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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availab.lity of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collab.rative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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Drug package recommendation via interaction-aware graph induction

arXiv

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arXiv 2021年

作者： Zheng, Zhi Wang, Chao Xu, Tong Shen, Dazhong Qin, Penggang Huai, Baoxing Liu, Tongzhu Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China Huawei Technologies China First Affiliated Hospital of USTC China

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support the intelligent medical services such as drug recommendation. However, prior arts mainly follow the traditional recommendation strategies like collab.rative filtering, which usually treat individual drugs as mutually independent, while the latent interactions among drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. Specifically, we first design a pre-training method based on neural collab.rative filtering to get the initial embedding of patients and drugs. Then, the drug interaction graph will be initialized based on medical records and domain knowledge. Along this line, we propose a new Drug Package Recommendation (DPR) framework with two variants, respectively DPR on Weighted Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in which each the interactions will be described as signed weights or attribute vectors. In detail, a mask layer is utilized to capture the impact of patient condition, and graph neural networks (GNNs) are leveraged for the final graph induction task to embed the package. Extensive experiments on a real-world data set from a first-rate hospital demonstrate the effectiveness of our DPR framework compared with several competitive baseline methods, and further support the heuristic study for the drug package generation task with adequate performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore 29

Taxi travel time based Geographically Weighted Regression Mo...

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29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Wang, Yi'an Cai, Fangyi Cheng, Shih-Fen Wu, Bo Cao, Kai School of Geographic Sciences East China Normal University Shanghai China East China Normal University Key Laboratory of Geographic Information Science Ministry of Education Shanghai China East China Normal University Key Lab. of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai China National University of Singapore Department of Geography Singapore School of Computing and Information Systems Singapore Management University Singapore School of Geography and Environment Jiangxi Normal University Jiangxi Nanchang China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling. © 2022 IEEE.

关键词： Travel time

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Multimodal Dialogue Systems via Capturing Context-aware Dependencies of Semantic Elements 20

Multimodal Dialogue Systems via Capturing Context-aware Depe...

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28th ACM International Conference on Multimedia, MM 2020

作者： He, Weidong Li, Zhi Lu, Dongcai Chen, Enhong Huai, Baoxing Yuan, Jing Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Data Science University of Science and Technology of China China HUAWEI Technologies Hangzhou China

ISBN: (纸本)9781450379885

Recently, multimodal dialogue systems have engaged increasing attention in several domains such as retail, travel, etc. In spite of the promising performance of pioneer works, existing studies usually focus on utterance-level semantic representations with hierarchical structures, which ignore the context-aware dependencies of multimodal semantic elements, i.e., words and images. Moreover, when integrating the visual content, they only consider images of the current turn, leaving out ones of previous turns as well as their ordinal information. To address these issues, we propose a Multimodal diAlogue systems with semanTic Elements, MATE for short. Specifically, we unfold the multimodal inputs and devise a Multimodal Element-level Encoder to obtain the semantic representation at element-level. Besides, we take into consideration all images that might be relevant to the current turn and inject the sequential characteristics of images through position encoding. Finally, we make comprehensive experiments on a public multimodal dialogue dataset in the retail domain, and improve the BLUE-4 score by 9.49, and NIST score by 1.8469 compared with state-of-the-art methods. © 2020 ACM.

关键词： Semantics

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VNF Service Chain Deployment Algorithm in 5G Communication based on Reinforcement Learning

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2021年第1期48卷 1页

作者： Xuan, Hejun Zhao, Xuelin Fan, Jianwei Xue, Yahui Zhu, Fangfang Li, Yanling School of Computer Science and Technology Xinyang Normal University Xinyang464000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University XinyangHenan464000 China

With the rapidly deployment of 5G communication, network functional virtualization has became one of key technology. However, how to achieve virtual network function service chaining (VNF-SC) adaptively and cost-effectively in an inter datacenter elastic optical network (interDC-EON) has become an interesting and challenging problem. In addition, network function virtualization has became one of the key technology for 5G Commons. In this paper, we propose a resource scheduling framework based on artificial intelligence. In addition, an effective VNF-SC deployment algorithm based on deep reinforcement learning (RL) is designed. In this algorithm, VNF-SC deployed scheme is obtained by using deep Q-learning according to the state of the network and thereward of the action. To verify the efficient of the proposed algorithm, a large number of experiments have been conducted. Experimental results demonstrate that the RL achieves better performance than several benchmarks, in terms of balancing the tradeoff among the overall resource utilization, the vNF-SC request blocking probability. © 2021, IAENG International Journal of Computer Science. All Rights Reserved.

