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A Game Theoretic Link Scheduler for Wireless Networks with Heterogeneous Energy Sources

China Communications 2024年第12期21卷 108-125页

作者： He Tengjiao Kwanwu Chin Wang Yishun Sieteng Soh College of Information Science and Technology Jinan UniversityGuangzhouChina School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongNSWAustralia School of Electrical Engineering Computing and Mathematical SciencesCurtin UniversityWAAustralia

This paper considers link scheduling in a wireless network comprising of two types of nodes:(i)hybrid access points(HAPs)that harvest solar en-ergy,and(ii)devices that harvest radio frequency(RF)energy whenever HAPs *** aim is to de-rive the shortest possible link schedule that determines the transmission time of inter-HAPs links,and uplinks from devices to *** first outline a mixed in-teger linear program(MILP),which can be run by a central node to determine the optimal schedule and transmit power of HAPs and *** then out-line a game theory based protocol called Distributed Schedule Minimization Protocol(DSMP)that is run by HAPs and ***,it does not require causal energy arrivals and channel gains *** results show that DSMP produces schedule lengths that are at most 1.99x longer than the schedule computed by MILP.

关键词： channel access finite battery mathemati-cal program renewable energy wireless power trans-fer

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Emotion-oriented Cross-modal Prompting and Alignment for Human-centric Emotional Video Captioning

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IEEE Transactions on Multimedia 2025年 27卷 3766-3780页

作者： Wang, Yu Liu, Yuanyuan Zhou, Shunping Huang, Yuxuan Tang, Chang Zhou, Wujie Chen, Zhe China University of Geosciences School of Computer Science School of Geography and Information Engineering Wuhan430074 China China University of Geosciences School of Computer Science Wuhan430074 China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310018 China La Trobe University Cisco-La Trobe Centre for AI and IoT School of Computing Engineering and Mathematical Sciences BundooraVIC3086 Australia

Human-centric Emotional Video Captioning (H-EVC) aims to generate fine-grained, emotion-related sentences for human-based videos, enhancing the understanding of human emotions and facilitating human-computer emotional interaction. However, existing video captioning methods primarily focus on overall event content, often overlooking sufficient subtle emotional clues and interactions in videos. As a result, the generated captions frequently lack emotional information. To address this, we propose a novel Emotion-oriented Cross-modal Prompting and Alignment (ECPA) approach for large foundation models to enhance H-EVC accuracy by effectively modeling fine-grained visual-textual emotion clues and interactions. Using large foundation models, our ECPA introduces two learnable prompting strategies: visual emotion prompting (VEP) and textual emotion prompting (TEP), as well as an emotion-oriented cross-modal alignment (ECA) module. In VEP, we develop two-level learnable visual prompts, i.e., emotion recognition (ER)-level and action unit (AU)-level prompting, to assist pre-trained vision-language foundation models to attend to both coarse and fine emotion-related visual information in videos. In TEP, we correspondingly devise two-level learnable textual prompts, i.e., sentence-level emotional tokens, and word-level masked tokens, for obtaining both whole and local textual prompt representations related to emotions. To further facilitate the interaction and alignment of visual-textual emotion prompt representations, our ECA introduces another two levels of emotion-oriented prompt alignment learning mechanisms: the ER-sentence level and the AU-word level alignment losses. Both enhance the model's ability to capture and integrate both global and local cross-modal emotion semantics, thereby enabling the generation of fine-grained emotional linguistic descriptions in video captioning. Extensive experiments not only demonstrate that our ECPA outperforms existing state-of-the-art ap

关键词： Semantics

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M4: A Framework for Per-Flow Quantile Estimation 40

M4: A Framework for Per-Flow Quantile Estimation

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Dong, Siyuan Fan, Zhuochen Bai, Tianyu Yang, Tong Xue, Hanyu Chen, Peiqing Wu, Yuhan School of Mathematical Sciences Peking University China School of Electronics Engineering and Computer Science School of Computer Science Peking University China University of Maryland Department of Computer Science United States

