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arXiv 2023年

作者： Xiong, Haoyi Bian, Jiang Yang, Sijia Zhang, Xiaofei Kong, Linghe Zhang, Daqing Big Data Lab. Baidu Inc. No.10 Xibeiwang East Road Haidian Beijing100085 China Beijing University of Posts and Telecommunications 10 Xitucheng Rd Haidian Beijing100876 China Baidu Inc. No.10 Xibeiwang East Road Haidian Beijing100085 China Department of Computer Science and Engineering Shanghai Jiaotong University 800 Dongchuan Road Minghang Shanghai200240 China Télécom SudParis Institut Polytechnique de Paris Evry Courcouronnes 9 Rue Charles Fourier Essonne 91000 France

Large language models (LLMs) have become phenomenally surging1, since 2018 – two decades after introducing context-awareness into computing systems [1–4]. Through taking into account the situations of ubiquitous devices, users and the societies, context-aware computing has enabled a wide spectrum of innovative applications, such as assisted living, location-based social network services and so on. To recognize contexts and make decisions for actions accordingly, various artificial intelligence technologies, such as Ontology and OWL, have been adopted as representations for context modeling and reasoning [5, 6]. Recently, with the rise of LLMs and their improved natural language understanding and reasoning capabilities, it has become feasible to model contexts using natural language and perform context reasoning by interacting with LLMs such as ChatGPT and GPT-4. In this tutorial, we demonstrate the use of texts, prompts, and autonomous agents (AutoAgents) that enable LLMs to perform context modeling and reasoning without requiring fine-tuning of the model. We organize and introduce works in the related field, and name this computing paradigm as the LLM-driven Context-aware Computing (LCaC). In the LCaC paradigm, users’ requests, sensors reading data, and the command to actuators are supposed to be represented as texts. Given the text of users’ request and sensor data, the AutoAgent models the context by prompting and sends to the LLM for context reasoning. LLM generates a plan of actions and responds to the AutoAgent, which later follows the action plan to foster context-awareness. To prove the concepts, we use two showcases – (1) operating a mobile z-arm in an apartment for assisted living, and (2) planning a trip and scheduling the itinerary in a context-aware and personalized manner. Furthermore, we analyze several factors that might affect the performance of LLM-driven context-awareness, and then discuss the future research directions. © 2023, CC BY.

关键词： Modeling languages

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Argumentation Based Ontology Alignment Extraction 6th

Argumentation Based Ontology Alignment Extraction

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Wu, Xiaojing Xue, Xingsi Hu, Wenyu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33 Xuefu South Road Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China School of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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Artificial intelligence-assisted niacin skin flush screening in early psychosis identification and prediction

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General Psychiatry 2022年第2期35卷 76-78页

作者： Tao Chen Haichun Liu Renfang Tian Ranpiao Gan Wenzuo Xu Tianhong Zhang Jijun Wang Department of Economics University of WaterlooWaterlooOntarioCanada Depatment of Statistics and Actuarial Science(Cross Appointed) University of Waterloo WaterlooOntarioCanada Big Data Research Lab Universty of WaterlooWaterlooOntarioCanada Shanghai Niacin Tech Co. LtdShanghal China Labor and Worklife Program Harvard UniversityCambridgeMassachusettsUSA Department of Automation Shanghai Jao Tong UniversityShanghaiChina Ningbo Artificial Intelligent Institute Shanghai Jiao Tong UniversityNingboChina School of Management Economicsand MathematicsKing's University CollegeLondon0ntarioCanada Shanghai Intelligent Psychological Evaluaton and Intervention Engineering Technology Research Center Shanghai Key Laboratory of Psychotic DisordersShanghai Mental Health CenterShanghai Jiao Tong University School of MedicineShanghaiChina Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders(Ministry of Education)ShanghaiChina CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina Institute of Psychology and Behavioral Science Shanghai Jiao Tong UniversiyShanghaiChina

Schizophrenia is a devastating mental disorder affecting 20 million people *** diagnosis is crucial for disease management and improvement in prognosis,and diagnostic biomarkerscan serveasobjective indicators for the early screening of the *** on the observation of diminished flush responses to niacin in patients with schizophrenia Horrobin proposed anoninvasive niacin skin flush screening for schizophrenia.

关键词： diagnosis prognosis prediction

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A Multi-population QUasi-Affine TRansformation Evolution Algorithm for Global Optimization 12th

A Multi-population QUasi-Affine TRansformation Evolution Alg...

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12th International Conference on Genetic and Evolutionary Computing, ICGEC 2018

作者： Liu, Nengxian Pan, Jeng-Shyang Liao, Xiangwen Chen, Guolong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fuzhou China

ISBN: (纸本)9789811358401

In this paper, we propose a new Multi-Population QUasi-Affine TRansformation Evolution (MP-QUATRE) algorithm for global optimization. The proposed MP-QUATRE algorithm divides the population into three sub-populations with a sort strategy to maintain population diversities, and each sub-population adopts a different mutation scheme to make a good balance between exploration and exploitation capability. In the experiments, we compare the proposed algorithm with DE algorithm and QUATRE algorithm on CEC2013 test suite for real-parameter optimization. The experimental results indicate that the proposed MP-QUATRE algorithm has a better performance than the competing algorithms. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Global optimization

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Adaptive gradient methods with dynamic bound of learning rate 7

Adaptive gradient methods with dynamic bound of learning rat...

