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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Yang, Tingting Wang, Yiqian Zhu, Jie Chang, Shuyu Huang, Haiping Nanjing University of Posts and Telecommunications Nanjing210003 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210003 China

ISBN: (纸本)9789819615278

Unmanned Aerial Vehicles (UAVs) have emerged as integral components in logistics systems, where their potential for efficient delivery services is being explored. However, the limited battery capacity of UAVs poses a significant challenge for long-distance delivery applications. In this paper, we investigate a hybrid problem involving UAV trajectory planning and charging scheduling for long-distance logistics systems with a charging network. In the problem under study, multiple UAVs are assigned long-distance delivery tasks. A UAV can stop at any charging station in the charging network to charge if it is in a low-energy state. An Ant Colony Optimization-based UAV Trajectory Planning and Charging Scheduling (ACO-TPCS) algorithm is proposed to minimize task completion time by optimizing UAV trajectory and charging plans. The main framework of the ACO-TPCS algorithm consists of several key components, including reachable graph construction, candidate flight path generation, pheromone matrix construction, ant generation, feasible solution generation, and pheromone update method. All these components are delicately designed. Through extensive experimentation and comparison with baseline algorithms, we demonstrate the effectiveness of the ACO-TPCS algorithm in addressing the problem under study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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Optimizing Self-training Sample Selection for Euphemism Detection in Special Scenarios 24th

Optimizing Self-training Sample Selection for Euphemism Dete...

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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Zhiwei Chang, Shuyu Huang, Haiping Wang, Rui Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China

ISBN: (纸本)9789819615476

Euphemism, which is often used to disguise true intentions or soften the tone of speech, has garnered significant attention for its detection in recent years. Current approaches to euphemism detection struggle with uncommon data, where new and rare vocabularies frequently emerge in the test set, leading to decreased model performance. To address these challenges, we propose a novel framework that combines data augmentation, self-training, and a special sample selection method. We expand the original dataset using lexical and sentence-level data augmentation techniques, enhancing the representation of rare vocabulary and allowing the model to familiarize itself with their contexts and usage characteristics. Next, we employ self-training as a semi-supervised learning approach, utilizing the augmented datasets to iteratively generate and refine pseudo-labels. During each iteration, a KNN framework is used to generate pseudo-labels by searching for the nearest neighbor texts, thus enhancing the quality of these labels. Finally, to further increase the presence of rare vocabulary in the training data, we implement a sample selection strategy based on euphemism frequency and predictive uncertainty, integrating it with the earlier steps. Experimental results demonstrate that our framework outperforms previous research on most metrics and achieves a 10% increase over the baseline model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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An effective meta-heuristics for trajectory planning problem in UAV-assisted vessel emission detection system

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-21页

作者： Zhu, Jie Cui, Weizhi Huang, Haiping Zhu, Tao Sun, Yuzhong School of Computer Science Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and Telecommunications Nanjing 210003 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100864 China

With the development of the shipping industry, the pollution problem caused by vessel fuels is becoming increasingly severe. Due to the great mobility and on-demand service capability of Unmanned Aerial Vehicles (UAVs), UAVs have become effective tools for detecting pollution in Emission Control Areas (ECAs). How to obtain the effective trajectories of UAVs within the energy limitation and the time limitation is a difficult problem to solve. The paper investigates the trajectory planning problem in UAV-assisted vessel emission detection system. In the problem, the sailing vessels in a given area are taken as the detection tasks and multiple UAVs are ready to detect these vessels. Each vessel is assigned a different detecting weight and sails to the port at a different speed. The objective is to complete as many detection tasks as possible before the deadline and maximize the total weight of these completed tasks. A Genetic Algorithm (GA)-based trajectory planning algorithm framework is proposed for the problem under study. The framework consists of key components, such as the chromosome generation method to generate chromosomes of each generation, the feasible solution generation method to compute the feasible solution according to the sequence defined in a chromosome, and the chromosome reconstruction method to improve the quality of chromosomes based on history information. The experimental results show that the proposed algorithm outperforms the baseline algorithms in terms of effectiveness and robustness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Emission control areas Genetic algorithm Trajectory planning UAV scheduling

