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Learning Social Graph for Inactive User Recommendation 29th

Learning Social Graph for Inactive User Recommendation

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Liu, Nian Fan, Shen Bai, Ting Wang, Peng Sun, Mingwei Mo, Yanhu Xu, Xiaoxiao Liu, Hong Shi, Chuan Alibaba Grp Holding Ltd Hangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9789819755714;9789819755721

Social relations have been widely incorporated into recommender systems to alleviate data sparsity problem. However, raw social relations don't always benefit recommendation due to their inferior quality and insufficient quantity, especially for inactive users, whose interacted items are limited. In this paper, we propose a novel social recommendation method called LSIR (Learning Social Graph for Inactive User Recommendation) that learns an optimal social graph structure for social recommendation, especially for inactive users. LSIR recursively aggregates user and item embeddings to collaboratively encode item and user features. Then, graph structure learning (GSL) is employed to refine the raw user-user social graph, by removing noisy edges and adding new edges based on the enhanced embeddings. Meanwhile, mimic learning is implemented to guide active users in mimicking inactive users during model training, which improves the construction of new edges for inactive users. Extensive experiments on real-world datasets demonstrate that LSIR achieves significant improvements of up to 129.58% on NDCG in inactive user recommendation. Our code is available at https://***/liun-online/LSIR.

关键词： Social recommendation Graph structure learning

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Leveraging e-Surveys for Investigating Sustainable Urban Mobility: A Case Study in Small Community 24th

Leveraging e-Surveys for Investigating Sustainable Urban Mob...

引用

24th International Conference on Computational science and Its Applications (ICCSA)

作者： D'Apuzzo, Mauro Nardoianni, Sofia Cappelli, Giuseppe Furioso, Martina Nicolosi, Vittorio Univ Cassino & Southern Lazio Via G Di Biasio 43 I-03043 Cassino Italy Univ Sch Adv Studies IUSS Piazza Vittoria 15 I-27100 Pavia Italy Univ Roma Tor Vergata Via Politecn 1 I-00133 Rome Italy

ISBN: (纸本)9783031653421;9783031653438

As urban environments grapple with the challenges posed by increased transportation demands, the quest for sustainable mobility solutions becomes paramount. This study explores the efficacy of electronic surveys (e-surveys) as a powerful tool to delve into the intricacies of sustainable mobility in urban settings. The present research work focusing on a small city in central Italy, and sees the diffusion of e-surveys to its residents. The originality of the research lies in investigating mobility needs and patterns and propensity towards sustainable mobility at a scale of a medium to small urban community on which there is nowadays a dramatic lack of information. As a matter of fact, last mobility data provided at urban level by Italian Statistical Institute date back to 2011 and take into account only commuting traffic whereas recent surveys provide commuting traffic pattern only at national level. It is foreseen that the survey outcomes may contribute valuable insights into sustainable mobility patterns, fostering a deeper understanding of urban transportation dynamics and providing the development of future mobility strategies.

关键词： E-Survey Sustainable mobility Sharing mobility

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Convolutional Neural Networks in Medical Imaging: A Review 15th

Convolutional Neural Networks in Medical Imaging: A Review

引用

15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Lin, Anjie Su, Bianping Ning, Yihe Zhang, Longqing He, Yantao Guangdong Univ Sci & Technol 99 Xihu Rd Dongguan 523083 Peoples R China Xijing Hosp 127 West Rd Xian 710032 Peoples R China

ISBN: (纸本)9789819771837;9789819771844

This review article traces the trajectory of Convolutional Neural Networks, providing an in-depth analysis of their foundational components: convolutional, pooling, and fully connected layers, and their integral function in feature extraction and image classification. It offers a comprehensive survey of landmark architectures such as LeNet, AlexNet, GoogLeNet, VGGNet, ResNet, ShuffleNet, and EfficientNet, examining their variants and their significant contributions to medical image processing. The review corroborates the transformative impact of CNNs in pivotal medical applications, including cancer detection, Alzheimer's diagnosis, and brain tumor identification. Additionally, It also examines the current challenges and issues, and outlines potential future directions for the field's evolution.

关键词： Convolutional Neural Networks Medical Imaging Deep Learning

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Enhancing the Parallel UC2B Framework: Approach Validation and Scalability Study 24th

Enhancing the Parallel UC2B Framework: Approach Validation a...