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7th International Conference on Learning Representations, ICLR 2019

作者： Luo, Liangchen Xiong, Yuanhao Liu, Yan Sun, Xu MOE Key Lab of Computational Linguistics School of EECS Peking University China College of Information Science and Electronic Engineering Zhejiang University China Department of Computer Science University of Southern California United States Center for Data Science Beijing Institute of Big Data Research Peking University China

Adaptive optimization methods such as ADAGRAD, RMSPROP and ADAM have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGRAD to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of ADAM and AMSGRAD, called ADABOUND and AMSBOUND respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://***/Luolc/AdaBound. © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

关键词： Gradient methods

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A Co-Verification Interface Design for High-Assurance CPS

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Computers, Materials & Continua 2019年第1期58卷 287-306页

作者： Yu Zhang Mengxing Huang Hao Wang Wenlong Feng Jieren Cheng State Key Laboratory of Marine Resource Utilization in South China Sea Hainan UniversityHaikou570228China College of Information Science and Technology Hainan UniversityHaikou570228China Big Data Lab Department of ICT and Natural SciencesNorwegian University of Science and TechnologyPostboks 1517N-6025 AalesundNorway

Cyber-Physical Systems(CPS)tightly integrate cyber and physical components and transcend traditional control systems and embedded *** systems are often mission-critical;therefore,they must be *** CPS require co-verification which takes a comprehensive view of the whole system to verify the correctness of a cyber and physical components *** of strict multiple semantic definition for interaction between the two domains has been considered as an obstacle to the CPS co-verification.A Cyber/Physical interface model for hierarchical a verification of CPS is ***,we studied the interaction mechanism between computation and physical *** further classify the interaction mechanism into two levels:logic interaction level and physical interaction *** define different types of interface model according to combinatorial relationships of the A/D(Analog to Digital)and D/A(Digital to Analog)conversion periodical *** interface model has formal semantics,and is efficient for simulation and formal *** experiment results show that our approach has major potential in verifying system level properties of complex CPS,therefore improving the high-assurance of CPS.

关键词： CPS interface co-verification co-simulation high-assurance

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Hyperbolic variational graph neural network for modeling dynamic graphs

arXiv

引用

arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Zhang, Jiawei Wang, Feiyang Peng, Hao Su, Sen Yu, Philip S. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China IFM Lab Department of Computer Science Florida State University FL United States Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Department of Computer Science University of Illinois ChicagoIL United States

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representation in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets. © 2021, CC BY-NC-SA.

关键词： Normal distribution

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XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

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Nature Communications 2023年第1期14卷 6870页

作者： Cai, Mingxuan Wang, Zhiwei Xiao, Jiashun Hu, Xianghong Chen, Gang Yang, Can Department of Biostatistics City University of Hong Kong Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute Guangzhou 511458 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data Shenzhen 518172 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha 410083 China WeGene Shenzhen Zaozhidao Technology Co. Ltd Shenzhen 518040 China Graduate Affairs Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand

Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution. © 2023, The Author(s).

关键词：

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T-EGAT: A Temporal Edge Enhanced Graph Attention Network for Tax Evasion Detection

T-EGAT: A Temporal Edge Enhanced Graph Attention Network for...

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IEEE International Conference on big data

作者： Yiyang Wang Qinghua Zheng Jianfei Ruan Yuda Gao Yan Chen Xuanya Li Bo Dong SPKLSTN Lab School of Computer Science and Technology Xi’an Jiaotong University Baidu Inc. National Engineering Lab of Big Data Analytics School of Distance Education Xi’an Jiaotong University

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Tax evasion refers to the illegal act of taxpayers using deception and concealment to avoid paying taxes. How to detect tax evasion effectively is always an important topic for the government and academic researchers. Recent research has proposed using machine learning technologies to detect tax evasion and has achieved good results in some specific conditions. However, recent methods have three shortcomings. First, recent methods mainly use the basic features extracted based on expert experience. Second, recent methods do not make full use of the edge features of the transaction network. Third, recent methods cannot adapt to a dynamic transaction network. To overcome these challenges, we propose a novel tax evasion detection method, the temporal edge enhanced graph attention network (T-EGAT), which combines the edge enhanced graph attention network (EGAT) and the recurrent weighted average unit (RWA). Specifically, the EGAT is used to learn complex topological structures for capturing spatial dependence and the RWA is used to learn the dynamic changes of transaction data for capturing temporal dependence. Experimental tests using real-world tax data demonstrate that our method achieves better performance at detecting tax evaders than existing methods.

关键词： Image edge detection Neural networks Finance big data Feature extraction data models Task analysis

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RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr

arXiv

引用

arXiv 2020年

作者： Li, Xingjian Xiong, Haoyi An, Haozhe Xu, Chengzhong Dou, Dejing Big Data Lab Baidu Research Beijing China Faculty of Science and Technology University of Macau Macau SAR China State Key Lab of IOTSC Department of Computer Science University of Macau Macau SAR China

Fine-tuning the deep convolution neural network (CNN) using a pre–trained model helps transfer knowledge learned from larger datasets to the target task. While the accuracy could be largely improved even when the training dataset is small, the transfer learning outcome is usually constrained by the pre-trained model with close CNN weights (Liu et al., 2019), as the backpropagation here brings smaller updates to deeper CNN layers. In this work, we propose RIFLE– a simple yet effective strategy that deepens backpropagation in transfer learning settings, through periodically Re-Initializing the Fullyconnected LayEr with random scratch during the fine-tuning procedure. RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning, while the effects of randomization can be easily converged throughout the overall learning procedure. The experiments show that the use of RIFLE significantly improves deep transfer learning accuracy on a wide range of datasets, outperforming known tricks for the similar purpose, such as Dropout, DropConnect, Stochastic Depth, Disturb label and Cyclic Learning Rate, under the same settings with 0.5%–2% higher testing accuracy. Empirical cases and ablation studies further indicate RIFLE brings meaningful updates to deep CNN layers with accuracy improved. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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