ISBN: (纸本)9798350317152

The field of quantile estimation has grown in importance due to its myriad practical applications. Recent research trends have evolved from estimating the quantile for a single data stream to developing data structures that can concurrently estimate quantiles for multiple sub-streams, also known as flows. This paper introduces a novel framework, M4, designed to estimate per-flow quantiles in data streams accurately. M4 is a versatile framework that can be integrated with a wide array of single-flow quantile estimation algorithms, thereby enabling them to perform per-flow estimation. The framework employs a sketch-based approach to provide a space-efficient method for recording and extracting distribution information. M4 incorporates two techniques: MINIMUM and SUM. The MINIMUM technique minimizes the noise on a flow from other flows caused by hash collisions, while the SUM technique efficiently categorizes flows based on their sizes and customizes treatment strategies accordingly. We demonstrate the application of M4 on three single-flow quantile estimation algorithms (DDSketch, t-digest, and ReqSketch), detailing the specific implementation of the MINIMUM and SUM techniques. We provide theoretical proof that M4 delivers high accuracy while utilizing limited memory. Additionally, we conduct extensive experiments to evaluate the performance of M4 regarding accuracy and speed. The experimental results indicate that across all three example algorithms, M4 significantly outperforms two comparison frameworks in terms of accuracy for per-flow quantile estimation while maintaining comparable speed. © 2024 IEEE.

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Masked-Enhanced Food Segment Anything Model for Automatic Dietary Intake Monitoring 25

Masked-Enhanced Food Segment Anything Model for Automatic Di...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Guo, Zhongsui Javadi, Bahman Singh, Sonit Sowmya, Arcot School of Computer Science and Engineering Unsw Sydney Australia School of Computer Data and Mathematical Sciences Western Sydney University NSW Australia

ISBN: (纸本)9798350379037

Dietary management plays a crucial role in maintaining long-term health, preventing diseases and aiding in recovery, particularly amidst the increasing prevalence of chronic conditions such as hypertension, cardiovascular diseases and diabetes. To achieve best practice in dietary management, providing insights into calorie intake based on the food items that people are eating is pivotal. In this paper, we propose a methodology to achieve this in three phases: instance segmentation, volume estimation and calorie calculation. Building on the foundations of Food Segment Anything Model (FoodSAM), we propose Masked-enhanced FoodSAM (MFoodSAM) focussing on enhancing semantic and instance masks and providing better food identification. MFoodSAM incorporates ResNet50 as a background model, improving its ability to separate non-food elements and reducing dependency on merging thresholds. We follow a coarse-to-fine segmentation approach to create detailed and semantically enriched masks, providing accurate instance and panoptic segmentation masks. After we accurately identify food items, we estimate food volume by calculating the pixel count from the food instance masks and combine this data with information from the USDA National Nutrient Database and NutritionData to compute the caloric content of the food. We applied the proposed method on both a public and a private food dataset, and obtained state-of-the-art results, with mean IoU of 87.99;mean Accuracy of 95.55;average Acc of 94.85 on MyFood dataset, and mean IoU of 82.38;mean Acc of 92.85;average Acc of 94.66 on UNSW-Food dataset. This intelligent food monitoring system provides users with precise caloric information, facilitating more effective nutrition monitoring and contributing to improved health outcomes. © 2024 IEEE.

关键词： Semantic Segmentation

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Data Quality-Driven Top-k Rule Discovery 3rd

Data Quality-Driven Top-k Rule Discovery

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

作者： Han, Ziyan Chen, Wanjia Han, Yunpeng School of Computer Science and Engineering Beihang University Beijing China School of Mathematical Sciences Shenzhen University Shenzhen China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge United States

ISBN: (纸本)9789819616978

Existing works on rule discovery often yield an overwhelming number of rules with a high computational cost. Moreover, few studies consider the impact of data quality on the reliability of rules produced. This paper studies the top-k rule discovery problem that returns a set of top-k reliable rules. We introduce a novel scoring function that incorporates data quality, providing a more comprehensive and fine-grained assessment of rules. Based on this scoring function, we develop an efficient algorithm for discovering top-k high-quality rules in relational data. To enhance the performance of our algorithm, we propose effective pruning and optimization strategies that significantly reduce the search space and improve computational efficiency;additionally, we parallelize it for further acceleration. Through extensive experiments on real-life datasets, we verify the effectiveness and efficiency of the proposed method, confirming its practical utility in data quality-driven top-k rule discovery. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data reliability

