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29th ACM International Conference on Multimedia, MM 2021

作者： Hao, Qianxiu Xu, Qianqian Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781450386517

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance;2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method. © 2021 ACM.

关键词： Embeddings

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Modeling Temporal Dependencies within the Target for Long-Term Time Series Forecasting

arXiv

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arXiv 2024年

作者： Xiong, Qi Tang, Kai Ma, Minbo Zhang, Ji Xu, Jie Li, Tianrui School of Computing and Artificial Intelligence Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education National Engineering Laboratory of Integrated Transportation Big Data Application Technology Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China School of Computing The University of Leeds LeedsLS2 9JT United Kingdom

Long-term time series forecasting (LTSF) is a critical task across diverse domains. Despite significant advancements in LTSF research, we identify a performance bottleneck in existing LTSF methods caused by the inadequate modeling of Temporal Dependencies within the Target (TDT). To address this issue, we propose a novel and generic temporal modeling framework, Temporal Dependency Alignment (TDAlign), that equips existing LTSF methods with TDT learning capabilities. TDAlign introduces two key innovations: 1) a loss function that aligns the change values between adjacent time steps in the predictions with those in the target, ensuring consistency with variation patterns, and 2) an adaptive loss balancing strategy that seamlessly integrates the new loss function with existing LTSF methods without introducing additional learnable parameters. As a plug-and-play framework, TDAlign enhances existing methods with minimal computational overhead, featuring only linear time complexity and constant space complexity relative to the prediction length. Extensive experiments on six strong LTSF baselines across seven real-world datasets demonstrate the effectiveness and flexibility of TDAlign. On average, TDAlign reduces baseline prediction errors by 1.47% to 9.19% and change value errors by 4.57% to 15.78%, highlighting its substantial performance improvements. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series analysis

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Study on Pedestrian Evacuation Model Base on Reinforcement Learning

SSRN

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SSRN 2024年

作者： Rui, Zhu Hu, Jun Ling, Fan Qi, Zhang Juan, Wei School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu611130 China School of Intelligent Manufacturing Chengdu Technological University Chengdu611730 China Key Laboratory of Multidimensional Data Sensing and Intelligent Information Processing Dazhou Key Laboratory Dazhou635000 China

This paper proposed a pedestrian evacuation model combined with reinforcement learning in order to study how to better guide pedestrians to complete evacuation in specific indoor scenes. This model introduced the way of establishing a scene in cellular automata and formulated reward rules according to the characteristics of the scene. It fitted the psychological activities of pedestrians in the actual evacuation process and trained the strategy of pedestrians at the overall level through the Q-learning algorithm from the reinforcement learning area. A speed control mechanism combined with real statistical data was introduced to simulate the speed attenuation. A simulation platform was built to compare the evacuation conditions under different scenarios and the different total numbers of pedestrians. The research showed that the model could automatically realize the exit selection function of pedestrians and part of conformity behavior. In the same evacuation scenario, this model could show adaptability for the different total numbers of pedestrians. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Method of Realizing External Network Access to Intranet for Embedded Web Server 2

A Method of Realizing External Network Access to Intranet fo...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Zhang, Xian Qu, Taiguo Tang, Pengju Liu, Yiwen Key Lab. of Intelligent Contr. Technol. for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China School of Computer Science and Engineering Huaihua University Huaihua China

ISBN: (纸本)9781665464017

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects. © 2022 IEEE.

关键词： Intranets

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Achieving Enhanced Bi-Linear Attention Network for Teaching Manner Analysis over Edge Cloud-assisted AIoT: Voice-Body Coordination Perspective

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IEEE Transactions on Cloud computing 2025年

作者： Zhou, Yu Zou, Sai Wu, Bochun Ni, Wei Du, Xiaojiang Guizhou University Micro-nano and Intelligent Manufacturing Engineering Research Centre of Ministry of Education College of Big Data and Information Engineering China Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai200433 China Data61 CSIRO Australia Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States

Edge computing, an advanced extension of cloud computing, provides superior computational capabilities and lowlatency processing at the network edge, facilitating its availability for real-time data analysis in resource-limited settings. When applied to the analysis of teaching methodologies, edge computing enables the seamless integration of vocal and physical cues, facilitating collaborative, dynamic, and real-time evaluations of teaching quality. However, the inherent complexity of human perception and multimodal interactions impose great challenges to the analysis of these aspects in Artificial Intelligence of Things (AIoT). This paper introduces an innovative mathematical model and a measurement index specifically designed to assess changes in voice-body coordination over time. To achieve this, we propose a cloud-enabled enhanced Bi-Linear Attention Network incorporating entropy and Fourier transforms (BAN-E-FT), which leverages both temporal and frequencydomain features. Specifically, by harnessing the computational and storage capabilities of edge computing, BAN-E-FT facilitates distributed training, expedites large-scale data processing, and enhances model scalability, where entropy measures and Fourier transforms capture modality dynamics, enhancing BAN's fusion capabilities. Moreover, a conditional domain adversarial network is embedded to address regional teaching variations, improving model generalizability. We also verify the robustness of BAN-EFT with accuracy and convergence through convex optimization analysis. Experiments on the eNTERFACE'05 dataset demonstrate 81% accuracy in assessing teaching adaptability, while real-world test at Guizhou University confirms 78% accuracy when using BAN-E-FT, matching human expert assessments. © 2013 IEEE.

