International Journal of Crowd Science最新文献

{"title":"Research on Data Management Capability Assessment","authors":"Tingting Guo;Leiju Qiu;Lianchong Zhang","doi":"10.26599/IJCS.2026.9100003","DOIUrl":"https://doi.org/10.26599/IJCS.2026.9100003","url":null,"abstract":"In the digital economy, data are crucial for enterprise value creation, yet many organizations still lack systematic assessment tools for data management capability. This paper presents a comprehensive review and develops a multidimensional framework encompassing five core dimensions: data strategy and governance, data resources and quality, technology and platform support, data application and value realization, and security and risk management. We further analyze three complementary assessment methodologies—maturity grading, quantitative assessment, and qualitative assessment. Recognizing that existing models primarily address data for internal operations, we introduce a product-oriented data management capability assessment model. To demonstrate its applicability, we conduct a case study on 11 commercial remote sensing satellite companies, constructing a tailored three-level indicator system and employing entropy-weighted TOPSIS for evaluation. Results reveal clear enterprise stratification, with data sustainable development capability and data market capability emerging as the primary differentiators. This paper contributes a theoretically grounded and operationally feasible assessment framework, offering both academic insights and practical guidance for enterprises seeking to enhance their data management capabilities and accelerate digital transformation.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 3","pages":"166-177"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Prediction of Cucumber Price and Yield Based on LSTM Weight Sharing","authors":"Wenzheng Song;Yong Zheng;Yuchun Wang;Lun Hao;Lianjun Guo;Qingfeng Hao;Lizhen Cui;Yonghui Xu","doi":"10.26599/IJCS.2024.9100034","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100034","url":null,"abstract":"The agricultural market is characterized by unpredictable price fluctuations influenced by diverse uncontrollable factors, often leading to a mismatch between production and income, thus creating a vicious cycle. Accurate forecasting of cucumber prices is paramount to enhancing the agricultural industry's development and safeguarding the interests of all stakeholders in the cucumber a supply chain. In this study, we introduce a novel multi-task learning model that leverages Long Short-Term Memory (LSTM) neural networks and self-attention mechanisms to jointly predict cucumber prices and production. This model is designed to capture the intricate relationship between these two variables, enhancing feature extraction through the incorporation of self-attention. By adopting a multi-task learning approach, we aim to gain a deeper understanding of the interplay between cucumber prices and production. Our experimental results confirm the superiority of the proposed model in terms of predictive accuracy and generalization ability when compared to traditional methods. A comprehensive robustness analysis further demonstrates the model's stability across various scenarios. Specifically, the self-attention multi-task LSTM model exhibits smaller average absolute and root mean square errors than traditional neural network models. This research contributes a valuable tool for agricultural decision-making, empowering farmers and policymakers with more precise predictions of cucumber prices and production. Thus, in turn, it facilitates better adaptation to market fluctuations and promotes overall agricultural productivity.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 3","pages":"178-185"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Attacks on Discrete Rating Reputation Systems Considering the Herd Effect","authors":"Zhanjiang Chen;Hangjing Zhang;Hong Vicky Zhao","doi":"10.26599/IJCS.2024.9100021","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100021","url":null,"abstract":"Reputation systems are critical as a cornerstone of digital interaction, serving to assess entities and exerting a corrective influence that encourages users to engage in proper conduct. It plays a pivotal role in various domains, especially in those employing discrete five-star rating systems such as numerous E-commerce platforms. However, reputation systems also encounter significant challenges, including susceptibility to malicious attacks and vulnerability to group effects like herd behavior, which offer attackers opportunities to realize their extreme objectives. Numerous researchers have designed and analyzed effective attack methods, with the ultimate aim of enhancing the defense mechanisms of reputation systems. Therefore, considering group effects in the rating process, in this paper, we model the herding of user ratings and introduce the corresponding discrete rating reputation system. Based on the proposed model, we give simple attack methods and detailed theoretical analysis to precisely predict the attack effect. In addition, we propose adaptive attack methods, which are fundamentally based on adapting to the randomized decisions of the affected users, with the goal of influencing group decisions as much as possible and thus obtaining a better attack. We also analyze these adaptive scenarios and give some qualitative results. Simulations and experiments validate the effectiveness of the attack method, while real rating data reflects the accuracy of the model. By examining the attack dynamics in more depth, this work seeks to inspire the further development of system defenses that can contribute to strengthening social trust.