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A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel

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computers, Materials & Continua 2025年第5期83卷 2025-2040页

作者： Xiaoying Qiu Xiaoyu Ma Guangxu Zhao Jinwei Yu Wenbao Jiang Zhaozhong Guo Maozhi Xu College of Computer Science Beijing Information Science and Technology UniversityBeijing100192China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China School of Mathematical Sciences Peking UniversityBeijing100871China

Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant *** with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium *** existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless ***,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing *** overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely *** be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep ***,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication *** with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.

关键词： Physical layer authentication blockchain squeeze and excitation module computational cost mobile scenario

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Topology Construction for Max-Min Rate Optimization in Heterogeneous UAVs Networks

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IEEE Internet of Things Journal 2025年第12期12卷 20203-20214页

作者： Wu, Kefeng Chin, Kwan-Wu Soh, Sieteng Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Curtin University School of Electrical Engineering Computing and Mathematical Sciences PerthWA6102 Australia

This paper considers a topology construction problem involving a fixed-wing Unmanned Aerial Vehicle (UAV), a set of quad-rotor UAVs and mobile ground users. The constructed topology must maximize the max-min flow rate of ground users over a given planning horizon. To this end, we outline a Mixed Integer Linear Program (MILP) that jointly optimizes the trajectory of the fixed-wing UAV, placement of quad-rotor UAVs, and routing of traffic from each source-destination ground user pair. Solving the MILP is challenging because it requires an exhaustive collection of topologies. To this end, this paper outlines a solution called Rollout to determine the network topology in each time slot of a given planning horizon iteratively. The main idea of Rollout is to generate a sequence of future decisions using a heuristic, where a decision corresponds to the placement of quad-rotor UAVs. Further, each sequence of decisions has a cost-to-go value. Rollout then selects the sequence with the highest cost-to-go value. The results show that the max-min flow rate achieved by Rollout is on average 81% that of MILP. © 2014 IEEE.

关键词： Antenna grounds

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

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Tsinghua science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Revenue-maximizing online stable task assignment on taxi-dispatching platforms

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Frontiers of computer science 2022年第6期16卷 175-177页

作者： Jingwei LV Ze ZHAO Shuzhen YAO Weifeng LV School of Computer Science and Engineering Beihang UniversityBeijing100191China School of Mathematical Science Beihang UniversityBeijing100191China

1 Introduction and contributions With the rapid development of mobile Internet,taxi-dispatching platforms have become increasingly popular and *** example,UBer and Didi are both famous O2O platforms covering the dominating portion of the taxidispatching market over the world.A central issue in taxidispatching platforms is task assignment.

关键词： O2O assignment platforms

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Homomorphic Encryption Based ECDSA Generation Over Five Party Protocol 19th

Homomorphic Encryption Based ECDSA Generation Over Five Part...

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19th International Workshop on Data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer Security, ESORICS 2024

作者： Aggarwal, Akshit Swain, Srinibas Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India School of Computer and Mathematical Sciences University of Adelaide Adelaide Australia

ISBN: (纸本)9783031823480

Secure computation of multiparty protocol (MPP) is being widely used to address privacy issues in various technologies, such as cryptocurrencies, blockchain, and many more. The security of MPP is computed in various steps, mainly via secret sharing of keys, encryption/decryption of messages, and signature generation. However, literature suggests the secure computation of multiparty is arduous in the presence of adversarial nature of parties. In this paper, we propose a notion for achieving ECDSA in a multiparty protocol using an Authentication server At. We demonstrate this using four steps: 1. proposal of multiparty (five) protocol (5PP) in which there are parties with adversarial natures. 2. setup of 5PP with At that ensures the mutual identification of parties. 3. Encryption of messages in the proposed 5PP. 4. ECDSA signature generation between the parties in the aforementioned setup. We ensure security in all the steps mainly by using modified Paillier cryptosystem and homomorphic encryption. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Authentication Protocol

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