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Enhancing the Uplink of Cell-Free Massive MIMO Through Prioritized Sampling and Personalized Federated Deep Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Mendoza, Charmae Franchesca Kaneko, Megumi Rupp, Markus Schwarz, Stefan Technische Universität Wien Institute of Telecommunications Sustainable Radio Access Networks Christian Doppler Laboratory Digital Twin Assisted AI Vienna1040 Austria National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan Department of Computer Science University of Tokyo Tokyo Japan Technische Universität Wien Institute of Telecommunications Vienna1040 Austria

Effective power control is key to solving the inter-user interference problem that degrades performance in cell-free massive multiple-input multiple-output (MIMO) systems. Motivated by its ability to operate online and model-free, without relying on training datasets, we leverage deep reinforcement learning (DRL) for uplink power control, aiming to maximize the guaranteed rate. We propose a fully centralized single-agent framework and two distributed schemes that employ several agents for improved scalability, leveraging prioritized experience replay to enable fast adaptation to the dynamic changes of the wireless environment. We investigate the performance of two multi-agent system architectures: (1) centralized training, decentralized execution (CTDE), where each agent forwards its RL experience to a central trainer, and (2) personalized federated learning (FedPer), where the training is performed locally at each agent, and only the base layer of the local deep neural network (DNN) model is forwarded periodically for aggregation at a server. We focus on the realistic scenario of dynamic device (de-)activation, combined with user mobility. Numerical evaluations demonstrate that the proposed FedPer with prioritized sampling achieves near-optimal rate and power performance while incurring the least amount of communication overhead. © 2015 IEEE.

关键词： Deep reinforcement learning

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Matching Traffic Lights to Routes for Real-World Deployments of Mobile GLOSA Apps 8

Matching Traffic Lights to Routes for Real-World Deployments...

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8th IEEE International Smart Cities Conference, ISC2 2022

作者： Matthes, Philipp Springer, Thomas Institute of Systems Architecture Computer Networks United States

ISBN: (数字)9781665485616

ISBN: (纸本)9781665485616

Green Light Optimized Speed Advisory (GLOSA) apps provide speed recommendations for drivers to pass traffic lights during their green phases. In this way, the comfort and efficiency of traveling can be significantly improved. Thus, GLOSA apps are a valuable contribution to smart mobility. Mobile GLOSA apps provide an attractive alternative to static info signs, but they need to anticipate upcoming traffic lights that the vehicle will pass. While this imposes no challenge for predominating research within simulation or test track environments, real-world deployments need to correctly match a few from thousands of traffic lights to a route. In this paper, we discuss in a novel approach that MAP topologies, an international ETSI standard for turn geometries of traffic lights, can be used to perform this matching. However, routing is usually performed on public map data, which is not aligned with the MAP topologies. We explore two computational methods, specifically map-matching as preprocessing for adjacency lookup and topologic feature matching, that account for discrepancies between the MAP topologies and the route. We show that the core problem can be addressed using these algorithms to enable large-area deployments of real-world mobile GLOSA apps. In a comparative evaluation, the topologic feature matching technique achieved an F1 score of 89.5%, while the map-matched adjacency lookup method only achieved an F1 score of 48.3%. We analyze this performance gap and conclude further research directions. © 2022 IEEE.

关键词： Topology

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices

Selecting Resource-Efficient ML Models for Transport Mode De...

