Concurrency and Computation-Practice & Experience最新文献

{"title":"A Robust Zero-Watermarking Algorithm for Spatiotemporal Trajectory Data Protection","authors":"Jianing Xie,&nbsp;Liming Zhang,&nbsp;Yan Jin,&nbsp;Ruigang Nan,&nbsp;Tao Tan,&nbsp;Haoran Wang","doi":"10.1002/cpe.70280","DOIUrl":"https://doi.org/10.1002/cpe.70280","url":null,"abstract":"<div>\u0000 \u0000 <p>As an important type of spatiotemporal big data, trajectory data faces severe challenges such as illegal copying and dissemination, which infringes upon the legitimate rights of copyright owners. Existing copyright protection methods for trajectory data often regard it as a collection of points for watermark embedding, ignoring its inherent spatiotemporal characteristics. These methods exhibit limitations in both robustness and specificity. To address this, this paper introduces a zero-watermarking algorithm for trajectory data that effectively integrates spatiotemporal features. The proposed algorithm begins by segmenting the trajectory into sub-trajectories using stay areas as key nodes, extracting feature points accordingly. Then, a watermark index is constructed based on the movement speed of each sub-trajectory in the X-direction, with the watermark information generated through a voting mechanism. Finally, an exclusive-ORing (XOR) operation is performed between the watermark information and the scrambled copyright image to produce the zero-watermark image. Experimental results demonstrate that the proposed method exhibits strong robustness, effectively resisting various attacks such as time shifting, cropping, and coordinate point deletion. Specifically, the normalized correlation (NC) value remains above 0.95 even when 50% of the coordinate points are removed, and achieves an NC value of 1.00 under geometric attacks including translation and scaling. Compared to existing watermarking schemes for trajectory data, the proposed approach exhibits significantly enhanced robustness.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Impact of AI on Consumer Behavior and Digital Marketing Through the Lens of Social Media","authors":"Cui Weijun,&nbsp;Muzamil Mohib","doi":"10.1002/cpe.70284","DOIUrl":"https://doi.org/10.1002/cpe.70284","url":null,"abstract":"<div>\u0000 \u0000 <p>The incorporation of Artificial Intelligence (AI) is rapidly advancing and significantly shaping consumer purchasing behavior and marketing approaches. This research examines AI's role in increasing the understanding of consumer preferences and digital marketing performance within Pakistan. Despite the potential of technologies such as analytics, machine learning, and recommender systems to enhance personalization, process automation, and user engagement, their application in moderating social media platforms remains underexplored. This study examines the effect and role of Artificial Intelligence (AI) on consumer-buyer behavior and digital marketing, with a key moderating variable being social media. The study, based on the Technology Acceptance Model (TAM), aims to investigate the impact of AI-based technologies on consumer decision-making, which promotes personalization and effectiveness in marketing. Data was collected from 658 Foodpanda users in Pakistan to complete a structured online survey with some standard Likert-scale measurement items. The findings indicate that significant and positive relationships exist among AI, consumer purchasing behavior, digital marketing, and social media engagement. The consumer's purchasing behavior to purchase a product mediates the connection between AI and digital marketing, and social media amplifies the effects of consumer behavior on marketing performance. This study is beneficial to both theory and practice since it validates the theory of TAM in an AI-driven marketing setting and provides practical steps targeted towards those marketers who want to maximize campaigns via AI and social media. There are also limitations and areas of future research discussed.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-Based Policy Network for Sensor-Free Robotic Arm Control With Deep Reinforcement Learning","authors":"Jin Wu,&nbsp;Yaqiao Zhu,&nbsp;Jinfu Li","doi":"10.1002/cpe.70250","DOIUrl":"https://doi.org/10.1002/cpe.70250","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a novel attention-based convolutional neural network (CNN) for sensor-free robotic arm control, aiming to improve six dimensional (6D) pose estimation and end-effector operation in an end-to-end manner. Unlike traditional methods that rely on explicit feature engineering or sensor feedback, our approach leverages a sophisticated attention mechanism within the convolutional backbone to enhance spatial awareness. The proposed localization sub-module scores each prior regime through a weighted average