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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Multi-behavior Guided Temporal Graph Attention Network for Recommendation 27th

Multi-behavior Guided Temporal Graph Attention Network for R...

引用

27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Xu, Weijun Li, Han Wang, Meihong Xiamen Univ Dept Software Engn Xiamen Peoples R China

ISBN: (纸本)9783031333798;9783031333804

The traditional recommendation approaches learn the representations of users and items utilizing only a single type of behavior data, which results in them facing the data sparsity issue. To alleviate the dilemma, multi-behavior recommendations leverage different types of behaviors to assist in modeling users' preferences. Despite their remarkable effectiveness, two significant challenges have remained less explored: 1) Effectively distinguishing the contributions of different types of behaviors during capturing users' preferences;2) Sufficiently exploiting the temporal information of user-item interactions. To tackle these challenges, we develop a new model named Multi-behavior Guided Temporal Graph Attention Network (MB-TGAT) to discriminate the diverse influence of various behaviors and to explore the evolutionary tendencies of users' recent preferences. In particular, we propose a behavior-aware attention mechanism to differentiate the strengths of different behaviors in the user-item aggregation phase. Furthermore, we tailor a phased message passing mechanism based on GNNs and design an evolution sequence self-attention to extract the users' preferences from static and dynamic perspectives, respectively. Extensive experiments on three real-world datasets demonstrate the superiority of our model, noticeably with 37.27%, 37.31% and 14.63% performance gain over the state-of-the-art baselines on the Taobao, IJCAI-15 and YooChoose datasets, respectively.

关键词： Multi-behavior Recommendation Collaborative Filtering Graph Neural Network

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cuSLINK: Single-Linkage Agglomerative Clustering on the GPU

cuSLINK: Single-Linkage Agglomerative Clustering on the GPU

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Nolet, Corey J. Gala, Divye Fender, Alex Doijade, Mahesh Eaton, Joe Raff, Edward Zedlewski, John Rees, Brad Oates, Tim NVIDIA Inc Santa Clara CA 95051 USA Univ Maryland Baltimore Cty Baltimore MD 21228 USA Booz Allen Hamilton Mclean VA USA

ISBN: (纸本)9783031434112;9783031434129

In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only O(Nk) space and uses a parameter k to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly optimized computational patterns for k-NN graph construction, spanning trees, and dendrogram cluster extraction. We show how we used our primitives to implement cuSLINK end-to-end on the GPU, further enabling a wide range of real-world data mining and machine learning applications that were once intractable. In addition to being a primary computational bottleneck in the popular HDBSCAN algorithm, the impact of our end-to-end cuSLINK algorithm spans a large range of important applications, including cluster analysis in social and computer networks, natural language processing, and computer vision.

关键词： KNN Graph Neighborhood Methods Nearest Neighbors Spanning Tree Single-Linkage Hierarchical Clustering Agglomerative Clustering Cluster Analysis Networks Forest Parallel Algorithms GPU

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Anonymisation of Judicial Rulings for Legal Analytics Purposes: Ethics, Law, and Compliance 22nd

Anonymisation of Judicial Rulings for Legal Analytics Purpos...

引用

22nd EPIA Conference on artificial intelligence (EPIA)

作者： Sciolla, Jacopo Ciani Paseri, Ludovica Univ Turin Dept Law Turin Italy

ISBN: (纸本)9783031490101;9783031490118

Legal Analytics (LA) techniques are a useful tool in the process of digitisation of judicial systems. However, they may imply processing of personal data contained in judicial rulings. This requires an assessment of the impact generated on the rights and freedoms of individuals. What happens if personal data are processed, with LA and AI systems, for research purposes, such as prediction? Should be taken additional technical and organisational measures for the protection of individuals, such as anonymisation or pseudonymisation? The EU legal framework does not interfere with data processing of courts acting in their judicial capacity, in order to safeguard the independence of the judiciary. Therefore, the decision to anonymise judgments is normally taken by the Court's rules or procedures. The paper provides an overview of the different policies adopted by the different EU countries, investigating whether they should apply to researchers performing LA of judicial rulings. The paper also illustrates how such issues have been dealt within the Legal Analytics for Italian LAw (LAILA) project, funded by the Italian Ministry of Education and Research within the "PRIN programme".

