Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reli...Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.展开更多
At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anom...At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation.In real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly detectors.In this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human feedback.In particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the future.Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure.Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark datasets.The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.展开更多
The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on dif...The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.展开更多
As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.I...As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.ICPCSEE 2023 was held in Harbin,China,during September 22–24,2023,and hosted by the Harbin Engineering University,the Harbin Institute of Technology,the Northeast Forestry University,the Harbin University of Science and Technology,the Heilongjiang Computer Federation,and the National Academy of Guo Ding Institute of Data Sciences.The goal of this conference series is to provide a forum for computer scientists,engineers,and educators.展开更多
As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.I...As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.ICPCSEE 2023 was held in Harbin,China,during September 22–24,2023,and hosted by the Harbin Engineering University,the Harbin Institute of Technology,the Northeast Forestry University,the Harbin University of Science and Technology,the Heilongjiang Computer Federation,and the National Academy of Guo Ding Institute of Data Sciences.The goal of this conference series is to provide a forum for computer scientists,engineers,and educators.展开更多
Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy ...Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,etc.The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data.In this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model.Ensemble learning,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified framework.Specifically,ensemble learning firstly extracts a set of features with a variety of transformations.Based on these learned features,multiple learning algorithms are utilized to produce weak predictive results.Finally,ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way.In this paper,we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics.In addition,we present challenges and possible research directions for each mainstream approach of ensemble learning,and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning,reinforcement learning,etc.展开更多
The widespread fake news in social networks is posing threats to social stability,economic development,and political democracy,etc.Numerous studies have explored the effective detection approaches of online fake news,...The widespread fake news in social networks is posing threats to social stability,economic development,and political democracy,etc.Numerous studies have explored the effective detection approaches of online fake news,while few works study the intrinsic propagation and cognition mechanisms of fake news.Since the development of cognitive science paves a promising way for the prevention of fake news,we present a new research area called Cognition Security(CogSec),which studies the potential impacts of fake news on human cognition,ranging from misperception,untrusted knowledge acquisition,targeted opinion/attitude formation,to biased decision making,and investigates the effective ways for fake news debunking.CogSec is a multidisciplinary research field that leverages the knowledge from social science,psychology,cognition science,neuroscience,AI and computer science.We first propose related definitions to characterize CogSec and review the literature history.We further investigate the key research challenges and techniques of CogSec,including human-content cognition mechanism,social influence and opinion dif-fusion,fake news detection,and malicious bot detection.Finally,we summarize the open issues and future research directions,such as the cognition mechanism of fake news,influence maximization of fact-checking information,early detection of fake news,fast refutation of fake news,and so on.展开更多
Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is...Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.展开更多
Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping ...Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View(FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems(GIS).Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called 'Spotlight', which performs passive localization using crowdsourced photos.Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.展开更多
Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge ...Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.展开更多
1 Introduction Withthe rapid development of the E-commerce,more consumers turn to shop online.When online shoppers search for products using keywords,the related products appear with their brief descriptions.We conduc...1 Introduction Withthe rapid development of the E-commerce,more consumers turn to shop online.When online shoppers search for products using keywords,the related products appear with their brief descriptions.We conduct a real-world survey and find that most shoppers are dissatisfied with the existing"onefit-all product descriptions"and they have to spend more time to scan detail pages.However,handcrafting the attractive product descriptions is always costly.展开更多
Figure 8 of this article shows YaleB and CMU PIE with incorrect legend titles:YaleB(Tr=1900,Te=514,NOC=100)should be YaleB(Tr=1900,Te=514,d=100)(Fig.8(a));TIE(Tr=1200,Te=2880,d=100)should be PIE(Tr=1200,Te=2880,d=100)...Figure 8 of this article shows YaleB and CMU PIE with incorrect legend titles:YaleB(Tr=1900,Te=514,NOC=100)should be YaleB(Tr=1900,Te=514,d=100)(Fig.8(a));TIE(Tr=1200,Te=2880,d=100)should be PIE(Tr=1200,Te=2880,d=100)(Fig.8(b)).In Fig.9,the legend keys and the legend texts are mismatched.The correct figure is ilustrated as follows.展开更多
基金supported by Science and Technology Project of China Southern Power Grid Company Limited under Grant Number 036000KK52200058(GDKJXM20202001).
文摘Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.
基金supported in part by the National Science Fund for Distinguished Young Scholars(61725205)the National Natural Science Foundation of China(Grant Nos.61960206008,61772428,61972319,and61902320).
文摘At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation.In real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly detectors.In this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human feedback.In particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the future.Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure.Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark datasets.The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.61960206008,62002294)the National Science Fund for Distinguished Young Scholars(61725205).
文摘The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.
文摘As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.ICPCSEE 2023 was held in Harbin,China,during September 22–24,2023,and hosted by the Harbin Engineering University,the Harbin Institute of Technology,the Northeast Forestry University,the Harbin University of Science and Technology,the Heilongjiang Computer Federation,and the National Academy of Guo Ding Institute of Data Sciences.The goal of this conference series is to provide a forum for computer scientists,engineers,and educators.
