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IEEE Conference on Wireless Communications and Networking

作者： Nam H. Chu Diep N. Nguyen Dinh Thai Hoang Khoa T. Phan Eryk Dutkiewicz Dusit Niyato Tao Shu School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences La Trobe University Melbourne Australia School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science and Software Engineering Auburn University USA

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

关键词： Deep learning Metaverse Heuristic algorithms Simulation Reinforcement learning Quality of service Dynamic scheduling

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PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model

PATR: Periodicity-Aware Trajectory Recovery for Express Syst...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Xinrui Zhang Xingyuan Liang Hai Wang Shuai Wang Tian He College of Software Engineering Southeast University School of Computer Science and Engineering Southeast University Department of Technology and Data Intelligence JD Logistics

ISBN: (纸本)9781665435413

The analysis of express trajectory is essential for delivery service optimization and courier management. Express trajectory consists of a large amount of community paths which is different from the trajectory of transportation tools such as taxis which run in the main road of a city. Due to the complexity of the community road networks and the energy constraint of devices, trajectories are collected under a low sampling rate and accuracy. These trajectories need to be recovered and mapped to the road networks for further analysis. Current recovery approaches utilize two-stage or end-to-end models to generate the upsampled trajectories. These methods fail to fully exploit the periodicity relation in historical data, which leads to accuracy losses for complex community road networks. In this paper, we design PATR, a periodicity-aware Seq2Seq model, to recover the couriers' trajectories by leveraging the periodical patterns of the historical data. We conduct experimental evaluations based on real-world GPS data of JD logistics consisting of 836,959 points and 25,726 road segments, and our model outperforms the state-of-the-art baselines by reducing recovery error by 2.4%-21.9%.

关键词： Roads Urban areas data models Trajectory Complexity theory Global communication Public transportation

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Regression with Cost-based Rejection

arXiv

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arXiv 2023年

作者： Cheng, Xin Cao, Yuzhou Wang, Haobo Wei, Hongxin An, Bo Feng, Lei College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Regression with cost-based rejection 23

Regression with cost-based rejection

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xin Cheng Yuzhou Cao Haobo Wang Hongxin Wei Bo An Lei Feng College of Computer Science Chongqing University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Software Technology Zhejiang University China Department of Statistics and Data Science Southern University of Science and Technology China

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on Computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data privacy

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ASAnalyzer: Attention based Sentiment analyzer for Real-world Sentiment Analysis

ASAnalyzer: Attention based Sentiment analyzer for Real-worl...

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Frontiers of Information Technology (FIT)

作者： Khadim Hussain Muhammad Azhar Bumshik Lee Asma Iqbal Muhammad Affan Sajid Ullah Khan Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong Department of Computer Science and Software Engineering Chosun University Korea Department of Computer Science Tsinghua University China Department of Computer Science University of Lakki Marwat

In this era of big data, a lot of data is produced in various forms every second through various sources. Text data is one of those types that is produced mainly through social media like Twitter, Facebook, YouTube comments, WhatsApp, etc. To know the public's point of view on a specific issue, we can perform sentiment analysis on the text data collected from the above sites. Even though various algorithms have been proposed for sentiment analysis, these algorithms have issues of high time complexity and low context awareness, leading to low classification accuracy. To resolve the above issues, we propose an attention-based sentiment analyzer for real-world sentiment analysis named ‘ASAnalyzer’, which uses CNN+Attention-Based BiGRU. CNN is used to extract local features from the tweets, and then these features are used by Attention-Based BiGRU to learn the contextual information of the tweets and the long-term dependencies in both directions of the text, which helps to improve accuracy. To validate our algorithm, we used tweet data about the anti-COVID-19 vaccine from Twitter, and the results have shown that our method outperformed other state-of-the-art methods.

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ExpertDRL: Request Dispatching and Instance Configuration for Serverless Edge Inference with Foundation Models

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IEEE Transactions on Mobile Computing 2025年

作者： Zeng, Yue Zhou, Junlong Ye, Baoliu Qu, Zhihao Guo, Song Gong, Tianjian Li, Pan Nanjing University of Science and Technology Department of Computer Science and Engineering Jiangsu Nanjing210094 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China Hong Kong University of Science and Technology Hong Kong Southwest University College of Electronic and Information Engineering Chongqing400715 China

