High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NR...High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.展开更多
Map matching has been widely investigated in indoor pedestrian navigation to improve positioning accuracy and robustness.This paper proposes an accurate map matching algorithm based on activity detection and crowdsour...Map matching has been widely investigated in indoor pedestrian navigation to improve positioning accuracy and robustness.This paper proposes an accurate map matching algorithm based on activity detection and crowdsourced Wi-Fi(AiFiMatch).Firstly, by taking indoor road segments between activity-related locations as nodes, and the activity type from one road segment to another as directed edge, the indoor floor plan is abstracted as a directed graph. Secondly, the smartphone’s motion sensors are utilized to detect different activities based on a decision tree and then the pedestrian’s walking trajectory is divided into subtrajectory sequence according to location-related activities. Finally, the sub-trajectory sequence is matched to the directed graph of indoor floor plan to position the pedestrian by using a Hidden Markov Model(HMM). Simultaneously, Wi-Fi fingerprints are bound to road segments based on timestamp. Through crowdsourcing, a radio map of indoor road segments is constructed. The radio map in turn inversely promotes the HMM based map matching algorithm. AiFiMatch is evaluated by the experiments using smartphones in a teaching building. Experimental results show that the pedestrian can be accurately tracked even without knowing the starting position and AiFiMatch is robust to a certain degree of step length and heading direction errors.展开更多
A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major d...A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major difficulties in generating land classification maps at the regional scale:the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time.Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens.As one of the most well-known VGI initiatives,OpenStreetMap(OSM),contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns.Whereas,most large-scale mapping approaches-including regional and national scales–confuse“land cover”and“land-use”,or build up the land-use database based on modeled land cover data-sets,in this study,we clearly distinguished and differentiated land-use from land cover.By focusing on our prime objective of mapping land-use and management practices,a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery.Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work.Furthermore,our novel approach in regional scale land-use mapping produced robust results in our study area:the overall internal accuracy of the classifier was 95.2%and the external accuracy of the classifier was measured at 74.8%.展开更多
Currently, mobile devices （e.g., smartphones） are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous ...Currently, mobile devices （e.g., smartphones） are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing （MCS） applications. Generally, an MCS system is composed of three components： a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods： reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher＇s feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.展开更多
Crowdsourced data can effectively observe environmental and urban ecosystem processes.The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems(EWS)to bette...Crowdsourced data can effectively observe environmental and urban ecosystem processes.The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems(EWS)to better perform while support decision-making to reduce the fatalities and economic losses due to inundation hazard.In this work,we develop a Data Assimilation(DA)method integrating Volunteered Geographic Information(VGI)and a 2D hydraulic model and we test its performances.The proposed framework seeks to extend the capabilities and performances of standard DA works,based on the use of traditional in situ sensors,by assimilating VGI while managing and taking into account the uncertainties related to the quality,and the location and timing of the entire set of observational data.The November 2012 flood in the Italian Tiber River basin was selected as the case study.Results show improvements of the model in terms of uncertainty with a significant persistence of the model updating after the integration of the VGI,even in the case of use of few-selected observations gathered from social media.This will encourage further research in the use of VGI for EWS considering the exponential increase of quality and quantity of smartphone and social media user worldwide.展开更多
The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields,including agriculture, transportation, energy, public health and safety, and more. Understanding the ...The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields,including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However,existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.展开更多
Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Beca...Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.展开更多
In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in descriptive natural language on mobile devices. Thus, these ...In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in descriptive natural language on mobile devices. Thus, these test reports generally lack important details and challenge developers in understanding the bugs. To improve the quality of inspected test reports, we issue a new problem of test report augmentation by leveraging the additional useful information contained in duplicate test reports. In this paper, we propose a new framework named test report augmentation framework (TRAF) towards resolving the problem. First, natural language processing (NLP) techniques are adopted to preprocess the crowdsourced test reports. Then, three strategies are proposed to augment the environments, inputs, and descriptions of the inspected test reports, respectively. Finally, we visualize the augmented test reports to help developers distinguish the added information. To evaluate TRAF, we conduct experiments over five industrial datasets with 757 crowdsourced test reports. Experimental results show that TRAF can recommend relevant inputs to augment the inspected test reports with 98.49% in terms of NDCG and 88.65% in terms of precision on average, and identify valuable sentences from the descriptions of duplicates to augment the inspected test reports with 83.58% in terms of precision, 77.76% in terms of recall, and 78.72% in terms of F-measure on average. Meanwhile, empirical evaluation also demonstrates that augmented test reports can help developers understand and fix bugs better.展开更多
