% bmc_article.bib % % An example of bibtex entries. % Entries taken from BMC instructions for authors page. % uncomment next line to make author-year bibliography % @settings{label, options="nameyear"} @article{blank, author = {}, title = {}, journal = {}, year = {}, month = {}, volume = {}, number = {}, pages = {}, note = {} } % Article within a journal @article{koon, author = {Koonin, E V and Altschul, S F and P Bork}, title = {BRCA1 protein products: functional motifs}, journal = {Nat. Genet.}, year = {1996}, volume = {13}, pages = {266-267} } %%%%%%%% % Article within conference proceedings @inproceedings{xjon, author = {X Jones}, title = {Zeolites and synthetic mechanisms}, booktitle = {Proceedings of the First National Conference on Porous Sieves: 27-30 June 1996; Baltimore}, year = {1996}, editor = {Y Smith}, pages = {16-27}, } %%%%%%%% % Book chapter, or article within a book @incollection{schn, author = {E Schnepf}, title = {From prey via endosymbiont to plastids: comparative studies in dinoflagellates}, booktitle = {Origins of Plastids}, editor = {R A Lewin}, publisher = {Chapman and Hall}, pages = {53-76}, year = {1993}, address = {New York}, edition = {2nd} } %%%%%%%% % Complete book @book{marg, author = {L Margulis}, title = {Origin of Eukaryotic Cells}, publisher = {Yale University Press}, year = {1970}, address = {New Haven} } %%%%%%%% % PHD Thesis @phdthesis{koha, author = {R Kohavi}, title = {Wrappers for performance enhancement and obvious decision graphs}, school = {Stanford University, Computer Science Department}, year = {1995} } %%%%%%%% % Miscellaneous: webpage link/urL, etc/ @misc{issnic, author = {{ISSN International Centre}}, title = {The ISSN register}, url = {http://www.issn.org}, year = {2006}, urldate={Accessed 20 Feb 2007} } @ARTICLE{refStart, author={P. {Mach} and Z. {Becvar}}, journal={IEEE Communications Surveys Tutorials}, title={Mobile Edge Computing: A Survey on Architecture and Computation Offloading}, year={2017}, volume={19}, number={3}, pages={1628-1656}, doi={10.1109/COMST.2017.2682318}} @INPROCEEDINGS{ref1, author={L. {Chiaraviglio} and F. {Cuomo} and A. {Gigli} and M. {Maisto} and Y. {Zhou} and {Zhifeng Zhao} and {Honggang Zhang}}, booktitle={2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}, title={A reality check of Base Station Spatial Distribution in mobile networks}, year={2016}, volume={}, number={}, pages={1065-1066}, doi={10.1109/INFCOMW.2016.7562255}} @article{ref2, title={Edge Computing: Vision and Challenges}, author={ Shi, Weisong and Cao, Jie and Zhang, Quan and Li, Youhuizi and Xu, Lanyu }, journal={Internet of Things Journal, IEEE}, volume={3}, number={5}, pages={637-646}, year={2016}, } @article{ref3, title={User mobility aware task assignment for mobile edge computing}, author={ Wang, Zi and Zhao, Zhiwei and Min, Geyong and Huang, Xinyuan and Ni, Qiang and Wang, Rong }, journal={Future Generation Computer Systems}, volume={85}, number={AUG.}, pages={1-8}, year={2018}, } @inproceedings{ref5, title={User mobility prediction based on Lagrange's interpolation in ultra-dense networks}, author={ Li, Bangxu and Zhang, Hongtao and Lu, Haitao }, booktitle={IEEE International Symposium on Personal}, year={2016}, } @article{ref4, title={QoS prediction for service recommendations in mobile edge computing}, author={ Wang, Shangguang and Zhao, Yali and Huang, Lin and Xu, Jinliang and Hsu, Ching Hsien }, journal={Journal of Parallel \& Distributed Computing}, volume={127}, number={MAY}, pages={134-144}, year={2017}, } @article{ref6, title={Personalized Web Service Recommendation via Normal Recovery Collaborative Filtering}, author={ Sun, Huifeng and Zheng, Zibin and Chen, Junliang and Lyu, Michael R. }, journal={IEEE Transactions on Services Computing}, volume={6}, number={4}, pages={573-579}, year={2013}, } @article{ref7, title={Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing}, author={ Li, Yan and Guo, Yan }, journal={entific programming}, volume={2018}, number={PT.1}, pages={2181974.1-2181974.8}, year={2018}, } @inproceedings{ref8, title={A Content-Based Recommendation System Using Neuro-Fuzzy Approach}, author={ Rutkowski, Tomasz and Romanowski, Jakub and Woldan, Piotr and Staszewski, Pawel and Rutkowski, Leszek }, booktitle={2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)}, year={2018}, } @article{ref9, title={Unified Collaborative and Content-Based Web Service Recommendation}, author={ Yao, Lina and Sheng, Quan Z. and Ngu, Anne. H. H. and Yu, Jian and Segev, Aviv}, journal={IEEE Transactions on Services Computing}, volume={8}, number={3}, pages={453-466}, year={2015}, } @patent{ref10, title = {Multilingual content based recommendation system}, number = {9898773}, author = {Nice, Nir (Kfar Veradim, IL), Koenigstein, Noam (Raanana, IL), Ben-elazar, Shay (Tel Aviv, IL), Keren, Shahar (Tel Aviv, IL), Paquet, Ulrich (Cambridge, GB), Finkelstein, Yehuda (Tel Aviv, IL)}, year = {2018}, month = {February}, url = {https://www.freepatentsonline.com/9898773.html}, } @inproceedings{ref11, title={Personalized QoS Prediction forWeb Services via Collaborative Filtering}, author={ Shao, Lingshuang and Zhang, Jing and Wei, Yong and Zhao, Junfeng and Mei, Hong }, booktitle={IEEE International Conference on Web Services}, year={2007}, } @inproceedings{ref12, title={WSRec: A Collaborative Filtering Based Web Service Recommender System}, author={ Zheng, Zibin and Ma, Hao and Lyu, Michael R. and King, Irwin }, booktitle={IEEE International Conference on Web Services}, year={2009}, } @article{ref13, title={QoS-Aware Web Service Recommendation by Collaborative Filtering}, author={ Zheng, Zibin and Ma, Hao and Lyu and M., R. and King and I.}, journal={Services Computing, IEEE Transactions on}, year={2011}, } @article{ref14, title={Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization}, author={Zheng and Zibin and Ma and Hao and Lyu and Michael, R and King and Irwin}, journal={IEEE Transactions on Services Computing}, volume={6}, number={3}, pages={289-299}, year={2013}, } @article{ref15, title={Multi-Dimensional QoS Prediction for Service Recommendations}, author={ Wang, Shangguang and Ma, You and Cheng, Bo and Yang, Fangchun and Chang, Rong N. }, journal={IEEE Transactions on Services Computing}, volume={PP}, number={99}, pages={1-1}, year={2016}, } @INPROCEEDINGS{ref16, author={M. {Tang} and Y. {Jiang} and J. {Liu} and X. {Liu}}, booktitle={2012 IEEE 19th International Conference on Web Services}, title={Location-Aware Collaborative Filtering for QoS-Based Service Recommendation}, year={2012}, volume={}, number={}, pages={202-209}, doi={10.1109/ICWS.2012.61} } @article{ref27, title={A personalised travel recommender system utilising social network profile and accurate GPS data}, author={ Logesh, R. and Subramaniyaswamy, V. and Vijayakumar, V. }, journal={Electronic Government An International Journal}, volume={14}, number={1}, year={2018}, } @inproceedings{ref28, author = {Wang, Hao and Terrovitis, Manolis and Mamoulis, Nikos}, title = {Location Recommendation in Location-Based Social Networks Using User Check-in Data}, year = {2013}, isbn = {9781450325219}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2525314.2525357}, doi = {10.1145/2525314.2525357}, booktitle = {Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, pages = {374–383}, numpages = {10}, location = {Orlando, Florida}, series = {SIGSPATIAL'13} } @inproceedings{ref29, author = {Park, Moon-Hee and Hong, Jin-Hyuk and Cho, Sung-Bae}, title = {Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices}, year = {2007}, isbn = {3540735488}, publisher = {Springer-Verlag}, address = {Berlin, Heidelberg}, abstract = {As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user's physical location. As increasing information and services, it becomes difficult to find a proper service that reflects the individual preference at proper time. Due to the small screen of mobile devices and insufficiency of resources, personalized services and convenient user interface might be useful. In this paper, we propose a map-based personalized recommendation system which reflects user's preference modeled by Bayesian Networks (BN). The structure of BN is built by an expert while the parameter is learned from the dataset. The proposed system collects context information, location, time, weather, and user request from the mobile device and infers the most preferred item to provide an appropriate service by displaying onto the mini map.}, booktitle = {Proceedings of the 4th International Conference on Ubiquitous Intelligence and Computing}, pages = {1130–1139}, numpages = {10}, location = {Hong Kong, China}, series = {UIC'07} } @inproceedings{ref17, author = {Zheng, Vincent W. and Cao, Bin and Zheng, Yu and Xie, Xing and Yang, Qiang}, title = {Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach}, year = {2010}, publisher = {AAAI Press}, abstract = {With the increasing popularity of location tracking services such as GPS, more and more mobile data are being accumulated. Based on such data, a potentially useful service is to make timely and targeted recommendations for users on places where they might be interested to go and activities that they are likely to conduct. For example, a user arriving in Beijing might wonder where to visit and what she can do around the Forbidden City. A key challenge for such recommendation problems is that the data we have on each individual user might be very limited, while to make useful and accurate recommendations, we need extensive annotated location and activity information from user trace data. In this paper, we present a new approach, known as user-centered collaborative location and activity filtering (UCLAF), to pull many users' data together and apply collaborative filtering to find like-minded users and like-patterned activities at different locations. We model the user-location-activity relations with a tensor representation, and propose a regularized tensor and matrix decomposition solution which can better address the sparse data problem in mobile information retrieval. We empirically evaluate UCLAF using a real-world GPS dataset collected from 164 users over 2.5 years, and showed that our system can outperform several state-of-the-art solutions to the problem.