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Elicitation for Decision Problems Under Severe Uncertainties 16th

Elicitation for Decision Problems Under Severe Uncertaintie...

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16th International Conference on Scalable Uncertainty Management, SUM 2024

作者： Nakharutai, Nawapon Troffaes, Matthias C. M. Destercke, Sébastien Department of Statistics Faculty of Science Chiang Mai University Chiang Mai50200 Thailand Department of Mathematical Sciences Durham University Durham United Kingdom UMR CNRS 7253 Heudiasyc Sorbonne Université Université de Technologie de Compiègne Compiègne France

ISBN: (纸本)9783031762345

In this paper, we investigate the problem of eliciting information from an expert, where the assumed uncertainty model is a coherent upper prevision (or equivalently a closed convex set of probabilities). The goal is to solve a decision problem under the maximality decision rule, with as few queries to the expert as possible. To address this, we study the range of coherent upper bounds an expert may give on a given query. In doing so, we provide new results and characterisations for this range. We then use these results to provide an algorithm of elicitation. We illustrate the algorithm on an example. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： coherence decision elicitation

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Trends in Computational science: Natural Language Processing and Network Analysis of 23 Years of ICCS Publications 24th

Trends in Computational Science: Natural Language Processing...

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24th International Conference on Computational science (ICCS)

作者： Luo, Lijing Kovalchuk, Sergey Krzhizhanovskaya, Valeria Paszynski, Maciej de Mulatier, Clelia Dongarra, Jack Sloot, Peter M. A. Univ Amsterdam Amsterdam Netherlands AGH Univ Krakow Krakow Poland Univ Tennessee Knoxville TN USA

ISBN: (纸本)9783031637537;9783031637513

We analyze 7826 publications from the International Conference on Computational science (ICCS) between 2001 and 2023 using natural language processing and network analysis. We categorize computer science into 13 main disciplines and 102 sub-disciplines sourced from Wikipedia. After lemmatizing full texts of these papers, we calculate the similarity scores between the papers and each sub-discipline using vectors built with TF-IDF evaluation. Among the 13 main disciplines, machine learning & AI have become the most popular topics since 2019, surpassing parallel & distributed computing, which peaked in the early 2010 s. Modeling & simulation, and algorithms & data structure have always been popular disciplines in ICCS over the past 23 years. The most frequently researched sub-disciplines, on average, are algorithms, numerical analysis, and machine learning. Deep learning shows the most rapid growth, while parallel computing has declined over the past 23 years in ICCS publications. The network of sub-disciplines exhibits a scale-free distribution, indicating certain disciplines are more connected than others. We also present correlation analysis of sub-disciplines, both within the same main disciplines and between different main disciplines.

关键词： natural language processing topic modelling computational science graph theory network analysis scientometrics ICCS

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Novel Clustering Aggregation and Multi-grained Alignment for Image-Text Matching 27th

Novel Clustering Aggregation and Multi-grained Alignment for...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Shuming Wu, Xiao-Jun Xu, Tianyang Zhang, Donglin School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9783031783043

As a challenging multi-modal task, image-text matching continues to be an attractive topic of research. The essence of this task lies in narrowing down the semantic disparity between vision and language to align them better. Existing works have either focused on coarse-grained alignment between global images and texts or fine-grained alignment between salient regions and words. However, they do not distinguish between considering related and redundant pairs (i.e., regions with no matching words or pairs with low relevance). We thereby propose a Novel Clustering Aggregation and multi-grained Alignment network (NCAA), which utilizes cross-modal contextual clustering to group regions based on semantic information consistent with the text content. Specifically, we leverage textual fragments as clustering centers, similarity between regions and fragments as propagation medium and delicately devise two mask mechanisms for simultaneous and distinguishable consideration of both related and redundant pairs. Two alignment modules of different granularities are also introduced to achieve the multi-grained alignment. By incorporating both global and local similarity into the training and inference phases, our model attains further enhancements. Finally, we conduct extensive experiments on two benchmark datasets, Flickr30K and MSCOCO, demonstrating the efficacy of our framework. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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A New Impressive and Expressive Features Based Model for Personality Traits Identification 27th