关键词： Legal Analytics artificial intelligence Judicial Rulings Data Protection Anonymisation Legal Information Legitimate Interest Proportionality

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A Global View-Guided Autoregressive Residual Network for Irregular Time Series Classification 1

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Zhu, Jianping Tang, Haocheng Zhang, Liang Jin, Bo Xu, Yi Wei, Xiaopeng Dalian Univ Technol Dalian Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Dongbei Univ Finance & Econ Dalian Peoples R China

ISBN: (数字)9783031333835

ISBN: (纸本)9783031333828;9783031333835

Irregularly sampled multivariate time series classification tasks become prevalent due to widespread application of sensors. However, different collection frequencies or sensor failures presents nontrivial challenges since mainstream methods generally assume aligned measurements across sensors (variables). Besides, most existing studies fail to account for the relationship between misaligned patterns and classification tasks. To this end, we propose a Global view-guided Autoregressive Residual Network (GARNet), which mainly adopts a generation-and-sampling strategy to deal with the partially observed data at each timestamp. Specifically, we first leverage a Structure-augmented Global Information Extractor (SGIE) to capture the global semantic information in the whole conditioning window. Then, a Global view-guided Autoregressive Recurrent Neural Network (GARNN) is developed to capture the local temporal dynamics hidden in latent factors. Finally, a Masked Temporal Information Aggregator (MTIA) is proposed to attentively aggregate the extracted latent factors at each timestamp for the classification task. Experimental results on two real-world datasets show that GARNet outperforms state-of-the-art methods.

关键词： Irregularly Sampled Multivariate Time Series Graph Neural Network Autoregressive Residual Network

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MIDFA: Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection 27th

MIDFA: Memory-Based Instance Division and Feature Aggregatio...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Chen, Qiaochuan Zhou, Min Yu, Hang Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Univ Minist Educ Key Lab Silicate Cultural Rel Conservat Shanghai Peoples R China

ISBN: (纸本)9783031333798;9783031333804

Previous video object detection methods focus on aggregating the features of other frames into the current frame to alleviate the image degradation, but they rarely focus on multi-class scenes. Aggregating features of different classes will generate confusing information and affect network performance. This problem can be solved by using a classifier to divide the features. However, classifier method has three problems: (a) Heterogeneous high-similarity objects and homogeneous low-similarity objects affect the accuracy of the classifier. (b) Two objects whose positions overlap also affect the classifier. (c) Previous classifier method did not exploit sufficient global information. Therefore, we propose a new method that divides the features of different instances to deal with the problems of (a) and (b). Then we designed two new memories (one is Init Memory and the other is MDR) to solve problem (c). These three parts constitute the MIDFA network. Experiments show that our method achieves 83.76% mAP on the ImageNet VID dataset based on ResNet-101, and 84.6% mAP on ResNeXt-101. In addition, we also conduct experiments on a custom-designed multi-class VID dataset, and adding Instance Division and MDR can increase the mAP of the network by 0.6% compared to using only Init Memory.

关键词： Video Object Detection Instance Division Memory Feature Aggregation

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Mitigating Algorithmic Bias with Limited Annotations

Mitigating Algorithmic Bias with Limited Annotations

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5th International Workshop on Learning with Imbalanced Domains - Theory and Applications / European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Wang, Guanchu Du, Mengnan Liu, Ninghao Zou, Na Hu, Xia Rice Univ Houston TX 77005 USA New Jersey Inst Technol Newark NJ 07102 USA Univ Georgia Athens GA 30602 USA Texas A&M Univ College Stn TX USA

ISBN: (纸本)9783031434143;9783031434150

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information. When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mitigate bias. However, the skewed distribution across different sensitive groups preserves the skewness of the original dataset in the annotated subset, which leads to non-optimal bias mitigation. To tackle this challenge, we propose Active Penalization Of Discrimination (APOD), an interactive framework to guide the limited annotations towards maximally eliminating the effect of algorithmic bias. The proposed APOD integrates discrimination penalization with active instance selection to efficiently utilize the limited annotation budget, and it is theoretically proved to be capable of bounding the algorithmic bias. According to the evaluation on five benchmark datasets, APOD outperforms the state-of-the-arts baseline methods under the limited annotation budget, and shows comparable performance to fully annotated bias mitigation, which demonstrates that APOD could benefit real-world applications when sensitive information is limited. The source code of the proposed method is available at: https://***/guanchuwang/APOD-fairness.

