocks for reducing redundant ***,we consider a large number of vertices and their associated edge-blocks to maintain a predictable execution *** also present to switch models in advance with few stalls using their state *** evaluation on a wide variety of graph algorithms for many real-world graphs shows that our approach achieves up to 3.69x speedup over state-of-the-art FPGA-based graph processing systems.

关键词： graph processing efficiency computational model FPGAs

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MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion

MacST: Multi-Accent Speech Synthesis via Text Transliteratio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sho Inoue Shuai Wang Wanxing Wang Pengcheng Zhu Mengxiao Bi Haizhou Li School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) Shenzhen China Fuxi AI Lab NetEase Inc. Hangzhou China Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In accented voice conversion or accent conversion, we seek to convert the accent in speech from one another while preserving speaker identity and semantic content. In this study, we formulate a novel method for creating multi-accented speech samples, thus pairs of accented speech samples by the same speaker, through text transliteration for training accent conversion systems. We begin by generating transliterated text with Large Language Models (LLMs), which is then fed into multilingual TTS models to synthesize accented English speech. As a reference system, we built a sequence-to-sequence model on the synthetic parallel corpus for accent conversion. We validated the proposed method for both native and non-native English speakers. Subjective and objective evaluations further validate our dataset’s effectiveness in accent conversion studies.

关键词： Training Large language models Semantics Signal processing Acoustics Text to speech Multilingual

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Multimodal Biological Knowledge Graph Completion via Triple Co-Attention Mechanism

Multimodal Biological Knowledge Graph Completion via Triple ...

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International Conference on data Engineering

作者： Derong Xu Jingbo Zhou Tong Xu Yuan Xia Ji Liu Enhong Chen Dejing Dou State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Business Intelligence Lab Baidu Research Intelligent Healthcare Unit Baidu Big Data Lab Baidu Research BCG X

ISBN: (数字)9798350322279

ISBN: (纸本)9798350322286

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset.

关键词： Measurement Knowledge engineering Adaptation models Fuses Biological system modeling Semantics Knowledge graphs

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Research Progress of Domestic big data Field Based on Social Network Method 3

Research Progress of Domestic Big Data Field Based on Social...

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3rd International Academic Exchange Conference on science and Technology Innovation, IAECST 2021

作者： Zhang, Chang-Lu Yang, Qiong Zhao, Dong-Yan Wu, Yan Beijing Information Science and Technology University School of Economic Management Beijing China Beijing Key Lab of Green Development Decision Based on Big Data Beijing China

ISBN: (纸本)9781665402675

big data is becoming the new engine of the economic and social development in the future. It has caused the concern worldwide being an important production factors besides the material assets and human capital. Numerous research achievements related to big data have been coming forth making it the hotspot. By the advanced search in the CNKI database, 4147 papers are got. By adopting the literature metrology and social network analysis method, the paper analyzed the research subjects, hotspot and topics, small-world property of the researchers' social network. The result shows that: Firstly, the core research subject is being formed and there is an obvious small-world characteristic in the researchers' social network. Secondly the three core research topics are big data analysis, data mining and cloud computing. Besides there are many other research perspectives showing a flourishing trend. © 2021 IEEE.

关键词： big data

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Incremental Learning Algorithms for Broad Learning System with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Guang-Ze Chen Junwei Jin Hai-Wei Sun C. L. Philip Chen Department of Mathematics Faculty of Science and Technology University of Macau Macau China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China South China University of Technology and Pazhou Lab School of Computer Science and Engineering Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Broad Learning System (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments.

关键词： Learning systems Incremental learning Accuracy Scalability Parallel processing Stability analysis Matrix decomposition Thermal stability Numerical stability Testing

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FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices

arXiv

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arXiv 2025年

作者： Yao, Dezhong Shi, Yuexin Liu, Tongtong Xu, Zhiqiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process in conventional FL introduces significant computation and communication overhead, which is unfriendly for resource-constrained edge devices. One-shot FL has emerged as a promising approach to mitigate communication overhead, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups. Our code is available at https://***/YXShi2000/FedMHO. © 2025, CC BY.

关键词： Federated learning

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ModelLock: Locking Your Model With a Spell

arXiv

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arXiv 2024年

作者： Gao, Yifeng Sun, Yuhua Ma, Xingjun Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

This paper presents a novel model protection paradigm Model Locking that locks the performance of a finetuned model on private data to make it unusable or unextractable without the right key. Specifically, we proposed a diffusion-based framework dubbed ModelLock that explores text-guided image editing to transform the private finetuning data into unique styles or blend new objects into the background. A model finetuned on this edited dataset will be locked and can only be unlocked by the key prompt, i.e., the same text prompt used to edit the data. We conduct extensive experiments on both image classification and segmentation tasks and show that 1) ModelLock can effectively lock finetuned models without significantly reducing their unlocked performance, and more importantly, 2) the locked model cannot be easily unlocked without knowing both the key prompt and the diffusion model. Our work opens up a new direction for intellectual property protection of private models. Copyright © 2024, The Authors. All rights reserved.

关键词： Keys (for locks)

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Energy-efficient transfer learning for water consumption forecasting

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Sustainable Computing: Informatics and Systems 2025年 46卷

作者： Gil-Gamboa, A. Torres, J.F. Martínez-Álvarez, F. Troncoso, A. Data Science and Big Data Lab Universidad Pablo de Olavide SevilleES-41013 Spain

Artificial intelligence is expanding at an unprecedented rate due to the numerous advantages it provides to all types of businesses and industries. Water utilities are adopting artificial intelligence models to optimize water management in cities nowadays. However, the substantial computational demands of artificial intelligence present challenges, particularly regarding energy consumption and environmental impact. This paper addresses this problem by proposing a transfer learning approach for water consumption forecasting that reduces computational time, energy usage, and CO2 emissions. The proposed methodology consists in developing a transfer learning approach based on a deep learning model already trained for a task with similar characteristics such as predicting electricity consumption. Thus, a pre-trained deep learning model designed for electricity consumption prediction is adapted to the water consumption domain, leveraging shared characteristics between these tasks. Experiments are conducted to determine the optimal amount of knowledge transfer and compare the performance of this approach with other state-of-the-art time-series forecasting models. Using real data from a water company in Spain, the transfer learning model achieves a similar or better accuracy than the other methods, while demonstrating significantly lower computational times, energy consumption and CO2 emissions. In addition, a scalability analysis has been conducted leading to the conclusion that the proposed transfer learning model is highly suitable to deal with big data. These findings highlight the potential of transfer learning as a sustainable and scalable solution for big data challenges in water management systems. © 2025 The Authors

关键词： Deep learning

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Find truth in the hands of the few:acquiring specific knowledge with crowdsourcing

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Frontiers of Computer science 2021年第4期15卷 5-16页

作者： Tao HAN Hailong SUN Yangqiu SONG Yili FANG Xudong LIU SKLSDE Lab School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyClearwater BayHong Kong 999077China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful ***,when we aggregate the crowd knowledge based on the currently developed voting algorithms,it often results in common knowledge that may not be *** this paper,we consider the problem of collecting specific knowledge via *** the help of using external knowledge base such as WordNet,we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more *** formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption,and solve it by the expectation-maximization(EM)*** increase algorithm compatibility,we also refine our method into semi-supervised *** results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected,especially for sparse data.

关键词： crowdsourcing knowledge acquisition EM algorithm label aggregation

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