of activation maps, allowing the network to focus on the most informative regions of the input. Additionally, we introduce a two-phase training methodology requiring only image-level annotations. In the first phase, the network learns to extract discriminative features from synthetic images, which are crucial for accurate 6D pose prediction. In the second phase, a reinforcement learning agent, equipped with the trained vision model as its sensory module, is optimized using a sparse reward function to refine action policies. Experimental evaluations in two virtual scenarios demonstrate that our method outperforms popular CNN-based approaches in terms of both accuracy and efficiency. Specifically, our method improves task success rates by 52.9% and reduces position error by 72.3% compared to baseline models, showcasing its effectiveness in sensor-free robotic arm control.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Distributed Geyser-Inspired Algorithm for Minimizing Losses in Flywheel Array Energy Storage Systems","authors":"Jianan Chen,&nbsp;Istas Fahrurrazi Nusyirwan,&nbsp;Robiah Ahmad,&nbsp;Fadhilah Abdul Razak,&nbsp;Lili Jing","doi":"10.1002/cpe.70181","DOIUrl":"https://doi.org/10.1002/cpe.70181","url":null,"abstract":"<div>\u0000 \u0000 <p>Flywheel array energy storage systems (FAESS), due to their high power density, rapid response time, and long operational lifespans, have come to be recognized as one of the best alternatives for renewable energy storage on a large scale. However, the scarcity of efficient working energy systems results in impeded performance and reliability of the entire system. This paper presents a new distributed architecture of the Geyser-Inspired Algorithm (GEA), which allows energy loss minimization using a dynamic load assignment among flywheels. This architecture uses dynamic load sharing among flywheels to minimize energy loss. The algorithm works in a distributed way, with each flywheel unit running its own version of the control logic that was inspired by geyser dynamics, enabling real-time responses to dynamic load changes and system failures. The effectiveness of the proposed GEA is verified through extensive simulations and experimental validation. In simulations, GEA outperforms conventional control strategies such as Proportional Allocation and Round-Robin Scheduling, showing a reduction in total energy losses by up to 30%, an average State of Charge (SoC) imbalance improvement to 6.2%, and a significantly enhanced real-time responsiveness with an average response time of about 0.8 s. Moreover, parameter sensitivity analysis demonstrated robust performance across different operational thresholds, with minimal variations in energy loss and response time, confirming the stability and adaptability of the proposed method. Additional validation scenarios, including random load fluctuations and multiple simultaneous flywheel failures, further confirmed the robustness and fault-tolerance of GEA. Scalability analysis also indicated efficient computational performance, with execution times increasing modestly from 0.85 ms for four flywheels to 4.60 ms for twenty-four flywheels, underscoring GEA's applicability in larger-scale energy storage applications. Through the integration of nature-influenced heuristics and engineering tools in a consolidated manner, our study highlights an avenue through which the design of robust, scalable, and fault-tolerant control methods in large-scale electrical energy storage systems is made possible. Given that the point of distribution of the Geyser-inspired algorithm allows for lesser losses and greater adaptability in the fast-changing power grid, the distributed Geyser-inspired algorithm is key in the development of FAESS, a type of battery energy storage system.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Machine Learning Models for Short-Term U.S. Treasury Yield Forecasting","authors":"Max Yue-Feng Wang,&nbsp;Yi-Fan Wang","doi":"10.1002/cpe.70265","DOIUrl":"https://doi.org/10.1002/cpe.70265","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examines historical trends in the U.S. 10-year Treasury yield and evaluates the effectiveness of four machine learning models, linear regression, decision tree, random forest, and multi-layer perceptron (MLP) neural networks, for short-term yield forecasting. As a key benchmark in global financial markets, the 10-year Treasury yield is influenced by multiple economic factors, including core inflation, the federal funds rate, GDP growth, and the U.S. Federal government's debt growth rate. Leveraging historical data from the Federal Reserve Economic Database (FRED), this study develops predictive models to assess the impact of these factors on yield fluctuations. Empirical results indicate that the random forest model