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ORCIDJames Caverlee
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- affiliation: Texas A&M University
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2020 – today
- 2023
[c148]Xiangjue Dong, Yun He, Ziwei Zhu, James Caverlee:
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts. ACL (Findings) 2023: 10651-10666- [c147]Karthic Madanagopal, James Caverlee:
Reinforced Sequence Training based Subjective Bias Correction. EACL 2023: 2577-2590 - [c146]Xiangjue Dong, Jiaying Lu, Jianling Wang, James Caverlee:
Closed-book Question Generation via Contrastive Learning. EACL 2023: 3142-3154 - [c145]Han Zhang
, Ziwei Zhu, James Caverlee:
Evolution of Filter Bubbles and Polarization in News Recommendation. ECIR (2) 2023: 685-693 - [c144]Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi:
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN). KDD 2023: 5608-5617 - [c143]Mostafa Rahmani, James Caverlee, Fei Wang:
Incorporating Time in Sequential Recommendation Models. RecSys 2023: 784-790 - [c142]Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee:
Enhancing User Personalization in Conversational Recommenders. WWW 2023: 770-778 - [i28]Han Zhang, Ziwei Zhu, James Caverlee:
Evolution of Filter Bubbles and Polarization in News Recommendation. CoRR abs/2301.10926 (2023) - [i27]Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee:
Enhancing User Personalization in Conversational Recommenders. CoRR abs/2302.06656 (2023) - [i26]Yingqiang Ge, Mostafa Rahmani, Athirai A. Irissappane, Jose Sepulveda, James Caverlee, Fei Wang:
Automated Data Denoising for Recommendation. CoRR abs/2305.07070 (2023) - [i25]Xiangjue Dong, Yun He, Ziwei Zhu, James Caverlee:
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts. CoRR abs/2306.04535 (2023) - [i24]Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang, James Caverlee:
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks. CoRR abs/2308.15614 (2023) - 2022
- [j23]Weiwen Liu, Yin Zhang, Jianling Wang, Yun He, James Caverlee, Patrick P. K. Chan, Daniel S. Yeung, Pheng-Ann Heng:
Item Relationship Graph Neural Networks for E-Commerce. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4785-4799 (2022) - [c141]Kaize Ding, Jianling Wang, James Caverlee, Huan Liu:
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. AAAI 2022: 6524-6531 - [c140]Zhuoer Wang, Qizhang Feng, Mohinish Chatterjee, Xing Zhao, Yezi Liu, Yuening Li, Abhay Kumar Singh, Frank M. Shipman, Xia Hu, James Caverlee:
RES: An Interpretable Replicability Estimation System for Research Publications. AAAI 2022: 13230-13232 - [c139]Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee:
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems. CIKM 2022: 1238-1247 - [c138]Ziwei Zhu, James Caverlee:
Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning. WSDM 2022: 1497-1506 - [c137]Karthic Madanagopal, James Caverlee:
Improving Linguistic Bias Detection in Wikipedia using Cross-Domain Adaptive Pre-Training. WWW (Companion Volume) 2022: 1301-1309 - [c136]Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo, James Caverlee:
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks. WWW 2022: 2205-2215 - [i23]Yun He, Xue Feng, Cheng Cheng, Geng Ji, Yunsong Guo, James Caverlee:
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks. CoRR abs/2203.06801 (2022) - [i22]Ziwei Zhu, Yun He, Xing Zhao, James Caverlee:
Evolution of Popularity Bias: Empirical Study and Debiasing. CoRR abs/2207.03372 (2022) - [i21]Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee:
