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Proceedings of the 41st International Conference on Machine Learning

作者： Shijie Lian Ziyi Zhang Hua Li Wenjie Li Laurence Tianruo Yang Sam Kwong Runmin Cong Hainan University The Hong Kong University of Science and Technology (Guangzhou) Hainan University and Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications (Southeast University) Ministry of Education China Hainan University and Huazhong University of Science and Technology and St. Francis Xavier University LIngnan University School of data science Hong Kong Shandong University School of Control Science and Engineering and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. datasets and codes are released on Github.

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Demand Response Scheme: Achieving Sustainability in Distributed data Processing

Demand Response Scheme: Achieving Sustainability in Distribu...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Xueying Zhai Yunfeng Peng Xiuping Guo Liang Zhang Wei Zhang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Guizhou Key Laboratory of Electric Power Big Data Guizhou Institute of Technology Guizhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization.

关键词： Renewable energy sources Energy consumption Costs Electricity Simulation Carbon dioxide Spatiotemporal phenomena Internet of Things Sustainable development Optimization

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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ERC DMSP: Emotion Recognition in Conversation Based on Dynamic Modeling of Speaker Personalities

ERC DMSP: Emotion Recognition in Conversation Based on Dynam...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoyang Li Zhenyu Yang Zhijun Li Yiwen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Emotion Recognition in Conversations (ERC) is a widely applicable task, whether in emotional chatbots or recommender systems for dialog scenarios. The ability of systems to accurately identify human emotions is a crucial component in ERC success. In everyday conversations, a speaker’s emotional expression is closely tied to their individual characteristics and personality traits. Furthermore, individuals exhibit significant variations in their expressions of the same emotion. Traditional methods for ERC often overlook these personality differences and dynamic changes, leading to unreliable results. To address this issue, we propose a conversation emotion recognition model based on dynamic modeling of speaker’s personality (ERC DMSP). In our approach, to extract a speaker’s personality profile, we design a personality capture module to model the personalities of individuals based on their historical utterances. Moreover, a bidirectional influence exists between a speaker’s personality tendencies and their linguistic behavior. The speaker’s personality profile is thus continually updated based on the ongoing dialog. Additionally, since the personalities of other speakers in the conversation can also affect the form and intensity of a speaker’s emotional expression, we construct a conversation emotion representation utilizing group personality traits. We then comprehensively incorporate the speaker’s personality into the emotion representation of each utterance at both the group and individual levels through the personality engagement module. Throughout this process, the representation of dialog and the speaker’s personality undergo hierarchical updates, leading to more accurate conversation emotion recognition. We conducted extensive experiments on four commonly used public benchmark datasets for ERC to evaluate the effectiveness of our proposed ERC DMSP model. The results demonstrate its efficacy, as it exhibits a significant improvement over other methods in popular

关键词： Measurement Emotion recognition Accuracy Codes Neural networks Oral communication Color

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Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation

arXiv

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arXiv 2022年

作者： Chen, Xiaoyu Zhong, Han Yang, Zhuoran Wang, Zhaoran Wang, Liwei Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China Center for Data Science Peking University China Peng Cheng Laboratory China Department of Statistics and Data Science Yale University United States Department of Industrial Engineering and Management Sciences Northwestern University United States

We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, the agent only receives preferences over trajectory pairs from a human overseer. The goal of the agent is to learn the optimal policy which is most preferred by the human overseer. Despite the empirical successes, the theoretical understanding of preference-based RL (PbRL) is only limited to the tabular case. In this paper, we propose the first optimistic model-based algorithm for PbRL with general function approximation, which estimates the model using value-targeted regression and calculates the exploratory policies by solving an optimistic planning problem. Our_ algorithm achieves the regret of Õ(poly(dH)√K), where d is the complexity measure of the transition and preference model depending on the Eluder dimension and log-covering numbers, H is the planning horizon, K is the number of episodes, and Õ(·) omits logarithmic terms. Our lower bound indicates that our algorithm is near-optimal when specialized to the linear setting. Furthermore, we extend the PbRL problem by formulating a novel problem called RL with n-wise comparisons, and provide the first sample-efficient algorithm for this new setting. To the best of our knowledge, this is the first theoretical result for PbRL with (general) function approximation. Copyright © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms

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Short-term Portfolio Optimization using Doubly Regularized Exponential Growth Rate

Short-term Portfolio Optimization using Doubly Regularized E...

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International Conference on Computer Supported Cooperative Work in Design

作者： Quanlong Guan Jinneng He Zhao-Rong Lai Yuyu Zhou Quming Jiang Ziliang Chen College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China Guangdong-Macao Advanced Intelligent Computing Joint Laboratory Zhuhai China Chutian Dragon Co. Ltd Dongguan China School of Intelligent Systems Science and Engineering Jinan University China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the realm of short-term portfolio optimization, the integration of machine learning with exponential growth rate techniques is gaining prominence. This paper introduces a novel approach for short-term portfolio optimization, termed Short-term Portfolio Optimization using Doubly Regularized EGR (SPODR), to address the challenges posed by limited data availability. SPODR utilizes radial basis functions for the effective identification of market trends, enabling improved stock market forecasts. The approach uniquely combines ℓ 1 and ℓ 2 -regularization, adhering to empirical financial principles, to strike a balance between risk and return in short-term portfolios. A key aspect of SPODR is addressing the complexity of its ElasticNet-like objective, which poses a challenge for traditional methods due to its online learning nature. To overcome this, we have developed an algorithm based on the log barrier interior-point method. This algorithm is adept at efficiently optimizing portfolio allocation, taking into account the specific constraints inherent in our approach. Extensive comparative experiments across five benchmark datasets demonstrate that SPODR significantly outperforms existing short-term portfolio optimization models. It achieves a right balance between return and risk. Furthermore, SPODR showcases efficient computational speed, enhancing its applicability in real-world financial settings.

