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arXiv 2025年

作者： Li, Yangning Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Zhao, Changyuan Niyato, Dusit College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

An expansion of Internet of Things (IoTs) has led to significant challenges in wireless data harvesting, dissemination, and energy management due to the massive volumes of data generated by IoT devices. These challenges are exacerbated by data redundancy arising from spatial and temporal correlations. To address these issues, this paper proposes a novel data-driven collaborative beamforming (CB)-based communication framework for IoT networks. Specifically, the framework integrates CB with an overlap-based multi-hop routing protocol (OMRP) to enhance data transmission efficiency while mitigating energy consumption and addressing hot spot issues in remotely deployed IoT networks. Based on the data aggregation to a specific node by OMRP, we formulate a node selection problem for the CB stage, with the objective of optimizing uplink transmission energy consumption. Given the complexity of the problem, we introduce a softmax-based proximal policy optimization with long-short-term memory (SoftPPO-LSTM) algorithm to intelligently select CB nodes for improving transmission efficiency. Simulation results show that the proposed OMRP improves network lifetime by 17% compared to benchmark routing protocols, while the SoftPPO-LSTM method for CB node selection achieves an 8.3% increase in throughput over benchmark algorithms. The results also reveal that the combined OMRP with the SoftPPO- Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Injecting Revenue-awareness into Cold-start Recommendation: The Case of Online Insurance 29

Injecting Revenue-awareness into Cold-start Recommendation: ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Li, Yu Zhang, Yi Chang, Helen He Li, Qiang Jilin University College of Computer Science and Technology China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China WeSure Inc. Ping An Technology Shenzhen Co. Ltd China

ISBN: (纸本)9798350330717

In online insurance, one of the central challenges is the cold-starting of new insurance products, which means there are no previous samples to refer to. Previous studies have mainly focused on improving the prediction accuracy of new items, but they have failed to consider the revenue generated by existing items. As a result, the total revenue may suffer a loss even if new items get conversions. To address this issue, in this paper we propose RACRec, a Revenue-Aware Cold-start Recommendation framework for online insurance. Unlike previous works, RACRec uses a revenue-based objective function to ensure profitability when cold-starting new items. With this dedicated objective, RACRec orchestrates the cold-start and warm-start model by predicting their expected revenue from each user, thereby preserving the total profit. In order to accurately predict revenue, a double-ranking scheme is designed for the warm-start model to mitigate position bias, while an item embedding alignment algorithm is proposed for the cold-start model to learn the revenue of new items from similar old items. Furthermore, a reinforced orchestrate update scheme is designed to eliminate the impact of sparse conversions and continuously update revenue estimation. Extensive offline and online experiments have been conducted to validate the effectiveness of RACRec. © 2023 IEEE.

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Multi-Target Track Initiation in Heavy Clutter

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Computers, Materials & Continua 2022年第9期72卷 4489-4507页

作者： Li Xu Ruzhen Lou Chuanbin Zhang Bo Lang Weiyue Ding College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Norinco Group Air Ammunition Research Institute Co.Ltd. Harbin150036China Harvard Medical School Boston02115USA

In the heavy clutter environment, the information capacity is large,the relationships among information are complicated, and track initiationoften has a high false alarm rate or missing alarm rate. Obviously, it is adifficult task to get a high-quality track initiation in the limited measurementcycles. This paper studies the multi-target track initiation in heavy *** first, a relaxed logic-based clutter filter algorithm is presented. In thealgorithm, the raw measurement is filtered by using the relaxed logic *** not only design a kind of incremental and adaptive filtering gate, but alsoadd the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with highdetection rate and less clutter. Then, we propose a fuzzy sequential Houghtransform-based track initiation algorithm. The algorithm establishes a newmeshing rule according to system noise to balance the relationship between thegrid granularity and the track initiation quality. And a flexible superpositionmatrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough *** addition, the algorithm allows the superposition matrixes of nonadjacentcycles to be associated to overcome the shortcoming that the track can’t beinitiated in time when the measurements appear in an intermittent way. Anda slope verification method is introduced to detect formation-intensive serialtracks. Last, the sliding window method is employed to feedback the trackinitiation results timely and confirm the track. Simulation results verify thatthe proposed algorithms can initiate the tracks accurately in heavy clutter.