outperforms the other approaches, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), alongside an <i>R</i>² of 0.6073. This suggests its superior ability to capture nonlinear relationships in yield movements. The decision tree model also demonstrates competitive accuracy but is more susceptible to overfitting. Conversely, linear regression provides useful interpretability but struggles to capture complex economic interactions, leading to lower predictive accuracy. Despite its potential for handling nonlinear dependencies, the MLP model underperforms compared to the random forest, yielding an <i>R</i>² of 0.5058. The findings underscore the advantages of machine learning, particularly ensemble-based methods, in short-term Treasury yield forecasting.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning-Assisted Method for Camellia Oleifera Trunk Detection Using the Enhanced Yolov8-SEAW Model","authors":"Yuyan Zhang,&nbsp;Shuhui Min,&nbsp;Lijun Li,&nbsp;Yang Liu,&nbsp;Fei Long,&nbsp;Shangshang Wu","doi":"10.1002/cpe.70227","DOIUrl":"https://doi.org/10.1002/cpe.70227","url":null,"abstract":"<div>\u0000 \u0000 <p>Efficient detection of <i>Camellia oleifera</i> trunks in complex environments is vital for advancing intelligent harvesting robotics. However, challenges such as occlusion, background noise, and varying trunk shapes often lead to missed detections and false positives. To address these, this study presents YOLOv8-SEAW, an enhanced version of the YOLOv8 model designed to improve detection accuracy in such conditions. The architecture integrates three key improvements: SPD-Conv boosts small target detection, Efficiency RepGFPN enhances multiscale feature fusion, and ACmix attention minimizes background interference. The WIoUv1 loss function further refines bounding box regression, improving localization accuracy. Experimental results show that YOLOv8-SEAW boosts mAP from 82.4% to 89.4% and precision from 70% to 96%, with a 26% relative increase. Additionally, the model reduces parameters from 3.1 million to 2.9 million, improving efficiency without sacrificing accuracy. Overall, YOLOv8-SEAW enhances trunk detection, particularly in cluttered and occluded scenes, and is well-suited for deployment in automated agricultural tasks.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy Text Clustering Method Based on Burst Feature of Words","authors":"Xia Wu,&nbsp;Zehan Li,&nbsp;Yong Wang,&nbsp;Qing Zhao,&nbsp;Ke Wang,&nbsp;Hangyu Hu","doi":"10.1002/cpe.70269","DOIUrl":"https://doi.org/10.1002/cpe.70269","url":null,"abstract":"<div>\u0000 \u0000 <p>Real-time detection of privacy-relevant events in social media faces two fundamental challenges: (1) cluster instability caused by sparse and noisy text data, which leads to center drift; and (2) poor event discernibility in traditional online clustering methods. These limitations severely impair effective privacy monitoring in dynamic social media environments. To address these challenges, we propose an innovative edge intelligence-driven framework that integrates adaptive burst word detection using wavelet-based signal analysis; spectral clustering of identified burst words to establish stable event anchors; and real-time incremental text clustering centered around these fixed anchors. We conduct a comprehensive evaluation on a dataset of 116 million COVID-19-related tweets and obtain the following results: Burst word identification accuracy of 86.28%; cluster purity of 0.875 (37% improvement over the baseline method); throughput of 3000 tweets per minute; and 78% reduction of irrelevant content through effective noise filtering. The key advantages of our approach include: Addressing the persistent cluster drift problem via burst anchoring centers; enabling efficient distributed processing via edge intelligence architecture; providing a practical and scalable solution for real-time social media monitoring; and establishing a new paradigm for privacy-aware event detection systems.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PUF-Based Lightweight Authentication and Key Agreement Protocol for Secure Internet of Drones in Smart City","authors":"Shengcai Zhang,&nbsp;Zhaoming Xu,&nbsp;Xiang Gong","doi":"10.1002/cpe.70252","DOIUrl":"https://doi.org/10.1002/cpe.70252","url":null,"abstract":"<div>\u0000 \u0000 <p>With the introduction of the concept of smart cities and the widespread application of drones in areas such as traffic monitoring, air quality monitoring, and emergency medical response, the rapid development and emergence of the Internet of Drones (IoD) have been driven forward. Drones, as a critical component of IoD, leverage their advantage in airspace coverage and real-time dynamic response capabilities. Through the IoD framework, they enable multi-drone collaboration and real-time data