text:
Available online 14 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Wei Yu, Wenjun Wang, Xue Chen, Lin Pan, Pengfei Jiao{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Temporal community detection could help us analyze and understand the meaningful substructure hidden within dynamic networks in the real world. Evolutionary clustering, as a popular framework for clustering stream data, has been denoted for mining the communities in dynamic networks. However, most of these methods ignore the varying characteristics of micro structure of the networks and lack of statistical interpretation. In this paper, we propose a powerful, interpretable and extensible evolutionary clustering framework based on nonnegative matrix factorization (NMF) for temporal community detection via combining the first-order varying information of micro structure in dynamic networks from the perspective of statistical model. Firstly, we consider the first-order varying information of nodes by constructing a temporal similarity matrix over time. Secondly, we present the framework, FVI-NMF, for detecting temporal community based on NMF combing the First-order Varying Information. Thirdly, we develop a effective algorithm to optimize the objective function of FVI-NMF and analyze its complexity. In addition, our model can discover the evolutionary pattern of temporal communities synchronously, which has a variety applications in the analysis of dynamic network. Experiments on both artificial and real dynamic networks demonstrate that our proposed framework has superior performance in comparison with state-of-art methods.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{yuNovelEvolutionaryClusteringa,
  title = {A Novel Evolutionary Clustering via the First-Order Varying Information for Dynamic Networks},
  author = {Yu, Wei and Wang, Wenjun and Chen, Xue and Pan, Lin and Jiao, Pengfei},
  doi = {S0378437119300196},
  abstract = {{$<$}p{$>$}Publication date: Available online 14 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Wei Yu, Wenjun Wang, Xue Chen, Lin Pan, Pengfei Jiao{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}Temporal community detection could help us analyze and understand the meaningful substructure hidden within dynamic networks in the real world. Evolutionary clustering, as a popular framework for clustering stream data, has been denoted for mining the communities in dynamic networks. However, most of these methods ignore the varying characteristics of micro structure of the networks and lack of statistical interpretation. In this paper, we propose a powerful, interpretable and extensible evolutionary clustering framework based on nonnegative matrix factorization (NMF) for temporal community detection via combining the first-order varying information of micro structure in dynamic networks from the perspective of statistical model. Firstly, we consider the first-order varying information of nodes by constructing a temporal similarity matrix over time. Secondly, we present the framework, FVI-NMF, for detecting temporal community based on NMF combing the First-order Varying Information. Thirdly, we develop a effective algorithm to optimize the objective function of FVI-NMF and analyze its complexity. In addition, our model can discover the evolutionary pattern of temporal communities synchronously, which has a variety applications in the analysis of dynamic network. Experiments on both artificial and real dynamic networks demonstrate that our proposed framework has superior performance in comparison with state-of-art methods.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@book{zallerNatureOriginsMass1992,
  title = {The Nature and Origins of Mass Opinion},
  author = {Zaller, John},
  year = {1992},
  publisher = {{Cambridge University Press}},
  address = {{Cambridge [England] ; New York, NY, USA}},
  isbn = {978-0-521-40449-5 978-0-521-40786-1},
  lccn = {HM261 .Z35 1992}
}

@article{zampieriSemEval2020Task122020,
  title = {{{SemEval}}-2020 {{Task}} 12: {{Multilingual Offensive Language Identification}} in {{Social Media}} ({{OffensEval}} 2020)},
  shorttitle = {{{SemEval}}-2020 {{Task}} 12},
  author = {Zampieri, Marcos and Nakov, Preslav and Rosenthal, Sara and Atanasova, Pepa and Karadzhov, Georgi and Mubarak, Hamdy and Derczynski, Leon and Pitenis, Zeses and {\c C}{\"o}ltekin, {\c C}a{\u g}r{\i}},
  year = {2020},
  month = jun,
  abstract = {We present the results and main findings of SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2020). The task involves three subtasks corresponding to the hierarchical taxonomy of the OLID schema (Zampieri et al., 2019a) from OffensEval 2019. The task featured five languages: English, Arabic, Danish, Greek, and Turkish for Subtask A. In addition, English also featured Subtasks B and C. OffensEval 2020 was one of the most popular tasks at SemEval-2020 attracting a large number of participants across all subtasks and also across all languages. A total of 528 teams signed up to participate in the task, 145 teams submitted systems during the evaluation period, and 70 submitted system description papers.},
  archivePrefix = {arXiv},
  eprint = {2006.07235},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Zampieri et al/Zampieri et al_2020_SemEval-2020 Task 12.pdf;/home/aleksandar/Zotero/storage/QBSL3S4E/2006.html},
  journal = {arXiv:2006.07235 [cs]},
  primaryClass = {cs}
}

