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9th International Conference on Big data Computing and Communications, BigCom 2023

作者： Wu, Xiaodong Wang, Ronghai Lin, Guoxin Quanzhou Normal University School of Mathmatics and Computer Science China Fujian Province University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian China Provincial Big Data Research Institute of Intelligent Manufacturing Quanzhou China

ISBN: (纸本)9798350331240

Virtual Machine Consolidation (VMC) is an effective means to improve the resource utilization and reduce energy consumption of data centers. However, the VMC optimization that only considers energy consumption may lead to SLA (Service-Level Agreements) violations and thus fail to guarantee the QoS (Quality of Service) provided. Therefore, it is necessary to consider both energy saving and SLA violations in the process of VMC. In this paper, we propose an energy and SLA-aware VMC algorithm that considers both energy saving and SLA violation optimization in data centers. The proposed algorithm uses the local regression method to predict the utilization of virtual machines, based on which it determines whether the host will be overloaded, so as to make the decision of virtual machine migrations. The proposed VMC algorithm is evaluated with CloudSim toolkit. Simulation results demonstrate that our algorithm has good energy saving performance and can effectively reduce SLA violation in cloud data center. Considering energy consumption and SLA violation, the ESV can be reduced by 7.57% on average and 16.36% at most compared with other algorithms. © 2023 IEEE.

关键词： Virtual machine

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LCS-based Visual Recognition for AGV Guide Paths under Complex Illumination Conditions 15

LCS-based Visual Recognition for AGV Guide Paths under Compl...

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15th International Conference on Measurement Technology and Mechanical Automation, ICMTMA 2023

作者： Wang, Z.M. Li, M. Gao, C. Jin, X.M. School of Computer Science and Technology Xi'An University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'An Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

Complex lighting is one of the most challenging problems in automatic guided vehicle (AGV) vision recognition system. In order to overcome the influence of uneven illumination on the accuracy and robustness of path recognition, this paper proposes the LCS based visual recognition for AGV guide paths under complex illumination conditions method, which converts the collected image into an invariant image through logarithmic chromaticity space (LCS) to eliminate the influence of illumination, and the minimum average entropy angle is used as the projection angle to generate the invariant image to improve the speed of image conversion to the invariant image. Experimental results show that the proposed method can effectively improve the robustness of AGV vision recognition system under complex lighting conditions. © Published under licence by IOP Publishing Ltd.

关键词： Lighting

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On the Gap Between AI-Generated and Human-Written Patent Texts 1

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14th International Conference on Brain Inspired Cognitive Systems, BICS 2024

作者： Xiao, Zhanhao Hu, Wei Wu, Yanqiang Chen, Weiqi Li, Huihui Liu, Xiaoyong School of Computer Science Guangdong Polytechnic Normal University Guangzhou510665 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangzhou510665 China School of Data Science and Engineering Guangdong Polytechnic Normal University Guangzhou510665 China

ISBN: (数字)9789819628827

ISBN: (纸本)9789819628810

Since the GPT-X models have made progress in generative tasks, a large number of large language models (LLMs) have sprung up. When the powerful features of LLMs have attracted the interest of numerous researchers, their misuse has also become a source of growing concern for human beings. In fact, LLMs have been used to generate fake news, fake academic papers, and fake patent application documents. Detecting whether content is generated by artificial intelligence (AI) has been a significant problem. Unfortunately, to our knowledge, there is currently no existing research focused on AI-generated patent text detection, nor are there any datasets tailored for patents publicly available. In this paper, to explore the differences between AI-generated and human-written patent texts, we generate a set of patent abstract texts by ChatGPT, in Chinese and English, from granted patent claims. Each generated patent abstract text corresponds to its original patent abstract. We analyze the linguistic characteristics of two types of patent texts by various comparison experiments. We anticipate that our work can assist people in identifying the patents generated by AI from the ocean of patents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Engineering education

