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Multimodal Summarization with Modality-Aware Fusion and Summarization Ranking 24th

Multimodal Summarization with Modality-Aware Fusion and Su...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Xuming Chaomurilige Liu, Zheng Luo, Haoyu Dong, Jun Luo, Yingzhe Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China Faculty of Data Science City University of Macau Macau China

ISBN: (纸本)9789819615278

Existing multimodal summarization methods primarily focus on multimodal fusion to efficiently utilize the visual information for summarization. However, they fail to exploit the deep interaction between textual and visual modality. Moreover, optimizing the model by maximum likelihood estimation (MLE) leads to exposure bias, causing the model to generate the next word that is based on the previously generated erroneous words during inference. To address these challenges, we propose a novel modality-aware fusion module (MAF) with a summarization ranking (SumR) training objective. Specifically, the MAF module exploits the interaction in the multimodal input through multiple fusion layers, and SumR aims to align the probability order predicted by the model with actual quality metrics, therefore it is able to reduce the exposure bias problem during inference. Extensive experiments on a large-scale dataset demonstrate that our method outperforms existing models, achieving superior results in both automatic and human evaluation metrics. the generated multimodal summaries provide richer context and enhance user’s comprehension by combining the key textual information with the relevant visual content. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Maximum likelihood estimation

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Enhancing Text-Image Person Re-identification via Intra-Class Relevance Learning 24th

Enhancing Text-Image Person Re-identification via Intra-Cla...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： He, Wenbin Gao, Yutong Liu, Wenjian Chaomurilige Liu, Zheng Guo, Ao Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China City University of Macau China

ISBN: (纸本)9789819615278

Text-Image Person Re-Identification (TIReID) is a computer vision task that involves identifying person in images or videos based on textual descriptions. Current works mainly employ Vision Language Pretrained (VLP) models for cross-modal alignment and to reestablish text-image fine-grained associations for re-identification. However, these methods neglect the variations in intra-class sample features, hindering accurate cross-modal alignment and discrimination of fine-grained features for the same person. In this paper, we propose a Cross-modal Intra-Class Learning (CICL) framework to enhance the model’s capability of learning fine-grained cross-modal features and intra-class sample variations. We propose an Image-Text Intra-class Relevance Learning (ITRL) method that considers sample relevance during text-image matching, boosting re-identification capability while maintaining fine-grained text-image alignment. Meanwhile, we propose a Bidirectional Masked Matching (BiMM) method that introduces masks to images and texts, prompting the model to attend to different content regions and establish finer-grained cross-modal associations. We test our approach using the CLIP model on three TIReID benchmarks, achieving results that surpass state-of-the-art performance on multiple metrics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image matching

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NPB-PSTL: C++ STL algorithms with parallel Execution Policies in NAS parallel Benchmarks

NPB-PSTL: C++ STL Algorithms with Parallel Execution Policie...

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33rd Euromicro international conference on parallel, Distributed, and Network-Based processing, PDP 2025

作者： Loff, Junior Hoffmann, Renato B. Bianchessi, Arthur S. Mallmann, Leonardo Griebler, Dalvan Binder, Walter Faculty of Informatics Lugano Switzerland Porto Alegre Brazil

ISBN: (纸本)9798331524937

the C++ language continually evolves through formal specifications established by its standards committee, proposing new features to maintain $\mathrm{C}++$ as a relevant programming language while improving usability, performance, and portability across platforms. With the addition of parallel Standard Template Library (STL) algorithms in C++17, programmers can now leverage parallel processing capabilities via vendor-neutral parallel execution policies. this study presents an adaptation of the NAS parallel Benchmarks (NPB) - a well-established suite of applications for evaluating parallel architectures-by porting its sequential C-style code to use C++ STL abstractions and performance-portable parallelism features. Our goals are to (1) assess the suitability of C++ STL for scientific applications like the ones in the NPB and (2) provide a comparative performance and portability of STL algorithms' parallel execution policies across different multicore architectures (x86 and AArch64). Results indicate that the performance of parallel STL algorithms is often close to that of optimized handwritten versions (OpenMP, Intel TBB, and FastFlow) on different architectures, with notable shortfalls. Across all NPB benchmarks, the STL algorithms' geometric mean shows sequential execution times that are between 3.76% and $\mathrm{6. 9 \%}$ higher, while parallel executions may reach a geometric mean of up to $\mathrm{2 1. 2 1 \%}$ higher execution time. © 2025 IEEE.

关键词： C++ (programming language)

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Neural Text Normalization for Luxembourgish Using Real-Life Variation Data 12

Neural Text Normalization for Luxembourgish Using Real-Life ...