High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on ...High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost.Hence,this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data.The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles.This allows users to modify the extraction process by using a more sophisticated neural network,thus achieving a more accurate detection result when compared with traditional binarization method.The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks.Finally,the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.展开更多
The City of Calgary did a comparative study between two techniques of traffic data collection： Bluetooth sensors and crowdsourcing, for measuring travel time reliability on two goods movement corridors in Calgary, Al...The City of Calgary did a comparative study between two techniques of traffic data collection： Bluetooth sensors and crowdsourcing, for measuring travel time reliability on two goods movement corridors in Calgary, Alberta. To estimate travel time and speed, we used the output of BIuFAX sensors, which were operated by monitoring Bluetooth signals at several points along a roadway. On the other hand, TomTom historical traffic data were extracted from the TomTom Traffic Stats portal. To calculate travel time reliability, we applied the buffer index, and the planning time index recommended by FHWA （Federal Highway Administration）. The Bluetooth traffic data were presumed as the benchmark in this study. Unlike the TomTom traffic data, the data provided by the Bluetooth technology met the minimum recommended sample size requirement, although data processing was time consuming and impractical for long study periods. Our study results showed that crowdsourcing technique can be a viable alternative and provide travel time reliability estimates with a reasonable accuracy, when there are adequate numbers of records registered. However, the TomTom sample sizes in Calgary were not large enough to provide a statistically reliable method of providing travel time indices. Further researches may verify the accuracy of crowdsourcing technologies for travel time studies.展开更多
The prevalence of smart phone and improvement of wireless net promote the usage of crowdsourced live streaming,where individual users act as live streaming sources to broadcast themselves online. Characterizing the pe...The prevalence of smart phone and improvement of wireless net promote the usage of crowdsourced live streaming,where individual users act as live streaming sources to broadcast themselves online. Characterizing the performance and identifying its bottleneck in such systems can shed light on the system design and performance optimization. TCP performance of a commercial crowdsourced live streaming system is examined by analyzing packet-level traces collected at streaming servers. TCP stalls that heavily hurt the Qo E of user have been identified. In particular,the TCP stalls account for as much as 31. 6% of the flow completion time for upload flows and result in abandonment of upload on the corresponding channels. Stalls caused by timeout retransmissions are further dissected and timeout retransmission characteristics are revealed to be dependent on the video encoding methods. These findings provide new insights in crowdsourced live streaming systems and can guide designers to improve the TCP efficiency.展开更多
Fingerprint⁃based Bluetooth positioning is a popular indoor positioning technology.However,the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution,which will resul...Fingerprint⁃based Bluetooth positioning is a popular indoor positioning technology.However,the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution,which will result in the decline of positioning accuracy.The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating.To address this problem,this paper presents an adaptive fingerprint database updating approach.First,the crowdsourced data including the Bluetooth Received Signal Strength(RSS)sequences and the speed and heading of the pedestrian were recorded.Second,the recorded crowdsourced data were fused by the Kalman Filtering(KF),and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints.Third,after enough candidate fingerprints were obtained at each Reference Point(RP),the Density⁃based Spatial Clustering of Applications with Noise(DBSCAN)approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise,and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP.Finally,the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations,the fingerprint⁃based Bluetooth positioning accuracy can be effectively improved.展开更多
Volunteered geographic information(VGI)can be considered a subset of crowdsourced data(CSD)and its popularity has recently increased in a number of application areas.Disaster management is one of its key application a...Volunteered geographic information(VGI)can be considered a subset of crowdsourced data(CSD)and its popularity has recently increased in a number of application areas.Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high.However,quality issues such as credibility,reliability and relevance are limiting many of the advantages of utilising CSD.Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens.VGI and CSD are also highly unstructured and the quality and metadata are often undocumented.In the 2011 Australian floods,the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards,evacuations,emergency services,road closures and property damage.This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems.The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90%using a forced classification methodology.展开更多
The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem o...The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.展开更多
基金funded by the National Key R&D Program of China(NO.2022YFB3903903)the National Natural Science Foundation of China(NO.41974008,NO.42074045).
