This paper studies the technics of reducing item exposure by utilizing automatic item generation methods. Known test item calibration method uses item parameter estimation with the statistical data, collected during e...This paper studies the technics of reducing item exposure by utilizing automatic item generation methods. Known test item calibration method uses item parameter estimation with the statistical data, collected during examinees prior testing. Disadvantage of the mentioned item calibration method is the item exposure; when test items become familiar to the examinees. To reduce the item exposure, automatic item generation method is used, where item models are being constructed based on already calibrated test items without losing already estimated item parameters. A technic of item model extraction method from the already calibrated and therefore exposed test items described, which can be used by the test item development specialists to integrate automatic item generation principles with the existing testing applications.展开更多
In real-world scenarios,dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage,which is important for identifying proh...In real-world scenarios,dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage,which is important for identifying prohibited items that are not visible in one view due to rotation or overlap.However,existing work still focuses mainly on single-view,and the limited dual-viewbasedwork only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view.To this end,this paper proposes an end-to-end dual-view prohibited item detection method,the core of which is an adaptive material-aware coordinate-aligned attention module(MACA)and an adaptive adjustment strategy(AAS).Specifically,we observe that in X-ray images,the material information of an object can be represented by color and texture features,and remains consistent across views,even under complex backgrounds.Therefore,our MACA first integrates the material information of the prohibited items in each view and then smoothly transfers these clearmaterial clues along the shared axis to the corresponding locations in the other view to enhance the feature representation of the blurred prohibited items in the other view.In addition,AAS can autonomously adjust the importance of the two views during feature learning to make joint optimizationmore stable and effective.Experiments on the DvXray dataset demonstrate that the proposed MACA and AAS can be plug-and-played into various detectors,such as Faster Region-based Convolutional Neural Network(Faster R-CNN)and Fully Convolutional One-Stage Object Detector(FCOS),and bring consistent performance gains.The entire framework performs favorably against state-of-the-art methods,especially on small-sized prohibited items,highlighting its potential application in reality.展开更多
Loneliness is a complex and usually unpleasant emotional response to isolation,which has been considered the latest global health epidemic exacerbated by the coronavirus disease 2019 pandemic,affecting nearly twothird...Loneliness is a complex and usually unpleasant emotional response to isolation,which has been considered the latest global health epidemic exacerbated by the coronavirus disease 2019 pandemic,affecting nearly twothirds of older adults.Some profound health implications carried by loneliness include depression,cognitive impairment,hypertension and frailty.Across the world,there is no consensus definition of loneliness,and its measure is based on the phenomenological perspective of the individual.The 20-item University of California Los Angeles Loneliness Scale version 3(UCLA-20)is the most common measure.This scale demonstrates acceptable psychometric properties but is too long and complex for a phone interview.This paper addresses the increasing need to shorten this scale by adopting classical item response theory and network psychometrics to advance scale development.Through an item reduction analysis,we trimmed the original scale into an effective short form,which is as valid as the original one.With respondents’time at a premium in most research nowadays,this shortform scale is an efficient and practical alternative to the original UCLA-20.展开更多
Qualification of equipment essential to safety in NPPs （nuclear power plants） ensures its capability to perform designated safety functions on demand under postulated service conditions. However, a number of inciden...Qualification of equipment essential to safety in NPPs （nuclear power plants） ensures its capability to perform designated safety functions on demand under postulated service conditions. However, a number of incidents identified by the NRC （Nuclear Regulatory Commission） since 1980s catalysed the US nuclear industry to adopt standard precautions to guard against counterfeit items. The purpose of this paper is to suggest the NFC （near field communication） based equipment qualification management system preventing counterfeit and fraudulent items. The NEQM （NFC based equipment qualification management） system works with the support of legacy systems such as PMS （procurement management system） and FMS （facility management system）.展开更多
A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimizati...A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.展开更多
Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the develo...Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the development of E-commerce,the difficulties of the extreme sparsity of user rating data have become more and more severe. Based on the traditional similarity measuring methods,we introduce the cloud model and combine it with the item-based collaborative filtering recommendation algorithms. The new collaborative filtering recommendation algorithm based on item and cloud model （IC-Based CF） computes the similarity de-gree between items by comparing the statistical characteristic of items. The experimental results show that this method can improve the performance of the present item-based collaborative filtering algorithm with extreme sparsity of data.展开更多
文摘This paper studies the technics of reducing item exposure by utilizing automatic item generation methods. Known test item calibration method uses item parameter estimation with the statistical data, collected during examinees prior testing. Disadvantage of the mentioned item calibration method is the item exposure; when test items become familiar to the examinees. To reduce the item exposure, automatic item generation method is used, where item models are being constructed based on already calibrated test items without losing already estimated item parameters. A technic of item model extraction method from the already calibrated and therefore exposed test items described, which can be used by the test item development specialists to integrate automatic item generation principles with the existing testing applications.
