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Study on Mode and Strategic Recommendation for New Urbanization Ecological Civilization Construction

Qian Yi,Wu Zhiqiang,Jiang Yi and Wen Zongguo

Strategic Study of CAE 2015, Volume 17, Issue 8,   Pages 81-87

Abstract:

Over the past 30 years, China’s rapid urbanization process has brought along some issues such as excessive consumption of resources and environmental pollution, as well as other phenomena contrary to ecological civilization, and therefore is unsustainable. The core of ecological civilization and urbanization are people. New urbanization and ecological civilization construction have the same target. Considering the feedback mechanism of the four subsystems (resources, environment, economy and population) to urban development, the concept of ecological civilization should be run through the whole course of urbanization development and urban construction in the aspects of economic, political, cultural, social, etc. In addition, the ecological planning and intelligent design should be done in the urban development on the aspects of production, consumption and infrastructure construction, so as to realize the new urbanization development model with the characters of environment-friendly and resource-saving.

Keywords： new urbanization     ecological civilization     development model     ecological carrying capacity     strategic    

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The Backdrop and Significance of Ecological Civilization Construction

Du Xiangwan,Wen Zongguo,Wang Ning2, and Cao Xin

Strategic Study of CAE 2015, Volume 17, Issue 8,   Pages 8-15

Abstract: Finally, the paper proposes strategic recommendations based on the current situation, in order to provide

Keywords： ecological civilization     time background     civilization evolution     significance     strategic recommendation    

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Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting Artical

Longbing Cao

Engineering 2016, Volume 2, Issue 2,   Pages 212-224 doi: 10.1016/J.ENG.2016.02.013

Abstract:

While recommendation plays an increasingly critical role in our living, study, work, and entertainmentIn this paper, the non-IID nature and characteristics of recommendation are discussed, followed by thetheoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendationThis non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research

Keywords： relationship     Coupling learning     Relational learning     IIDness learning     Non-IIDness learning     Recommender system     Recommendation     Non-IID recommendation    

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Current Status and Development Strategy on Clean Coal Conversion Technology in China and the United States of America

Wu Yanli,Li Wenying,Yi Qun and Xie Kechang

Strategic Study of CAE 2015, Volume 17, Issue 9,   Pages 133-139

Abstract: Strategic recommendations are put forward that collaboration should be done in fundamental part such

Keywords： China and the United States of America     clean coal technology     current situation     development trend     strategicrecommendation    

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Toward Privacy-Preserving Personalized Recommendation Services Review

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Engineering 2018, Volume 4, Issue 1,   Pages 21-28 doi: 10.1016/j.eng.2018.02.005

Abstract:

Recommendation systems are crucially important for the delivery of personalized services to users.With personalized recommendation services, users can enjoy a variety of targeted recommendations suchIn addition, personalized recommendation services have become extremely effective revenue drivers forWe present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services.

Keywords： Privacy protection     Personalized recommendation services     Targeted delivery     Collaborative filtering     Machine    

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Rare tumors: a blue ocean of investigation

Frontiers of Medicine 2023, Volume 17, Issue 2,   Pages 220-230 doi: 10.1007/s11684-023-0984-z

Abstract: Lastly, we pinpointed the current recommendation chance for patients with rare tumors to be involved

Keywords： rare tumors     diagnosis flowchart     treatment strategy     clinical trials recommendation    

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A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 7,   Pages 532-540 doi: 10.1631/FITEE.1400368

Abstract: In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interestA questionnaire survey proved user satisfaction with recommendation results when the cold-start problem

Keywords： Recommender system     Collaborative filtering     Social tagging     Interest evolution model    

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EncyCatalogRec: catalog recommendation for encyclopedia article completion Article

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3,   Pages 436-447 doi: 10.1631/FITEE.1800363

Abstract: So, the recommendation problem is changed to a transductive learning problem in the product graph.Experimental results demonstrate that our approach achieves state-of-the-art performance on catalog recommendation

Keywords： Catalog recommendation     Encyclopedia article completion     Product graph     Transductive learning    

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DAN: a deep association neural network approach for personalization recommendation Research Articles

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-980 doi: 10.1631/FITEE.1900236