关键词： data integration

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Circuit area optimization of multi-output MPRM based on ERWOA algorithm

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2023年第5期49卷 1193-1200页

作者： He, Juncai He, Zhenxue Wang, Fushun Huo, Zhisheng Xiao, Limin Intelligent Agricultural Equipment Research Institute Hebei Agricultural University Baoding071001 China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University Baoding071001 China High Performance Computing Platform Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

Mixed polarity Reed-Muller (MPRM) circuit area optimization has become a research hotspot in the field of integrated circuit design. It is a combinatorial optimization, aiming at finding the MPRM expression with the least number of terms among many MPRM expressions. A explosion strategy and restart strategy based whale optimization algorithm (ERWOA) is proposed. In addition, a multi-output MPRM circuit area optimization method is proposed, which uses the improved whale algorithm and the improved polarity conversion algorithm to search for the MPRM circuit with the least number of AND terms. Results on the MCNC Benchmark circuits show that the proposed algorithm increases the conversion efficiency by 99.93% and 99.96% at most, compared with the mixed polarity and inter-polarity conversion algorithms based on the list technology, respectively. Compared with the genetic algorithm and the artificial bee colony algorithm, the improved whale optimization algorithm saves the circuit area up to 18.32% with an average of 5.54%, and 14.41% with an average of 5.00%, respectively. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Genetic algorithms

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Design of a clever carbon monoxide monitoring system

Design of a clever carbon monoxide monitoring system

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Chinese Control and Decision Conference, CCDC

作者： Yanxi Cao Hongyan Ma Yingda Zhang Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

Carbon monoxide is a colorless and odorless gas that is extremely dangerous. Carbon monoxide cannot be accurately detected or eliminated by the senses in high concentrations in the body under relatively airtight conditions. As a result, the human body can only inhale it passively. Carbon monoxide inhalation can deplete the body's oxygen supply, causing symptoms such as nausea, dizziness, headaches, and, in severe cases, death. In response to the event's insidiousness, urgency, and gravity. Simultaneously, because the current carbon monoxide monitoring system is not portable, it is proposed to design a microcontroller as the core, with a concentration collection module, a wireless transmission module, an LCD module, and so on. Interact with information between intelligent monitoring systems via wireless transmission module and mobile phone APP. The system consists of a microcontroller, concentration acquisition module, wireless transmission module, and LCD display module. And the performance of the system is tested by changing the environmental conditions, while a control test is conducted. The results show that the system has a fast and stable transmission rate, small size, and wireless monitoring function. Users can be informed of environmental conditions via their mobile phones, providing early warning and quick alarms.

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A Unified Stream and Batch Graph computing Model for Community Detection 17th

A Unified Stream and Batch Graph Computing Model for Commu...

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17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Dai, Jinkun Wu, Ling Guo, Kun Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

An essential challenge in graph data analysis and mining is to simply and effectively deal with large-scale network data that is expanding dynamically. Although batch-based parallel graph computation frameworks have better accuracy, they cannot process incremental data on time and need to be recomputed. To process real-time data, stream processing applications need to be redeveloped, which increases the redundancy of work, and some existing dynamic graph computation schemes are not generalizable. This paper proposes a unified stream and batch graph computing model(USBGM). The model is compatible with both stream and batch graph computing. Graph operators and algorithms developed based on the model can handle stream and batch graph data in a unified manner. The experiments on real-world and artificial networks verified the effectiveness and efficiency of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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A Uniformly Spaced Double-Latent Variable Probabilistic Model for Monitoring of Dynamic Processes with Multiple Sampling Rates

SSRN

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SSRN 2024年

作者： Ying, Ze Chang, Yuqing He, Yuchen Wang, Fuli College of Information Science and Engineering Northeastern University Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China

The collections and statistical analysis of production data are essential for process monitoring in modern industries, yet often faces challenges such as multi-sample rates and dynamics. In this paper, we propose a uniformly spaced double-latent variable probabilistic (USDLVP) model that can obtain the measurement correlations within diverse sampling rates (SR). The model utilizes the greatest common divisors of the SR to construct a latent variable structure, effectively addressing the challenges encountered by conventional methods in handling practical multi-sampling rate data. Moreover, to gain a comprehensive understanding of the dynamics in multi-sampling rate processes, a Markov-enhanced double latent variable structure is presented to extract data autocorrelation features from both quality-correlated and uncorrelated perspectives. The expectation maximization (EM) algorithm is employed for model training. Finally, the model is adopted to develop a fault detection method, which is subsequently applied in two industrial plants. The experimental results demonstrate the superiority of the proposed model in handling multi-sampling rate issues. © 2024, The Authors. All rights reserved.

关键词： Process monitoring

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Multi-View Mutual Learning Network for Multimodal Fake News Detection

SSRN

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SSRN 2023年

作者： Cui, Wei Zhang, Xuerui Shang, Mingsheng College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China School of Electronic Information and Communication Engineering Chongqing Aerospace Polytechnic Chongqing400021 China

Multimodal fake news is more deceptive than unimodal content and often has adverse social and economic impacts. However, most existing methods learn modal features from a single perspective, without considering simultaneously learning and sharing knowledge across modalities from different perspectives. Our work presents a novel Multi-View Mutual Learning (MVML) network for multimodal fake news detection, which explores the semantic relationship between words and scenes, as well as words and objects, and investigates the semantic connection that exists between global concepts and local objects from multiple perspectives. We first construct text-scenes and text-objects graphs respectively, and perform intra-graph inference to obtain multi-view features. Then, the multi-view fusion layer based on the cross-view attention mechanism interactively models the cross-view dependencies between the image and text. The extracted features before feeding into the final classifier are further processed via a feed-forward attention module, which adaptively reweights and aggregates the features for redundancy reduction. In addition, to exploit the respective advantages of two multi-view classifiers, we propose a mutual learning mechanism that allows them to perform knowledge distillation and align the learning targets. The proposed MVML is thoroughly assessed on four publicly available benchmark datasets, and the findings demonstrate that it outperforms the existing standard approaches. © 2023, The Authors. All rights reserved.

关键词： Semantics

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