文摘High-quality spatial atmospheric delay correction information is essential for achieving fast integer ambiguity resolution(AR)in precise positioning.However,traditional real-time precise positioning frameworks(i.e.,NRTK and PPP-RTK)depend on spatial low-resolution atmospheric delay correction through the expensive and sparsely distributed CORS network.This results in limited public appeal.With the mass production of autonomous driving vehicles,more cost-effective and widespread data sources can be explored to create spatial high-resolution atmospheric maps.In this study,we propose a new GNSS positioning framework that relies on dual base stations,massive vehicle GNSS data,and crowdsourced atmospheric delay correction maps(CAM).The map is easily produced and updated by vehicles equipped with GNSS receivers in a crowd-sourced way.Specifically,the map consists of between-station single-differenced ionospheric and tropospheric delays.We introduce the whole framework of CAM initialization for individual vehicles,on-cloud CAM maintenance,and CAM-augmented user-end positioning.The map data are collected and preprocessed in vehicles.Then,the crowdsourced data are uploaded to a cloud server.The massive data from multiple vehicles are merged in the cloud to update the CAM in time.Finally,the CAM will augment the user positioning performance.This framework forms a beneficial cycle where the CAM’s spatial resolution and the user positioning performance mutually improve each other.We validate the performance of the proposed framework in real-world experiments and the applied potency at different spatial scales.We highlight that this framework is a reliable and practical positioning solution that meets the requirements of ubiquitous high-precision positioning.
基金supported by the National Natural Science Foundation of China(Grant No.61702288)the Natural Science Foundation of Tianjin in China(Grant No.16JCQNJC00700)the Fundamental Research Funds for the Central Universities
文摘Map matching has been widely investigated in indoor pedestrian navigation to improve positioning accuracy and robustness.This paper proposes an accurate map matching algorithm based on activity detection and crowdsourced Wi-Fi(AiFiMatch).Firstly, by taking indoor road segments between activity-related locations as nodes, and the activity type from one road segment to another as directed edge, the indoor floor plan is abstracted as a directed graph. Secondly, the smartphone’s motion sensors are utilized to detect different activities based on a decision tree and then the pedestrian’s walking trajectory is divided into subtrajectory sequence according to location-related activities. Finally, the sub-trajectory sequence is matched to the directed graph of indoor floor plan to position the pedestrian by using a Hidden Markov Model(HMM). Simultaneously, Wi-Fi fingerprints are bound to road segments based on timestamp. Through crowdsourcing, a radio map of indoor road segments is constructed. The radio map in turn inversely promotes the HMM based map matching algorithm. AiFiMatch is evaluated by the experiments using smartphones in a teaching building. Experimental results show that the pedestrian can be accurately tracked even without knowing the starting position and AiFiMatch is robust to a certain degree of step length and heading direction errors.
文摘A land-use map at the regional scale is a heavy computation task yet is critical to most landowners,researchers,and decision-makers,enabling them to make informed decisions for varying objectives.There are two major difficulties in generating land classification maps at the regional scale:the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time.Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens.As one of the most well-known VGI initiatives,OpenStreetMap(OSM),contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns.Whereas,most large-scale mapping approaches-including regional and national scales–confuse“land cover”and“land-use”,or build up the land-use database based on modeled land cover data-sets,in this study,we clearly distinguished and differentiated land-use from land cover.By focusing on our prime objective of mapping land-use and management practices,a robust regional land-use mapping approach was developed by integrating OSM data with the earth observation remote sensing imagery.Our novel approach incorporates a vital temporal component to large-scale land-use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work.Furthermore,our novel approach in regional scale land-use mapping produced robust results in our study area:the overall internal accuracy of the classifier was 95.2%and the external accuracy of the classifier was measured at 74.8%.
基金supported in part by the National Natural Science Foundation of China(No.61171092)in part by the Jiangsu Educational Bureau Project(No.14KJA510004)
文摘Currently, mobile devices （e.g., smartphones） are equipped with multiple wireless interfaces and rich builtin functional sensors that possess powerful computation and communication capabilities, and enable numerous Mobile Crowdsourced Sensing （MCS） applications. Generally, an MCS system is composed of three components： a publisher of sensing tasks, crowd participants who complete the crowdsourced tasks for some kinds of rewards, and the crowdsourcing platform that facilitates the interaction between publishers and crowd participants. Incentives are a fundamental issue in MCS. This paper proposes an integrated incentive framework for MCS, which appropriately utilizes three widely used incentive methods： reverse auction, gamification, and reputation updating. Firstly, a reverse-auction-based two-round participant selection mechanism is proposed to incentivize crowds to actively participate and provide high-quality sensing data. Secondly, in order to avoid untruthful publisher feedback about sensing-data quality, a gamification-based verification mechanism is designed to evaluate the truthfulness of the publisher＇s feedback. Finally, the platform updates the reputation of both participants and publishers based on their corresponding behaviors. This integrated incentive mechanism can motivate participants to provide high-quality sensed contents, stimulate publishers to give truthful feedback, and make the platform profitable.