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2022 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2022

作者： Matthes, Philipp Springer, Thomas Institute of Systems Architecture Chair of Computer Networks Tu Dresden Germany

ISBN: (数字)9798350396454

ISBN: (纸本)9798350396454

Processing data closer to the source to minimize latency and the amount of data to be transmitted is a major driver for research on the Internet of Things (IoT). Since data processing in many IoT scenarios heavily depends on machine learning (ML), designing ML models for resource constraint devices at the edge of IoT infrastructures is one of the big challenges. Which ML model performs best highly depends on the problem domain but also on the availability of resources. Thus, to find an appropriate ML model in the broad search space of options, the trade-off between accuracy and resource consumption in terms of memory, CPU, and energy needs to be considered. However, there are ML problems where most current research focuses on accuracy, and the resource consumption of applicable models is not well investigated yet. We show that transport mode detection (TMD) is such a problem and present a case study for designing an ML model running on smartphones. To transform the search for the needle in the haystack into a structured design process, we propose an engineering workflow to systematically evolve ML model candidates, considering portability and resource consumption in addition to model accuracy. At the example of the Sussex-Huawei-Locomotion (SHL) dataset, we apply this process to multiple ML architectures and find a suitable model that convinces with high accuracy and low measured resource consumption for smartphone deployment. We discuss lessons learned, enabling engineers and researchers to use our workflow as a blueprint to identify solutions for their ML problems systematically. © 2022 IEEE.

关键词： Internet of things

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Middleware support for transparent client-side caching

Middleware support for transparent client-side caching

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SC 2002, Workshop on Software Composition Affiliated with ETAPS 2002 (Satellite Event of ETAPS 2002)

作者： Pohl, Christoph Schill, Alexander Dresden University of Technology Institute for System Architecture Chair for Computer Networks Germany

This paper reflects on existing caching concepts in proxies and stubs of component technologies and lines out their advantages and deficiencies. A new concept is introduced that averts proliferation of component stubs on client side while transparently providing efficient caching of attributes that doesn't require any changes to existing code at all. Common object-oriented design facilities and code generation tools are leveraged to support seamless integration of these caching concepts into the development cycle. ©2002 Published by Elsevier Science B.V.

关键词： Middleware

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Fusing Bluetooth with Pedestrian Dead Reckoning: A Floor Plan-Assisted Positioning Approach

arXiv

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arXiv 2025年

作者： Pan, Wenxuan Yang, Yang Chen, Mingzhe Wei, Dong Guo, Caili Mao, Shiwen Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering the Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Wireless Engineering Research and Education Center Auburn University AuburnAL36849 United States

Floor plans can provide valuable prior information that helps enhance the accuracy of indoor positioning systems. However, existing research typically faces challenges in efficiently leveraging floor plan information and applying it to complex indoor layouts. To fully exploit information from floor plans for positioning, we propose a floor plan-assisted fusion positioning algorithm (FP-BP) using Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). In the considered system, a user holding a smartphone walks through a positioning area with BLE beacons installed on the ceiling, and can locate himself in real time. In particular, FP-BP consists of two phases. In the offline phase, FP-BP programmatically extracts map features from a stylized floor plan based on their binary masks, and constructs a mapping function to identify the corresponding map feature of any given position on the map. In the online phase, FP-BP continuously computes BLE positions and PDR results from BLE signals and smartphone sensors, where a novel grid-based maximum likelihood estimation (GML) algorithm is introduced to enhance BLE positioning. Then, a particle filter is used to fuse them and obtain an initial estimate. Finally, FP-BP performs post-position correction to obtain the final position based on its specific map feature. Experimental results show that FP-BP can achieve a real-time mean positioning accuracy of 1.19 m, representing an improvement of over 28% compared to existing floor plan-fused baseline algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Photomapping

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A description language for user interface services

A description language for user interface services

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IADIS International Conference WWW/Internet 2010

作者： Hübsch, Gerald Liebing, Christian Spillner, Josef Schill, Alexander Technische Universität Dresden Institute for Systems Architecture Department of Computer Networks Germany

ISBN: (纸本)9789728939250

In this paper, we present the User Interface Service Description Language (UISDL) for the description of UI Services. UI Services allow UI component implementations to be globally distributed, flexibly integrated and dynamically bound into interactive applications including, but not limited to, Rich Internet Applications. We present abstraction concepts, motivate design decisions, and discuss analogies between UISDL and WSDL as a classical service description language. © 2010 IADIS.

关键词： User interfaces

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