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QUERT: Continual Pre-training of Language Model for Query Understanding in Travel Domain Search

arXiv

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arXiv 2023年

作者： Xie, Jian Liang, Yidan Liu, Jingping Xiao, Yanghua Wu, Baohua Ni, Shenghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Alibaba Group Hangzhou China School of Information Science and Engineering East China University of Science and Technology Shanghai China

In light of the success of the pre-trained language models (PLMs), continual pre-training of generic PLMs has been the paradigm of domain adaption. In this paper, we propose QUERT, A Continual Pre-trained Language Model for QUERy Understanding in Travel Domain Search. QUERT is jointly trained on four tailored pre-training tasks to the characteristics of query in travel domain search: Geography-aware Mask Prediction, Geohash Code Prediction, User Click Behavior Learning, and Phrase and Token Order Prediction. Performance improvement of downstream tasks and ablation experiment demonstrate the effectiveness of our proposed pre-training tasks. To be specific, the average performance of downstream tasks increases by 2.02% and 30.93% in supervised and unsupervised settings, respectively. To check on the improvement of QUERT to online business, we deploy QUERT and perform A/B testing on Fliggy APP. The feedback results show that QUERT increases the Unique Click-Through Rate and Page Click-Through Rate by 0.89% and 1.03% when applying QUERT as the encoder. Resources are available at https://***/hsaest/QUERT. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Improving Automatic Piano Transcription by Refined Feature Fusion and Weighted Loss 9th

Improving Automatic Piano Transcription by Refined Feature ...

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9th Conference on Sound and Music Technology, CSMT 2021

作者： Zhao, Jiahao Wu, Yulun Wen, Liang Ma, Lianhang Ruan, Linping Wang, Wantao Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China CETHIK Group Co. Ltd Hangzhou311199 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789811947025

Automatic Piano Transcription is to transcribe raw audio files into annotated piano rolls. In recent studies, jointly estimating pitch, onset, offset, and velocity of each note is commonly used. The previous state-of-the-art "Onsets and Frames" model chooses to concatenate the output of both onset and offset sub-tasks with extracted features to improve the frame-wise pitch detection, which is, however, low-efficiency in our opinion. In this paper, we proposed an improved piano transcription model by doing feature fusion and loss weighting. Our proposed model outperforms the baselines by a large margin. It also shows comparable performance with the state-of-the-art "High-Res PT" model in note metrics and outperforms it in frame metrics with an F1 score of 90.27%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Musical instruments

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HOTVCOM: Generating Buzzworthy Comments for Videos

arXiv

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arXiv 2024年

作者： Chen, Yuyan Qian, Yiwen Yan, Songzhou Jia, Jiyuan Li, Zhixu Xiao, Yanghua Li, Xiaobo Yang, Ming Guo, Qingpei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Arizona State University United States Ant Group China Southern University of Science and Technology China

In the era of social media video platforms, popular "hot-comments" play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or "danmaku" in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces HOTVCOM, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the ComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets. © 2024, CC BY.

关键词： Video analysis

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Real-time Energy Dispatching Strategy for Microgrid Based on Improved SD3 Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1338-1357页

作者： Hang, Tian-Xiao He, Wen-Wu Pei, Bo-Yu Lin, Mei-Li Li, Pei-Qiang School of Computer Science and Mathematics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China

Deep reinforcement learning algorithms has attracted attention in the field of energy management in recent years, which can effectively interact with the environment and learn relevant policies to achieve specific goals. To eliminate Q-value estimation bias, in this paper we propose a real-time energy dispatching strategy for microgrid based on SD3 algorithm, and introduce prioritized experience replay and Huber loss, to develop an improved algorithm, called SD3 with loss-adjusted prioritized experience replay (SD3-LAP). A grid-connected microgrid that includes photovoltaic system, wind turbine, micro-turbine, energy storage system and electrical load is built to simulate the environment and validate the proposed methods. The problem of real-time energy dispatching for microgrid is recast as a Markov Decision Process, which aims to minimize the operating costs under the condition that the constraints of power balance and the upper and lower limits of SOC of the energy storage system are satisfied. Experimental results show that compared with baseline models, the energy dispatching strategies provide by SD3 algorithm and SD3-LAP algorithm are more effective and robust, and the mechanism of LAP is a general purposed plug-in that can effectively improve the performance of related reinforcement learning algorithm. The daily average operating cost of SD3-LAP algorithm and SD3 algorithm are respectively 37.9% and 22.89% lower than TD3 algorithm, while the daily average operating cost of of TD3 algorithm is 21.9% lower than DQN. In addition, compared with the original algorithms, namely SD3, TD3 and DQN, the LAP mechanism reduces respectively the daily average operating cost by 19.5%, 11.7%, and 20.1%. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Reinforcement learning

