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共 742 条记录，以下是1-10 订阅

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The Fourth International Workshop on Smart data for Blockchain and Distributed Ledger (SDBD'24) 24

The Fourth International Workshop on Smart Data for Blockcha...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Zhu, Feida Pei, Jian Zeller, Michael Zhang, Bingxue Singapore Management Univ Singapore Singapore Singapore Duke Univ Durham NC USA Temasek San Diego CA USA Univ Shanghai Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798400704901

With the advent of Bitcoin, a cryptographically-enabled peer-topeer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, process automation and secure data sharing across otherwise isolated data silos. Furthermore, as the recognition of the data value began to sink in, data assets has become an essential part of the development of enterprises and countries. Blockchain technology is regarded as the foundation of digital economy and provides an effective approach for data ownership, pricing and transactions, which are the core issues of data asset management. However, the potential implications of Blockchain technologies go far beyond their application as the technological backbone for cryptocurrencies. Web3.0, using blockchain as underlying technology, allow for various novel application scenarios, which are built upon distributed consensus and thus are hard to block or censor while providing public verifiability of peer-to-peer transactions without a trusted central party. Web3.0 are expected to become the main front for a plethora of highly expressive applications. To more thoroughly explore the potential of blockchain and web3.0 and promote their progress, SDBD'24 will provide a forum for the most recent blockchain and web3.0 research, innovations, and applications, bridging the gap between theory and practice in the design.

关键词： distributed ledger technology data governance AI in blockchain data asset data pricing privacy data provenance data auditing

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Bridging and Compressing Feature and Semantic Spaces for Robust Graph Neural Networks: An Information theory Perspective 24

Bridging and Compressing Feature and Semantic Spaces for Rob...

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30th ACM SIGKDD conference on knowledge discovery and data mining

作者： Zhong, Luying Lin, Renjie Li, Jiayin Wang, Shiping Chen, Zheyi Fuzhou Univ Fuzhou Peoples R China Fujian Normal Univ Fuzhou Peoples R China

ISBN: (纸本)9798400704901

The emerging Graph Convolutional Networks (GCNs) have attracted widespread attention in graph learning, due to their good ability of aggregating the information between higher-order neighbors. However, real-world graph data contains high noise and redundancy, making it hard for GCNs to accurately depict the complete relationships between nodes, which seriously degrades the quality of graph representations. Moreover, existing studies commonly ignore the distribution difference between feature and semantic spaces in graphs, causing inferior model generalization. To address these challenges, we propose DIB-RGCN, a novel robust GCN framework, to explore the optimal graph representation with the guidance of the well-designed dual information bottleneck principle. First, we analyze the reasons for distribution differences and theoretically prove that minimal sufficient representations in specific spaces cannot promise optimal performance for downstream tasks. Next, we design new dual channels to regularize feature and semantic spaces, eliminating the sharing of task-irrelevant information between spaces. Different from existing denoising algorithms that adopt a random dropping manner, we innovatively replace potential noisy features and edges with local neighboring representations. This design lowers edge-specific coefficient assignment, alleviating the interference of original representations while retaining graph structures. Further, we maximize the sharing of task-relevant information between feature and semantic spaces to alleviate the difference between them. Using real-world datasets, extensive experiments demonstrate the robustness of the proposed DIB-RGCN, which outperforms state-of-the-art methods on classification tasks.

关键词： Semi-supervised Learning Information theory Graph Neural Networks Robustness

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TCMRD-KG: Design and Development of a Rheumatism knowledge Graph Based on Ancient Chinese Literature 2

TCMRD-KG: Design and Development of a Rheumatism Knowledge G...

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2024 International conference on Medical Artificial Intelligence

作者： Li, Haotian Xia, Congmin Hou, Youjuan Hu, Sile Quan, Jiang Liu, Yanjun Beijing Univ Chinese Med Sch Tradit Chinese Med Beijing Peoples R China China Acad Chinese Med Sci Guanganmen Hosp Beijing Peoples R China China Acad Chinese Med Sci Inst Informat Tradit Chinese Med Beijing Peoples R China China Acad Chinese Med Sci Inst Basic Theory Chinese Med Beijing Peoples R China

ISBN: (纸本)9798350377620;9798350377613

The use of Traditional Chinese medicineTCM in rheumatic diseases dates back to thousands of years ago. Compared with standardized treatment, TCM has the advantages of low cost, low side effects, and flexible medication. Ancient books of traditional Chinese medicine play an important role in clinical and scientific research. This study lakes the content related to rheumatism in ancient books of traditional Chinese medicine as the research object, integrates the ontology theory and technology in the knowledge graph, realizes the reconstruction of traditional Chinese medicine information knowledge, and provides basic data structure for data mining and knowledge discovery. This study is the first rheumatism-specific knowledge graph constructed based on ancient books of traditional Chinese medicine;it has tried the construction method of knowledge graph of ancient books of traditional Chinese medicine by combining automatic labeling of mainstream large language models with manual review;and according to the knowledge characteristics of ancient books of traditional Chinese medicine, the existing word segmentation technology is difficult to accurately reproduce the accurate meaning of the original text of ancient hooks, a new type of entity extraction method is given.