sharing, making them an essential part of smart cities. However, drones, as resource-constrained devices, are unable to perform overly complex computations or utilize public-key cryptographic techniques. Therefore, we have designed a lightweight authentication and key agreement scheme based on Physical Unclonable Function (PUF) for the smart city environment. By completing the user registration phase over a public channel, it is better suited for the actual deployment of IoD in smart cities. Using the Real-or-Random (RoR) model and the widely recognized formal analysis tool ProVerif, we conducted a formal analysis of the proposed scheme, ensuring its security. We also evaluated the computational cost and communication overhead of the designed scheme. The results show that the proposed scheme is better suited for IoD-based smart city environments, offering lower computational costs, reduced communication overhead, and improved security.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research of Key Technologies of Distributed Stream Processing Based on FaaS","authors":"Qinlu He,&nbsp;Fan Zhang,&nbsp;Genqing Bian,&nbsp;Weiqi Zhang,&nbsp;Zhen Li","doi":"10.1002/cpe.70274","DOIUrl":"https://doi.org/10.1002/cpe.70274","url":null,"abstract":"<div>\u0000 \u0000 <p>Serverless computing has emerged as a promising paradigm for cloud-based stream processing applications characterized by fluctuating workloads and latency sensitivity. While existing Function-as-a-Service (FaaS) implementations primarily focus on homogeneous CPU/memory resource scaling, they fail to address the challenges of heterogeneous resource management and coordinated elasticity in distributed stream processing. This study proposes HFaaS, a novel serverless framework that integrates dataflow programming with heterogeneous resource orchestration for stream processing applications. The key innovations include: (1) a dataflow-oriented function composition model enabling dynamic scaling of individual processing stages through peer-to-point communication mechanisms, (2) a fine-grained GPU resource allocation strategy achieving 15% + utilization improvement through device sharing and elastic scaling capabilities, and (3) a locality-aware scheduling algorithm optimizing task placement based on data proximity and heterogeneous resource availability. Experimental results demonstrate that HFaaS effectively coordinates multi-stage function scaling while maintaining sub-second latency guarantees. The proposed resource allocation strategy improves GPU utilization by 15.2% compared to conventional static allocation approaches, with network overhead reduced by 31.6% through data-local scheduling. This work bridges the gap between serverless architectures and modern stream processing requirements, providing a unified platform for building resource-efficient, latency-sensitive distributed applications in heterogeneous cloud environments.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 23-24","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144935283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anomaly Detection Model Based on Anomaly Representation Reinforcement and Path Iterative Modeling","authors":"Xiangling Chen,&nbsp;Weifu Zhu,&nbsp;Zhixia Zeng,&nbsp;Zhipeng Qiu,&nbsp;Ruliang Xiao","doi":"10.1002/cpe.70245","DOIUrl":"https://doi.org/10.1002/cpe.70245","url":null,"abstract":"<div>\u0000 \u0000 <p>In complex systems, mutual influence among sensors can lead to gradual accumulation and spread of anomalies, eventually triggering systemic failures. During this process, abnormal features evolve slowly over time, blurring the distinction between normal and abnormal patterns, and anomalies in high-dimensional spaces are difficult to detect, increasing detection difficulty. We propose an anomaly detection model based on spatiotemporal graphs ARR-PIM to address this issue, which obtains anomalous representations from multiple perspectives and models anomalous propagation paths to extract spatiotemporal features. The model consists of two core modules: the anomaly representation enhancement module and the multilevel feature extraction module. The former can enlarge the feature difference and provide prior information for potential anomaly recognition by modeling multigranularity time series as positive and negative sample pairs; the latter learns time-varying spatial features through iterative modeling of the anomaly propagation process. Experiments on six public datasets show that the proposed anomaly detection model ARR-PIM improves the average F1 score by 1.84% compared to 14 benchmark methods, significantly improving the anomaly detection performance of multivariate time series.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 21-22","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}