The prevalence of the pre-training & fine-tuning paradigm enables machine learning models to quickly adapt to various downstream tasks by fine-tuning pre-trained foundation models (FMs), greatly facilitating various IoT applications that rely on model inference in dynamic edge serverless environments. Efficiently dispatching inference requests and configuring instances to batch inference requests can significantly enhance resource efficiency. However, existing serverless inference solutions are tailored for traditional models, make coarse-grained request dispatching and instance configuration decisions, fail to exploit the shared model backbone characteristics of the FM and capture delayed rewards in dynamic environments, and ignore communication latency between edge sites, resulting in high costs and constraint violations. In this paper, we leverage our insight that fine-grained batch inference requests can effectively exploit the shared model backbone feature of FM to save monetary costs. We propose an algorithm that incorporates deep reinforcement learning (DRL) and expert intervention for fine-grained request dispatching and instance configuration, where the DRL component outputs fractional solutions as guidance, while the expert intervention module integrates our insights—batching reduces monetary costs at the expense of increased inference latency, whereas higher configurations shorten inference latency. This module rounds fractional solutions and adjusts instance configurations to search for optimal solutions while satisfying constraints, with theoretical guarantees rigorously proved. Finally, we conducted our experiments on an OpenFaas-based platform and simulator, and extensive trace-driven evaluation results show that ExpertDRL can save costs by up to 85.14% and improve request acceptance ratio by up to 26.93%, compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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MTTM: Metamorphic Testing for Textual Content Moderation software

arXiv

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arXiv 2023年

作者： Wang, Wenxuan Huang, Jen-Tse Wu, Weibin Zhang, Jianping Huang, Yizhan Li, Shuqing He, Pinjia Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University Zhuhai China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

The exponential growth of social media platforms such as Twitter and Facebook has revolutionized textual communication and textual content publication in human society. However, they have been increasingly exploited to propagate toxic content, such as hate speech, malicious advertisement, and pornography, which can lead to highly negative impacts (e.g., harmful effects on teen mental health). Researchers and practitioners have been enthusiastically developing and extensively deploying textual content moderation software to address this problem. However, we find that malicious users can evade moderation by changing only a few words in the toxic content. Moreover, modern content moderation software's performance against malicious inputs remains underexplored. To this end, we propose MTTM, a Metamorphic Testing framework for Textual content Moderation software. Specifically, we conduct a pilot study on 2, 000 text messages collected from real users and summarize eleven metamorphic relations across three perturbation levels: character, word, and sentence. MTTM employs these metamorphic relations on toxic textual contents to generate test cases, which are still toxic yet likely to evade moderation. In our evaluation, we employ MTTM to test three commercial textual content moderation software and two state-of-the-art moderation algorithms against three kinds of toxic content. The results show that MTTM achieves up to 83.9%, 51%, and 82.5% error finding rates (EFR) when testing commercial moderation software provided by Google, Baidu, and Huawei, respectively, and it obtains up to 91.2% EFR when testing the state-of-the-art algorithms from the academy. In addition, we leverage the test cases generated by MTTM to retrain the model we explored, which largely improves model robustness (0% ∼ 5.9% EFR) while maintaining the accuracy on the original test set. A demo can be found in this link. Copyright © 2023, The Authors. All rights reserved.

关键词： software testing

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Design Automation for Continuous-Flow Lab-on-a-Chip Systems: A One-Pass Paradigm

Design Automation for Continuous-Flow Lab-on-a-Chip Systems:...

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作者： Huang, Xing Pan, Youlin Chen, Zhen Guo, Wenzhong Wang, Lu Li, Qingshan Wille, Robert Ho, Tsung-Yi Schlichtmann, Ulf Technical University of Munich Electronic Design Automation Munich80333 Germany Fuzhou University College of Computer and Data Science Fuzhou350116 China Xidian University School of Computer Science and Technology Xi'an710071 China Technical University of Munich Chair for Design Automation Munich80333 Germany Software Competence Center Hagenberg GmbH Hagenberg4232 Austria The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

Owing to the high complexity of chip architecture and assay protocol, considerable effort has been directed toward the design automation of continuous-flow microfluidics over the past decade. Existing methods, however, perform the corresponding design tasks, including binding, scheduling, placement, and routing separately, leading to serious gaps between different steps and potentially even cause design failure. To overcome these drawbacks, in this article, we propose a one-pass design paradigm for continuous-flow microfluidic lab-on-a-chip systems, integrating all the design steps into an 'organic whole,' which has never been considered in prior work. With the proposed paradigm, all the design tasks can be synchronized seamlessly and performed in a combined manner, thereby eliminating the gaps between design steps. Consequently, optimized biochip architectures can be generated without any design adjustments and modifications. The experimental results demonstrate the effectiveness of the proposed automation flows. © 1982-2012 IEEE.

关键词： Microfluidics

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A Lightweight and Robust Access Control Protocol for IoT-based e-Healthcare Network

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IEEE Transactions on Mobile Computing 2025年

作者： Ghaffar, Zahid Kuo, Wen-Chung Mahmood, Khalid Tariq, Tayyaba Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J.F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University Department of Computer Science and Engineering College of Informatics 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency. © 2002-2012 IEEE.

关键词： Authentication

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