Abstract: The collaborative filtering technology used in traditional systems has a problem of data sparsity. The traditional matrix decomposition algorithm simply decomposes users and items into a linear model of potential factors. These limitations have led to the low accuracy in traditional algorithms, thus leading to the emergence of systems based on . At present, s mostly use deep s to model some of the auxiliary information, and in the process of modeling, multiple mapping paths are adopted to map the original input data to the potential vector space. However, these deep algorithms ignore the combined effects of different categories of data, which can have a potential impact on the effectiveness of the . Aimed at this problem, in this paper we propose a feedforward deep method, called the deep association (DAN), which is based on the joint action of multiple categories of information, for implicit feedback . Specifically, the underlying input of the model includes not only users and items, but also more auxiliary information. In addition, the impact of the joint action of different types of information on the is considered. Experiments on an open data set show the significant improvements made by our proposed method over the other methods. Empirical evidence shows that deep, joint s can provide better performance.

Keywords： Neural network     Deep learning     Deep association neural network (DAN)     Recommendation    

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Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 9,   Pages 1273-1286 doi: 10.1631/FITEE.2200304

Abstract: Next point-of-interest (POI) recommendation is an important personalized task in location-based social

Keywords： Point-of-interest recommendation     Spatiotemporal effects     Long short-term memory (LSTM)     Attention mechanism    

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Strategic Reflection on Promoting Energy Revolution of Production and Consumption in China

The Research Group of “Strategic Research on Promoting Energy Revolution of Production and Consumption

Strategic Study of CAE 2015, Volume 17, Issue 9,   Pages 11-17

Abstract:

Energy is an important basis for the sustained and sound development of Chinese economy and society. Energy revolution of production and consumption is the only way to promote China’s development. As a national development strategy of promoting energy revolution of production and consumption, it has been gradually implementing. This paper indicates the significance of driving energy revolution of production and consumption from the perspective of the relationship between energy, economy and society. It elaborates the background of China’s energy revolution, and analyzes the major issues which affect the energy revolution through the historical experience of developed countries. Finally it proposes strategies for energy revolution based on current situation, in order to provide reference and support for energy revolution in China.

Keywords： energy revolution     economy and society     sustainable development     major issues     strategic measure    

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Applying system dynamics to strategic decision making in construction

SangHyun LEE

Frontiers of Engineering Management 2017, Volume 4, Issue 1,   Pages 35-40 doi: 10.15302/J-FEM-2017002

Abstract: The author discusses the application of System Dynamics to high-level strategic simulation in constructionFrom this exercise, it is concluded that System Dynamics offers a great potential for strategic simulationcomprehensive simulation framework that integrates System Dynamics and Discrete Event Simulation for a strategic

Keywords： strategic project management     construction management     system dynamics     feedback process     hybrid simulation    

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Fast code recommendation via approximate sub-tree matching Research Article

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8,   Pages 1205-1216 doi: 10.1631/FITEE.2100379

Abstract: Software developers often write code that has similar functionality to existing code segments. A tool that helps developers reuse these code fragments can significantly improve their efficiency. Several methods have been proposed in recent years. Some use sequence matching algorithms to find the related recommendations. Most of these methods are time-consuming and can leverage only low-level textual information from code. Others extract features from code and obtain similarity using numerical feature vectors. However, the similarity of feature vectors is often not equivalent to the original code’s similarity. Structural information is lost during the process of transforming abstract syntax trees into vectors. We propose an approximate sub-tree matching based method to solve this problem. Unlike existing tree-based approaches that match feature vectors, it retains the tree structure of the query code in the matching process to find code fragments that best match the current query. It uses a fast approximation sub-tree matching algorithm by transforming the sub-tree matching problem into the match between the tree and the list. In this way, the structural information can be used for tasks that have high time requirements. We have constructed several real-world code databases covering different languages and granularities to evaluate the effectiveness of our method. The results show that our method outperforms two compared methods, SENSORY and Aroma, in terms of the recall value on all the datasets, and can be applied to large datasets.