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 3","pages":"129-142"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wire Drawing Die Recognition Based on Visual Inspection","authors":"Ye Ding;Hongbo Sun","doi":"10.26599/IJCS.2024.9100041","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100041","url":null,"abstract":"The wire drawing die is a key consumable in metal-wire processing, and diamonds are generally implanted as die cores by metal metallurgy for its high hardness. But the uncertainty of diamonds position greatly obstucts the efficiency of the automatic control of early stage of this. What's more, to achieve efficient and precise production, machine vision is widely used. Since diamonds are invisible, the primary difficulty lies in the detection of the diamond exposure in initial stages of processing. However, due to the unfixed position of diamonds, precise positioning is difficult to achieve during the automation control process. To address this issue, this paper leverages machine vision to enhance the flexibility of processing and proposes an improved U-Net network model specifically designed for detecting diamond exposure in wire drawing die. Among them, considering the model's generality, the U-Net convolutional networks down-sampling part is replaced by the feature extraction part of visual geometry group network-16 for training. Appropriate online data enhancement methods were also used. The experimental results show that the method has a missed detection rate of 6.67% and a misjudgment rate of 3.67% on the wire drawing die dataset. Compared with the original model, the missed detection and misjudgment of diamond exposure are reduced, and the training time is shortened from the original 94 792 to 48 526 s, which greatly shortens the training time and reduces the time cost. The improved model reduces the misjudgment and missed detection to a certain extent, and greatly shortens the training time. Meanwhile, the paper achieves adaptive feedback of machine vision through artificial intelligence technology.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 3","pages":"186-194"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148928364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Sampling Frequency and Window Size on Identifying Risky Driving Maneuvers","authors":"Xuehao Yang;Di Wang;Shengfei Lyu;Ying Shu;Chunyan Miao","doi":"10.26599/IJCS.2024.9100042","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100042","url":null,"abstract":"The identification of risky driving maneuvers, such as harsh acceleration, harsh braking, harsh cornering, and speeding, has always been an important topic in monitoring driving safety, which lays the groundwork of a wide range of real-world applications ranging from personal car insurance to risk management in fleets. Although extensive research has been carried out on the identification of risky driving maneuvers using various driving trajectory datasets, to the best of our knowledge, there is not yet a thorough study on the effect of the sampling frequency of the telematics devices and the window size used to efficiently identify risky driving maneuvers. In this paper, we focus on the evaluation of the effect of sampling frequency and window size on the identification of risky driving maneuvers. Specifically, we first implement an enhanced multi-stage approach named SCALE to identify risky driving maneuvers, comprising the following procedures: Sampling raw data, Clustering sampled data, lAbeLing clustered data, and training a classifiEr using the labeled data. We then apply SCALE to extensively evaluate the effect of different sampling frequency rates and window sizes using a real-world comprehensive driving trajectory dataset. Experimental results suggest that a sampling frequency of 5 Hz with a window size of 3 s would be sufficient to capture a vast majority of risky driving maneuvers.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 3","pages":"143-151"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trustworthy Evaluation Method for User Psychological Assessment Based on Large-Scale Heterogeneous Graph Networks","authors":"Haojie Wan;Wei Yu;Zhiyu Li;Shijun Li;Fang Yu;Juncheng Yang","doi":"10.26599/IJCS.2025.9100008","DOIUrl":"https://doi.org/10.26599/IJCS.2025.9100008","url":null,"abstract":"Online psychological assessment information system can quickly pre-screen users with potential mental health risks. Therefore, accurate and effective psychological assessment results are the foundation and guarantee of psychological health work. The process data, including response time and answer patterns, reflect the behavioral patterns of users during the assessment. However, the current predominant abnormal detection methods rely on constructing global or contextual data distributions, distances, or error calculation models. These methods fail to account for the influence of user personality traits and question characteristics on behavior patterns, making it challenging to capture personalized differences and the combined effects of multiple behaviors. To address these challenges, this paper proposes a knowledge-enhanced heterogeneous graph model based on largescale data. It embeds a large number of factors affecting the answering process, including user profiles, question semantics, user relationships, question relationships, user-question interactions, contextual environments, and temporal dynamics, into a heterogeneous multi-graph network. It adaptively learns the importance of each node and edge through graph attention mechanisms and employs Transformer's encoding layers to represent user personality traits, question knowledge features, contextual features, and multi-behavior features. The primary contribution of this approach lies in designing a unified multi-graph network model on a large-scale dataset for jointly learning the complex characteristics of users and questions. It explicitly distinguishes the abnormal characteristics of different users and questions in various contextual environments, effectively achieving personalized abnormal detection in user psychological assessments. Experimental results demonstrate that this method exhibits high accuracy and recall in identifying the trustworthiness of psychological assessments, enabling a fine-grained assessment of the credibility of each response.