关键词： Traditional Chinese Medicine Rheumatic Diseases knowledge Graph

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Staying or Leaving: A knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation 27th

Staying or Leaving: A Knowledge-Enhanced User Simulator for ...

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27th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Yang, Zhaoqi Liu, Hongyan Tsinghua Univ Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9783031333798;9783031333804

Reinforcement learning has been widely used in recommender systems in order to optimize users' long-term utilities. An accurate and explainable user simulator is crucial for reinforcement learning based recommendation, as an online interactive environment is often unavailable. On short video platforms, it is very important to keep users on the platform as long as possible in each session. Thus, session-based user utilities depend on two factors: how much users like every single video (video preference) and the number of videos watched (video views) in each session. To this end, the simulator should simultaneously model the user's degree of liking for each video and video views. However, most previous studies on the short video recommendation only paid attention to the former. In this work, we propose KESWA, a knowledge-Enhanced Session-Wide Attention method for short video user simulation. KESWA fuses information foraging theory with a deep learning model for both video preference and video views modeling, providing an explainable prediction for users' staying and leaving behavior. Comparative experiments demonstrate that KESWA provides a better simulation of video views compared with existing models. Meanwhile, reinforcement learning agents can achieve higher session-based user utilities trained by KESWA than by other user simulators.

关键词： Session-based recommendation Reinforcement learning Information foraging theory

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Pyramid Graph Neural Network: A Graph Sampling and Filtering Approach for Multi-scale Disentangled Representations 23

Pyramid Graph Neural Network: A Graph Sampling and Filtering...

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29th ACM SIGKDD conference on knowledge discovery and data mining (KDD)

作者： Geng, Haoyu Chen, Chao He, Yixuan Zeng, Gang Han, Zhaobing Chai, Hua Yan, Junchi Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Oxford Oxford England Didi Chuxing Beijing Peoples R China Shanghai Jiao Tong Univ Dept CSE Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab AI Shanghai Peoples R China

ISBN: (纸本)9798400701030

Spectral methods for graph neural networks (GNNs) have achieved great success. Despite their success, many works have shown that existing approaches are mainly focused on low-frequency information which may not be pertinent to the task at hand. Recent efforts have been made to design new graph filters for wider frequency profiles, but it remains an open problem how to learn multi-scale disentangled node embeddings in the graph Fourier domain. In this paper, we propose a graph (signal) sampling and filtering framework, entitled Pyramid Graph Neural Network (PyGNN), which follows the Downsampling-Filtering-Upsampling-Decoding scheme. To be specific, we develop the.. -bandlimited downsampling approach to split input graph into subgraphs for the reduction of high-frequency components, then perform spectral graph filters on subgraphs to achieve node embeddings with different frequency bands, and propose the Laplacian smoothing-based upsampling approach to extrapolate the node embedding on subgraphs to the full set of vertices on the original graph. In the end, we add the frequency-aware gated units to decode node embeddings of different frequencies for the downstream tasks. Results on both homophilic and heterophilic graph datasets show that our proposed PyGNN model can outperform many state-of-the-art baselines for node classification.

关键词： Graph Neural Networks Spectral Graph theory Graph Algorithms

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cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series 27th

cPNN: Continuous Progressive Neural Networks for Evolving St...

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27th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Giannini, Federico Ziffer, Giacomo Della Valle, Emanuele DEIB Politecn Milano Via Ponzio 34-5 I-20133 Milan Italy

ISBN: (纸本)9783031333828;9783031333835

Dealing with an unbounded data stream involves overcoming the assumption that data is identically distributed and independent. A data stream can, in fact, exhibit temporal dependencies (i.e., be a time series), and data can change distribution over time (concept drift). The two problems are deeply discussed, and existing solutions address them separately: a joint solution is absent. In addition, learning multiple concepts implies remembering the past (a.k.a. avoiding catastrophic forgetting in Neural Networks' terminology). This work proposes Continuous Progressive Neural Networks (cPNN), a solution that tames concept drifts, handles temporal dependencies, and bypasses catastrophic forgetting. cPNN is a continuous version of Progressive Neural Networks, a methodology for remembering old concepts and transferring past knowledge to fit the new concepts quickly. We base our method on Recurrent Neural Networks and exploit the Stochastic Gradient Descent applied to data streams with temporal dependencies. Results of an ablation study show a quick adaptation of cPNN to new concepts and robustness to drifts.

关键词： data streams Catastrophic forgetting Concept drift

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Modern Theoretical tools for Designing Information Retrieval System 22

Modern Theoretical Tools for Designing Information Retrieval...