关键词： Computational modeling Benchmark testing Market research Robustness Regulation Computational efficiency Resource management

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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N-Doped Carbon-Stabilized Pdnicu Nanoparticles Derived from Pdni@Cu-Zif-8 as Highly Active and Durable Electrocatalyst for Boosting Ethanol Oxidation

SSRN

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SSRN 2023年

作者： Chen, Yizhe Yang, Jian Ma, Yanyun Tang, Junping Zhao, Xueling Chen, Cheng Wang, Lijun Zhang, Binwei Zhou, Xinwen Sun, Shigang Lin, Donghai School of Energy and Materials Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional Materials Shanghai Key Laboratory of Engineering Materials Application and Evaluation Shanghai Polytechnic University Shanghai201209 China State Key Laboratory of Physical Chemistry of Solid Surfaces Department of Chemistry College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China School of Chemistry and Chemical Engineering Chongqing University Chongqing401331 China Center of Advanced Energy Technology and Electrochemistry Institute of Advanced Interdisciplinary Studies Chongqing University Chongqing400044 China College of Biological and Pharmaceutical Sciences China Three Gorges University Yichang443002 China

Controlling the component, tailoring the size and designing the carbon supporting materials are three promising strategies to improve electrocatalytic activity. Herein, a monatomic Cu carrier (CuCN) with larger specific surface area was used to anchor PdNi alloy nanoparticles by a facile one-step reduction method. Physical characterization revealed that the PdNi nanoparticles in the synthesized material possess good dispersion, which is mainly attributed to the high specific surface area of CuCN-900. Electrochemical results revealed that CuCN supported PdNi nanoparticles (Pd1Ni1/CuCN-900) nanohybrids had superior durability and catalytic activity for the ethanol oxidation (EOR) compared with other PdxNiy/CuCN and commercial Pd/C. Meanwhile, the rotating disk electrode (RDE) results also further demonstrated that Pd1Ni1/CuCN-900 has a faster kinetic process of ethanol oxidation compared to commercial Pd/C. DFT calculation implies that OH adsorption is preferentially near the Ni atom, the OH species on Ni1Pd1 surface play an important role in the process of EOR. This is of great research implications for finding new carbon carriers and regulating catalyst components to improve the catalytic efficiency of PdNi catalysts. © 2023, The Authors. All rights reserved.

关键词： Nanoparticles

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Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation

arXiv

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arXiv 2024年

作者： Liu, Kang Ma, Zhuoqi Kang, Xiaolu Zhong, Zhusi Jiao, Zhicheng Baird, Grayson Bai, Harrison Miao, Qiguang School of Computer Science and Technology Xidian University Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Vision Xi’an China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an China Warren Alpert Medical School Brown University Providence United States School of Electronic Engineering Xidian University Xi’an China Department of Radiology and Radiological Sciences Johns Hopkins University School of Medicine Baltimore United States

The automated generation of imaging reports proves invaluable in alleviating the workload of radiologists. A clinically applicable reports generation algorithm should demonstrate its effectiveness in producing reports that accurately describe radiology findings and attend to patient-specific indications. In this paper, we introduce a novel method, Structural Entities extraction and patient indications Incorporation (SEI) for chest X-ray report generation. Specifically, we employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports and improve the quality of factual entity sequences. This reduces the noise in the following cross-modal alignment module by aligning X-ray images with factual entity sequences in reports, thereby enhancing the precision of cross-modal alignment and further aiding the model in gradient-free retrieval of similar historical cases. Subsequently, we propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications. This process allows the text decoder to attend to discriminative features of X-ray images, assimilate historical diagnostic information from similar cases, and understand the examination intention of patients. This, in turn, assists in triggering the text decoder to produce high-quality reports. Experiments conducted on MIMIC-CXR validate the superiority of SEI over state-of-the-art approaches on both natural language generation and clinical efficacy metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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Randomized algorithms for computing the tensor train approximation and their applications

arXiv

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arXiv 2024年

作者： Che, Maolin Wei, Yimin Yan, Hong School of Mathematics and Statistics Guizhou University Guizhou Guiyang550025 China Center for Intelligent Multidimensional Data Analysis Hong Science Park Shatin Hong Kong School of Mathematical Sciences Key Laboratory of Mathematics for Nonlinear Sciences Fudan University Shanghai200433 China Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong

In this paper, we focus on the fixed TT-rank and precision problems of finding an approximation of the tensor train (TT) decomposition of a tensor. Note that the TT-SVD and TT-cross are two well-known algorithms for these two problems. Firstly, by combining the random projection technique with the power scheme, we obtain two types of randomized algorithms for the fixed TT-rank problem. Secondly, by using the non-asymptotic theory of sub-random Gaussian matrices, we derive the upper bounds of the proposed randomized algorithms. Thirdly, we deduce a new deterministic strategy to estimate the desired TT-rank with a given tolerance and another adaptive randomized algorithm that finds a low TT-rank representation satisfying a given tolerance, and is beneficial when the target TT-rank is not known in advance. We finally illustrate the accuracy of the proposed algorithms via some test tensors from synthetic and real databases. In particular, for the fixed TT-rank problem, the proposed algorithms can be several times faster than the TT-SVD, and the accuracy of the proposed algorithms and the TT-SVD are comparable for several test *** Codes 15A18, 15A69, 65F55, 68W20 Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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