The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications.
{"title":"Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment","authors":"Amer Tahseen Abu-Jassar;Hani Attar;Ayman Amer;Vyacheslav Lyashenko;Vladyslav Yevsieiev;Ahmed Solyman","doi":"10.26599/IJCS.2023.9100018","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100018","url":null,"abstract":"The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"29-43"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106333","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}
Pub Date : 2025-01-29DOI: 10.26599/IJCS.2023.9100035
Ahmad Al-Qerem;Ali Mohd Ali;Issam Jebreen;Ahmad Nabot;Mohammed Rajab;Mohammad Alauthman;Amjad Aldweesh;Faisal Aburub;Someah Alangari;Musab Alzgol
Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings.
{"title":"Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes","authors":"Ahmad Al-Qerem;Ali Mohd Ali;Issam Jebreen;Ahmad Nabot;Mohammed Rajab;Mohammad Alauthman;Amjad Aldweesh;Faisal Aburub;Someah Alangari;Musab Alzgol","doi":"10.26599/IJCS.2023.9100035","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100035","url":null,"abstract":"Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"64-78"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106334","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}
This paper presents a novel approach to enhance energy harvesting systems from ambient Radio Frequency (RF) sources in overcrowded environments. In environments like shopping malls, coffee shops, and airports, where wireless devices are prevalent, the electromagnetic energy emitted by these devices can be harvested and converted into electrical energy to power small devices, specifically those associated with the Social Internet of Things (SIoT). However, due to the high density of devices in such environments, the RF signals can be weak, resulting in low energy harvesting efficiency. This study focuses on developing technologies for wireless power transfer through a radio frequency ambient energy harvesting scheme, specifically designing to improve energy harvesting systems in crowded social environments. Recognizing the growing importance of energy harvesting for low-power devices in intelligent environments, our proposed method utilizes the ambient environment to capture energy in the downlink radio frequency range of the GSM-900 band. The system architecture comprises four main stages: a supercapacitor, a Villard voltage doubler circuit with seven stages, a lumped element matching network, and a microstrip patch antenna. The voltage doubler circuit is designed and simulated using the Agilent Advanced Design System (ADS) 2014 environment, and simulations and tests are conducted across different input power levels. Throughout the study, several key factors are identified as crucial to the system's efficiency, including the frequency band, input power level, voltage doubler circuit design, impedance matching, diode selection, number of rectification stages, and load resistance. The proposed method demonstrates significant potential in enhancing the energy harvesting efficiency from ambient RF sources in crowded social environments. By providing a sustainable power source for SIoT devices in such settings, our approach contributes to the advancement of energy harvesting capabilities and supports the practical implementation of energy-efficient technologies in intelligent and socially interconnected environments.
{"title":"Improving Energy Harvesting System from Ambient RF Sources in Social Systems with Overcrowding","authors":"Ramy Agieb;Ayman Amer;Ibrahim Mansour;Ahmed Solyman;Khalid Yahya;Ahmed Samir","doi":"10.26599/IJCS.2023.9100022","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100022","url":null,"abstract":"This paper presents a novel approach to enhance energy harvesting systems from ambient Radio Frequency (RF) sources in overcrowded environments. In environments like shopping malls, coffee shops, and airports, where wireless devices are prevalent, the electromagnetic energy emitted by these devices can be harvested and converted into electrical energy to power small devices, specifically those associated with the Social Internet of Things (SIoT). However, due to the high density of devices in such environments, the RF signals can be weak, resulting in low energy harvesting efficiency. This study focuses on developing technologies for wireless power transfer through a radio frequency ambient energy harvesting scheme, specifically designing to improve energy harvesting systems in crowded social environments. Recognizing the growing importance of energy harvesting for low-power devices in intelligent environments, our proposed method utilizes the ambient environment to capture energy in the downlink radio frequency range of the GSM-900 band. The system architecture comprises four main stages: a supercapacitor, a Villard voltage doubler circuit with seven stages, a lumped element matching network, and a microstrip patch antenna. The voltage doubler circuit is designed and simulated using the Agilent Advanced Design System (ADS) 2014 environment, and simulations and tests are conducted across different input power levels. Throughout the study, several key factors are identified as crucial to the system's efficiency, including the frequency band, input power level, voltage doubler circuit design, impedance matching, diode selection, number of rectification stages, and load resistance. The proposed method demonstrates significant potential in enhancing the energy harvesting efficiency from ambient RF sources in crowded social environments. By providing a sustainable power source for SIoT devices in such settings, our approach contributes to the advancement of energy harvesting capabilities and supports the practical implementation of energy-efficient technologies in intelligent and socially interconnected environments.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"13-28"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106224","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}