}, booktitle = {Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence}, pages = {236–241}, numpages = {6}, location = {Atlanta, Georgia}, series = {AAAI'10} } @ARTICLE{ref18, author={X. {Chen} and Z. {Zheng} and Q. {Yu} and M. R. {Lyu}}, journal={IEEE Transactions on Parallel and Distributed Systems}, title={Web Service Recommendation via Exploiting Location and QoS Information}, year={2014}, volume={25}, number={7}, pages={1913-1924}, doi={10.1109/TPDS.2013.308} } @ARTICLE{ref19, author={Ben Zion E., and Lerner, B.}, journal={EPJ Data Sci}, title={Identifying and predicting social lifestyles in people\'s trajectories by neural networks}, year={2018}, volume={45}, number={7}, url = {https://doi.org/10.1140/epjds/s13688-018-0173-5}, } @article{ref20, ISSN = {00278424}, URL = {http://www.jstor.org/stable/4143304}, abstract = {Equilibrium models of isolation by distance predict an increase in genetic differentiation with geographic distance. Here we find a linear relationship between genetic and geographic distance in a worldwide sample of human populations, with major deviations from the fitted line explicable by admixture or extreme isolation. A close relationship is shown to exist between the correlation of geographic distance and genetic differentiation (as measured by Fst) and the geographic pattern of heterozygosity across populations. Considering a worldwide set of geographic locations as possible sources of the human expansion, we find that heterozygosities in the globally distributed populations of the data set are best explained by an expansion originating in Africa and that no geographic origin outside of Africa accounts as well for the observed patterns of genetic diversity. Although the relationship between Fst and geographic distance has been interpreted in the past as the result of an equilibrium model of drift and dispersal, simulation shows that the geographic pattern of heterozygosities in this data set is consistent with a model of a serial founder effect starting at a single origin. Given this serial-founder scenario, the relationship between genetic and geographic distance allows us to derive bounds for the effects of drift and natural selection on human genetic variation.}, author = {Sohini Ramachandran and Omkar Deshpande and Charles C. Roseman and Noah A. Rosenberg and Marcus W. Feldman and L. Luca Cavalli-Sforza}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {44}, pages = {15942--15947}, publisher = {National Academy of Sciences}, title = {Support from the Relationship of Genetic and Geographic Distance in Human Populations for a Serial Founder Effect Originating in Africa}, volume = {102}, year = {2005} } @article{ref21, author = {Khaleghi, Bahador and Khamis, Alaa and Karray, Fakhreddine O. and Razavi, Saiedeh N.}, title = {Multisensor Data Fusion: A Review of the State-of-the-Art}, year = {2013}, issue_date = {January, 2013}, publisher = {Elsevier Science Publishers B. V.}, address = {NLD}, volume = {14}, number = {1}, issn = {1566-2535}, url = {https://doi.org/10.1016/j.inffus.2011.08.001}, doi = {10.1016/j.inffus.2011.08.001}, abstract = {There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.}, journal = {Inf. Fusion}, month = jan, pages = {28–44}, numpages = {17}, keywords = {Taxonomy, Fusion methodologies, Multisensor data fusion} } @ARTICLE{ref22, author={F. A. {Gers} and J. {Schmidhuber} and F. {Cummins}}, journal={Neural Computation}, title={Learning to Forget: Continual Prediction with LSTM}, year={2000}, volume={12}, number={10}, pages={2451-2471}, doi={10.1162/089976600300015015}} @INPROCEEDINGS{ref23, author={Y. {Duan} and Y. {L.V.