@article{zareiDetectingCommunityStructure2020,
  title = {Detecting Community Structure in Signed and Unsigned Social Networks by Using Weighted Label Propagation},
  author = {Zarei, Bagher and Meybodi, Mohammad Reza and Masoumi, Behrooz},
  year = {2020},
  month = oct,
  volume = {30},
  pages = {103118},
  publisher = {{American Institute of Physics}},
  issn = {1054-1500},
  doi = {10.1063/1.5144139},
  abstract = {Detecting community structure is one of the most important problems in analyzing complex networks such as technological, informational, biological, and social networks and has great importance in understanding the operation and organization of these networks. One of the significant properties of social networks is the communication intensity between the users, which has not received much attention so far. Most of the proposed methods for detecting community structure in social networks have only considered communications between users. In this paper, using MinHash and label propagation, an algorithm called weighted label propagation algorithm (WLPA) has been proposed to detect community structure in signed and unsigned social networks. WLPA takes into account the intensity of communications in addition to the communications. In WLPA, first, the similarity of all adjacent nodes is estimated by using MinHash. Then, each edge is assigned a weight equal to the estimated similarity of its end nodes. The weights assigned to the edges somehow indicate the intensity of communication between users. Finally, the community structure of the network is determined through the weighted label propagation. Experiments on the benchmark networks indicate that WLPA is efficient and effective for detecting community structure in both signed and unsigned social networks.},
  file = {/home/aleksandar/Documents/Zotero/Zarei et al/Zarei et al_2020_Detecting community structure in signed and unsigned social networks by using.pdf},
  journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
  number = {10}
}

@article{zegaracMemristiveNetworksGraph,
  title = {Memristive Networks: {{From}} Graph Theory to Statistical Physics},
  author = {Zegarac, A. and Caravelli, F.},
  doi = {10.1209/0295-5075/125/10001},
  abstract = {We provide an introduction to a very specific toy model of memristive networks, for which an exact   differential equation for the internal memory which contains the Kirchhoff laws is known. In   particular, we highlight how the circuit topology enters the dynamics via an analysis of directed   graph. We try to highlight in particular the connection between the asymptotic states of memristors   and the Ising model, and the relation to the dynamics and statics of disordered systems.},
  journal = {Europhysics Letters}
}

@article{zegaracMemristiveNetworksGrapha,
  title = {Memristive Networks: {{From}} Graph Theory to Statistical Physics},
  author = {Zegarac, A. and Caravelli, F.},
  doi = {10.1209/0295-5075/125/10001},
  abstract = {We provide an introduction to a very specific toy model of memristive networks, for which an exact   differential equation for the internal memory which contains the Kirchhoff laws is known. In   particular, we highlight how the circuit topology enters the dynamics via an analysis of directed   graph. We try to highlight in particular the connection between the asymptotic states of memristors   and the Ising model, and the relation to the dynamics and statics of disordered systems.},
  journal = {Europhysics Letters}
}

@article{zehmakanSpreadInfluenceGraphs2020,
  title = {Spread of {{Influence}} in {{Graphs}}},
  author = {Zehmakan, Ahad N.},
  year = {2020},
  month = jun,
  abstract = {Consider a graph \$G\$ and an initial configuration where each node is black or white. Assume that in each round all nodes simultaneously update their color based on a predefined rule. One can think of graph \$G\$ as a social network, where each black/white node represents an individual who holds a positive/negative opinion regarding a particular topic. In the \$r\$-threshold (resp. \$\textbackslash alpha\$-threshold) model, a node becomes black if at least \$r\$ of its neighbors (resp. \$\textbackslash alpha\$ fraction of its neighbors) are black, and white otherwise. The \$r\$-monotone (resp. \$\textbackslash alpha\$-monotone) model is the same as the \$r\$-threshold (resp. \$\textbackslash alpha\$-threshold) model, except that a black node remains black forever. What is the number of rounds that the process needs to stabilize? How many nodes must be black initially so that black color takes over or survives? Our main goal in the present paper is to address these two questions},
  archivePrefix = {arXiv},
  eprint = {2006.03440},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Zehmakan/Zehmakan_2020_Spread of Influence in Graphs.pdf;/home/aleksandar/Zotero/storage/HVAN6MQZ/2006.html},
  journal = {arXiv:2006.03440 [cs, math]},
  keywords = {DONE},
  primaryClass = {cs, math}
}

