Evolutionary Computation (EC) deals with problem solving, optimization, and machine learning techniques inspired by principles of natural evolution and - netics. Just from this basic de?nition, it is clear that one of...
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ISBN:
(数字)9783540246534
ISBN:
(纸本)9783540213789
Evolutionary Computation (EC) deals with problem solving, optimization, and machine learning techniques inspired by principles of natural evolution and - netics. Just from this basic de?nition, it is clear that one of the main features of theresearchcommunityinvolvedinthestudyofitstheoryandinitsapplications is multidisciplinarity. For this reason, EC has been able to draw the attention of an ever-increasing number of researchers and practitioners in several ?elds. In its 6-year-long activity, EvoNet, the European Network of Excellence in Evolutionary Computing, has been the natural reference and incubator for that multifaceted community. EvoNet has provided logistic and material support for thosewhowerealreadyinvolvedinECbut,inthe?rstplace,ithashadacritical role in favoring the signi?cant growth of the EC community and its interactions with longer-established ones. The main instrument that has made this possible has been the series of events, ?rst organized in 1998, that have spanned over both theoretical and practical aspects of EC. Ever since 1999, the present format, in which the EvoWorkshops, a collection of workshops on the most application-oriented aspects of EC, act as satellites of a core event, has proven to be very successful and very representative of the multi-disciplinarity of EC. Up to 2003, the core was represented by EuroGP, the main European event dedicated to Genetic Programming. EuroGP has been joined as the main event in 2004 by EvoCOP, formerly part of EvoWorkshops, which has become the European Conference on Evolutionary Computation in Combinatorial Optimization.
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Scale Space Methods and Variational Methods in computer Vision, SSVM 2011, held in Ein-Gedi, Israel in...
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ISBN:
(数字)9783642247859
ISBN:
(纸本)9783642247842
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Scale Space Methods and Variational Methods in computer Vision, SSVM 2011, held in Ein-Gedi, Israel in May/June 2011.;The 24 revised full papers presented together with 44 poster papers were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on denoising and enhancement, segmentation, image representation and invariants, shape analysis, and optical flow.
Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived...
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Few-shot learning (FSL) aims to classify a novel object into a specific category under limited training samples. This is a challenging task since (1) the features expressed by pre-trained knowledge introduce perceived bias and then constrain the classification space, and (2) the use of general hallucination techniques based on global features fails to escape the limited classification space, resulting in suboptimal improvements. To solve these issues, this paper proposes an interventional feature generation (IFG) method. Specifically, we first use the relations of the categories or instances as interventional operations to implicitly constrain the feature representations (pre-trained knowledge) into different classification subsets. Then, we employ a parameter-free feature generation strategy to enrich each subset’s training samples of the support category. In other words, IFG provides a multi-subsets learning strategy to reduce the influence of perceived bias, enrich the diversity of generated features, and improve the robustness of the few-shot classifier. We apply our method to four benchmark datasets and observe state-of-the-art performance across all experiments. Specifically, compared to the baseline on the Mini-ImageNet dataset, our approach yields accuracy improvements of 6.03% and 3.46% for 1 and 5 support training samples, respectively. Furthermore, the proposed interventional feature generation technique can improve classifier performance in other FSL methods, demonstrating its versatility and potential for broader applications. The code is available at https://***/ShuoWangCS/IFG-FSL/.
Agents are software processes that perceive and act in an environment, processing their perceptions to make intelligent decisions about actions to achieve their goals. Multi-agent systems have multiple agents that wor...
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ISBN:
(数字)9783642111617
ISBN:
(纸本)9783642111600
Agents are software processes that perceive and act in an environment, processing their perceptions to make intelligent decisions about actions to achieve their goals. Multi-agent systems have multiple agents that work in the same environment to achieve either joint or conflicting goals. Agent computing and technology is an exciting, emerging paradigm expected to play a key role in many society-changing practices from disaster response to manufacturing to agriculture. Agent and mul- agent researchers are focused on building working systems that bring together a broad range of technical areas from market theory to software engineering to user interfaces. Agent systems are expected to operate in real-world environments, with all the challenges complex environments present. After 11 successful PRIMA workshops/conferences (Pacific-Rim International Conference/Workshop on Multi-Agents), PRIMA became a new conference titled “International Conference on Principles of Practice in Multi-Agent Systems” in 2009. With over 100 submissions, an acceptance rate for full papers of 25% and 50% for posters, a demonstration session, an industry track, a RoboCup competition and workshops and tutorials, PRIMA has become an important venue for multi-agent research. Papers submitted are from all parts of the world, though with a higher representation of Pacific Rim countries than other major multi-agent research forums. This volume presents 34 high-quality and exciting technical papers on multimedia research and an additional 18 poster papers that give brief views on exciting research.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of appl...
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ISBN:
(数字)9783030054113
ISBN:
(纸本)9783030054106
This book highlights cutting-edge research in the field of network science, offering scientists, researchers, students and practitioners a unique update on the latest advances in theory, together with a wealth of applications. It presents the peer-reviewed proceedings of the VII International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2018), which was held in Cambridge on December 11–13, 2018. The carefully selected papers cover a wide range of theoretical topics such as network models and measures; community structure and network dynamics; diffusion, epidemics and spreading processes; and resilience and control; as well as all the main network applications, including social and political networks; networks in finance and economics; biological and neuroscience networks; and technological networks.
Body joint modeling and human pose reconstruction provide precise motion and quantitative geometric information about human dynamics. The rich motion information obtained from human pose estimation plays important rol...
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Body joint modeling and human pose reconstruction provide precise motion and quantitative geometric information about human dynamics. The rich motion information obtained from human pose estimation plays important roles in a wide range of digital twin and connected health applications. However, current related researches have difficulties in extracting the joints’ spatial-temporal correlations from different levels. This is due to the poses being at various complexities in moving various joints differently. Hence, the typical conventional transformer method is non-adaptable and barely meets the aforementioned requirement. In this paper, we propose the Body Joint Interactive transFormers (BJIFormer) to extract the multi-level joints’ spatial-temporal information. The design enables the model to learn the inner joints’ correlation inside the body parts across frames and propagate the extracted information across the body parts with shared joints. The multi-level body joint interactive scheme has greater efficiency improvement by restricting the self-attention computation to partial body parts and connecting each body part by torso. The proposed interactive approach explores the spatial-temporal correlation following the hierarchical paradigm and effectively estimates and reconstructs 3D human poses.
In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. One of the key problems for a practical conversational search system is how to ask high...
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In legal case retrieval, existing work has shown that human-mediated conversational search can improve users’ search experience. One of the key problems for a practical conversational search system is how to ask high-quality clarifying questions to initiate conversations with users and understand their search intents. Previous works demonstrated that human-annotated external domain knowledge (such as event schemas) can improve the legal utility of clarifying questions generated by Large Language Models. However, these methods are restricted to specific law systems or languages and can not be generalized to others. To this end, we propose to generate context and domain-specific questions with LLMs without external annotations or knowledge by extracting information from top retrieved documents given the current conversation context. Specifically, we construct a conversational legal case retrieval system CARQ that iteratively selects neighbor candidate case documents from the retrieved list at each conversation step to ask clarifying questions. We pretrain CARQ to capture the differences between legal cases and employ the reward augmented maximum likelihood to optimize the system directly for retrieval metrics. Extensive automated and human evaluations on three widely adopted legal case retrieval datasets demonstrate the superior effectiveness of our approach as compared with the state-of-the-art baselines.
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