Pub Date : 2025-01-29DOI: 10.26599/IJCS.2024.9100044
Yuebo Jin;Yadong Huang
Depression is a critical mental health issue that increasingly affects millions worldwide. Traditional monitoring methods, relying on self-reported symptoms and periodic clinical assessments, are often subjective and infrequent. Wearable devices, offering continuous and real-time data on various physiological parameters, present a promising alternative. These devices provide a comprehensive picture of a patient's condition by tracking vital signs such as heart rate, sleep patterns, and physical activity. Our study utilized wearable devices to monitor 302 hospitalized depression patients over six months. We collected data on heart rate, sleep conditions, and physical activity, which were then correlated with Hamilton Anxiety (HAMA) and Hamilton Depression (HAMD) scales. The results showed significant differences in these vital signs between mild and severe depression cases. The logistic regression model yielded promising results, with an Area Under the Curve (AUC) value of 0.84 on the Receiver Operating Characteristic (ROC) curve, indicating a high level of classification accuracy. The model's performance suggests that the selected features are significantly correlated with depression severity and can effectively aid in clinical classification. In conclusion, wearable devices offer significant advancements in monitoring and managing depression. By integrating continuous physiological data with clinical assessments, these devices can improve the understanding and treatment of depression, potentially transforming mental health care into a more precise, personalized, and proactive field.
{"title":"Long-Term Vital Sign Tracking Study of Depression Patients Based on Wearable Devices","authors":"Yuebo Jin;Yadong Huang","doi":"10.26599/IJCS.2024.9100044","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100044","url":null,"abstract":"Depression is a critical mental health issue that increasingly affects millions worldwide. Traditional monitoring methods, relying on self-reported symptoms and periodic clinical assessments, are often subjective and infrequent. Wearable devices, offering continuous and real-time data on various physiological parameters, present a promising alternative. These devices provide a comprehensive picture of a patient's condition by tracking vital signs such as heart rate, sleep patterns, and physical activity. Our study utilized wearable devices to monitor 302 hospitalized depression patients over six months. We collected data on heart rate, sleep conditions, and physical activity, which were then correlated with Hamilton Anxiety (HAMA) and Hamilton Depression (HAMD) scales. The results showed significant differences in these vital signs between mild and severe depression cases. The logistic regression model yielded promising results, with an Area Under the Curve (AUC) value of 0.84 on the Receiver Operating Characteristic (ROC) curve, indicating a high level of classification accuracy. The model's performance suggests that the selected features are significantly correlated with depression severity and can effectively aid in clinical classification. In conclusion, wearable devices offer significant advancements in monitoring and managing depression. By integrating continuous physiological data with clinical assessments, these devices can improve the understanding and treatment of depression, potentially transforming mental health care into a more precise, personalized, and proactive field.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"56-63"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106301","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}
Pub Date : 2025-01-29DOI: 10.26599/IJCS.2023.9100015
Issa Haitham M. Ata
Because of its superior multipath channel performance and spectrum efficiency, orthogonal frequency-division multiplexing (OFDM) is one of the most suitable candidates for the 6G physical layer in contemporary applications like smart cities and crowded environments in a social Internet of Things (S-IoT) ecosystem. The terahertz (THz) band, 1Tbit/s data rate, low latency, spectrum efficacy, and mobility of 1000 km/h are just a few of the requirements that 6G must meet. Using single-tap equalization, OFDM may reduce the multipath channel impact optimally. However, when the channel has doubly dispersive fading, inter-carrier interference starts to create errors. The worst doubly dispersive fading channel will be produced when THz and high-speed mobility are combined with OFDM as the physical layer for 6G. OFDM requires a complicated equalizer when using channels with doubly dispersive fading. On the basis of band factorization, time domain least squares QR (LSQR) iterative computing, and banded minimum mean squared error (BMMSE), a number of low-complexity equalizers for OFDM have been proposed. Conjugate gradient least squares (CGLS), a revolutionary iterative computation algorithm, is proposed in this study; the proposed equalization technique obtains the trade-off between computations and performance. Simulation results show that the proposed equalizer outperforms the existing BMMSE and LSQR algorithms over doubly dispersive fading channels.