基金by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021B1515120064.
文摘In real-world scenarios,dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage,which is important for identifying prohibited items that are not visible in one view due to rotation or overlap.However,existing work still focuses mainly on single-view,and the limited dual-viewbasedwork only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view.To this end,this paper proposes an end-to-end dual-view prohibited item detection method,the core of which is an adaptive material-aware coordinate-aligned attention module(MACA)and an adaptive adjustment strategy(AAS).Specifically,we observe that in X-ray images,the material information of an object can be represented by color and texture features,and remains consistent across views,even under complex backgrounds.Therefore,our MACA first integrates the material information of the prohibited items in each view and then smoothly transfers these clearmaterial clues along the shared axis to the corresponding locations in the other view to enhance the feature representation of the blurred prohibited items in the other view.In addition,AAS can autonomously adjust the importance of the two views during feature learning to make joint optimizationmore stable and effective.Experiments on the DvXray dataset demonstrate that the proposed MACA and AAS can be plug-and-played into various detectors,such as Faster Region-based Convolutional Neural Network(Faster R-CNN)and Fully Convolutional One-Stage Object Detector(FCOS),and bring consistent performance gains.The entire framework performs favorably against state-of-the-art methods,especially on small-sized prohibited items,highlighting its potential application in reality.
文摘Loneliness is a complex and usually unpleasant emotional response to isolation,which has been considered the latest global health epidemic exacerbated by the coronavirus disease 2019 pandemic,affecting nearly twothirds of older adults.Some profound health implications carried by loneliness include depression,cognitive impairment,hypertension and frailty.Across the world,there is no consensus definition of loneliness,and its measure is based on the phenomenological perspective of the individual.The 20-item University of California Los Angeles Loneliness Scale version 3(UCLA-20)is the most common measure.This scale demonstrates acceptable psychometric properties but is too long and complex for a phone interview.This paper addresses the increasing need to shorten this scale by adopting classical item response theory and network psychometrics to advance scale development.Through an item reduction analysis,we trimmed the original scale into an effective short form,which is as valid as the original one.With respondents’time at a premium in most research nowadays,this shortform scale is an efficient and practical alternative to the original UCLA-20.
文摘Qualification of equipment essential to safety in NPPs （nuclear power plants） ensures its capability to perform designated safety functions on demand under postulated service conditions. However, a number of incidents identified by the NRC （Nuclear Regulatory Commission） since 1980s catalysed the US nuclear industry to adopt standard precautions to guard against counterfeit items. The purpose of this paper is to suggest the NFC （near field communication） based equipment qualification management system preventing counterfeit and fraudulent items. The NEQM （NFC based equipment qualification management） system works with the support of legacy systems such as PMS （procurement management system） and FMS （facility management system）.
基金supported by the Key Project of National Social Science Foundation(12AZD111)the National Project for Education Science Planning(EFA110351)+2 种基金the Humanities and Social Science Foundation of Ministry of Education of China(12YJCZH207)the Key Project for Jiangsu Province Social Science Foundation(12DDA011)the Jiangsu College of Humanities and Social Sciences outside Campus Research Base:Chinese Development of Strategic Research Base for Internet of Things
文摘A combination method of optimization of the back-ground value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM（1,1） model are ful y expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to il ustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM（1,1） model relatively.
基金Supported by the National Basic Research Program of China (973 Program) (2006CB701305, 2007CB310804)the National Natural Science Foundation of China (60743001)+1 种基金Best National Thesis Fund (2005047)the Natural Science Foundation of Hubei Province (CDB132, 2010j0049)
文摘Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the development of E-commerce,the difficulties of the extreme sparsity of user rating data have become more and more severe. Based on the traditional similarity measuring methods,we introduce the cloud model and combine it with the item-based collaborative filtering recommendation algorithms. The new collaborative filtering recommendation algorithm based on item and cloud model （IC-Based CF） computes the similarity de-gree between items by comparing the statistical characteristic of items. The experimental results show that this method can improve the performance of the present item-based collaborative filtering algorithm with extreme sparsity of data.
