As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an ele...As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.展开更多
The Chinese Software Developer Network(CSDN)is one of the largest information technology communities and service platforms in China.This paper describes the user profiling for CSDN,an evaluation track of SMP Cup 2017....The Chinese Software Developer Network(CSDN)is one of the largest information technology communities and service platforms in China.This paper describes the user profiling for CSDN,an evaluation track of SMP Cup 2017.It contains three tasks:(1)user document keyphrase extraction,(2)user tagging and(3)user growth value prediction.In the first task,we treat keyphrase extraction as a classification problem and train a Gradient-Boosting-Decision-Tree model with comprehensive features.In the second task,to deal with class imbalance and capture the interdependency between classes,we propose a two-stage framework:(1)for each class,we train a binary classifier to model each class against all of the other classes independently;(2)we feed the output of the trained classifiers into a softmax classifier,tagging each user with multiple labels.In the third task,we propose a comprehensive architecture to predict user growth value.Our contributions in this paper are summarized as follows:(1)we extract various types of features to identify the key factors in user value growth;(2)we use the semi-supervised method and the stacking technique to extend labeled data sets and increase the generality of the trained model,resulting in an impressive performance in our experiments.In the competition,we achieved the first place out of 329 teams.展开更多
In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infe...In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact tracing.Through this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance pressure.The practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.展开更多
Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network e...Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.展开更多
Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced b...Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’motion.While existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information utilization.To this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view.Particularly,the first-level view is the inter-trajectory view.In this level,we observe that the difficulty in predicting different trajectory samples varies.We define trajectory difficulty and train the proposed framework in an“easy-to-hard”schema.The second-level view is the intra-trajectory level.We find the influencing factors for a particular trajectory can be divided into two parts.The first part is global features,which keep stable within a trajectory,i.e.,the expected destination.The second part is local features,which change over time,i.e.,the current position.We believe that the two types of information should be handled in different ways.The hierarchical view is beneficial to take full advantage of the information in a fine-grained way.Experimental results validate the effectiveness of the proposed model-agnostic framework.展开更多
Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods in...Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples （Steve Jobs, PlaceOfBrith, California） and （Apple Inc., Location, California） share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe （Translation-Based Method via Modeling the Correlations of Relations）, learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.展开更多
基金Supported by the National Natural Science Foundation of China(No.61672494,61402437)the National High Technology Research and Development Program of China(No.2015AA016005)
文摘As a completely new residential distribution infrastructure,energy internet facilitates transactions of equipment,energy and services. However,there is security risk under all the facilities.This paper proposes an electricity pricing model based on insurance from the perspective of maximizing the benefits of Energy Internet service providers by using the principal-agent theory. The consumer prepays the provider insurance premiums and signs a contract. The provider sets electricity price according to the premiums and therefore provides differentiated electric services for the consumer. Loss suffered by the consumer due to the power failure is compensated by the provider according to the contract. The equivalent model is presented and a necessary condition of the optimal strategy is obtained on the basis of Pontryagin's maximum principle. At last,a numerical example is presented,which illustrates the effectiveness of the proposed model.
基金The work is supported by the National Natural Science Foundation of China(NSFC)under grant numbers 61472400,91746301 and 61802371H.Shen is also funded by K.C.Wong Education Foundation and the Youth Innovation Promotion Association of the Chinese Academy of Sciences.
文摘The Chinese Software Developer Network(CSDN)is one of the largest information technology communities and service platforms in China.This paper describes the user profiling for CSDN,an evaluation track of SMP Cup 2017.It contains three tasks:(1)user document keyphrase extraction,(2)user tagging and(3)user growth value prediction.In the first task,we treat keyphrase extraction as a classification problem and train a Gradient-Boosting-Decision-Tree model with comprehensive features.In the second task,to deal with class imbalance and capture the interdependency between classes,we propose a two-stage framework:(1)for each class,we train a binary classifier to model each class against all of the other classes independently;(2)we feed the output of the trained classifiers into a softmax classifier,tagging each user with multiple labels.In the third task,we propose a comprehensive architecture to predict user growth value.Our contributions in this paper are summarized as follows:(1)we extract various types of features to identify the key factors in user value growth;(2)we use the semi-supervised method and the stacking technique to extend labeled data sets and increase the generality of the trained model,resulting in an impressive performance in our experiments.In the competition,we achieved the first place out of 329 teams.
文摘In 2020,the COVID-19 pandemic has brought“digital contact tracing”to the forefront of public attention.In the context of COVID-19,technology has offered public health investigators a new capability for locating infected individuals,i.e.,digital contact tracing.Through this technology,investigators were able to track the location of patients without relying on their memory,which alleviated disease surveillance pressure.The practical application of this technology is known as“Exposure Notification.”Developers were able to complete the creation and operation of this digital contact tracing system within a few weeks,and they made the code open-source to ensure that Apple and Android users worldwide could utilize it.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.U21B2046 and 62102402the National Key Research and Development Program of China under Grant No.2020AAA0105200.
文摘Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.
基金supported by the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No.2023112the National Natural Science Foundation of China under Grant No.62206266Zhao Zhang is supported by the China Postdoctoral Science Foundation under Grant No.2021M703273.
文摘Predicting the future trajectories of multiple agents is essential for various applications in real life,such as surveillance systems,autonomous driving,and social robots.The trajectory prediction task is influenced by many factors,including the individual historical trajectory,interactions between agents,and the fuzzy nature of the observed agents’motion.While existing methods have made great progress on the topic of trajectory prediction,they treat all the information uniformly,which limits the effectiveness of information utilization.To this end,in this paper,we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view.Particularly,the first-level view is the inter-trajectory view.In this level,we observe that the difficulty in predicting different trajectory samples varies.We define trajectory difficulty and train the proposed framework in an“easy-to-hard”schema.The second-level view is the intra-trajectory level.We find the influencing factors for a particular trajectory can be divided into two parts.The first part is global features,which keep stable within a trajectory,i.e.,the expected destination.The second part is local features,which change over time,i.e.,the current position.We believe that the two types of information should be handled in different ways.The hierarchical view is beneficial to take full advantage of the information in a fine-grained way.Experimental results validate the effectiveness of the proposed model-agnostic framework.
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2014CB340405, the National Key Research and Development Program of China under Grant No. 2016YFB1000902, and the National Natural Science Foundation of China under Grant Nos. 61402442, 61272177, 61173008, 61232010, 61303244, 61572469, 91646120 and 61572473.
文摘Knowledge graph embedding, which maps the entities and relations into low-dimensional vector spaces, has demonstrated its effectiveness in many tasks such as link prediction and relation extraction. Typical methods include TransE, TransH, and TransR. All these methods map different relations into the vector space separately and the intrinsic correlations of these relations are ignored. It is obvious that there exist some correlations among relations because different relations may connect to a common entity. For example, the triples （Steve Jobs, PlaceOfBrith, California） and （Apple Inc., Location, California） share the same entity California as their tail entity. We analyze the embedded relation matrices learned by TransE/TransH/TransR, and find that the correlations of relations do exist and they are showed as low-rank structure over the embedded relation matrix. It is natural to ask whether we can leverage these correlations to learn better embeddings for the entities and relations in a knowledge graph. In this paper, we propose to learn the embedded relation matrix by decomposing it as a product of two low-dimensional matrices, for characterizing the low-rank structure. The proposed method, called TransCoRe （Translation-Based Method via Modeling the Correlations of Relations）, learns the embeddings of entities and relations with translation-based framework. Experimental results based on the benchmark datasets of WordNet and Freebase demonstrate that our method outperforms the typical baselines on link prediction and triple classification tasks.