{"title":"CGLS Method for Efficient Equalization of OFDM Systems Under Doubly Dispersive Fading Channels with an Application Into 6G Communications in Smart Overcrowded","authors":"Issa Haitham M. Ata","doi":"10.26599/IJCS.2023.9100015","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100015","url":null,"abstract":"Because of its superior multipath channel performance and spectrum efficiency, orthogonal frequency-division multiplexing (OFDM) is one of the most suitable candidates for the 6G physical layer in contemporary applications like smart cities and crowded environments in a social Internet of Things (S-IoT) ecosystem. The terahertz (THz) band, 1Tbit/s data rate, low latency, spectrum efficacy, and mobility of 1000 km/h are just a few of the requirements that 6G must meet. Using single-tap equalization, OFDM may reduce the multipath channel impact optimally. However, when the channel has doubly dispersive fading, inter-carrier interference starts to create errors. The worst doubly dispersive fading channel will be produced when THz and high-speed mobility are combined with OFDM as the physical layer for 6G. OFDM requires a complicated equalizer when using channels with doubly dispersive fading. On the basis of band factorization, time domain least squares QR (LSQR) iterative computing, and banded minimum mean squared error (BMMSE), a number of low-complexity equalizers for OFDM have been proposed. Conjugate gradient least squares (CGLS), a revolutionary iterative computation algorithm, is proposed in this study; the proposed equalization technique obtains the trade-off between computations and performance. Simulation results show that the proposed equalizer outperforms the existing BMMSE and LSQR algorithms over doubly dispersive fading channels.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"3-12"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857982","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106300","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}
Pub Date : 2025-01-29DOI: 10.26599/IJCS.2024.9100045
Mohamadreza Khosravi;Lianyong Qi
Smart environments are now an undeniable part of our life. A day-by-day increase in the use of new technologies based on Artificial Intelligence (AI), Internet of Things (IoT), communication and information systems, human-machine interactions, multimedia sensors, and bio-sensing devices is happening. The good research practice in these areas is absolutely the main factor of advancing our knowledge in this regard. Smart environments contain a variety of applications in different industries including entertainment industry, manufactures, healthcare systems, and Information Technology (IT). From the viewpoint of social systems, the main large-scale applications (not personal) of Cyber-Physical Systems (CPS) to make a smart environment are mainly smart homes and cities, health systems, intelligent transportation systems, green energy systems, and environmental protection and monitoring systems (including remote sensing). The personal use of such technologies is mostly around entertainment including IPTVs, virtual reality, games, other multimedia services, as well as personal healthcare services.
{"title":"Editorial of Cyber-Physical Social Systems and Smart Environments","authors":"Mohamadreza Khosravi;Lianyong Qi","doi":"10.26599/IJCS.2024.9100045","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100045","url":null,"abstract":"Smart environments are now an undeniable part of our life. A day-by-day increase in the use of new technologies based on Artificial Intelligence (AI), Internet of Things (IoT), communication and information systems, human-machine interactions, multimedia sensors, and bio-sensing devices is happening. The good research practice in these areas is absolutely the main factor of advancing our knowledge in this regard. Smart environments contain a variety of applications in different industries including entertainment industry, manufactures, healthcare systems, and Information Technology (IT). From the viewpoint of social systems, the main large-scale applications (not personal) of Cyber-Physical Systems (CPS) to make a smart environment are mainly smart homes and cities, health systems, intelligent transportation systems, green energy systems, and environmental protection and monitoring systems (including remote sensing). The personal use of such technologies is mostly around entertainment including IPTVs, virtual reality, games, other multimedia services, as well as personal healthcare services.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106299","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}
Pub Date : 2025-01-29DOI: 10.26599/IJCS.2023.9100033
Ahmad Al-Qerem;Ali Mohd Ali;Ahmad Nabot;Issam Jebreen;Mohammad Alauthman;Someah Alangari;Faisal Aburub;Amjad Aldweesh
In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate quality constructs relevant to contemporary technologies, including cyber-physical systems, cloud services, and crowdsensing, holds promise for further enriching our understanding of the evolving digital landscape.
