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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Liang, Xin Cheng, Dawei Yang, Fangzhou Luo, Yifeng Qian, Weining Zhou, Aoying Shanghai Engineering Research Center on Big Data Management System Shanghai China School of Data Science and Engineering East China Normal University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China SeekData Inc. Shanghai China

ISBN: (纸本)9780999241165

The share prices of listed companies in the stock trading market are prone to be influenced by various events. Performing event detection could help people to timely identify investment risks and opportunities accompanying these events. The financial events inherently present hierarchical structures, which could be represented as tree-structured schemes in real-life applications, and detecting events could be modeled as a hierarchical multi-label text classification problem, where an event is designated to a tree node with a sequence of hierarchical event category labels. Conventional hierarchical multi-label text classification methods usually ignore the hierarchical relationships existing in the event classification scheme, and treat the hierarchical labels associated with an event as uniform labels, where correct or wrong label predictions are assigned with equal rewards or penalties. In this paper, we propose a neural hierarchical multi-label text classification method, namely F-HMTC, for a financial application scenario with massive event category labels. F-HMTC learns the latent features based on bidirectional encoder representations from transformers, and directly maps them to hierarchical labels with a delicate hierarchy-based loss layer. We conduct extensive experiments on a private financial dataset with elaborately-annotated labels, and F-HMTC consistently outperforms state-of-art baselines by substantial margins. We will release both the source codes and dataset on a public repository1,. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Forestry

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Analysis of Age of Information in Dual Updating systems

arXiv

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arXiv 2022年

作者： Chen, Zhengchuan Deng, Dapeng Yang, Howard H. Pappas, Nikolaos Hu, Limei Jia, Yunjian Wang, Min Quek, Tony Q.S. The School of Microelectronics and Communication Engineering Chongqing University China The Zhejiang University/UIUC Institute Zhejiang University China The Department of Computer and Information Science Linköping University Linköping581 83 Sweden The School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications China The Information System Technology and Design Pillar Sutd Singapore487372 Singapore

We study the average Age of Information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. Although the double queue parallel transmission is instrumental in reducing AoI, the out of order of data arrivals also imposes a significant challenge to the performance analysis. We consider two settings: the M-M system where the service time of two servers is exponentially distributed;the M-D system in which the service time of one server is exponentially distributed and that of the other is deterministic. For the two dual-queue systems, closed-form expressions of average AoI and PAoI are derived by resorting to the graphic method and state flow graph analysis method. Our analysis reveals that compared with the single-queue system with an exponentially distributed service time, the average PAoI and the average AoI of the M-M system can be reduced by 33.3% and 37.5%, respectively. For the M-D system, the reduction in average PAoI and the average AoI are 27.7% and 39.7%, respectively. Numerical results show that the two dual-queue systems also outperform the M/M/2 single queue dual-server system with optimized arrival rate in terms of average AoI and PAoI. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network

arXiv

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arXiv 2022年

作者： Hu, Han Song, Weiwei Wang, Qun Hu, Rose Qingyang Zhu, Hongbo Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China Department of Electrical and Computer Engineering Utah State University LoganUT United States

Mobile Edge Computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation intensive and delay critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering non-stationary computation demands, user mobility, and wireless fading channels. This paper aims to study the tradeoff between energy efficiency (EE) and service delay for multi-user multi-server MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a [O(1/V), O(V)] tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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End-to-end 2D-3D Registration between Image and LiDAR Point Cloud for Vehicle Localization

arXiv

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arXiv 2023年

作者： Wang, Guangming Zheng, Yu Guo, Yanfeng Liu, Zhe Zhu, Yixiang Burgard, Wolfram Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China Department of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Electrical and Computer Engineering University of California Los Angeles United States Institute of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Technology Nuremberg Germany