} and F. {Wang}}, booktitle={2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)}, title={Travel time prediction with LSTM neural network}, year={2016}, volume={}, number={}, pages={1053-1058}, doi={10.1109/ITSC.2016.7795686}} %确认一下这篇文章的引用，不行就换一篇 @misc{ref24, title={Human Trajectory Prediction using Spatially aware Deep Attention Models}, author={Daksh Varshneya and G. Srinivasaraghavan}, year={2017}, eprint={1705.09436}, archivePrefix={arXiv}, primaryClass={cs.LG} } @inproceedings{ref25, author = {Bengio, Samy and Vinyals, Oriol and Jaitly, Navdeep and Shazeer, Noam}, title = {Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks}, year = {2015}, publisher = {MIT Press}, address = {Cambridge, MA, USA}, abstract = {Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current approach to training them consists of maximizing the likelihood of each token in the sequence given the current (recurrent) state and the previous token. At inference, the unknown previous token is then replaced by a token generated by the model itself. This discrepancy between training and inference can yield errors that can accumulate quickly along the generated sequence. We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead. Experiments on several sequence prediction tasks show that this approach yields significant improvements. Moreover, it was used succesfully in our winning entry to the MSCOCO image captioning challenge, 2015.}, booktitle = {Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1}, pages = {1171–1179}, numpages = {9}, location = {Montreal, Canada}, series = {NIPS'15} } @article{ref26, author = {Jaeger, Herbert}, year = {2001}, month = {01}, pages = {}, title = {The \"Echo State\" Approach to Analysing and Training Recurrent Neural Networks}, journal = {GMD-Report 148, German National Research Institute for Computer Science} } @inproceedings{ref27, author = {Karatzoglou, Antonios and Jablonski, Adrian and Beigl, Michael}, title = {A Seq2Seq Learning Approach for Modeling Semantic Trajectories and Predicting the next Location}, year = {2018}, isbn = {9781450358897}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3274895.3274983}, doi = {10.1145/3274895.3274983}, abstract = {Proactive mobile applications and services have the advantage of providing their users with timely and customized solutions improving in this way the human-machine interaction. For this reason, Location Based Services (LBS) rely increasingly on predictive models that estimate how likely it is for a user to visit a certain location. Recently, Artificial Neural Networks, and especially recurrent architectures such as the LSTMs, have shown a particularly good performance in this field. In this work, we extend a LSTM network by applying Sequence to Sequence (Seq2Seq) learning on human semantic trajectories. In particular, we explore whether and to what extent Attention-based Seq2Seq learning in combination with neural networks can contribute to improving the accuracy in a location prediction scenario. We compare the performance of our framework with the performance of a standard LSTM, a semantic trajectory tree-based approach and a probabilistic graph of first and higher order on two different real-world datasets. It can be shown that Sequence to Sequence learning may well be used to model semantic trajectories and predict future human movement patterns.}, booktitle = {Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems}, pages = {528–531}, numpages = {4}, keywords = {attention-based learning, location prediction, semantic locations, Seq2Seq learning, embedding layer, semantic trajectories}, location = {Seattle, Washington}, series = {SIGSPATIAL '18} } @misc{T-CONV, title={T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction}, author={Jianming Lv and Qing Li and Xintong Wang}, year={2017}, eprint={1611.07635}, archivePrefix={arXiv}, primaryClass={cs.CV} } @inproceedings{Handoff, author = {Arshad, Rabe and Elsawy, Hesham and Sorour, Sameh and Al-Naffouri, Tareq and Alouini, Mohamed-Slim}, year = {2016}, month = {04}, pages = {}, title = {Handover Management in Dense Cellular Networks: A Stochastic Geometry Approach}, doi = {10.1109/ICC.2016.7510709} } @ARTICLE{refLPre, author = {{Arshad}, Rabe and {ElSawy}, Hesham and {Sorour}, Sameh and {Al-Naffouri}, Tareq Y. and {Alouini}, Mohamed-Slim}, title = "{Handover Management in Dense Cellular Networks: A Stochastic Geometry Approach}", journal = {arXiv e-prints}, keywords = {Computer Science - Networking and Internet Architecture}, year = 2016, month = apr, eid = {arXiv:1604.08552}, pages = {arXiv:1604.08552}, archivePrefix = {arXiv}, eprint = {1604.08552}, primaryClass = {cs.NI}, adsurl = {https://ui.adsabs.harvard.edu/abs/2016arXiv160408552A}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } @ARTICLE{ReLU, author = {{Schmidt-Hieber}, Johannes}, title = "{Nonparametric regression using deep neural networks with ReLU activation function}", journal = {arXiv e-prints}, keywords = {Mathematics - Statistics Theory, Computer Science - Machine Learning, Statistics - Machine Learning, 62G08}, year = 2017, month = aug, eid = {arXiv:1708.06633}, pages = {arXiv:1708.06633}, archivePrefix = {arXiv}, eprint = {1708.06633}, primaryClass = {math.ST}, adsurl = {https://ui.adsabs.harvard.edu/abs/2017arXiv170806633S}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }