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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Chen Shi, Fan Cheng, Xu School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350368741

4D light field imaging captures rich spatial-angular information, providing essential geometric cues for semantic segmentation tasks. In this paper, we introduce a novel backbone network called the Light Field Extraction Interaction Network (LFEI-Net). LFEI-Net excels in extracting global structures and multi-scale spatial-angular features, capturing feature dependencies through channel modeling and diverse feature interactions. Unlike traditional methods that depend on pyramid and dilated feature extraction, LFEI-Net pioneers an efficient method by integrating large-scale horizontal depth-wise convolution (HDWC) and vertical depth-wise convolution (VDWC) with interactive operations for comprehensive spatial multi-scale feature extraction. Furthermore, we present the Multi-Angular Modeling (MAM) module, which effectively captures scene angle variations from multiple perspectives and precisely delineates object boundaries, thereby improving model adaptability. Our experimental evaluations on two datasets demonstrate that LFEI-Net significantly outperforms state-of-the-art (SOTA) 2D and 4D light field semantic segmentation methods, achieving mean Intersection over Union (mIoU) of 83.72% and 86.88%, respectively. © 2025 IEEE.

关键词： LFEI-Net light field imaging MAM module semantic segmentation

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An AI Planning Approach to Supply Chain Logistics Planning and Scheduling 6

An AI Planning Approach to Supply Chain Logistics Planning a...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Tu, Mingjun Liang, Ruishi Zheng, Fanghao University of Electronic Science and Technology of China School of Computer Science and Engineering ChengDu China University of Electronic Science and Technology of China Zhongshan Institute School of Computer Science Zhongshan China

ISBN: (纸本)9798350386974

Fuelled by rapid advancements in logistics technology and the internet, the transaction volume in the food industry continues to soar, accompanied by escalating consumer expectations regarding the freshness and safety of various foods, notably perishable produce, chilled products, and pre-prepared dishes. As the key step ensuring the freshness and safety of food, efficient planning and optimization of supply chain logistics have become increasingly vital. This paper introduces an AI planning model for supply chain logistics planning and scheduling. Firstly, it extracts knowledge from the supply chain logistics process, with particular emphasis on controlling cargo damage and costs induced by temperature fluctuations in cold chain logistics. Secondly, incorporating elements of numerical and preference planning, a dual-objective optimization model is established, aiming at concurrently minimizing costs and reducing cargo losses during planning. Formulated using PDDL (Planning Domain Definition Language), this model is transformed into an AI-planning task. Ultimately, the model's effectiveness is demonstrated through scenario-based resolution by planning algorithms, showcasing its capability to make optimized decisions tailored to diverse objectives. It adeptly balances cost reduction with cargo damage mitigation, thereby enhancing customer satisfaction and furnishing invaluable guidance for supply chain logistics planning and scheduling. © 2024 IEEE.

关键词： Customer satisfaction

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A Hybrid Model Combining Vision Transformer and Generative Pre-trained Transformer for Image Captioning

A Hybrid Model Combining Vision Transformer and Generative P...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Hina Vishwesh, Jatangi Y D M V Prasada Jagadeesh, M. Sucharitha, M. Pradyothan, Vitta Sai Bennett University School of Computer Science Engineering & Technology Noida India

ISBN: (纸本)9798331508685

Creating descriptive captions for images is now becoming a mission-critical application area in the intersection of natural language processing and computer vision. This work provides the hybrid model VisionGPT2, combining Vision Transformer for encoding images and GPT2 for text generation, which enables accurate, coherent, and context-aware image descriptions. The model is tested over a dataset by using BLEU score metrics and has achieved a high BLEU score of 0.4877, which ascertains the strength of cross-modal architectures for image captioning. In this paper, the design and training process of VisionGPT2 has been discussed along with the benefits and challenges faced while proceeding and the potential applications the model has gained in performance for future use. © 2025 IEEE.

关键词： computer vision

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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1571-1583页

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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Research on patent classification technology based on deep learning 5

Research on patent classification technology based on deep l...