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 3","pages":"152-165"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11686145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Identification of Coal Geographical Origin Based on Improved U-Net3+ and Near-Infrared Spectroscopy","authors":"Meng Lei;Shifan Xu;Chuanda Yang;Zhiyi Tan;Zhibin Xu;Liang Zou","doi":"10.26599/IJCS.2024.9100028","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100028","url":null,"abstract":"The determination of coal's geographical origin is pivotal for assessing coal quality and improving import and export inspections. Conventional methods for determining coal's geographical origin, which primarily involve time-intensive, labor-intensive, and costly chemical experiments, necessitate optimization for greater efficiency. Near-Infrared Spectroscopy (NIRS) offers a viable solution with its accuracy, speed, and non-destructive nature in detecting chemical compositions, which has achieved success in various research fields. This study introduces a non-destructive technique that integrates NIRS with deep learning for identifying the geographical origin of coal, addressing the limitations of conventional approaches. In tackling the issue of abnormal Near-Infrared (NIR) data, the study employs a data cleaning method based on Euclidean distance to identify and eliminate outliers. Standard normal variate transformation is utilized to extract features from the NIR data in the experiment. Furthermore, we introduce an improved U-Net3+ model by integrating residual modules into the encoder and incorporating the concurrent spatial and channel squeeze & excitation attention block to enrich the model's feature capturing ability. Experimental results demonstrate that the optimized U-Net3+ model attains an impressive 97.45% identification accuracy, outperforming other algorithms. The proposed method stands as a promising solution in the realm of coal quality assessment and inspection.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 2","pages":"78-85"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11548561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Overview of Intelligent Game: From Game AI to Military Combat Simulation","authors":"Wanpeng Ma;Yang Xiao;Liping Wang;Shaojie Lv;Xiaoting Sun;Qiancheng Ding","doi":"10.26599/IJCS.2024.9100027","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100027","url":null,"abstract":"There has been a growing focus on employing intelligent game technology within the realms of game AI and military combat simulation in recent times. This article offers a comprehensive overview of the development of intelligent game theory technology, delving into notable research milestones in game AI and military combat simulation. It examines the technical mechanisms employed. Furthermore, it discusses the challenges associated with integrating intelligent game theory in military contexts and summarizes some future developments to overcome these challenges. This article aims to provide a guidance for the adaptation of intelligent game theory technology from game AI to military combat simulation.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 2","pages":"94-105"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11548556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Blockchain-Based Intrinsic Security Mechanisms for Satellite Internet","authors":"Jiandong Liu;Hailong Kang;Feipeng Wu;Wenbo Wei;Xichao Hou","doi":"10.26599/IJCS.2024.9100031","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100031","url":null,"abstract":"In the context of rapid globalization and the swift advancement of information technology, satellite internet has emerged as a critical infrastructure for global communication services, drawing unprecedented attention to its security challenges. Traditional security measures are increasingly inadequate, struggling to meet the high standards of security, transparency, and reliability required by modern communication networks. Blockchain technology, with its unique attributes such as decentralization, immutability, and smart contracts, presents a promising solution to these security concerns. This paper explores blockchain-based intrinsic security mechanisms for satellite internet, with a particular emphasis on key components such as trusted accounts, data encryption, and access control. The objective is to propose a robust set of strategies and provide technical support for the security management of satellite internet, ultimately aiming to enhance the security and reliability of satellite communication networks on a global scale.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 2","pages":"86-93"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11548558","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement and Network Analysis for the Data Service Market Based on Heterogeneous Multi-Agent Modeling","authors":"Deyu Zhou;Yuwei Guo;Xudong Lu;Linhao Zhang;Wei Gio;Lizhen Cui","doi":"10.26599/IJCS.2025.9100001","DOIUrl":"https://doi.org/10.26599/IJCS.2025.9100001","url":null,"abstract":"With the increasing complexity of collaboration among various social entities and user demands, the factors affecting the stable development of the data service market are also growing. These factors include the widespread dissemination of information enhancing subjective consciousness, the continuous improvement in intelligence, and the complexification of structural relationships. To achieve effective governance and regulation of the data service market, it is crucial to conduct simulation experiments before making regulatory decisions. However, current research and analysis of the data service market primarily focus on data-level performance, proving inadequate when it comes to measurement and analysis of multiple heterogeneous entities and the integration of various social elements within the data service market. Based on this, this paper innovatively proposes a data service market measurement and network analysis method based on heterogeneous multi-agent modeling. By introducing the service ecosystem theory, we clarify the participants and external factors of the data service market and conduct utility measurements for three-level entities based on value creation. Furthermore, an analytical methodology is devised to precisely assess the influence of heterogeneous networks on utility. Finally, the paper verifies the effectiveness of the proposed method through the analysis of experimental results.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"10 2","pages":"67-77"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11548559","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148141559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}