文摘Crowdsourced data can effectively observe environmental and urban ecosystem processes.The use of data produced by untrained people into flood forecasting models may effectively allow Early Warning Systems(EWS)to better perform while support decision-making to reduce the fatalities and economic losses due to inundation hazard.In this work,we develop a Data Assimilation(DA)method integrating Volunteered Geographic Information(VGI)and a 2D hydraulic model and we test its performances.The proposed framework seeks to extend the capabilities and performances of standard DA works,based on the use of traditional in situ sensors,by assimilating VGI while managing and taking into account the uncertainties related to the quality,and the location and timing of the entire set of observational data.The November 2012 flood in the Italian Tiber River basin was selected as the case study.Results show improvements of the model in terms of uncertainty with a significant persistence of the model updating after the integration of the VGI,even in the case of use of few-selected observations gathered from social media.This will encourage further research in the use of VGI for EWS considering the exponential increase of quality and quantity of smartphone and social media user worldwide.
文摘The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields,including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However,existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.
基金‘This research is funded by Taif University,TURSP-2020/115’.
文摘Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.
基金This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61370144, 61722202, 61403057, and 61772107)Jiangsu Prospective Project of Industry- University-Research (BY2015069-03)Besides, the authors would thank the three graduate students who devote their efforts for the data annotation.
文摘In crowdsourced mobile application testing, workers are often inexperienced in and unfamiliar with software testing. Meanwhile, workers edit test reports in descriptive natural language on mobile devices. Thus, these test reports generally lack important details and challenge developers in understanding the bugs. To improve the quality of inspected test reports, we issue a new problem of test report augmentation by leveraging the additional useful information contained in duplicate test reports. In this paper, we propose a new framework named test report augmentation framework (TRAF) towards resolving the problem. First, natural language processing (NLP) techniques are adopted to preprocess the crowdsourced test reports. Then, three strategies are proposed to augment the environments, inputs, and descriptions of the inspected test reports, respectively. Finally, we visualize the augmented test reports to help developers distinguish the added information. To evaluate TRAF, we conduct experiments over five industrial datasets with 757 crowdsourced test reports. Experimental results show that TRAF can recommend relevant inputs to augment the inspected test reports with 98.49% in terms of NDCG and 88.65% in terms of precision on average, and identify valuable sentences from the descriptions of duplicates to augment the inspected test reports with 83.58% in terms of precision, 77.76% in terms of recall, and 78.72% in terms of F-measure on average. Meanwhile, empirical evaluation also demonstrates that augmented test reports can help developers understand and fix bugs better.
基金This work was supported in part by National Natural Science Foundation of China(U186420361773234 and 52102464)Project Funded by China Postdoctoral Science Foundation(2019M660622)in part by the International Science and Technology Cooperation Program of China(2019YFE0100200).
文摘High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost.Hence,this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data.The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles.This allows users to modify the extraction process by using a more sophisticated neural network,thus achieving a more accurate detection result when compared with traditional binarization method.The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks.Finally,the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.
文摘The City of Calgary did a comparative study between two techniques of traffic data collection： Bluetooth sensors and crowdsourcing, for measuring travel time reliability on two goods movement corridors in Calgary, Alberta. To estimate travel time and speed, we used the output of BIuFAX sensors, which were operated by monitoring Bluetooth signals at several points along a roadway. On the other hand, TomTom historical traffic data were extracted from the TomTom Traffic Stats portal. To calculate travel time reliability, we applied the buffer index, and the planning time index recommended by FHWA （Federal Highway Administration）. The Bluetooth traffic data were presumed as the benchmark in this study. Unlike the TomTom traffic data, the data provided by the Bluetooth technology met the minimum recommended sample size requirement, although data processing was time consuming and impractical for long study periods. Our study results showed that crowdsourcing technique can be a viable alternative and provide travel time reliability estimates with a reasonable accuracy, when there are adequate numbers of records registered. However, the TomTom sample sizes in Calgary were not large enough to provide a statistically reliable method of providing travel time indices. Further researches may verify the accuracy of crowdsourcing technologies for travel time studies.