关键词： Track initiation heavy clutter multi-target Hough transform

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Privacy-Preserving Average Consensus for Directed Graphs via State Decomposition and External Input

Privacy-Preserving Average Consensus for Directed Graphs via...

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第43届中国控制会议

作者： Jimin Wang Mengjie Xu School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper investigates the privacy-preserving average consensus problem for directed graphs.A privacy-preserving method is designed based on the state decomposition and external *** node arbitrarily decomposes its initial state into two substates,one of the substates is exchanged with neighbors and the other substate is retained to prevent leakage of initial state *** reduce the communication burdens,a new method instead of the push-sum algorithm is provided by introducing the external *** on this,the average consensus is achieved for the strongly connected graph.A privacy parameter during the decomposition process is designed to meet the different privacy needs of different ***,we show that this algorithm can effectively protect the privacy of the node's initial state,and meet the different privacy needs of different ***,the validity of the results is verified through a numerical example.

关键词： Average consensus privacy-preserving sate decomposition external input

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Multivariate Time Series Co-evolution Shapelet Learning Method 6

Multivariate Time Series Co-evolution Shapelet Learning Meth...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Wang, Ling Chu, Zhongkun Liu, Wei School of Automation and Electrical Engineering University of Science and Technology of Beijing Key Laboratory of Knowledge Automation for Industrial Process Ministry of Education Beijing China Shunde Innovation School University of Science and Technology Beijing Guangdong528399 China

ISBN: (纸本)9798400709241

Shapelets are discriminative subsequences in time series that effectively capture local shape characteristics. However, the time complexity of the existing shapelet extraction process remains high. To address this issue, this paper proposes an unsupervised shapelet extraction algorithm. The algorithm utilizes the coherence of multiple wavelets and constraints on multivariate collaborative evolution intervals to constrain the generation range of shapelets, thereby improving the efficiency of shapelet learning while preserving their significance. The superiority of the proposed algorithm is validated through experiments conducted on the UEA dataset. © 2023 ACM.

关键词： Extraction

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Unsupervised Statistical Text Simplification

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IEEE Transactions on knowledge and data engineering 2021年第4期33卷 1802-1806页

作者： Qiang, Jipeng Wu, Xindong Department of Computer Science Yangzhou University Yangzhou Jiangsu225127 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui10084 China

Most recent approaches for Text Simplification (TS) have drawn on insights from machine translation to learn simplification rewrites from the monolingual parallel corpus of complex and simple sentences, yet their effectiveness strongly relies on large amounts of parallel sentences. However, there has been a serious problem haunting TS for decades, that is, the availability of parallel TS corpora is scarce or not fit for the learning task. In this paper, we will focus on one especially useful and challenging problem of unsupervised TS without a single parallel sentence. To the best of our knowledge, we present the first unsupervised text simplification system based on phrase-based machine translation system, which leverages a careful initialization of phrase tables and language models. On the widely used WikiLarge and WikiSmall benchmarks, our system respectively obtains 39.08 and 25.12 SARI points, even outperforms some supervised baselines. © 1989-2012 IEEE.

关键词： Machine translation

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Design and implementation of fire alarm module for smart home

Design and implementation of fire alarm module for smart hom...