{"title":"Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing","authors":"Ahmad Al-Qerem;Ali Mohd Ali;Ahmad Nabot;Issam Jebreen;Mohammad Alauthman;Someah Alangari;Faisal Aburub;Amjad Aldweesh","doi":"10.26599/IJCS.2023.9100033","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100033","url":null,"abstract":"In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate quality constructs relevant to contemporary technologies, including cyber-physical systems, cloud services, and crowdsensing, holds promise for further enriching our understanding of the evolving digital landscape.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"9 1","pages":"44-55"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106336","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}
Pub Date : 2024-09-16DOI: 10.26599/IJCS.2024.9100019
Leiju Qiu;Dongmei Guo
Housing market can be viewed as a big intelligent system which gathers various intelligent agents including the demand side, the supply side, as well as the brokerage. Real estate agents are a large group that can lubricate housing transactions, but due to the principal-agent problem, this housing-transaction intelligent system shows low service efficiency. This paper claims that the non-price competition among real estate agents could possibly reconcile the principal-agent problem. With a merged dataset of housing listings and transaction records in Beijing, we find that the competition in the brokerage market raises housing prices significantly. This provides the first empirical evidence that the non-price competition of real estate agents could reconcile the principal-agent problem. Furthermore, we find that exclusive listing and the agent's effort positively relate to higher housing prices. The impact is even stronger in a hotter market. This paper explores the role of competition in the performance of the housing-transaction intelligent system, and therefore has important theoretical and practical significance for the system.
{"title":"Competition of Real Estate Agents and Housing Prices in Urban China","authors":"Leiju Qiu;Dongmei Guo","doi":"10.26599/IJCS.2024.9100019","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100019","url":null,"abstract":"Housing market can be viewed as a big intelligent system which gathers various intelligent agents including the demand side, the supply side, as well as the brokerage. Real estate agents are a large group that can lubricate housing transactions, but due to the principal-agent problem, this housing-transaction intelligent system shows low service efficiency. This paper claims that the non-price competition among real estate agents could possibly reconcile the principal-agent problem. With a merged dataset of housing listings and transaction records in Beijing, we find that the competition in the brokerage market raises housing prices significantly. This provides the first empirical evidence that the non-price competition of real estate agents could reconcile the principal-agent problem. Furthermore, we find that exclusive listing and the agent's effort positively relate to higher housing prices. The impact is even stronger in a hotter market. This paper explores the role of competition in the performance of the housing-transaction intelligent system, and therefore has important theoretical and practical significance for the system.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"8 4","pages":"205-211"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235871","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}
Pub Date : 2024-09-16DOI: 10.26599/IJCS.2024.9100022
Pengzhi Gao;Xiangwei Zheng;Tao Wang;Yuang Zhang
Emotion recognition plays an important role in Human Computer Interaction (HCI) and the evaluation of human behavior based on emotional state is an important research topic. The purpose of emotion recognition is to automatically identify human's emotional states by analyzing physiological or non-physiological signals. The conventional emotion classification methods cannot comprehensively leverage global and local features which are extracted from Electroencephalogram (EEG) signal generated after being stimulated. Therefore, we propose the graph convolutional neural network based emotion recognition with brain functional connectivity network (GERBN). Firstly, raw EEG data of the public DEAP and SEED datasets is preprocessed and adopted in this study. Secondly, emotion-related brain functional connection pattern is constructed using Phase-Locking Value (PLV) adjacency matrix to measure connectivity between the signals of different EEG channels according to phase synchronization. A novel graph structure is constructed where the EEG electrode channels are defined as the vertex, and the edge is strong connection of the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two-dimensional emotional models of valence and arousal. Finally, extensive experiments are conducted on DEAP and SEED datasets. Experimental results demonstrate that the proposed method can improve classification accuracies, in which average accuracies of 80.43% and 88.47% on DEAP are attained on valence and arousal dimensions, respectively. On the SEED dataset, the accuracy reaches 92.37% higher than some of the other methods.