关键词： Bias mitigation Limitied annotation

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Canonicity of Proofs in Constructive Modal Logic 32nd

Canonicity of Proofs in Constructive Modal Logic

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32nd International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX)

作者： Acclavio, Matteo Catta, Davide Olimpieri, Federico Univ Southern Denmark Odense Denmark Univ Napoli Federico II Naples Italy Univ Leeds Leeds W Yorkshire England

ISBN: (纸本)9783031435126;9783031435133

In this paper we investigate the Curry-Howard correspondence for constructive modal logic in light of the gap between the proof equivalences enforced by the lambda calculi from the literature and by the recently defined winning strategies for this logic. We define a new lambda-calculus for a minimal constructive modal logic by enriching the calculus from the literature with additional reduction rules and we prove normalization and confluence for our calculus. We then provide a typing system in the style of focused proof systems allowing us to provide a unique proof for each term in normal form, and we use this result to show a one-to-one correspondence between terms in normal form and winning innocent strategies.

关键词： Constructive Modal Logic Lambda Calculus Game Semantics

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Ethics by Design for Intelligent and Sustainable Adaptive Systems 1

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21st International Conference of the Italian-Association-for-artificial-intelligence (AIxIA)

作者： Squadrone, Luca Croce, Danilo Basili, Roberto Univ Roma Tor Vergata Dept Enterprise Engn Via Politecn 1 I-00133 Rome Italy

ISBN: (数字)9783031271816

ISBN: (纸本)9783031271809;9783031271816

AI systems are increasingly dependent on the data and information sources they are developed with. In particular, learning machines are highly exposed to undesirable problems due to biased and incomplete coverage of training data. The autonomy exhibited by machines trained on low-quality data raises an ethical concern, as it may infringe on social rules and security constraints. In this paper, we extensively experiment with a learning framework, called Ethics by Design, which aims to ensure a supervised learning policy that can pursue both the satisfaction of ethical constraints and the optimization of task (i.e., business) accuracy. The results obtained on tasks and datasets confirm the positive impact of the method in ensuring ethical compliance. This paves the way for a large set of industrial applications, whose ethical dimension is critical to increasing the trustworthiness with respect to this technology.

关键词： Ethical issues of AI Ethics by design in machine learning Bias in deep learning Empirical evaluation of ethical AI systems

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PDF-VQA: A New Dataset for Real-World VQA on PDF Documents 1

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作者： Ding, Yihao Luo, Siwen Chung, Hyunsuk Han, Soyeon Caren Unvers Sydney Sydney NSW Australia Univ Western Australia Perth WA Australia FortifyEdge Sydney NSW Australia

ISBN: (数字)9783031434273

ISBN: (纸本)9783031434266;9783031434273

Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions. We proposed a new document-based VQA dataset, PDF-VQA, to comprehensively examine the document understanding from various aspects, including document element recognition, document layout structural understanding as well as contextual understanding and key information extraction. Our PDF-VQA dataset extends the current scale of document understanding that limits on the single document page to the new scale that asks questions over the full document of multiple pages. We also propose a new graph-based VQA model that explicitly integrates the spatial and hierarchically structural relationships between different document elements to boost the document structural understanding. The performances are compared with several baselines over different question types and tasks (The full dataset is released in https://***/adlnlp/pdfvqa).

关键词： Document Understanding Document Information Extraction Visual Question Answering

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Reinforcing Assessment Processes Using Proactive Case-Based Reasoning Mechanisms 18th

Reinforcing Assessment Processes Using Proactive Case-Based ...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Leite, Jaime Belo, Orlando Univ Minho ALGORITMI R&D Ctr LASI Campus Gualtar P-4710057 Braga Portugal

ISBN: (纸本)9783031407246;9783031407253

The integration of case-based reasoning mechanisms into E-learning systems is not a recent option. These mechanisms allow us to implement reasoning systems using similar experiences in a retrospectively way, which is quite attractive for problem solving in some application areas. Its application promotes the achievement of very interesting solutions, especially when an interesting case base exists a priori, integrating knowledge and appropriate problem-solving methods. In this paper, we present and describe a hybrid reasoning system especially developed to support student knowledge assessment processes in an E-learning platform. The system integrates two autonomous reasoning modules, which combine their functioning during knowledge assessment processes, combining rule-based and case-based knowledge, respecting a set of assessment restrictions defined a priori by a human supervisor.

关键词： E-Learning Systems Rule-based Reasoning Case-based Reasoning E-learning User Assessment Supervised Reasoning

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