引用

24th International Conference on Computational science (ICCS)

作者： Ziani, Zineb Emad, Nahid Tsuji, Miwako Sato, Mitsuhisa Univ Paris Saclay Li PaRAD MDLS Paris France Numeryx 17 Rue Jeanne Braconnier F-92360 Meudon France RIKEN Ctr Computat Sci Kobe Hyogo 6500047 Japan

ISBN: (纸本)9783031637858;9783031637834

Anomaly detection is a critical aspect of uncovering unusual patterns in data analysis. This involves distinguishing between normal patterns and abnormal ones, which inherently involves uncertainty. This paper presents an enhanced version of the parallel UC2B framework for anomaly detection, previously introduced in a different context. In this work, we present an extension of the framework and present its large-scale evaluation on the Supercomputer Fugaku. The focus is on assessing its scalability by leveraging a great number of nodes to process large-scale datasets within the cybersecurity domain, using the UNSW-NB15 dataset. The ensemble learning techniques and inherent parallelizability of the Unite and Conquer approach are highlighted as key components, contributing to the framework's computational efficiency, scalability, and accuracy. This study expands upon the framework's capabilities and emphasizes its potential integration into an existing Security Orchestration, Automation, and Response (SOAR) system for enhancing cyber threat detection and response.

关键词： Anomaly Detection Linear Algebra Unite and Conquer Approach Machine Learning High performance computing Ensemble learning Uncertainties UC2B UCEL Cybersecurity

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Variable Neighborhood Search in Hamming Space 14th

Variable Neighborhood Search in Hamming Space

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14th International Conference on Large-Scale Scientific Computations (LSSC)

作者： Hengeveld, S. B. Mucherino, A. Univ Rennes IRISA Rennes France

ISBN: (纸本)9783031562075;9783031562082

Variable Neighbourhood Search (VNS) is one of the most used meta-heuristics for global optimization. We focus our attention on combinatorial problems and propose a new implementation of VNS in Hamming space. Together with the basic VNS approach initially proposed by Hansen and Mladenovic, we present a comparison among the most known VNS variants and we adapt them for the Hamming space. Our VNS is coded in Java programming language and implements the interfaces of the binMeta public project, where other meta-heuristics were previously included. Our computational experiments, on a small selection of combinatorial problems, show that our new VNS implementations outperform the other meta-heuristics present in binMeta.

关键词： Global optimization

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MGSD: Multi-Graph Joint Framework Based on Semantic Dependency for Chinese NER 29th

MGSD: Multi-Graph Joint Framework Based on Semantic Dependen...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： Feng, Zefeng Zhang, Yingqi Mao, Shun Jiang, Yuncheng South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China South China Normal Univ Sch Artificial Intelligence Foshan 528225 Peoples R China South China Normal Univ Sch Software Foshan 528225 Peoples R China

ISBN: (纸本)9789819755684;9789819755691

In the realm of Natural Language Processing (NLP), Named Entity Recognition (NER) holds a pivotal position as a foundational component. Recently, the majority of existing approaches addressed the Chinese NER task by leveraging the lexicon enhancement method. However, these lexicon-dependent approaches can be susceptible to confusion caused by lexicon words, which can result in the recognition of false entities. Moreover, using only the lexicon in complex texts, dialects, and irregular sentences could lead to relatively poor results due to the lack of dependency information between Chinese words. To address these issues, in this paper, we propose a Multi-graph joint framework based on semantic dependency for Chinese NER (MGSD). We use semantic dependency relationships and lexical knowledge to construct four graphs that describe the connections between characters and words. After that, we leverage Graph Attention Network to extract features from these four graphs. With these features of Chinese phrases, our model can explicitly improve the issue of relying solely on lexicons. Experimental outcomes obtained from four Chinese NER datasets demonstrate the effectiveness of our model and outperform the state-of-the-art (SOTA) results.

关键词： Chinese Named Entity Recognition Semantic Dependency Relationhip Lexical Knowledge Graph Attention Network

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Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction 21st

Treat Different Negatives Differently: Enriching Loss Functi...

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21st International Conference on The Semantic Web (ESWC)

作者： Hubert, Nicolas Monnin, Pierre Brun, Armelle Monticolo, Davy Univ Lorraine ERPI Nancy France Univ Lorraine LORIA CNRS Nancy France Univ Cote Azur Inria I3S CNRS Sophia Antipolis France

ISBN: (纸本)9783031606250;9783031606267

Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that consider batches of true and false triples. However, different kinds of false triples exist and recent works suggest that they should not be valued equally, leading to specific negative sampling procedures. In line with this recent assumption, we posit that negative triples that are semantically valid w.r.t. signatures of relations (domain and range) are high-quality negatives. Hence, we enrich the three main loss functions for link prediction such that all kinds of negatives are sampled but treated differently based on their semantic validity. In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results which demonstrates both the generality and superiority of our proposed approach. In fact, the proposed loss functions (1) lead to better MRR and Hits@10 values, and (2) drive KGEMs towards better semantic correctness as measured by the Sem@K metric. This highlights that relation signatures globally improve KGEMs, and thus should be incorporated into loss functions. Domains and ranges of relations being largely available in schema-defined KGs, this makes our approach both beneficial and widely usable in practice.