基金Supported by the National Basic Research Program of China(2012CB315801)the National Natural Science Foundation of China(No.6157060397)
文摘The prevalence of smart phone and improvement of wireless net promote the usage of crowdsourced live streaming,where individual users act as live streaming sources to broadcast themselves online. Characterizing the performance and identifying its bottleneck in such systems can shed light on the system design and performance optimization. TCP performance of a commercial crowdsourced live streaming system is examined by analyzing packet-level traces collected at streaming servers. TCP stalls that heavily hurt the Qo E of user have been identified. In particular,the TCP stalls account for as much as 31. 6% of the flow completion time for upload flows and result in abandonment of upload on the corresponding channels. Stalls caused by timeout retransmissions are further dissected and timeout retransmission characteristics are revealed to be dependent on the video encoding methods. These findings provide new insights in crowdsourced live streaming systems and can guide designers to improve the TCP efficiency.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.61771083,61704015)the Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT1299)+3 种基金the Special Fund of Chongqing Key Laboratory(CSTC)Fundamental Science and Frontier Technology Research Project of Chongqing(Grant Nos.cstc2017jcyjAX0380,cstc2015jcyjBX0065)the Scientific and Technological Research Foundation of Chongqing Municipal Education Commission(Grant No.KJ1704083)the University Outstanding Achievement Transformation Project of Chongqing(Grant No.KJZH17117).
文摘Fingerprint⁃based Bluetooth positioning is a popular indoor positioning technology.However,the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution,which will result in the decline of positioning accuracy.The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating.To address this problem,this paper presents an adaptive fingerprint database updating approach.First,the crowdsourced data including the Bluetooth Received Signal Strength(RSS)sequences and the speed and heading of the pedestrian were recorded.Second,the recorded crowdsourced data were fused by the Kalman Filtering(KF),and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints.Third,after enough candidate fingerprints were obtained at each Reference Point(RP),the Density⁃based Spatial Clustering of Applications with Noise(DBSCAN)approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise,and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP.Finally,the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations,the fingerprint⁃based Bluetooth positioning accuracy can be effectively improved.
基金Authors wish to acknowledge the Australian Government for providing support for the research work through the Research Training Program(RTP)and Monique Potts,ABC–Australia for providing the 2011 Australian Flood’s Ushahidi Crowdmap data.
文摘Volunteered geographic information(VGI)can be considered a subset of crowdsourced data(CSD)and its popularity has recently increased in a number of application areas.Disaster management is one of its key application areas in which the benefits of VGI and CSD are potentially very high.However,quality issues such as credibility,reliability and relevance are limiting many of the advantages of utilising CSD.Credibility issues arise as CSD come from a variety of heterogeneous sources including both professionals and untrained citizens.VGI and CSD are also highly unstructured and the quality and metadata are often undocumented.In the 2011 Australian floods,the general public and disaster management administrators used the Ushahidi Crowd-mapping platform to extensively communicate flood-related information including hazards,evacuations,emergency services,road closures and property damage.This study assessed the credibility of the Australian Broadcasting Corporation’s Ushahidi CrowdMap dataset using a Naïve Bayesian network approach based on models commonly used in spam email detection systems.The results of the study reveal that the spam email detection approach is potentially useful for CSD credibility detection with an accuracy of over 90%using a forced classification methodology.
基金the National High Technology Research and Development Program of China(No.2012AA120802)National Natural Science Foundation of China(No.61771186)+1 种基金Postdoctoral Research Project of Heilongjiang Province(No.LBH-Q15121)Undergraduate University Project of Young Scientist Creative Talent of Heilongjiang Province(No.UNPYSCT-2017125).
文摘The crowdsourcing-based WLAN indoor localization system has been widely promoted for the effective reduction of the workload from the offline phase data collection while constructing radio maps.Aiming at the problem of the inaccurate location annotation of the crowdsourced samples,the existing invalid access points(APs)in collected samples,and the uneven sample distribution,as well as the diverse terminal devices,which will result in the construction of the wrong radio map,an effective WLAN indoor radio map construction scheme(WRMCS)is proposed based on crowdsourced samples.The WRMCS consists of 4 main modules:outlier detection,key AP selection,fingerprint interpolation,and terminal device calibration.Moreover,an online localization algorithm is put forward to estimate the position of the online test fingerprint.The simulation results show that the proposed scheme can achieve higher localization accuracy than the peer schemes,and possesses good effectiveness and robustness at the same time.
