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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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Chinese image captioning via double decoding based on visual prompts 5

Chinese image captioning via double decoding based on visual...

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5th International Conference on computer Vision and Data Mining, ICCVDM 2024

作者： Tan, Weiguo Xiao, Yannan Wen, Yayuan Deng, Zhenrong Huang, Wenming School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510682863

The significant progress of vision-language pre-trained models (VLMs) and large language models (LLMs) have provided a feasible new mode for image captioning, which relies on VLMs to process images and then utilizes LLMs to generate captions, simplifying the caption generation process and making it lighter. Based on this mode, to address the issues of deviation between the generated captions and the content of image expressions, incomplete descriptive information, and resource hungry in previous Chinese image captioning models, we propose a Chinese image captioning model via double decoding based on Visual Prompts, DDVP. This model employs the CLIP model as the encoder, the GPT2 model as the decoder, and introduces Visual Prompts, which are keywords related to image content. The model adopts a double decoding approach, first decoding to generate Visual Prompts and then decoding to generate the final captions based on the Visual Prompts. Through evaluation, we have demonstrated that our model achieves competitive results on the AIC-ICC dataset, and while maintaining fluency, the generated captions of DDVP can also cover the information in the image more comprehensively and accurately. © 2024 SPIE.

关键词： Visual languages

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Combining Cache and Refresh to Optimize SSD Read Performance Scheme 15th

Combining Cache and Refresh to Optimize SSD Read Performance...

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15th International Symposium on Advanced Parallel processing Technologies, APPT 2023

作者： Chen, Jinli Li, Peixuan Xie, Ping College of Computer of Qinghai Normal University Xining810016 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China The Key Laboratory of Internet of Things of Qinghai Province Xining810016 China Academy of Plateau Science and Sustainability Xining810016 China

ISBN: (纸本)9789819978717

In the era of continuous advances in flash technology, the storage density of NAND flash memory is increasing, but the availability of data is declining. In order to improve data availability, low-density parity check codes (LDPC), which have been highly corrected in recent years, are used in flash memory. However, although LDPC can solve the problem of low data availability, it also brings the problem of long decoding time. Moreover, the LDPC decoding delay time is related to its decoding level, and the higher the LDPC decoding level, the longer the delay time. Long decoding delays can have an impact on the read performance of the flash. Therefore, in order to improve the speed of reading data in flash memory, this paper proposes a scheme combining cache and flushing, the main idea of which is to use the cache to reduce LDPC decoding time, and at the same time refresh the pages with high latency replaced by the cache, so that the pages with high latency can be restored to the state of low latency pages Experimental results show that this scheme can significantly reduce LDPC decoding delay and improve data availability with less overhead, and optimize the read performance of flash memory. The experimental results show that compared with the original strategy, the average response time is reduced by 24%, and the average IOPS value is increased by 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decoding

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Robust Electric Vehicle Routing Problem with Time Windows under Demand Uncertainty and Weight-Related Energy Consumption

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Complex System Modeling and Simulation 2022年第1期2卷 18-34页

作者： Yindong Shen Leqin Yu Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle routing problem with time windows(VRPTW)is a core combinatorial optimization problem in distribution *** electric vehicle routing problem with time windows under demand uncertainty and weight-related energy consumption is an extension of the *** some researchers have studied either the electric VRPTW with nonlinear energy consumption model or the impact of the uncertain customer demand on the conventional vehicles,the literature on the integration of uncertain demand and energy consumption of electric vehicles is still ***,practically,it is usually not feasible to ignore the uncertainty of customer demand and the weight-related energy consumption of electronic vehicles(EVs)in actual ***,we propose the robust optimization model based on a route-related uncertain set to tackle this ***,adaptive large neighbourhood search heuristic has been developed to solve the problem due to the NP-hard nature of the *** effectiveness of the method is verified by experiments,and the influence of uncertain demand and uncertain parameters on the solution is further explored.

关键词： electric vehicle routing problem time windows uncertain demand energy consumption model robust optimization adaptive large neighbourhood search

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Optimizing Client Selection in Federated Learning Base on Genetic Algorithm

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Cluster Computing 2025年第6期28卷 1-21页

作者： Wu, Jiagao Ji, Hongyan Yi, Jing Liu, Linfeng School of Computer Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Federated Learning (FL) significantly advances the field of distributed machine learning by allowing devices to collaboratively train models without the need to exchange sensitive local data, thus maintaining privacy and security. However, FL faces serious challenges in heterogeneous network environments where devices vary greatly in system resources (system heterogeneity) and data distributions (data heterogeneity), which impact the efficiency and effectiveness of the model training. To address these issues, we present FedCSGA, a novel FL client selection algorithm based on the genetic algorithm with adaptive genetic operators to maximize the number of selected clients within a training time deadline per round. Furthermore, we introduce the model accuracy into the fitness function of FedCSGA and enable the algorithm to handle the system and data heterogeneity simultaneously. Our comprehensive experiments demonstrate that the proposed FedCSGA algorithm can significantly outperform several state-of-the-art baselines and increase the number of clients selected per round by 45.6%, 54.3%, the model accuracy by 2.4%, 7.7% on average in both IID and non-IID settings, respectively.