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems. CoRR abs/2208.03298 (2022) - [i20]Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee:
Towards Fair Conversational Recommender Systems. CoRR abs/2208.03854 (2022) - [i19]Xiangjue Dong, Jiaying Lu, Jianling Wang, James Caverlee:
Closed-book Question Generation via Contrastive Learning. CoRR abs/2210.06781 (2022) - [i18]Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi:
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN). CoRR abs/2210.14309 (2022) - 2021
- [c135]Yin Zhang, Yun He, James Caverlee:
Vibe check: social resonance learning for enhanced recommendation. ASONAM 2021: 164-167 - [c134]Ziwei Zhu, Yun He, Xing Zhao, James Caverlee:
Popularity Bias in Dynamic Recommendation. KDD 2021: 2439-2449 - [c133]Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee:
Session-based Recommendation with Hypergraph Attention Networks. SDM 2021: 82-90 - [c132]Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, James Caverlee:
Fairness among New Items in Cold Start Recommender Systems. SIGIR 2021: 767-776 - [c131]Jianling Wang, Kaize Ding, James Caverlee:
Sequential Recommendation for Cold-start Users with Meta Transitional Learning. SIGIR 2021: 1783-1787 - [c130]Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, James Caverlee:
Popularity-Opportunity Bias in Collaborative Filtering. WSDM 2021: 85-93 - [c129]Karthic Madanagopal, James Caverlee:
Towards Ongoing Detection of Linguistic Bias on Wikipedia. WWW (Companion Volume) 2021: 629-631 - [c128]Xing Zhao, Ziwei Zhu, James Caverlee:
Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests. WWW 2021: 888-899 - [i17]Ziwei Zhu, Jianling Wang, James Caverlee:
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning. CoRR abs/2103.07849 (2021) - [i16]Jian Wu, Rajal Nivargi, Sree Sai Teja Lanka, Arjun Manoj Menon, Sai Ajay Modukuri, Nishanth Nakshatri, Xin Wei, Zhuoer Wang, James Caverlee, Sarah Michele Rajtmajer, C. Lee Giles:
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models. CoRR abs/2104.04580 (2021) - [i15]Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu:
Weakly-supervised Graph Meta-learning for Few-shot Node Classification. CoRR abs/2106.06873 (2021) - [i14]Monika Daryani, James Caverlee:
Identifying Hijacked Reviews. CoRR abs/2107.05385 (2021) - [i13]Jianling Wang, Kaize Ding, James Caverlee:
Sequential Recommendation for Cold-start Users with Meta Transitional Learning. CoRR abs/2107.06427 (2021) - [i12]Kaize Ding, Jianling Wang, James Caverlee, Huan Liu:
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning. CoRR abs/2112.09810 (2021) - [i11]Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee:
Session-based Recommendation with Hypergraph Attention Networks. CoRR abs/2112.14266 (2021) - 2020
- [c127]Parisa Kaghazgaran, James Caverlee:
Towards an Automated Writing Assistant for Online Reviews. AutomationXP@CHI 2020 - [c126]Jianling Wang, James Caverlee:
Recommending Music Curators: A Neural Style-Aware Approach. ECIR (1) 2020: 191-204 - [c125]Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee:
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. EMNLP (1) 2020: 4604-4614 - [c124]Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, James Caverlee:
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge. EMNLP (1) 2020: 7572-7582 - [c123]Yin Zhang, Ziwei Zhu, Yun He, James Caverlee:
Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. RecSys 2020: 43-52 - [c122]Ziwei Zhu, Yun He, Yin Zhang, James Caverlee:
Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. RecSys 2020: 551-556 - [c121]Ziwei Zhu, Jianling Wang, James Caverlee:
Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. SIGIR 2020: 449-458 - [c120]Parisa Kaghazgaran, Jianling Wang, Ruihong Huang, James Caverlee:
ADORE: Aspect Dependent Online REview Labeling for Review Generation. SIGIR 2020: 1021-1030 - [c119]Jianling Wang, Kaize Ding, Liangjie Hong, Huan Liu, James Caverlee:
Next-item Recommendation with Sequential Hypergraphs. SIGIR 2020: 1101-1110 - [c118]Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah, James Caverlee:
Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. SIGIR 2020: 1121-1130 - [c117]Yun He, Yin Zhang, Weiwen Liu, James Caverlee:
Consistency-Aware Recommendation for User-Generated Item List Continuation. WSDM 2020: 250-258 - [c116]Jianling Wang, Ziwei Zhu, James Caverlee:
User Recommendation in Content Curation Platforms. WSDM 2020: 627-635 - [c115]Jianling Wang, Kaize Ding, Ziwei Zhu, Yin Zhang, James Caverlee:
Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. WSDM 2020: 636-644 - [c114]Jianling Wang, Raphael Louca, Diane Hu, Caitlin Cellier, James Caverlee, Liangjie Hong:
Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce. WSDM 2020: 645-653 - [c113]Xing Zhao, Ziwei Zhu, Yin Zhang, James Caverlee:
Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations. WSDM 2020: 762-770 - [c112]Xing Zhao, Ziwei Zhu, Majid Alfifi, James Caverlee:
Addressing the Target Customer Distortion Problem in Recommender Systems. WWW 2020: 2969-2975 - [c111]Yin Zhang, Yun He, Jianling Wang, James Caverlee:
Adaptive Hierarchical Translation-based Sequential Recommendation. WWW 2020: 2984-2990 - [e5]James Caverlee, Xia (Ben) Hu, Mounia Lalmas, Wei Wang:
WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020. ACM 2020, ISBN 978-1-4503-6822-3 [contents] - [i10]Habeeb Hooshmand, James Caverlee:
Understanding Car-Speak: Replacing Humans in Dealerships. CoRR abs/2002.02070 (2020) - [i9]Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, James Caverlee:
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge. CoRR abs/2010.03725 (2020) - [i8]Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, James Caverlee:
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. CoRR abs/2010.03746 (2020)
2010 – 2019
- 2019
- [j22]Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu:
Tensor Completion Algorithms in Big Data Analytics. ACM Trans. Knowl. Discov. Data 13(1): 6:1-6:48 (2019) - [c110]Parisa Kaghazgaran, Majid Alfifi, James Caverlee:
Wide-Ranging Review Manipulation Attacks: Model, Empirical Study, and Countermeasures. CIKM 2019: 981-990 - [c109]Yun He, Jianling Wang, Wei Niu, James Caverlee:
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists. CIKM 2019: 1481-1490 - [c108]Yin Zhang, James Caverlee:
Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence. CIKM 2019: 1583-1592 - [c107]Majid Alfifi, Parisa Kaghazgaran, James Caverlee, Fred Morstatter:
A Large-Scale Study of ISIS Social Media Strategy: Community Size, Collective Influence, and Behavioral Impact. ICWSM 2019: 58-67 - [c106]Parisa Kaghazgaran, Majid Alfifi, James Caverlee:
TOmCAT: Target-Oriented Crowd Review Attacks and Countermeasures. ICWSM 2019: 302-312 - [c105]Yin Zhang, Ninghao Liu, Shuiwang Ji, James Caverlee, Xia Hu:
An Interpretable Neural Model with Interactive Stepwise Influence. PAKDD (3) 2019: 528-540 - [c104]Jianling Wang, James Caverlee:
Recurrent Recommendation with Local Coherence. WSDM 2019: 564-572 - [c103]Ziwei Zhu, Jianling Wang, James Caverlee:
Improving Top-K Recommendation via JointCollaborative Autoencoders. WWW 2019: 3483-3482 - [i7]Yun He, Haochen Chen, Ziwei Zhu, James Caverlee:
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation. CoRR abs/1901.00597 (2019) - [i6]Yun He, Jianling Wang, Wei Niu, James Caverlee:
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists. CoRR abs/1912.13023 (2019) - [i5]Yun He, Yin Zhang, Weiwen Liu, James Caverlee:
Consistency-Aware Recommendation for User-Generated ItemList Continuation. CoRR abs/1912.13031 (2019) - 2018
- [j21]Benjamin A. Knott, Jonathan Gratch, Angelo Cangelosi, James Caverlee:
ACM Transactions on Interactive Intelligent Systems (TiiS) Special Issue on Trust and Influence in Intelligent Human-Machine Interaction. ACM Trans. Interact. Intell. Syst. 8(4): 25:1-25:3 (2018) - [j20]Victor Bahl, Barbara Carminati, James Caverlee, Ing-Ray Chen, Wynne Hsu, Toru Ishida, Valérie Issarny, Surya Nepal, Indrakshi Ray, Kui Ren, Shamik Sural, Mei-Ling Shyu:
Editorial. IEEE Trans. Serv. Comput. 11(1): 1-4 (2018) - [c102]Wei Niu, James Caverlee, Haokai Lu:
Location-Sensitive User Profiling Using Crowdsourced Labels. AAAI 2018: 386-393 - [c101]Chenxi Qiu, Anna Cinzia Squicciarini, Dev Rishi Khare, Barbara Carminati, James Caverlee:
CrowdEval: A Cost-Efficient Strategy to Evaluate Crowdsourced Worker's Reliability. AAMAS 2018: 1486-1494 - [c100]Ziwei Zhu, Xia Hu, James Caverlee:
Fairness-Aware Tensor-Based Recommendation. CIKM 2018: 1153-1162 - [c99]Cheng Cao, Zhengzhang Chen, James Caverlee, Lu-An Tang, Chen Luo, Zhichun Li:
Behavior-based Community Detection: Application to Host Assessment In Enterprise Information Networks. CIKM 2018: 1977-1985 - [c98]Hancheng Ge, Kai Zhang, Majid Alfifi, Xia Hu, James Caverlee:
DisTenC: A Distributed Algorithm for Scalable Tensor Completion on Spark. ICDE 2018: 137-148 - [c97]Yun He, Haochen Chen, Ziwei Zhu, James Caverlee:
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation. ICDM 2018: 1025-1030 - [c96]Xing Zhao, Qingquan Song, James Caverlee, Xia Hu:
TrailMix: An Ensemble Recommender System for Playlist Curation and Continuation. RecSys Challenge 2018: 8:1-8:6 - [c95]Yin Zhang, Haokai Lu, Wei Niu, James Caverlee:
Quality-aware neural complementary item recommendation. RecSys 2018: 77-85 - [c94]Haokai Lu, Wei Niu, James Caverlee:
Learning Geo-Social User Topical Profiles with Bayesian Hierarchical User Factorization. SIGIR 2018: 205-214 - [c93]Xing Zhao, James Caverlee:
Vitriol on Social Media: Curation and Investigation. SocInfo (1) 2018: 487-504 - [c92]Parisa Kaghazgaran, James Caverlee, Anna Cinzia Squicciarini:
Combating Crowdsourced Review Manipulators: A Neighborhood-Based Approach. WSDM 2018: 306-314 - [c91]Wei Niu, James Caverlee, Haokai Lu:
Neural Personalized Ranking for Image Recommendation. WSDM 2018: 423-431 - [r9]James Caverlee:
Data Dictionary. Encyclopedia of Database Systems (2nd ed.) 2018 - [r8]James Caverlee:
Topic Maps. Encyclopedia of Database Systems (2nd ed.) 2018 - [r7]James Caverlee, Prasenjit Mitra, Mary Lynette Larsgaard:
Dublin Core. Encyclopedia of Database Systems (2nd ed.) 2018 - [r6]James Caverlee, Zhiyuan Cheng:
Geography and Web Communities. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i4]Ziwei Zhu, Jianling Wang, Yin Zhang, James Caverlee:
Fairness-Aware Recommendation of Information Curators. CoRR abs/1809.03040 (2018) - 2017
- [c90]Chenxi Qiu, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, James Caverlee:
Dynamic Contract Design for Heterogenous Workers in Crowdsourcing for Quality Control. ICDCS 2017: 1168-1177 - [c89]Parisa Kaghazgaran, James Caverlee, Majid Alfifi:
Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond. ICWSM 2017: 560-563 - [c88]Qingquan Song, Xiao Huang, Hancheng Ge, James Caverlee, Xia Hu:
Multi-Aspect Streaming Tensor Completion. KDD 2017: 435-443 - [c87]Wenlin Yao, Zeyu Dai, Ruihong Huang, James Caverlee:
Online Deception Detection Refueled by Real World Data Collection. RANLP 2017: 793-802 - [c86]Cheng Cao, Hancheng Ge, Haokai Lu, Xia Hu, James Caverlee:
What Are You Known For?: Learning User Topical Profiles with Implicit and Explicit Footprints. SIGIR 2017: 743-752 - [c85]Shanshan Li, James Caverlee, Wei Niu, Parisa Kaghazgaran:
Crowdsourced App Review Manipulation. SIGIR 2017: 1137-1140 - [c84]Majid Alfifi, James Caverlee:
Badly Evolved? Exploring Long-Surviving Suspicious Users on Twitter. SocInfo (1) 2017: 218-233 - [i3]Wenlin Yao, Zeyu Dai, Ruihong Huang, James Caverlee:
Online Deception Detection Refueled by Real World Data Collection. CoRR abs/1707.09406 (2017) - [i2]Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu:
Tensor Completion Algorithms in Big Data Analytics. CoRR abs/1711.10105 (2017) - 2016
- [j19]Wei Niu, Zhijiao Liu, James Caverlee:
On Local Expert Discovery via Geo-Located Crowds, Queries, and Candidates. ACM Trans. Spatial Algorithms Syst. 2(4): 14:1-14:24 (2016) - [c83]Hancheng Ge, James Caverlee:
College Towns, Vacation Spots, and Tech Hubs: Using Geo-Social Media to Model and Compare Locations. AAAI 2016: 129-136 - [c82]Wei Niu, James Caverlee, Haokai Lu, Krishna Yeswanth Kamath:
Community-based geospatial tag estimation. ASONAM 2016: 279-286 - [c81]Chenxi Qiu, Anna Cinzia Squicciarini, Barbara Carminati, James Caverlee, Dev Rishi Khare:
CrowdSelect: Increasing Accuracy of Crowdsourcing Tasks through Behavior Prediction and User Selection. CIKM 2016: 539-548 - [c80]Hancheng Ge, James Caverlee, Nan Zhang, Anna Cinzia Squicciarini:
Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information. CIKM 2016: 1493-1502 - [c79]Wei Niu, Zhijiao Liu, James Caverlee:
LExL: A Learning Approach for Local Expert Discovery on Twitter. ECIR 2016: 803-809 - [c78]Haokai Lu, James Caverlee, Wei Niu:
Discovering What You're Known For: A Contextual Poisson Factorization Approach. RecSys 2016: 253-260 - [c77]Hancheng Ge, James Caverlee, Haokai Lu:
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation. RecSys 2016: 261-268 - [e4]Ravi Kumar, James Caverlee, Hanghang Tong:
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, CA, USA, August 18-21, 2016. IEEE Computer Society 2016, ISBN 978-1-5090-2846-7 [contents] - 2015
- [c76]Haokai Lu, James Caverlee, Wei Niu:
BiasWatch: A Lightweight System for Discovering and Tracking Topic-Sensitive Opinion Bias in Social Media. CIKM 2015: 213-222 - [c75]Cheng Cao, James Caverlee, Kyumin Lee, Hancheng Ge, Jin-Wook Chung:
Organic or Organized?: Exploring URL Sharing Behavior. CIKM 2015: 513-522 - [c74]Yuan Liang, James Caverlee, Cheng Cao:
A Noise-Filtering Approach for Spatio-temporal Event Detection in Social Media. ECIR 2015: 233-244 - [c73]Cheng Cao, James Caverlee:
Detecting Spam URLs in Social Media via Behavioral Analysis. ECIR 2015: 703-714 - [c72]Hancheng Ge, James Caverlee, Kyumin Lee:
Crowds, Gigs, and Super Sellers: A Measurement Study of a Supply-Driven Crowdsourcing Marketplace. ICWSM 2015: 120-129 - [c71]Haokai Lu, James Caverlee:
Exploiting Geo-Spatial Preference for Personalized Expert Recommendation. RecSys 2015: 67-74 - [c70]Amir Fayazi, Kyumin Lee, James Caverlee, Anna Cinzia Squicciarini:
Uncovering Crowdsourced Manipulation of Online Reviews. SIGIR 2015: 233-242 - [c69]Klaus Berberich, James Caverlee, Miles Efron, Claudia Hauff, Vanessa Murdock, Milad Shokouhi, Bart Thomee:
SIGIR 2015 Workshop on Temporal, Social and Spatially-aware Information Access (#TAIA2015). SIGIR 2015: 1149-1150 - [c68]Natwar Modani, Elham Khabiri, Harini Srinivasan, James Caverlee:
Creating Diverse Product Review Summaries: A Graph Approach. WISE (1) 2015: 169-184 - 2014
- [j18]