关键词： Reinforcement learning

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Unavailab.e time aware scheduling of hybrid task on heterogeneous distributed system

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IAENG International Journal of Applied Mathematics 2020年第1期50卷 133-146页

作者： Xuan, Hejun Wei, Shiwei Zhao, Xuelin Zhou, Yang Ma, Xingpo Liu, Daohua Li, Yanling School of Computer and Information Technology Xinyang Normal University Henan Xinyang China School of Computer and Technology Guilin University of Aerospace Technology Guilin541000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University Henan Xinyang464000 China

The resource allocation for tasks in heterogeneous distributed system is a well known NP-hard problem. For the sake of making the makespan is minimized, it is hard to distribute the tasks to proper processors. The problem is even more complex and challenging when the processors have unavailab.e time and the tasks type are various. This paper investigates a resource allocation problem for hybrid tasks comprising both divisible and bag-of-tasks(BoT) in heterogeneous distributed system when the processors has unavailab.e time. First, the mathematical model, which minimizes the makespan of the hybrid tasks when the processors have unavailab.e time, is established. Second, we propose a scheduling algorithm referred to as bag-of-tasks allocate-pull and divisible task allocation (BoTAPDTA) algorithm for handling hybrid tasks on heterogeneous distributed systems. In addition, to solving the optimization model efficiently, a generic algorithm(GA) is proposed. For the sake of reducing the search space and solving the optimization model effectively, a two step scheduling algorithm(TSGA), which first allocate bag-of-tasks(BoT) using generic algorithm and then assign divisible task to processors like BoTAPDTA, is designed. Finally, numerical simulation experiments are conducted, and experimental results indicate the effectiveness of the proposed model and algorithm. © 2020.

关键词： Scheduling algorithms

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SIFN: A sentiment-aware interactive fusion network for review-based item recommendation

arXiv

引用

arXiv 2021年

作者： Zhang, Kai Qian, Hao Liu, Qi Zhang, Zhiqiang Zhou, Jun Ma, Jianhui Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application School of Data Science University of Science and Technology of China Ant Group Hangzhou China School of Computer Science and Technology University of Science and Technology of China

Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, despite being extensively studied, these methods still suffer from some limitations. First, previous studies either encode the document or extract latent sentiment via neural networks, which are difficult to interpret the sentiment of reviewers intuitively. Second, they neglect the personalized interaction of reviews with user/item, i.e., each review has different contributions when modeling the sentiment preference of user/item. To remedy these issues, we propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment lab.ls. Finally, we design a rating prediction task that contains a rating learner with an interactive and fusion module to fuse the identity (i.e., user and item ID) and each review representation so that various interactive features can synergistically influence the final rating score. Experimental results on five real-world datasets demonstrate that the proposed model is superior to state-of-the-art models. © 2021, CC BY.

关键词： Encoding (symbols)

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Boosting Factorization Machines via Saliency-Guided Mixup

arXiv

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arXiv 2022年

作者： Wu, Chenwang Lian, Defu Ge, Yong Zhou, Min Chen, Enhong Tao, Dacheng School of Data Science University of Science and Technology of China Anhui Hefei230000 China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Anhui Hefei230000 China Eller College of Management University of Arizona United States Huawei Noah’s Ark Lab Hong Kong JD Explore Academy in *** China University of Sydney Australia