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12th Workshop on NLP for Similar Languages, Varieties and Dialects, VarDial 2025 - co-located with the 31st international conference on Computational Linguistics, COLING 2025

作者： Lutgen, Anne-Marie Plum, Alistair Purschke, Christoph Plank, Barbara University of Luxembourg Esch-sur-Alzette Luxembourg MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany

ISBN: (纸本)9798891762084

Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult task given the lack of annotated and parallel data, which is exacerbated by ongoing standardization. In this paper, we propose the first sequence-to-sequence normalization models using the ByT5 and mT5 architectures with training data obtained from word-level real-life variation data. We perform a fine-grained, linguistically-motivated evaluation to test byte-based, word-based and pipeline-based models for their strengths and weaknesses in text normalization. We show that our sequence model using real-life variation data is an effective approach for tailor-made normalization in Luxembourgish. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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SwinMixer Detector: A Two-Level Traffic Flow Detection System Based on Swin Transformer and MLP-Mixer 24th

SwinMixer Detector: A Two-Level Traffic Flow Detection Syste...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Lu, Lu Fang, Menghao Li, Xia Li, Minghui Lei, Tianwei Kong, Zixiao School of Cyber Science and Engineering University of International Relations Beijing100091 China Institute of Disaster and Emergency Medicine Tianjin University Tianjin300072 China Marine Engineering of College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789819615414

this paper explores the problem of boundary data classification ambiguity that arises when machine learning techniques are applied in the field of intrusion detection. the features and attributes of the boundary data place them in the boundaries or boundaries between different categories in the classification decision, and these data points may be close to the decision boundaries of different categories in the feature space, resulting in relative uncertainty or some ambiguity in their classification. To address this problem, this study innovatively proposes a secondary detection system called the SwinMixer Detector, which utilises the Swin Transformer and MLP-Mixer architecture. the system consists of a primary triage detection platform utilizing Swin Transformer classifier and a secondary detection platform utilizing MLP-Mixer, which can effectively solve the problem of fuzzy classification of boundary data. By validating on the SR-BH2020 dataset and the CIC-DDOS2019 dataset, the method in this paper achieves a precision of 96.40% and 95.81%, respectively, which provides a new research idea and solution for the field of intrusion detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mixers (machinery)

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Data Center Infrastructure in the Era of Generative Artificial Intelligence

Data Center Infrastructure in the Era of Generative Artifici...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Antonio Cortés Castillo Departamento de Informática Universidad de Panamá Ciudad de Panamá Panamá

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Nowadays, Generative Artificial Intelligence (GenAI) is increasingly making inroads in Data Centers, helping to improve aspects related to latency and high speed. Data center-level network infrastructures require managing large volumes of data using high-speed protocols such as InfiniBand, as it allows low latency and parallel processing, as well as various applied switches at different network architecture levels. In this article, we examine those protocols at the data communication level that GenAI uses to optimize and guarantee data flow between nodes, as well as the design and reference of network architectures in the field of GenIA. On the other hand, the components of the GenAI networks used are analyzed. Similarly, a case study is proposed where the performance of the Graphic processing Units (GPUs) is analyzed based on the definition of a series of metrics such as memory access, memory bandwidth, throughput, energy consumption, temperature, and clock speed, and that through the simulation of neural networks specifically using the Radial Basic Function (RBF) and Multilayer Perceptron (MLP) algorithms, it helps to understand and analyze to what extent these metrics behave and generate possible solutions in the field of data centers.

关键词： Measurement Data centers Protocols Generative AI Optical switches Memory management Graphics processing units Bandwidth throughput Servers

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Language-Based Colorization with Sparse Attention and Multi-scale Cross-Modal Semantic Alignment 24th

Language-Based Colorization with Sparse Attention and Mult...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhang, Ying Gao, Yutong Liu, Xuan Zhang, Lefeng Liu, Xianggan Jiang, Shan Ethnic Language Intelligent Analysis and Security Governance Minzu University of China Beijing China Faculty of Data Science City university of Macau China Natural Language Processing and Knowledge Graph Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615476

Language-based colorization generates realistic and aesthetically appealing colors by leveraging the guidance of intuitive and user-friendly natural language descriptions. Previous methods in language-based image colorization face several significant challenges, including limited color richness, color bleeding, color distortions, and inconsistent color styles. these issues often arise due to the reliance on dense attention mechanisms, which can lead to an overemphasis on global features at the expense of local details. Furthermore, many feature fusion modules for text and grayscale images are not designed to achieve adequate alignment of the diverse feature representations, resulting in suboptimal colorization outcomes. In this paper, we explore a novel module that employs sparse attention to mitigate the issue of color bleeding and limited colorfulness in image colorization tasks. By introducing a sparse attention mechanism, our method enables more flexible allocation of computational resources with a focus on color awareness. Additionally, we propose a module that effectively aligns grayscale images with color descriptions, thereby improving the consistency and quality of the colorization results. this module leverages the synergy between image features and language descriptions to solve long-standing issues like color bleeding. Empirical evaluations demonstrate that our approach surpasses recent state-of-the-art techniques in both automatic and language-based colorization tasks, validating the effectiveness and robustness of our proposed method in generating high-quality, visually appealing colorized images. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Enhancing Delay-Sensitive Task Offloading: A Multi-agent Deep Reinforcement Learning Algorithm for MEC-Based AIoT Systems 24th