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28th ACM SIGKDD conference on knowledge discovery and data mining (KKD)

作者： Xu, Da Ruan, Chuanwei Walmart Labs Sunnyvale CA 94086 USA Instacart San Francisco CA USA

ISBN: (纸本)9781450393850

In the past decade, deep learning has significantly reshaped the landscape of information retrieval (IR). The community has recently begun to notice the potential dangers of overusing less-understood mechanisms and over-simplified assumptions to learn patterns and make decisions. In particular, there is growing concerns on the interpretation, reliability, social impact, and long-term utility of real-world IR systems. Therefore, it has become a pressing issue to bring the IR community comprehensive and systematic tools to understand empirical domain solutions and motivate principled design ideas. We focus on the three pillar stones of modern IR systems: pattern recognition with deep learning, causal inference analysis, and online decision making (with bandits and reinforcement learning). Our objectives are as follows. For pattern recognition, we introduce theoretical tools that address the expressivity, optimization, generalization, and model diagnostic for widespread domain practices, including models from unsupervised, (semi-)supervised, meta-learning, and online learning. For causal inference analysis, we emphasize both learning from observational studies and optimizing online experiment design, leveraging the recent theoretical advancements from various domains. Finally, for online decision making (with bandits and reinforcement learning), we aim to resolve both the conceptual and practical learning, evaluation and deployment challenges by introducing powerful tools from robust optimization and optimal control. Our tutorial is inclusive: we not only cover a broad range of heating topics, more importantly, we substantiate our discussion with the production examples at Walmart and Instacart such that audiences with different backgrounds can learn to leverage the tools as instructed. Our tutorial can serve as a guideline for practitioners seeking justifications and principled design ideas, a playbook for researchers landing their innovations on IR productions, and a

关键词： Information Retrieval theory

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Deep Learning for Prawn Farming Forecasting and Anomaly Detection 26th

Deep Learning for Prawn Farming Forecasting and Anomaly Dete...

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26th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Dabrowski, Joel Janek Rahman, Ashfaqur Hellicar, Andrew Rana, Mashud Arnold, Stuart CSIRO Data61 Canberra ACT Australia CSIRO Agr & Food Canberra ACT Australia

ISBN: (纸本)9783031059810;9783031059803

We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-h forecasting and anomaly detection of water quality parameters. It provides prawn farmers with tools to proactively avoid a poor growing environment, thereby optimising growth and reducing the risk of losing stock. This is a major shift for farmers who are forced to manage ponds by reactively correcting poor water quality conditions. To our knowledge, we are the first to apply Transformer as an anomaly detection model, and the first to apply anomaly detection in general to this aquaculture problem. Our technical contributions include adapting ForecastNet for multivariate data and adapting Transformer and the Attention model to incorporate weather forecast data into their decoders. We attain an average mean absolute percentage error of 12% for dissolved oxygen forecasts and we demonstrate two anomaly detection case studies. The system is successfully running in its second year of deployment on a commercial prawn farm.

关键词： Forecasting Anomaly detection Dissolved oxygen prediction Water quality

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Joint Latent Topic discovery and Expectation Modeling for Financial Markets 27th

Joint Latent Topic Discovery and Expectation Modeling for Fi...

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27th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Wang, Lili Huang, Chenghan Gao, Chongyang Ma, Weicheng Vosoughi, Soroush Dartmouth Coll Hanover NH 03755 USA Millennium Management LLC New York NY 10022 USA Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9783031333798;9783031333804

In the pursuit of accurate and scalable quantitative methods for financial market analysis, the focus has shifted from individual stock models to those capturing interrelations between companies and their stocks. However, current relational stock methods are limited by their reliance on predefined stock relationships and the exclusive consideration of immediate effects. To address these limitations, we present a groundbreaking framework for financial market analysis. This approach, to our knowledge, is the first to jointly model investor expectations and automatically mine latent stock relationships. Comprehensive experiments conducted on China's CSI 300, one of the world's largest markets, demonstrate that our model consistently achieves an annual return exceeding 10%. This performance surpasses existing benchmarks, setting a new state-of-the-art standard in stock return prediction and multiyear trading simulations (i.e., backtesting).

关键词： stock trend prediction trading simulation expectation modeling

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Large-Scale Acoustic Automobile Fault Detection: Diagnosing Engines Through Sound 22

Large-Scale Acoustic Automobile Fault Detection: Diagnosing ...

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28th ACM SIGKDD conference on knowledge discovery and data mining (KKD)

作者： Fedorishin, Dennis Birgiolas, Justas Mohan, Deen Dayal Forte, Livio, iii Schneider, Philip Setlur, Srirangaraj Govindaraju, Venu Univ Buffalo Buffalo NY 14203 USA ACV Auct Inc Ronin Inst Buffalo NY USA ACV Auct Inc Buffalo NY USA

ISBN: (纸本)9781450393850

In this paper we present AMPNet, an acoustic abnormality detection model deployed at ACV Auctions to automatically identify engine faults of vehicles listed on the ACV Auctions platform. We investigate the problem of engine fault detection and discuss our approach of deep-learning based audio classification on a largescale automobile dataset collected at ACV Auctions. Specifically, we discuss our data collection pipeline and its challenges, dataset preprocessing and training procedures, and deployment of our trained models into a production setting. We perform empirical evaluations of AMPNet and demonstrate that our framework is able to successfully capture various engine anomalies agnostic of vehicle type. Finally we demonstrate the effectiveness and impact of AMPNet in the real world, specifically showing a 20.85% reduction in vehicle arbitrations on ACV Auctions' live auction platform.

关键词： Engine fault detection classification multi-modal feature fusion audio vibration

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