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5th International Conference on Telecommunications, Optics, and computer science, TOCS 2024

作者： Kan, Bowen Fei, Rong Zhang, Facun Peng, Hailong School of Computer Science and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

With the rapid development of natural language processing technologies in computer science, machine learning models have demonstrated remarkable capabilities in handling complex textual data. However, traditional machine learning approaches often underperform in patent classification due to the texts' intricate semantics and cross-domain technical content. Therefore, this paper applies deep learning techniques to the automatic classification of patent texts, aiming to effectively improve classification performance. We use pre-trained language models like Word2Vec for the vector representation of patent texts and introduce models such as attention mechanisms, Gated Recurrent Unit (GRU) networks, and Convolutional Neural Networks (CNNs) to automatically learn and extract the deep semantic features of patent texts. We have constructed five deep learning-based patent classification models and obtained full-text data of patents from seven categories covering multiple domains using web crawlers and built a high-quality corpus based on these data. Experimental data indicates that deep learning methods achieve substantially higher performance metrics compared to traditional machine learning models. © 2025 SPIE.

关键词： Convolutional neural networks

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Promising Multi-Granularity Linguistic Steganography by Jointing Syntactic and Lexical Manipulations 39

Promising Multi-Granularity Linguistic Steganography by Join...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ou, Chengfu Xiang, Lingyun Liu, Yangfan School of Computer and Communication Engineering Changsha University of Science and Technology China

ISBN: (纸本)157735897X

Existing modification-based linguistic steganography methods primarily perform linguistic manipulations within a single embedding space to conceal secret information. However, these methods are stringently constrained by the original semantics of the cover text, making it struggle to achieve a satisfactory embedding capacity in a single embedding space. In this paper, we propose a novel Multi-granularity Modification-based Linguistic Steganography framework (MMLS) that hides secret information in both syntactic space and symbolic space, enhancing syntactic naturalness and semantic coherence while further increasing embedding capacity. Specifically, MMLS utilizes a paraphrase generation model to automatically modify the syntactic structure of the given original sentence, which enables the generation of paraphrases and the preservation of semantics simultaneously. Moreover, MMLS employs a distance-aware syntactic bins coding strategy to embed part of secret information into the syntactic space. This strategy utilizes a cluster-based way to partition the implicit syntactic space into a finite number of separate zones, thus increasing the number of candidate paraphrases and avoiding the selection of semantically distorted steganographic texts. Finally, the pre-trained BERT is used to replace some words in candidate paraphrases with their synonyms. Such a design embeds the remaining secret information into symbolic space while ensuring syntactic and semantic naturalness. Experimental results demonstrate that MMLS significantly outperforms existing methods in terms of semantic coherence, embedding capacity, and security. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Syntactics

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Opponent Modeling Based Dynamic Auction for UAV-Assisted Task Offloading 16

Opponent Modeling Based Dynamic Auction for UAV-Assisted Tas...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wang, Shuyu Wang, Zhe Shi, Long Li, Bin Li, Jun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350390643

This paper proposes a dynamic auction based resource trading scheme for unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network, where multiple mobile users (MUs) compete to purchase the task offloading service from the UAV by bidding strategically. The UAV-assisted MEC server aims to maximize its long-term profit at the least energy costs by adaptively adjusting its service location via path planning. With dynamic task arrivals, each MU optimizes its sequential bidding strategy to minimize its task computation delay and payment under the budget constraint. Since each MU's remaining budget is a private information, we model this dynamic auction as a partially observable stochastic game, where the MU with the highest bid and sufficient remaining budget in each round wins the UAV's exclusive computation service and pays the second-highest price to the UAV. To tackle the issue of non-stationarity in a multi-agent environment, we propose an opponent modeling based reinforcement learning algorithm, namely neural fictitious self-play with dueling double deep Q Network (NFSP-D3QN), to optimize the trading policies for both the UAV and MUs. Specifically, the proposed algorithm incorporates fictitious self-play to implicitly predict the opponents' behavior, assisting each agent's sequential decision making in a fully decentralized manner. Simulation results demonstrate that, compared with the baseline algorithm, our proposed algorithm enhances both the UAV's and MUs' utilities, achieving efficient utilization of computing resources. © 2024 IEEE.

关键词： Stochastic systems

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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of computer science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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