Keywords： Code reuse     Code recommendation     Tree similarity     Structure information    

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APFD: an effective approach to taxi route recommendation with mobile trajectory big data Research Article

Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU, Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1494-1510 doi: 10.1631/FITEE.2100530

Abstract:

With the rapid development of data-driven intelligent transportation systems, an efficient method for taxis has become a hot topic in smart cities. We present an effective taxi approach (called APFD) based on the (APF) method and method with mobile trajectory big data. Specifically, to improve the efficiency of , we propose a method that searches for a region including the optimal route through the origin and destination coordinates. Then, based on the APF method, we put forward an effective approach for removing redundant nodes. Finally, we employ the method to determine the optimal . In particular, the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing. On the map, we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony (AC) algorithm, greedy algorithm (A∗), APF, rapid-exploration random tree (RRT), non-dominated sorting genetic algorithm-II (NSGA-II), particle swarm optimization (PSO), and for the shortest . Compared with AC, A∗, APF, RRT, NSGA-II, and PSO, concerning shortest route planning, APFD improves route planning capability by 1.45%–39.56%, 4.64%–54.75%, 8.59%–37.25%, 5.06%–45.34%, 0.94%–20.40%, and 2.43%–38.31%, respectively. Compared with , the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency. In addition, in the real-world road network, on the Fourth Ring Road in Beijing, the ability of APFD to recommend the shortest route is better than those of AC, A∗, APF, RRT, NSGA-II, and PSO, and the execution efficiency of APFD is higher than that of the method.

Keywords： Big data analytics     Region extraction     Artificial potential field     Dijkstra     Route recommendation     GPS    

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RepoLike: amulti-feature-based personalized recommendation approach for open-source repositories None

Cheng YANG, Qiang FAN, Tao WANG, Gang YIN, Xun-hui ZHANG, Yue YU, Hua-min WANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 222-237 doi: 10.1631/FITEE.1700196

Abstract:

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness, a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called “RepoLike,” to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

Keywords： Social coding     Open-source software     Personal recommendation     GitHub    

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Title Author Date Type Operation

Study on Mode and Strategic Recommendation for New Urbanization Ecological Civilization Construction

Qian Yi,Wu Zhiqiang,Jiang Yi and Wen Zongguo

Journal Article

The Backdrop and Significance of Ecological Civilization Construction

Du Xiangwan,Wen Zongguo,Wang Ning2, and Cao Xin

Journal Article

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

Longbing Cao

Journal Article

Current Status and Development Strategy on Clean Coal Conversion Technology in China and the United States of America

Wu Yanli,Li Wenying,Yi Qun and Xie Kechang

Journal Article

Toward Privacy-Preserving Personalized Recommendation Services

Cong Wang, Yifeng Zheng, Jinghua Jiang, Kui Ren

Journal Article

Rare tumors: a blue ocean of investigation

Journal Article

A microblog recommendation algorithm based on social tagging and a temporal interest evolution model

Zhen-ming YUAN,Chi HUANG,Xiao-yan SUN,Xing-xing LI,Dong-rong XU

Journal Article

EncyCatalogRec: catalog recommendation for encyclopedia article completion

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Journal Article

DAN: a deep association neural network approach for personalization recommendation

Xu-na Wang, Qing-mei Tan,Xuna@nuaa.edu.cn,tanchina@nuaa.edu.cn

Journal Article

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

孙曦,吕志民

Journal Article

Strategic Reflection on Promoting Energy Revolution of Production and Consumption in China

The Research Group of “Strategic Research on Promoting Energy Revolution of Production and Consumption

Journal Article

Applying system dynamics to strategic decision making in construction

SangHyun LEE

Journal Article

Fast code recommendation via approximate sub-tree matching

Yichao SHAO, Zhiqiu HUANG, Weiwei LI, Yaoshen YU,shaoyichao@nuaa.edu.cn,zqhuang@nuaa.edu.cn

Journal Article

APFD: an effective approach to taxi route recommendation with mobile trajectory big data

Wenyong ZHANG, Dawen XIA, Guoyan CHANG, Yang HU, Yujia HUO, Fujian FENG, Yantao LI, Huaqing LI

Journal Article

RepoLike: amulti-feature-based personalized recommendation approach for open-source repositories

Cheng YANG, Qiang FAN, Tao WANG, Gang YIN, Xun-hui ZHANG, Yue YU, Hua-min WANG

Journal Article