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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： Rayaan Faiz Tisha Agarwal D Rajeswari Dept. of Data Science and Business Systems School of Computing College of engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

India’s air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai’s air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost’s efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management.

关键词： Gases Atmospheric modeling Urban areas Lead Predictive models Air pollution Prediction algorithms

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A modified universal pedestrian motion model: Revisiting pedestrian simulation with bottlenecks

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Building Simulation 2022年第4期15卷 631-644页

作者： Jianyu Wang Majid Sarvi Jian Ma Milad Haghani Abdullah Alhawsawi Juan Chen Peng Lin School of Transportation and Logistics Notional Engineering Laboratory of Integrated Transportation Big Data Application TechnologySouthwest Jiaotong UniversityChengdu 610031China Transport Engineering Group Melbourne School of Engineering&ITThe University of MelbourneVictoria 3070Australia Institute of Transport and Logistics Studies Business SchoolThe University of SydneyNSWAustralia Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 610031China

Pedestrian flow through narrow exits is one the most important features of crowd dynamics and *** is a particularly important aspect of pedestrian simulation models in that the accuracy is highly dependent on their ability to produce realistic exit flow *** firstly identified the four parameters that are most critical for physical interactions of the social force model and then calibrated them against two well-controlled pedestrian *** these calibrated parameters,we discussed the reasonable settings of sensitive parameters for different levels of pedestrian ***,we revisited the basic questions about the effect of the exit location,the bottleneck length,and the effect of obstacles on pedestrian *** simulation results indicated that:(1)The effect of the exit location on the pedestrian egress efficiency is uncertain,and the evacuation efficiency is also related to the exit width and the level of urgency.(2)The"pass-way"after the exit also named as the bottleneck length has a negative impact on the evacuation performance only in the scenarios that the bottleneck length is not more than 2.0 *** the bottleneck length exceeds 2.0 meters,pedestrian outflow efficiency reaches an asymptotic.(3)Setting an obstacle near an exit is not leading to a longer pedestrian evacuation time,instead,it is effectively improving pedestrian evacuation.

关键词： bottleneck pedestrian crowd simulation exit location bottleneck length obstacles location

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Semantic-Reconstructed Graph Transformer Network for Event Detection

Semantic-Reconstructed Graph Transformer Network for Event D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhuochun Miao Xingrui Zhuo Gongqing Wu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Event detection (ED) is a key subtask of information extraction to extract key events, such as stock rise and fall and social public opinion, from news or social media. Although current GCN-based event detection methods achieve remarkable success via building graphs with dependency trees, they typically suffer from two challenges: 1) They use sequence models to learn contextual information of sentences, ignoring the longterm dependencies problem of sequence models might learn ineffective information and make it propagate in GCN layers. 2) Most methods do not exploit global dependency label information and grammatical structure information that convey rich linguistic knowledge directly, and only consider local dependency label information. To cope with these challenges, we propose a novel event detection model via semantic-reconstructed graph transformer networks (SRGTNED), which incorporates semantic reconstruction and path information collection methods. Using the semantic reconstruction method, we assign a pruned sequence to each word based on the path information to capture contextual information consistent with sentence semantics. Moreover, to better utilize global dependency label information and grammatical structure information, a Graph Transformer Network (GTN)-based heterogeneous graph embedding framework is introduced to automatically learn path information between important words by converting sentences as heterogeneous graphs. We conduct experiments on the ACE2005 dataset and the Commodity News dataset, and the experimental results demonstrate that our method significantly outperforms 11 state-of-the-art baselines in terms of the F1-score.

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Exploiting the Phenomena of Performance Degradation Distribution for Reliability Evaluation of Aerospace Engines

Exploiting the Phenomena of Performance Degradation Distribu...

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2022 IEEE Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2022

作者： Haque, Md Musa Sheikder, Chandan Hu, Qing Hebei University of Technology Mechatronic Engineering Tianjin300131 China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300131 China

ISBN: (纸本)9781665477192

An aerospace engine is among the most critical components of a spacecraft, and its performance is directly tied to the spacecraft's ability to function effectively and consistently. According to studies, 90% of all aircraft accidents happen because of engine failure. As a result, forecasting the RUL (Remaining Useful Life) of aircraft engines is critical for assuring astronaut security and decreasing property losses. Based on past data, this paper presents a reliability of space engine evaluation approach depending on the performance deterioration that may be used to anticipate the space engines RUL. The experimental findings reveal that the approach provided in this work may quantitatively describe the remaining usable life as a probability, which has significant technical and practical implications for aeronautical engineering. © 2022 IEEE.

关键词： Aircraft accidents

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Object Detection Algorithm Based on Second-Order Pooling Network and Gaussian Mixture Attention 5

Object Detection Algorithm Based on Second-Order Pooling Net...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Li, Ningbo Yang, Xiaobao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To improve the feature representation ability of the YOLOX algorithm and obtain better detection performance, an object detection algorithm based on second-order pooling network and gaussian mixture attention is proposed. Firstly, the second-order pooling network is added after the PAFPN, and the higher-order statistical information is obtained by calculating the covariance matrix between different channels, to enhance the non-linear modeling capability. Secondly, the mixture attention based on the gaussian function is added after the SPP to model global contexts in the spatial and channel dimensions respectively, which improves the network performance with almost no extra parameters. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 82.6 %, which is 1.6 % higher than the YOLOX algorithm. © 2022 ACM.

关键词： Object detection

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Duo: Improving data Sharing of Stateful Serverless Applications by Efficiently Caching Multi-Read data

Duo: Improving Data Sharing of Stateful Serverless Applicati...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Zhuo Huang Hao Fan Chaoyi Cheng Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ***, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1×-2.1× and reduces the data sharing overhead by 25%-62%.

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KLILO: Kalman Filter based LiDAR-Inertial-Leg Odometry for Legged Robots

KLILO: Kalman Filter based LiDAR-Inertial-Leg Odometry for L...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shaohang Xu Wentao Zhang Lijun Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Data Science City University of Hong Kong HKSAR

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper presents a Kalman filter based LiDAR-Inertial-Leg Odometry (KLILO) system for legged robots to navigate in challenging environments. In particular, we employ the iterated error-state extended Kalman filter framework on manifolds to fuse measurements from the inertial measurement unit (IMU), LiDAR, joint encoders, and contact force sensors in a tightly coupled manner. To assess the performance of KLILO, we build a dataset that encompasses intricate environments with challenging conditions such as dynamic objects and deformable terrains. The results demonstrate that our algorithm can provide efficient and reliable localization in all tests. It exhibits an average improvement of around 40% in positioning accuracy compared to the baselines. Furthermore, we validate KLILO in a challenging navigation task on a real robot, where the LiDAR encounters ineffective measurements.

关键词： Legged locomotion Laser radar Atmospheric measurements Fuses Measurement uncertainty Kinematics Robot sensing systems Particle measurements Kalman filters Odometry

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Distributionally Robust Chance Constrained Trajectory Optimization for Mobile Robots within Uncertain Safe Corridor

Distributionally Robust Chance Constrained Trajectory Optimi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shaohang Xu Haolin Ruan Wentao Zhang Yian Wang Lijun Zhu Chin Pang Ho School of Artificial Intelligence and Automation Huazhong University of Science and Technology China School of Data Science City University of Hong Kong HKSAR

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, because its convex formulation can guarantee global optimality. The safe corridor is constructed based on the obstacle map, however, the non-ideal perception induces uncertainty, which is rarely considered in the context of trajectory generation. In this paper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) to consider the uncertainty of the safe corridor. Then, we integrate DRSCCs into the trajectory optimization framework using Bernstein basis polynomials. Theoretically, we rigorously prove that the proposed trajectory optimization problem is equivalent to a convex quadratic program, which is computationally efficient to deploy onto real robots. The simulation results show that our method enhances navigation safety by significantly reducing the infeasible motions compared to the baseline. Moreover, the proposed approach is validated through two robotic applications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robot Unitree A1.

关键词： Uncertainty Navigation Simulation Autonomous aerial vehicles Numerical simulation Polynomials Safety

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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