Enhancing Delay-Sensitive Task Offloading: A Multi-agent Dee...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Tang, Fengjie Xu, Jia Liu, Xiao Yao, Aiting Fang, Mingyan Li, Xuejun Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Hefei China School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9789819615414

Artificial Intelligence of things (AIoT) systems are widely utilized in various domains such as smart logistics and smart health. In AIoT systems, computational tasks earmarked for offloading by IoT devices often have different task priorities and heterogeneous resource requirements. However, these factors will affect the QoS of the solution and the task response time. therefore, motivated by a typical smart warehouse system, a Multi-access Edge Computing (MEC) based distributed task offloading framework is proposed. Afterward, a delay-sensitive mixed-integer programming model is defined to address the imbalanced utilization of computing resources between edge and cloud servers during the offloading process. Finally, based on the attention mechanism, a Priority-Driven Multi-Agent Deep Reinforcement Learning algorithm (PDMA-DRL) is proposed to fulfill real-time resource requirements while reducing the task response time. the PDMA-DRL algorithm consists of two agents, and each agent corresponds to a sub-problem. Comprehensive experimental results demonstrate that our proposed PDMA-DRL algorithm can outperform representative existing methods in generating delay-sensitive task offloading plans and effectively reducing the task response time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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A Dual-Defense Self-balancing Framework Against Bilateral Model Attacks in Federated Learning 24th

A Dual-Defense Self-balancing Framework Against Bilateral Mo...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Wu, Xiang Yao, Aiting Pal, Shantanu Jiang, Frank Li, Xuejun Xu, Jia Dong, Chengzu Chen, Xuefei Zhang, Xiuyi Liu, Xiao Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui China Deakin University Melbourne Australia School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9789819615247

With the rapid expansion of Artificial Intelligence (AI) services, smart devices generate a large amount of user data at the edge network, which urgently needs to be protected while effectively extracting information. Federated learning (FL) is an important technology for handling dispersed data and strict privacy requirements in this context. However, the security threats caused by model inversion attacks and poisoning attacks can affect the mutual trust between the client and server. Yet, for these two types of attacks, the existing defense mechanisms are contradictory in terms of whether the model parameters are publicly disclosed. In addition, the data distribution of the clients is imbalanced which will increase the bias of model, reducing its practicality. To address this issue, this study proposes a dual defense self-balanced federated learning (DDSFL) framework, aiming to introduce a novel lightweight defense mechanism during the model parameter aggregation stage, combating these two types of attacks simultaneously by applying differential privacy and adjusting learning rates. In addition, this method also integrates a middleware-based reordering algorithm to enhance the robustness of the framework. Experimental results show that DDSFL effectively improves the ability to resist imbalanced data, forged data, and malicious behavior, significantly enhancing the generalization performance and security of the FL system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Defense Against Textual Backdoors via Elastic Weighted Consolidation-Based Machine Unlearning 24th

Defense Against Textual Backdoors via Elastic Weighted Cons...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Xuan, Haojun Wang, Yajie Wu, Huishu Liu, Tao Zhang, Chuan Zhu, Liehuang Beijing Institute of Technology Beijing China China University of Political Science and Law Beijing China China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9789819615506

Backdoor attacks pose significant threats to Natural Language processing (NLP) models. Various backdoor defense methods for NLP models primarily function by identifying and subsequently manipulating backdoor triggers within provided samples. However, such methods predominantly operate at the level of data filtering, essentially failing to cleanse the affected model. To solve this problem, we present ELUDE—a groundbreaking method designed to excise the backdoor triggers embedded within the corrupted model. ELUDE’s architecture comprises two core components: the backdoor trigger identifier and the backdoor trigger remover, operating synergistically in a pipeline procedure. While the former employs a perplexity-based approach to locate the backdoor trigger, the latter eradicates the inserted backdoor’s influence on the tainted model using machine unlearning. To counteract the issue of catastrophic forgetting engendered by machine unlearning, we incorporate Elastic Weight Consolidation (EWC) within the backdoor trigger remover. Our experiments on SST-2, OLID, and AG News text classification datasets exemplify the efficacy of ELUDE, as comparative results indicate that ELUDE effectively reduces the success rate of three cutting-edge backdoor attack methods by an average of 60%—simultaneously maintaining comparable performance on the original task. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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