{"title":"Graph Convolutional Neural Network Based Emotion Recognition with Brain Functional Connectivity Network","authors":"Pengzhi Gao;Xiangwei Zheng;Tao Wang;Yuang Zhang","doi":"10.26599/IJCS.2024.9100022","DOIUrl":"https://doi.org/10.26599/IJCS.2024.9100022","url":null,"abstract":"Emotion recognition plays an important role in Human Computer Interaction (HCI) and the evaluation of human behavior based on emotional state is an important research topic. The purpose of emotion recognition is to automatically identify human's emotional states by analyzing physiological or non-physiological signals. The conventional emotion classification methods cannot comprehensively leverage global and local features which are extracted from Electroencephalogram (EEG) signal generated after being stimulated. Therefore, we propose the graph convolutional neural network based emotion recognition with brain functional connectivity network (GERBN). Firstly, raw EEG data of the public DEAP and SEED datasets is preprocessed and adopted in this study. Secondly, emotion-related brain functional connection pattern is constructed using Phase-Locking Value (PLV) adjacency matrix to measure connectivity between the signals of different EEG channels according to phase synchronization. A novel graph structure is constructed where the EEG electrode channels are defined as the vertex, and the edge is strong connection of the binary brain network. Thirdly, the GERBN model that includes six layers is designed to classify and recognize emotional states on the two-dimensional emotional models of valence and arousal. Finally, extensive experiments are conducted on DEAP and SEED datasets. Experimental results demonstrate that the proposed method can improve classification accuracies, in which average accuracies of 80.43% and 88.47% on DEAP are attained on valence and arousal dimensions, respectively. On the SEED dataset, the accuracy reaches 92.37% higher than some of the other methods.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"8 4","pages":"195-204"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681263","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235950","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}
Pub Date : 2024-09-16DOI: 10.26599/IJCS.2023.9100036
Xiaomeng Liu;Hongbo Sun;Jiarui Lin
Construction is the pillar industry of Chinese national economy, but the profit rate has continued to decline in recent years. The conventional job-centric construction information system cannot meet the requirements for safety, quality, and efficiency. In order to solve the above problems, a digital selves based intelligent construction framework has been proposed which is centred on workers, equipment, and sites. It changes the conventional job-centered construction information system and realizes efficient coordination of information among various departments and stages. Applying the digital selves based intelligent construction framework to the case has been proved to be a good solution to the problem of information sharing. Meanwhile, the authenticity and reliability of the information have been ensured. The system has improved management efficiency and information systematization for all participants. It has enabled companies and workers to have a more comprehensive understanding of themselves and to make timely and effective strategies against hazards. In addition, the system has effectively raised the level of digitalization, automation, and intelligence of construction, making governance more accurate and more effective.
{"title":"Digital Selves Based Intelligent Construction Framework","authors":"Xiaomeng Liu;Hongbo Sun;Jiarui Lin","doi":"10.26599/IJCS.2023.9100036","DOIUrl":"https://doi.org/10.26599/IJCS.2023.9100036","url":null,"abstract":"Construction is the pillar industry of Chinese national economy, but the profit rate has continued to decline in recent years. The conventional job-centric construction information system cannot meet the requirements for safety, quality, and efficiency. In order to solve the above problems, a digital selves based intelligent construction framework has been proposed which is centred on workers, equipment, and sites. It changes the conventional job-centered construction information system and realizes efficient coordination of information among various departments and stages. Applying the digital selves based intelligent construction framework to the case has been proved to be a good solution to the problem of information sharing. Meanwhile, the authenticity and reliability of the information have been ensured. The system has improved management efficiency and information systematization for all participants. It has enabled companies and workers to have a more comprehensive understanding of themselves and to make timely and effective strategies against hazards. In addition, the system has effectively raised the level of digitalization, automation, and intelligence of construction, making governance more accurate and more effective.","PeriodicalId":32381,"journal":{"name":"International Journal of Crowd Science","volume":"8 4","pages":"184-194"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235772","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}
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