关键词： Knowledge Graph Embeddings Link Prediction Schema-based Learning Loss Functions

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Correlation Matters: A Stock Price Predication Model Based on the Graph Convolutional Network 20th

Correlation Matters: A Stock Price Predication Model Based o...

引用

20th International Conference on Intelligent Computing (ICIC)

作者： Xin, Chengkun Han, Qian Pan, Gang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Sch Future Technol Tianjin Peoples R China Sun Yat Sen Univ Lingnan Coll Guangzhou Peoples R China

ISBN: (纸本)9789819755875;9789819755882

In the financial markets, accurate prediction of stocks is crucial for formulating investment strategies. Previous research predominantly relied on a stock's historical information for prediction, but overlooked the cross-effects between stocks. However, stocks are closely connected rather than independent of each other. This work introduces a deep learning framework named StockGCN for stock prediction, which can be easily extended by integrating other modules. By constructing a stock graph structure, the model transforms the prediction of individual stocks into the prediction of the entire graph. Experiments show that StockGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale stock networks and consistently outperforms state-of-the-art baselines on real-world stock datasets.

关键词： Stock Predicition Graph Convolutional Network Interrelationship Industry Graph

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Closing the Efficiency Gap Between Synchronous and Network-Agnostic Consensus 43rd

Closing the Efficiency Gap Between Synchronous and Network-A...

引用

43rd Annual International Conference on the Theory and Applications of Cryptographic Techniques (EUROCRYPT)

作者： Deligios, Giovanni Erbes, Mose Mizrahi Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9783031587399;9783031587405

In the consensus problem, n parties want to agree on a common value, even if some of them are corrupt and arbitrarily misbehave. If the parties have a common input m, then they must agree on m. Protocols solving consensus assume either a synchronous communication network, where messages are delivered within a known time, or an asynchronous network with arbitrary delays. Asynchronous protocols only tolerate t(alpha) < n/3 corrupt parties. Synchronous ones can tolerate t(s)< n/2 corruptions with setup, but their security completely breaks down if the synchrony assumptions are violated. Network-agnostic consensus protocols, as introduced by Blum, Katz, and Loss [TCC'19], are secure regardless of network conditions, tolerating up to t(s) corruptions with synchrony and t(alpha) without, under provably optimal assumptions t(alpha) = t(s) and 2t(s) + t(alpha) < n. Despite efforts to improve their efficiency, all known network-agnostic protocols fall short of the asymptotic complexity of state-of-the-art purely synchronous protocols. In this work, we introduce a novel technique to compile any synchronous and any asynchronous consensus protocols into a network-agnostic one. This process only incurs a small constant number of overhead rounds, so that the compiled protocol matches the optimal round complexity for synchronous protocols. Our compiler also preserves under a variety of assumptions the asymptotic communication complexity of state-of-the-art synchronous and asynchronous protocols. Hence, it closes the current efficiency gap between synchronous and network-agnostic consensus. As a plus, our protocols support L-bit inputs, and can be extended to achieve communication complexity O(n(2)kappa + Ln) under the assumptions for which this is known to be possible for purely synchronous protocols.

关键词： Efficiency

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Fast Path Planning Algorithm for 3D Indoor Scene Roaming Based on Path Table 20th

Fast Path Planning Algorithm for 3D Indoor Scene Roaming Bas...

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20th International Conference on Intelligent Computing (ICIC)

作者： Song, Pei-Hua Li, Ying Jia, Jin-Yuan Nanning Normal Univ Guangxi Key Lab Human Machine Interact & Intellig Nanning 530001 Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9789819755806;9789819755813

Automatic roaming of indoor scenes has become a research hotspot in three-dimensional graphics, and its core issue is the rapid generation of roaming paths. This paper proposes an indoor hierarchical roaming path planning algorithm based on path tables to solve the problems of long calculation time and large memory overhead when using the A* algorithm for roaming path planning in large-scale indoor scenes. First, we construct the path table based on the closed characteristics of the indoor room. The path table enables quick discovery of the path from any position in the room to the door. Then, we implement the hierarchical idea and search the roaming path in segments, using the door position as a local target point. We generate and store the path between each door offline to improve the efficiency of path planning. Finally, the Bezier curve is used to smooth the roaming path. Experimental results show that compared with the A* algorithm, our proposed algorithm significantly reduces the number of search nodes, the number of path corners, and path planning time, effectively improving the efficiency of indoor scene roaming path planning.

关键词： Fast path planning 3D indoor roaming Indoor path table A* algorithm

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