Robot localization using a previously built map is essential for a variety of tasks including highly accurate navigation and mobile manipulation. A popular approach to robot localization is based on image-to-point cloud registration, which combines illumination-invariant LiDAR-based mapping with economical image-based localization. However, the recent works for image-to-point cloud registration either divide the registration into separate modules or project the point cloud to the depth image to register the RGB and depth images. In this paper, we present I2PNet, a novel end-to-end 2D-3D registration network. I2PNet directly registers the raw 3D point cloud with the 2D RGB image using differential modules with a unique target. The 2D-3D cost volume module for differential 2D-3D association is proposed to bridge feature extraction and pose regression. 2D-3D cost volume module implicitly constructs the soft point-to-pixel correspondence on the intrinsic-independent normalized plane of the pinhole camera model. Moreover, we introduce an outlier mask prediction module to filter the outliers in the 2D-3D association before pose regression. Furthermore, we propose the coarse-to-fine 2D-3D registration architecture to increase localization accuracy. We conduct extensive localization experiments on the KITTI Odometry and nuScenes datasets. The results demonstrate that I2PNet outperforms the state-of-the-art by a large margin. In addition, I2PNet has a higher efficiency than the previous works and can perform the localization in real-time. Moreover, we extend the application of I2PNet to the camera-LiDAR online calibration and demonstrate that I2PNet outperforms recent approaches on the online calibration task. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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Dynamic Hierarchical Skeleton-Based Network for Human Action Recognition in Video Content

SSRN

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SSRN 2024年

作者： Yang, Hualin Li, Xiying Chen, Calvin Yu-Chian Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China - Preferred Program Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China Department of Medical Research China Medical University Hospital Taichung40447 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung41354 Taiwan Guangdong Provincial Key Laboratory of Intelligent Transportation System Sun Yat-sen University Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China

Human skeleton-based action recognition represents a pivotal field of study, capturing the intricate interplay between physical dynamics and intentional actions. Current research primarily focuses on extracting structural and temporal information from static skeleton-based graphs, but they grapple with a myriad of challenges. These include 1) an absence of hierarchical structure in encoding the skeleton-based graphs;2) A requirement for substantial prior knowledge to interpret the diverse spatial dynamics within singular action labels;3) An intricate task of representing the multifaceted temporal dynamics of individual actions. To counter this issue, we propose DHS-Net, an innovative dynamic hierarchical skeleton-based network in video content. The proposed DHS-Net incorporates the DHS Block as a central element, effectively facilitating information exchange through node concentration and diffusion across both structural and temporal dimensions. Through this architectural design, DHS-Net extracts hierarchical representations, enhancing the comprehensive understanding of spatio-temporal dynamics and marking a significant advancement in human action recognition. Our rigorous experimental evaluations further substantiate DHS-Net's supremacy, outperforming several state-of-the-art benchmarks in skeleton-based action recognition and achieving remarkable prediction accuracy. © 2024, The Authors. All rights reserved.

关键词： Dynamics

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Research on Joint Operation of Wind and Solar Storage system Based on Genetic Algorithm

SSRN

引用

SSRN 2022年

作者： Lv, You Qin, Ruijun Niu, Yuguang Zhang, Yanfei Zhang, Lei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China

With the large-scale integration of renewable energy sources such as wind power and photovoltaics, how to use energy storage systems to coordinate and control them has become an indispensable research content. After in-depth research on the output characteristics of wind power and photovoltaic power in a certain area of Ulanqab City, a strategy that can smooth the fluctuation of new energy output and configure energy storage, which the active power output fluctuates as little as possible and meets the national new energy grid-connected fluctuation index. The required parameters are obtained by solving the simulation through the measured data and genetic algorithm in the region, thus proving the effectiveness of the proposed strategy. © 2022, The Authors. All rights reserved.

关键词： Solar power generation

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A new family of feasible methods for distributed resource allocation

arXiv

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arXiv 2021年

作者： Wu, Xuyang Magnússon, Sindri Johansson, Mikael The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden The Department of Computer and System Science Stockholm University StockholmSE-164 07 Sweden

Distributed resource allocation is a central task in network systems such as smart grids, water distribution networks, and urban transportation systems. When solving such problems in practice it is often important to have non-asymptotic feasibility guarantees for the iterates, since overallocation of resources easily causes systems to break down. In this paper, we develop a distributed resource reallocation algorithm where every iteration produces a feasible allocation. The algorithm is fully distributed in the sense that nodes communicate only with neighbors over a given communication network. We prove that under mild conditions the algorithm converges to a point arbitrarily close to the optimal resource allocation. Numerical experiments demonstrate the competitive practical performance of the algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Water distribution systems

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Evaluation of algorithms and security protocols to mitigate the risks in commercial transactions carried out by public companies

Evaluation of algorithms and security protocols to mitigate ...