A New Impressive and Expressive Features Based Model for Per...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Biswas, Kunal Palaiahnakote, Shivakumara Pal, Umapada Chanda, Sukalpa Wu, Xiao-Jun Jadavpur University Kolkata India School of Science Engineering and Environment University of Salford Manchester United Kingdom Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Østfold University College Halden Norway Jiangnan University Wuxi China

ISBN: (纸本)9783031781858

Personality traits identification is challenging due to unpredictable changes in the foreground and background of images. In this work, we propose a new deep learning model for personality traits image classification by exploiting visual and textual information from dating images. We believe that there is a strong correlation between the features (visual + textual) of dating images and personality traits images. The features which draw attention are defined as impressive features of dating images and features that convey emotions are expressive features of personality traits images. This observation motivated us to combine the features of dating images to improve the performance of personality traits images classification. To affirm the above observation, we propose multiple convolutional layers followed by max pool layers which extract features from dating and personality traits images simultaneously. To integrate the strengths of impressive and expressive features, the proposed work introduces a dual fusion approach, which fuses features and modalities at different levels. The experiments are conducted on different standard datasets of personality traits images to demonstrate the effectiveness of impressive features in terms of personality trait image classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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FineFACE: Fair Facial Attribute Classification Leveraging Fine-Grained Features 27th

FineFACE: Fair Facial Attribute Classification Leveraging Fi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Manzoor, Ayesha Rattani, Ajita Department of Computer Science and Engineering University of North Texas at Denton DentonTX United States

ISBN: (纸本)9783031781094

Published research highlights the presence of demographic bias in automated facial attribute classification algorithms, particularly impacting women and individuals with darker skin tones. Existing bias mitigation techniques typically require demographic annotations and often obtain a trade-off between fairness and accuracy, i.e., Pareto inefficiency. Facial attributes, whether common ones like gender or others such as "chubby" or "high cheekbones", exhibit high interclass similarity and intraclass variation across demographics leading to unequal accuracy. This requires the use of local and subtle cues using fine-grained analysis for differentiation. This paper proposes a novel approach to fair facial attribute classification by framing it as a fine-grained classification problem. Our approach effectively integrates both low-level local features (like edges and color) and high-level semantic features (like shapes and structures) through cross-layer mutual attention learning. Here, shallow to deep CNN layers function as experts, offering category predictions and attention regions. An exhaustive evaluation on facial attribute annotated datasets demonstrates that our FineFACE model improves accuracy by 1.32% to 1.74% and fairness by 67% to 83.6%, over the SOTA bias mitigation techniques. Importantly, our approach obtains a Pareto-efficient balance between accuracy and fairness between demographic groups. In addition, our approach does not require demographic annotations and is applicable to diverse downstream classification tasks. To facilitate reproducibility, the code and dataset information is available at https://***/VCBSL-Fairness/FineFACE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Population statistics

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Leveraging LLMs and Generative Models for Interactive Known-Item Video Search 30th

Leveraging LLMs and Generative Models for Interactive Know...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Ma, Zhixin Wu, Jiaxin Ngo, Chong Wah School of Computing and Information Systems Singapore Management University Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9783031533013

While embedding techniques such as CLIP have considerably boosted search performance, user strategies in interactive video search still largely operate on a trial-and-error basis. Users are often required to manually adjust their queries and carefully inspect the search results, which greatly rely on the users’ capability and proficiency. Recent advancements in large language models (LLMs) and generative models offer promising avenues for enhancing interactivity in video retrieval and reducing the personal bias in query interpretation, particularly in the known-item search. Specifically, LLMs can expand and diversify the semantics of the queries while avoiding grammar mistakes or the language barrier. In addition, generative models have the ability to imagine or visualize the verbose query as images. We integrate these new LLM capabilities into our existing system and evaluate their effectiveness on V3C1 and V3C2 datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantics

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Intent Identification Using Few-Shot and Active Learning with User Feedback 25th

Intent Identification Using Few-Shot and Active Learning wi...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Yuvaraj, Senthil Ganesan Benatallah, Boualem Motahari-Nezhad, Hamid Reza Rabhi, Fethi School of Computer Science and Engineering University of New South Wales Sydney Australia Insight SFI Research Centre on Data Analytics Dublin City University Dublin Ireland UpBrains AI Inc. RestonVA United States Macquarie University Sydney Australia