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Temporal Knowledge Graph Reasoning with Dual Guiding Constraints 24

Temporal Knowledge Graph Reasoning with Dual Guiding Constra...

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24th IEEE International Conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Li, Jinpeng Jiao, Shanshan Guo, Shengming Luo, Xiangfeng School of Computer Engineering and Science Shanghai University Shanghai China Joint Operation College National Defense University Peking China Unit 93046 of the People's Liberation Army Shenyang China State Key Laboratory of Intelligent Game Peking China

ISBN: (纸本)9798350365658

Temporal knowledge graphs (TKGs) can store and model dynamic relational information. Recently, research on generative models has addressed the limitations of embedding methods in terms of generality and scalability. However, generative models still face two challenges in the context of TKGs: (1) Inability to effectively handle a large volume of contextual information;(2) Inability to constrain the rationality of generated results. To this end, we propose a new temporal graph reasoning framework, called the Dual Guiding constrained generative model (DuGen), which primarily consists of a context summarizer and a generative reasoner. The former generates crucial contextual information using question-guided prompts, while the latter employs a type decoder to infer the credibility of candidate entities. DuGen maintains the integrity and flexibility of individual model structures through external nesting. Experimental results demonstrate that DuGen outperforms state-of-the-art baseline techniques. © 2024 IEEE.

关键词： Knowledge graph

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BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation

BERTScore is Unfair: On Social Bias in Language Model-Based ...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Sun, Tianxiang He, Junliang Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

Automatic evaluation metrics are crucial to the development of generative systems. In recent years, pre-trained language model (PLM) based metrics, such as BERTScore (Zhang et al., 2020), have been commonly adopted in various generation tasks. However, it has been demonstrated that PLMs encode a range of stereotypical societal biases, leading to a concern on the fairness of PLMs as metrics. To that end, this work presents the first systematic study on the social bias in PLM-based metrics. We demonstrate that popular PLM-based metrics exhibit significantly higher social bias than traditional metrics on 6 sensitive attributes, namely race, gender, religion, physical appearance, age, and socioeconomic status. In-depth analysis suggests that choosing paradigms (matching, regression, or generation) of the metric has a greater impact on fairness than choosing PLMs. In addition, we develop debiasing adapters that are injected into PLM layers, mitigating bias in PLM-based metrics while retaining high performance for evaluating text generation. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Analyzing the Convergence of Federated Learning with Biased Client Participation 9th

Analyzing the Convergence of Federated Learning with Bias...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Tan, Lei Hu, Miao Zhou, Yipeng Wu, Di School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

ISBN: (纸本)9783031466632

Federated Learning (FL) is a promising decentralized machine learning framework that enables a massive number of clients (e.g., smartphones) to collaboratively train a global model over the Internet without sacrificing their privacy. Though FL’s efficacy in non-convex problems is proven, its convergence amidst biased client participation lacks theoretical study. In this paper, we analyze the convergence of FedAvg on non-convex problems, which is the most renowned FL algorithm. We assume even data distribution but non-IID among clients, and elucidate the convergence rate of FedAvg in situations characterized by biased client participation. Our analysis reveals that biased client participation can significantly reduce the precision of the FL model. We validate this through trace-driven experiments, demonstrating that unbiased client participation results in 11% to 50% higher test accuracy compared to extremely biased client participation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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