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2020 Asia Conference on computers and Communications, ACCC 2020

作者： Toapanta, Segundo Moises Toapanta Gallegos, Luis Enrique Mafla Alvarado, Alex Enrique Aranda Solis, Maximo Prado Quito Ecuador Faculty of System Engineering Quito Ecuador Department of Computer Science Guayaquil Ecuador Universidad Técnica Luis Vargas Torres de Esmeraldas Career Social Sciences Esmeraldas Ecuador

ISBN: (纸本)9781728189673

The security problems in electronic commerce in transactions are very evident in public companies. The objective of this research is to analyze security algorithms and protocols to mitigate risks and vulnerabilities within commercial transactions that help us identify computer attacks, whether internal or external. Deductive method and exploratory research were used to analyze the information from the reference articles and information available online;regarding different types of protocols and algorithms that protect the information in its entirety and confidentiality, thus providing the respective security to the information of public companies. It resulted in a control of the security and privacy factors, by having RSA algorithms with formulas to encrypt and decrypt public keys, combinations of commercial transactions, asymmetric and symmetric cryptography and the SET protocol, which facilitates the algorithms in decision-making within the organizations. It was concluded that the SET protocol is the best in facilitating greater security to mitigate vulnerabilities, threats and risks of its processes in public organizations. © 2020 IEEE.

关键词： Decision making

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MS-SAE: A General Model of Sentiment Analysis Based on Multimode Semantic Extraction and Sentiment Attention Enhancement Mechanism 6th

MS-SAE: A General Model of Sentiment Analysis Based on Multi...

引用

6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Yang, Kai Qu, Zhaowei Wang, Xiaoru Li, Fu Li, Yueli Jia, Dongbai Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Portland States University PortlandOR97207-0751 United States

ISBN: (纸本)9783030578800

Recently, there is a lot of research on sentiment analysis. When existing models extract text features, semantic information can’t be fully obtained, because they ignore context connection between historical texts and current texts. Meanwhile, models cannot self-optimize extraction algorithms, making key sentiment semantics be neglected, because they can’t track and feedback analysis results. Furthermore, models extract insufficient sentiment features, resulting in imperfect semantic extraction and unsatisfactory analysis results, because they only extract POS features. To solve the above problems, we propose a general model of sentiment analysis based on multimode semantic extraction and sentiment attention enhancement mechanism (MS-SAE). The model includes a multimode semantic extraction processing (a multimode semantic extraction module and a sentiment attention enhancement mechanism), an extended dictionary (ExWordNet) and a sentiment analysis module. The multimode semantic extraction module extracts semantic features from multiple perspectives and pays close attention to extracted features, which solves the problem of insufficient semantic extraction. We propose a sentiment attention enhancement mechanism to solve the problem that sentiment semantics is neglected. We construct a general extended dictionary to support MS-SAE in semantic extraction processing. The LSTM-based sentiment analysis module ensures the accuracy of sentiment analysis. We evaluate MS-SAE on SST-2, MR and Subj datasets. Extensive experiments have been conducted and the results demonstrate that MS-SAE could achieve better sentiment analysis performance than the state-of-the-art algorithms in accuracy. It solves the problems including poor understanding of text semantics and errors in analysis results. © 2020, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Backdoor Defense with Machine Unlearning

arXiv

引用

arXiv 2022年

作者： Liu, Yang Fan, Mingyuan Chen, Cen Liu, Ximeng Ma, Zhuo Wang, Li Ma, Jianfeng Xi'An China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'An China College of Computer and Data Science Fuzhou University Fuzhou China School of Data Science and Engineering East China Normal University Shanghai China Ant Group Hangzhou China

Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper, we propose BAERASER, a novel method that can erase the backdoor injected into the victim model through machine unlearning. Specifically, BAERASER mainly implements backdoor defense in two key steps. First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model. Here, the trigger pattern recovery problem is equivalent to the one of extracting an unknown noise distribution from the victim model, which can be easily resolved by the entropy maximization based generative model. Subsequently, BAERASER leverages these recovered trigger patterns to reverse the backdoor injection procedure and induce the victim model to erase the polluted memories through a newly designed gradient ascent based machine unlearning method. Compared with the previous machine unlearning solutions, the proposed approach gets rid of the reliance on the full access to training data for retraining and shows higher effectiveness on backdoor erasing than existing fine-tuning or pruning methods. Moreover, experiments show that BAERASER can averagely lower the attack success rates of three kinds of state-of-the-art backdoor attacks by 99% on four benchmark datasets. © 2022, CC BY-NC-ND.

关键词： Network security

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