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2023 International Conference on Electronic Information engineering and data Processing, EIEDP 2023

作者： Liu, Xiaohong Yan, Kai College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9781510666160

With the continuous development of information technology, artificial intelligence fire automatic alarm system is gradually developed. However, the current system is based on a single sensor for identification, when a fire still uses manual active fire extinguishing, low efficiency in handling fire, high failure rate, poor system scalability, poor anti-interference ability, high false alarm rate. At the same time, the recognition accuracy is low, the processing speed and intelligence are also insufficient. Therefore. To address the above problems, this study designs a multi-sensor artificial intelligence fire prevention system using smoke and temperature sensors. The fire prevention system can send alarm signals at the earliest stage of fire and can intelligently extinguish the fire, so that the disaster can be controlled and extinguished by taking measures in advance. Meanwhile, the system can continue to work even if one of the sensors is not working properly. © 2023 SPIE.

关键词： Fires

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Hierarchical Flocking Based on a Strictly Metric-Free Model with Restricted Visual Field 5th

Hierarchical Flocking Based on a Strictly Metric-Free Mode...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Zhang, Lingwei Sun, Wenwei Jia, Yongnan Zhang, Weilin Han, Jiali Li, Qing School of Automation and Electrical Engineering Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education University of Science and Technology Beijing Beijing100083 China Shunde Graduate School University of Science and Technology Beijing528399 China

ISBN: (纸本)9789811939976

The biological swarm model based on the limited visual field is necessary to be discussed due to the experimental evidence that the vision of a single individual in biological swarms cannot fully cover the environment. Besides, studies also have found that the leader-follower hierarchical mechanism exists widely in biological swarms. Therefore, we presented a strictly metric-free model with restricted visual field and hierarchical structure in this paper to explore the impact of hierarchical mechanisms on the emergence of swarm intelligence. Numerical simulation results preliminarily indicate that the leader-preferred coefficient can accelerate the emergence process of the collective behaviors of these self-propelled particles but has no concern with their synchronization behaviors. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vision

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TraSculptor: Visual Analytics for Enhanced Decision-Making in Road Traffic Planning

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IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Deng, Zikun Liu, Yuanbang Zhu, Mingrui Xiang, Da Yu, Haiyue Su, Zicheng Lu, Qinglong Schreck, Tobias Cai, Yi South China University of Technology School of Software Engineering China South China University of Technology Ministry of Education Key Laboratory of Big Data and Intelligent Robot China Tongji University Key Laboratory of Road and Traffic Engineering of the Ministry of Education China National University of Singapore Department of Civil and Environmental Engineering Singapore Graz University of Technology Austria

The design of urban road networks significantly influences traffic conditions, underscoring the importance of informed traffic planning. Traffic planning experts rely on specialized platforms to simulate traffic systems, assessing the efficacy of the road network across various states of modifications. Nevertheless, a prevailing issue persists: many existing traffic planning platforms exhibit inefficiencies in flexibly interacting with the road network's structure and attributes and intuitively comparing multiple states during the iterative planning process. This paper introduces TraSculptor, an interactive planning decision-making system. To develop TraSculptor, we identify and address two challenges: interactive modification of road networks and intuitive comparison of multiple network states. For the first challenge, we establish flexible interactions to enable experts to easily and directly modify the road network on the map. For the second challenge, we design a comparison view with a history tree of multiple states and a road-state matrix to facilitate intuitive comparison of road network states. To evaluate TraSculptor, we provided a usage scenario where the Braess's paradox was showcased, invited experts to perform a case study on the Sioux Falls network, and collected expert feedback through interviews. © 1995-2012 IEEE.

关键词： Visual analytics

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Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale dataset 41

Diving into Underwater: Segment Anything Model Guided Underw...

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41st International Conference on Machine Learning, ICML 2024

作者： Lian, Shijie Zhang, Ziyi Li, Hua Li, Wenjie Yang, Laurence Tianruo Kwong, Sam Cong, Runmin Hainan University China China Ministry of Education China Huazhong University of Science and Technology China St. Francis Xavier University Canada LIngnan University School of Data Science Hong Kong Shandong University School of Control Science and Engineering China 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. Copyright 2024 by the author(s)

关键词： Pixels

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