文摘As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.ICPCSEE 2023 was held in Harbin,China,during September 22–24,2023,and hosted by the Harbin Engineering University,the Harbin Institute of Technology,the Northeast Forestry University,the Harbin University of Science and Technology,the Heilongjiang Computer Federation,and the National Academy of Guo Ding Institute of Data Sciences.The goal of this conference series is to provide a forum for computer scientists,engineers,and educators.
基金Acknowledgements This work was partially supported by the National Basic Research Program of China （2015CB352400）, the National Natural Science Foundation of China （Grant Nos. 61402360, 61402369）, the Foundation of Shaanxi Educational Committee （16JK1509）. The authors are grateful to the anonymous referees for their helpful comments and suggestions.
基金the National Natural Science Foundation of China(Grant Nos.61722205,61751205,61572199,61502174,61872148,and U 1611461)the grant from the key research and development program of Guangdong province of China(2018B010107002)+1 种基金the grants from Science and Technology Planning Project of Guangdong Province,China(2016A050503015,2017A030313355)the grant from the Guangzhou science and technology planning project(201704030051).
文摘Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,etc.The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data.In this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model.Ensemble learning,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified framework.Specifically,ensemble learning firstly extracts a set of features with a variety of transformations.Based on these learned features,multiple learning algorithms are utilized to produce weak predictive results.Finally,ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way.In this paper,we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics.In addition,we present challenges and possible research directions for each mainstream approach of ensemble learning,and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning,reinforcement learning,etc.
基金Acknowledgements The research leading to these results was supported by the National Natural Science Foundation of China （Grants No. 61070090, 61273363, 61003174 and 60973083）, the Guangdong Natural Science Funds for Distinguished Young Scholar （$2013050014677）, the Fundamental Research Funds for the Central Universities （2014G0007）, China Postdoctoral Science Foundation （2013M540655）, NSFC-Guangdong Joint Fund （U1035004）, and Natural Science Foundation of Guangdong Province, China （10451064101004233 and S2012040008022）.
基金supported by the National Key R&D Program of China(2019QY0600)the National Natural Science Foundation of China(Grant Nos.61772428,61725205,61902320,61925203,U1636210)Beijing Advanced Innovation Center for Big Data and Brain Computing。
文摘The widespread fake news in social networks is posing threats to social stability,economic development,and political democracy,etc.Numerous studies have explored the effective detection approaches of online fake news,while few works study the intrinsic propagation and cognition mechanisms of fake news.Since the development of cognitive science paves a promising way for the prevention of fake news,we present a new research area called Cognition Security(CogSec),which studies the potential impacts of fake news on human cognition,ranging from misperception,untrusted knowledge acquisition,targeted opinion/attitude formation,to biased decision making,and investigates the effective ways for fake news debunking.CogSec is a multidisciplinary research field that leverages the knowledge from social science,psychology,cognition science,neuroscience,AI and computer science.We first propose related definitions to characterize CogSec and review the literature history.We further investigate the key research challenges and techniques of CogSec,including human-content cognition mechanism,social influence and opinion dif-fusion,fake news detection,and malicious bot detection.Finally,we summarize the open issues and future research directions,such as the cognition mechanism of fake news,influence maximization of fact-checking information,early detection of fake news,fast refutation of fake news,and so on.
基金supported by the National Basic Research Program of China(973)(2012CB316402)The National Natural Science Foundation of China(Grant Nos.61332005,61725205)+3 种基金The Research Project of the North Minzu University(2019XYZJK02,2019xYZJK05,2017KJ24,2017KJ25,2019MS002)Ningxia first-classdisciplinc and scientific research projects(electronic science and technology,NXYLXK2017A07)NingXia Provincial Key Discipline Project-Computer ApplicationThe Provincial Natural Science Foundation ofNingXia(NZ17111,2020AAC03219).
文摘Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.
文摘Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View(FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems(GIS).Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called 'Spotlight', which performs passive localization using crowdsourced photos.Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61772136,61672159)the Technology Innovation Platform Project of Fujian Province(2014H2005)+1 种基金the Research Project for Young and Middle-aged Teachers of Fujian Province(JT 180045)the Fujian Collaborative Innovation Center for Big Data Application in Governments,the Fujian Engineering Research Center of Big Data Analysis and Processing.
文摘Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.
基金supported by the National Key RD Program of China (2019QY0600)the National Science Fund for Distinguished Young Scholars (62025205)+1 种基金the National Natural Science Foundation of China (Grant Nos.62032020,61960206008,62102317,62002292)the Natural Science Basic Research Plan in Shaanxi Province of China (2020JQ-207).
文摘1 Introduction Withthe rapid development of the E-commerce,more consumers turn to shop online.When online shoppers search for products using keywords,the related products appear with their brief descriptions.We conduct a real-world survey and find that most shoppers are dissatisfied with the existing"onefit-all product descriptions"and they have to spend more time to scan detail pages.However,handcrafting the attractive product descriptions is always costly.
文摘Figure 8 of this article shows YaleB and CMU PIE with incorrect legend titles:YaleB(Tr=1900,Te=514,NOC=100)should be YaleB(Tr=1900,Te=514,d=100)(Fig.8(a));TIE(Tr=1200,Te=2880,d=100)should be PIE(Tr=1200,Te=2880,d=100)(Fig.8(b)).In Fig.9,the legend keys and the legend texts are mismatched.The correct figure is ilustrated as follows.
