Yiqin Wang¹, Yufeng Wang^1,*, Jianhua Ma², Qun Jin³

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4431-4449, 2025, DOI:10.32604/cmc.2025.059880 - 06 March 2025

Abstract Opportunistic mobile crowdsensing (MCS) non-intrusively exploits human mobility trajectories, and the participants’ smart devices as sensors have become promising paradigms for various urban data acquisition tasks. However, in practice, opportunistic MCS has several challenges from both the perspectives of MCS participants and the data platform. On the one hand, participants face uncertainties in conducting MCS tasks, including their mobility and implicit interactions among participants, and participants’ economic returns given by the MCS data platform are determined by not only their own actions but also other participants’ strategic actions. On the other hand, the platform can… More >

Jianyong Wang¹, Mingliang Gao¹, Qilei Li², Hyunbum Kim³, Gwanggil Jeon^3,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3561-3582, 2024, DOI:10.32604/cmc.2024.058637 - 19 December 2024

Abstract Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety. Crowd counting has attracted considerable attention in the field of computer vision, leading to the development of numerous advanced models and methodologies. These approaches vary in terms of supervision techniques, network architectures, and model complexity. Currently, most crowd counting methods rely on fully supervised learning, which has proven to be effective. However, this approach presents challenges in real-world scenarios, where labeled data and ground-truth… More >

Peicong He, Yang Xin^*, Yixian Yang

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3067-3084, 2024, DOI:10.32604/cmc.2024.054777 - 15 August 2024

Abstract The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain More >

Tianyu Wang^1,2, Kongyang Chen^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2297-2314, 2024, DOI:10.32604/cmes.2024.049964 - 08 July 2024

Abstract Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking… More >

Yun-Jung Choi^1,#,*, Jae-Won Kwak^2,#, Hae-In Namgung³

International Journal of Mental Health Promotion, Vol.26, No.6, pp. 439-447, 2024, DOI:10.32604/ijmhp.2024.050458 - 28 June 2024

Abstract This study evaluated the state of anxiety, depression, post-traumatic stress disorder, general mental health, and mental well-being among citizens after a crowd-crush disaster in Korea. Individuals who experienced the crowd crush had significantly higher anxiety, depression, and post-traumatic stress disorder (PTSD) scores than those who did not (p < 0.001). Additionally, people who avoided the disaster area had significantly higher depression and PTSD scores than those who did not avoid the area (p < 0.001). Those who directly witnessed the Seoul Halloween crowd crush had a significant difference in PTSD levels in either group than those More >

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Muhammad Hamza Faheem², Syed Shah Amanullah Quadri³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4001-4020, 2024, DOI:10.32604/cmc.2024.048208 - 20 June 2024

Abstract Estimation of crowd count is becoming crucial nowadays, as it can help in security surveillance, crowd monitoring, and management for different events. It is challenging to determine the approximate crowd size from an image of the crowd’s density. Therefore in this research study, we proposed a multi-headed convolutional neural network architecture-based model for crowd counting, where we divided our proposed model into two main components: (i) the convolutional neural network, which extracts the feature across the whole image that is given to it as an input, and (ii) the multi-headed layers, which make it easier More >

Mingze Li, Diwen Zheng, Shuhua Lu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2105-2122, 2024, DOI:10.32604/cmc.2024.048928 - 15 May 2024

Abstract Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis, achieving tremendous success recently with the development of deep learning. However, there have been still many challenges including crowd multi-scale variations and high network complexity, etc. To tackle these issues, a lightweight Res-connection multi-branch network (LRMBNet) for highly accurate crowd counting and localization is proposed. Specifically, using improved ShuffleNet V2 as the backbone, a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters. A light multi-branch structure with different expansion rate… More >

Maria Gul¹, Imran Ali Khan¹, Gohar Zaman², Atta Rahman^3,*, Jamaluddin Mir², Sardar Asad Ali Biabani^4,5, May Issa Aldossary⁶, Mustafa Youldash⁷, Ashraf Saadeldeen⁸, Maqsood Mahmud⁹, Asiya Abdus Salam⁶, Dania Alkhulaifi³, Abdullah AlTurkey³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1631-1657, 2024, DOI:10.32604/cmc.2024.048289 - 25 April 2024

Abstract Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, many researchers proposed game theoretic models to avoid and remove obstacles for crowd evacuation. Game theoretical models aim to study and analyze the strategic behaviors of individuals within a crowd and their interactions during the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. These models consider a group of individuals as homogeneous objects with the same goals, involve complex mathematical formulation, and cannot model real-world scenarios such as panic, environmental information, crowds that move dynamically,… More >

Kai Wei¹, Song Yu², Qingxian Pan^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240 - 25 April 2024

Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value More >

Yu Li, Mingxiao Li, Dongyang Ou^*, Junjie Guo, Fangyuan Pan

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 893-909, 2024, DOI:10.32604/cmes.2023.031350 - 30 December 2023

Abstract With the rapid development of mobile Internet, spatial crowdsourcing has become more and more popular. Spatial crowdsourcing consists of many different types of applications, such as spatial crowd-sensing services. In terms of spatial crowd-sensing, it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models. Besides collecting sensing data, spatial crowdsourcing also includes spatial delivery services like DiDi and Uber. Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications. Previous research conducted task assignments via traditional matching approaches or using simple… More > Graphic Abstract