关键词： client selection distributed machine learning Federated learning genetic algorithm heterogeneity

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An Anti-Noise Hybrid Clustering Oversampling Technique for Imbalanced Data Classification 5

An Anti-Noise Hybrid Clustering Oversampling Technique for I...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Pan, Xiaoying Feng, Congcong Liu, Chen Mu, Yuanzhen Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China

ISBN: (纸本)9781450396899

Oversampling techniques have always been favored in the field of imbalanced data classification. Noise processing plays an important role in the field of unbalanced data classification because the noise data directly affects the distribution of newly synthesized samples. We propose a new anti-noise hybrid clustering oversampling technique for imbalanced data classification to synthesize high-quality samples (ANCO). The algorithm is the first to propose a combination of noise filtering and optimization in an oversampling method. Firstly, based on the sample location information, the processing strategy of noise filtering and optimization is designed. Then, sample nearest neighbor interpolation is used to create a new sample. To compare the performance of our approach with representative oversampling, five datasets with variable imbalance rates in KEEL are chosen for testing. The results show that the ANCO algorithm improves the classifier's overall performance. © 2022 ACM.

关键词： Classification (of information)

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A Discrete Artificial Bee Colony Algorithm for Stochastic Vehicle Scheduling

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Complex System Modeling and Simulation 2022年第3期2卷 238-252页

作者： Yuanyuan Li Yindong Shen Jingpeng Li Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Division of Computer Science and Mathematics University of StirlingStirlingFK94LAUK

Vehicle scheduling plays a profound role in public ***,stochastic vehicle scheduling may lead to more robust *** solve the stochastic vehicle scheduling problem(SVSP),a discrete artificial bee colony algorithm(DABC)is *** to the discreteness of SVSP,in DABC,a new encoding and decoding scheme with small dimensions is designed,whilst an initialization rule and three neighborhood search schemes(i.e.,discrete scheme,heuristic scheme,and learnable scheme)are devised individually.A series of experiments demonstrate that the proposed DABC with any neighborhood search scheme is able to produce better schedules than the benchmark results and DABC with the heuristic scheme performs the best among the three proposed search schemes.

关键词： public transportation stochastic vehicle scheduling discrete artificial bee colony search scheme

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Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

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Frontiers of computer Science 2022年第6期16卷 103-121页

作者： Qingqing GAN Joseph K.LIU Xiaoming WANG Xingliang YUAN Shi-Feng SUN Daxin HUANG Cong ZUO Jianfeng WANG Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology/School of Cyber SecurityGuangdong University of Foreign StudiesGuangzhou510006China Department of Computer Science Jinan UniversityGuangzhou510632China Faculty of Information Technology Monash UniversityClayton3168Australia State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXi’an710071China

Searchable symmetric encryption(SSE)has been introduced for secure outsourcing the encrypted database to cloud storage,while maintaining searchable *** various SSE schemes,most of them assume the server is honest but curious,while the server may be trustless in the real *** a malicious server not honestly performing the queries,verifiable SSE(VSSE)schemes are constructed to ensure the verifiability of the search ***,existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during *** address this challenge,we present an efficient VSSE scheme,built on OXT protocol(Cash et al.,CRYPTO 2013),for conjunctive keyword queries with sublinear search *** proposed VSSE scheme is based on a privacy-preserving hash-based accumulator,by leveraging a well-established cryptographic primitive,Symmetric Hidden Vector Encryption(SHVE).Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations,thus greatly reducing the computational cost in the verification ***,the proposed VSSE scheme can still provide a proof when the search result is ***,the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.

关键词： searchable symmetric encryption verifiability conjunctive keyword queries hash-based accumulator cloud storage

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Ship Target Detection Method Based on Local Saliency Enhancement 5

Ship Target Detection Method Based on Local Saliency Enhance...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Li, Nana Pan, Xueli Yang, Lixia Huang, Zhixiang Wu, Zhenhua Zhang, Xianqi Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China East China Institute of Optoelectronic Integrated Devices Suzhou China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Due to the non-uniformity of ship target scattering, speckle noises and side lobes, synthetic aperture radar (SAR) ship target detection is facing great challenges. Therefore, to improve the detection performance in this scenario, the ship detection method based on the local saliency enhancement is proposed in this paper. Firstly, the proposed method improves the target-to-clutter ratio (TCR) by combining the relative differences of multi-features between ship target and the clutter background, and the threshold segmentation is performed based on the final local enhancement results. The experimental results show that the proposed method can suppress clutter background and speckle noises, improve the TCR and the algorithm has a high detection rate and a low false alarm rate in the complex background and multi-target marine environment. © 2022 IEEE.

关键词： Synthetic aperture radar

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Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network

arXiv

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arXiv 2024年

作者： Wang, Kunpeng Chen, Keke Li, Chenglong Tu, Zhengzheng Luo, Bin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University China Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University China

Alignment-free RGB-Thermal (RGB-T) salient object detection (SOD) aims to achieve robust performance in complex scenes by directly leveraging the complementary information from unaligned visible-thermal image pairs, without requiring manual alignment. However, the labor-intensive process of collecting and annotating image pairs limits the scale of existing benchmarks, hindering the advancement of alignment-free RGB-T SOD. In this paper, we construct a large-scale and high-diversity unaligned RGB-T SOD dataset named UVT20K, comprising 20,000 image pairs, 407 scenes, and 1256 object categories. All samples are collected from real-world scenarios with various challenges, such as low illumination, image clutter, complex salient objects, and so on. To support the exploration for further research, each sample in UVT20K is annotated with a comprehensive set of ground truths, including saliency masks, scribbles, boundaries, and challenge attributes. In addition, we propose a Progressive Correlation Network (PCNet), which models inter- and intra-modal correlations on the basis of explicit alignment to achieve accurate predictions in unaligned image pairs. Extensive experiments conducted on unaligned and aligned datasets demonstrate the effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

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