Factorization machines (FMs) are widely used in recommender systems due to their adaptability and ability to learn from sparse data. However, for the ubiquitous non-interactive features in sparse data, existing FMs can only estimate the parameters corresponding to these features via the inner product of their embeddings. Undeniably, they cannot learn the direct interactions of these features, which limits the model’s expressive power. To this end, we first present MixFM, inspired by Mixup, to generate auxiliary training data to boost FMs. Unlike existing augmentation strategies that require lab.r costs and expertise to collect additional information such as position and fields, these extra data generated by MixFM only by the convex combination of the raw ones without any professional knowledge support. More importantly, if the parent samples to be mixed have non-interactive features, MixFM will establish their direct interactions. Second, considering that MixFM may generate redundant or even detrimental instances, we further put forward a novel Factorization Machine powered by Saliency-guided Mixup (denoted as SMFM). Guided by the customized saliency, SMFM can generate more informative neighbor data. Through theoretical analysis, we prove that the proposed methods minimize the upper bound of the generalization error, which hold a beneficial effect on enhancing FMs. Significantly, we give the first generalization bound of FM, implying the generalization requires more data and a smaller embedding size under the sufficient representation capability. Finally, extensive experiments on five datasets confirm that our approaches are superior to baselines. Besides, the results show that"poisoning" mixed data is likewise beneficial to the FM variants. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective

Group-Level Cognitive Diagnosis: A Multi-Task Learning Persp...

引用

IEEE International Conference on data Mining (ICDM)

作者： Jie Huang Qi Liu Fei Wang Zhenya Huang Songtao Fang Runze Wu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Fuxi AI Lab NetEase Inc. Hangzhou China IFLYTEK Research

ISBN: (纸本)9781665423991

Most cognitive diagnosis research in education has been concentrated on individual assessment, aiming at discovering the latent characteristics of students. However, in many real-world scenarios, group-level assessment is an important and meaningful task, e.g., class assessment in different regions can discover the difference of teaching level in different contexts. In this work, we consider assessing cognitive ability for a group of students, which aims to mine groups’ proficiency on specific knowledge concepts. The significant challenge in this task is the sparsity of group-exercise response data, which seriously affects the assessment performance. Existing works either do not make effective use of additional student-exercise response data or fail to reasonably model the relationship between group ability and individual ability in different learning contexts, resulting in sub-optimal diagnosis results. To this end, we propose a general Multi-Task based Group-Level Cognitive Diagnosis (MGCD) framework, which is featured with three special designs: 1) We jointly model student-exercise responses and group-exercise responses in a multi-task manner to alleviate the sparsity of group-exercise responses; 2) We design a context-aware attention network to model the relationship between student knowledge state and group knowledge state in different contexts; 3) We model an interpretable cognitive layer to obtain student ability, group ability and exercise factors (e.g., difficulty), and then we leverage neural networks to learn complex interaction functions among them. Extensive experiments on real-world datasets demonstrate the generality of MGCD and the effectiveness of our attention design and multi-task learning.

关键词： Knowledge engineering Conferences Education Neural networks Multitasking data models data mining

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Graph self-supervised learning for optoelectronic properties of organic semiconductors

arXiv

引用

arXiv 2021年

作者： Zhang, Zaixi Liu, Qi Zhang, Shengyu Hsieh, Chang Yu Shi, Liang Lee, Chee Kong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Anhui Hefei230026 China Tencent Quantum Lab Guangdong Shenzhen518057 China University of California MercedCA95343 United States Tencent America Palo AltoCA94306 United States

The search for new high-performance organic semiconducting molecules is challenging due to the vastness of the chemical space, machine learning methods, particularly deep learning models like graph neural networks (GNNs), have shown promising potential to address such challenge. However, practical applications of GNNs for chemistry are often limited by the availab.lity of lab.lled data. Meanwhile, unlab.lled molecular data is abundant and could potentially be utilized to alleviate the scarcity issue of lab.lled data. Here, we advocate the use of self-supervised learning to improve the performance of GNNs by pre-training them with unlab.led molecular data. We investigate regression problems involving ground and excited state properties, both relevant for optoelectronic properties of organic semiconductors. Additionally, we extend the self-supervised learning strategy to molecules in non-equilibrium configurations which are important for studying the effects of disorder. In all cases, we obtain considerable performance improvement over results without pre-training, in particular when lab.lled training data is limited, and such improvement is attributed to the capability of self-supervised learning in identifying structural similarity among unlab.led molecules. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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