ISBN: (纸本)9789819605729

Collaboration tools contain many intents in workplace conversation, and identifying these intents is important to increase workplace productivity. However, labelling these intents for a large collection of conversations is expensive and data availability for new intents is quite limited. The pre-trained models show outstanding results in text classification tasks and large language models have recently produced accurate models with few samples. We explored few-shot learning methods and active learning strategies for this problem. Our proposed method, "SetFit with AL" is a combination of Sentence Transformer Fine-tuning (SetFit) and active learning for intent identification. This method fine-tunes a sentence-transformer model to develop accurate models. The intent classification evaluation dataset was used to evaluate this method. The results show that our proposed method outperforms state-of-the-art large language model GPT-3.5 and is comparable to GPT-4. This method also can utilize user feedback to adapt to new data and develop personalized models. Thus, the contribution of this paper is that fine-grained intents are identified using minimal data and the model is adaptable based on user feedback. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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Quality Scalable Video Coding Based on Neural Representation 30th

Quality Scalable Video Coding Based on Neural Representatio...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Cao, Qian Zhang, Dongdong Sun, Chengyu Department of Computer Science and Technology Tongji University Shanghai China Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology Tongji University Shanghai China

ISBN: (纸本)9783031533044

Neural Representation for Videos (NeRV) encodes each video into a network, providing a promising solution to video compression. However, existing NeRV methods are limited to representing single-quality videos with fixed-size models. To accommodate varying quality requirements, NeRV methods need multiple separate networks with different sizes, resulting in additional training and storage costs. To address this, we propose a Quality Scalable Video Coding method based on Neural Representation, in which a hierarchical network consisting of a base layer (BL) and several enhancement layers (ELs) represents the same video with coarse-to-fine qualities. As the smallest subnetwork, the BL represents basic content. The larger subnetworks can be formed by gradually adding the ELs which capture residuals between the lower-quality reconstructed frames and original ones. Since the larger subnetworks share the parameters of the smaller ones, our method saves 40% of storage space. In addition, our structural design and training strategy enable each subnetwork to outperform the baseline on average +0.29 PSNR. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image compression

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Evaluation of Escapade: An Engaging, Collaborative Multi-role Learning Game on History 13th

Evaluation of Escapade: An Engaging, Collaborative Multi-rol...

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13th International Conference on Games and Learning Alliance, GALA 2024

作者： Wang, Alf Inge Kielland, Jakob Endrestad Zhao, Wictor Norwegian University of Science and Technology TrondheimN7491 Norway

ISBN: (纸本)9783031782688

Game-based learning positively impacts learning performance, classroom dynamics, and attitudes of both students and teachers. While digital learning games have existed since the 1960s, most remain single-player. Although social learning games have gained popularity, they are often competitive, like student response systems. There is a growing need for collaborative learning games, especially those with distinct student roles. Escapade is a multi-role collaborative game for teaching history, inspired by GeoGuessr. This paper examines its effects on motivation, collaboration, learning, engagement, and enjoyment, analyzing factors such as collaboration mode, gaming interest, history class interest, and player roles. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Collaborative learning

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Path Optimization Method Under UAV Charging Scheduling Network 24th

Path Optimization Method Under UAV Charging Scheduling Netwo...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Tingting Wang, Yiqian Zhu, Jie Chang, Shuyu Huang, Haiping Nanjing University of Posts and Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9789819615278

Unmanned Aerial Vehicles (UAVs) have emerged as integral components in logistics systems, where their potential for efficient delivery services is being explored. However, the limited battery capacity of UAVs poses a significant challenge for long-distance delivery applications. In this paper, we investigate a hybrid problem involving UAV trajectory planning and charging scheduling for long-distance logistics systems with a charging network. In the problem under study, multiple UAVs are assigned long-distance delivery tasks. A UAV can stop at any charging station in the charging network to charge if it is in a low-energy state. An Ant Colony Optimization-based UAV Trajectory Planning and Charging Scheduling (ACO-TPCS) algorithm is proposed to minimize task completion time by optimizing UAV trajectory and charging plans. The main framework of the ACO-TPCS algorithm consists of several key components, including reachable graph construction, candidate flight path generation, pheromone matrix construction, ant generation, feasible solution generation, and pheromone update method. All these components are delicately designed. Through extensive experimentation and comparison with baseline algorithms, we demonstrate the effectiveness of the ACO-TPCS algorithm in addressing the problem under study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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