@article{zengNetworkStructureAdolescent2019,
  title = {The {{Network Structure}} of {{Adolescent Well}}-{{Being Traits}}: {{Results From}} a {{Large}}-{{Scale Chinese Sample}}},
  shorttitle = {The {{Network Structure}} of {{Adolescent Well}}-{{Being Traits}}},
  author = {Zeng, Guang and Peng, Kaiping and Hu, Chuan-Peng},
  year = {2019},
  month = dec,
  volume = {10},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2019.02783},
  abstract = {Background: The mental health and well-being of adolescents are becoming increasingly important globally. Understanding the relationship between different aspects of well-being is crucial for effective interventions of the well-being of adolescents. The present study aims to analyze the network structure of adolescent well-being and identify the central well-being traits., Methods: We used a network model to analyze the network structure of a psychometrically sound measurement of adolescent well-being \textendash{} the engagement, perseverance, optimism, connectedness, and happiness (EPOCH) scale. The dataset comes from a representative sample of Chinese adolescents (17, 854 participants from rural and urban areas from Southern, Northern, and the middle part of China)., Results: The 20 items of EPOCH formed a highly interconnected network. The item H4 (``I am a cheerful person.''), item E2 (``I get completely absorbed in what I am doing''), and item O4 (``I believe that things will work out, no matter how diffcult they seem'') were the traits with the highest centrality in the network., Conclusions: Cheerfulness, engagement in current activity, and optimism for the future are most central to the psychological well-being of Chinese adolescents. Future studies should further test the dynamics between these central traits and other well-being traits to find effective interventions of well-being of adolescents.},
  journal = {Frontiers in Psychology},
  pmcid = {PMC6932967},
  pmid = {31920831}
}

@article{zercherComparabilityUniversalismValue2015,
  title = {The Comparability of the Universalism Value over Time and across Countries in the {{European Social Survey}}: Exact vs. Approximate Measurement Invariance},
  shorttitle = {The Comparability of the Universalism Value over Time and across Countries in the {{European Social Survey}}},
  author = {Zercher, Florian and Schmidt, Peter and Cieciuch, Jan and Davidov, Eldad},
  year = {2015},
  month = jun,
  volume = {6},
  issn = {1664-1078},
  doi = {10.3389/fpsyg.2015.00733},
  abstract = {Over the last decades, large international datasets such as the European Social Survey (ESS), the European Value Study (EVS) and the World Value Survey (WVS) have been collected to compare value means over multiple time points and across many countries. Yet analyzing comparative survey data requires the fulfillment of specific assumptions, i.e., that these values are comparable over time and across countries. Given the large number of groups that can be compared in repeated cross-national datasets, establishing measurement invariance has been, however, considered unrealistic. Indeed, studies which did assess it often failed to establish higher levels of invariance such as scalar invariance. In this paper we first introduce the newly developed approximate approach based on Bayesian structural equation modeling (BSEM) to assess cross-group invariance over countries and time points and contrast the findings with the results from the traditional exact measurement invariance test. BSEM examines whether measurement parameters are approximately (rather than exactly) invariant. We apply BSEM to a subset of items measuring the universalism value from the Portrait Values Questionnaire (PVQ) in the ESS. The invariance of this value is tested simultaneously across 15 ESS countries over six ESS rounds with 173,071 respondents and 90 groups in total. Whereas, the use of the traditional approach only legitimates the comparison of latent means of 37 groups, the Bayesian procedure allows the latent mean comparison of 73 groups. Thus, our empirical application demonstrates for the first time the BSEM test procedure on a particularly large set of groups.},
  file = {/home/aleksandar/Dropbox/Zotero/Zercher et al/Zercher et al_2015_The comparability of the universalism value over time and across countries in.pdf},
  journal = {Frontiers in Psychology},
  language = {en}
}

@article{zhangAnalysisCollegeStudents,
  ids = {zhangAnalysisCollegeStudent,zhangAnalysisCollegeStudentsa},
  title = {Analysis of {{College Students}}' {{Public Opinion Based}} on {{Machine Learning}} and {{Evolutionary Algorithm}}},
  author = {Zhang, Jinqing and Zhang, Pengchao and Xu, Bin},
  doi = {10.1155/2019/1712569},
  abstract = {The recent information explosion may have many negative impacts on college students, such as distraction from learning and addiction to meaningless and fake news. To avoid these phenomena, it is necessary to verify the students' state of mind and give them appropriate guidance. However, many peculiarities, including subject focused, multiaspect, and low consistency on different samples' interests, bring great challenges while leveraging the mainstream opinion mining method. To solve this problem, this paper proposes a new way by using a questionnaire which covers most aspects of a student's life to collect comprehensive information and feed the information into a neural network. With reliable prediction on students' state of mind and awareness of feature importance, colleges can give students guidance associated with their own experience and make macroscopic policies more effective. A pipeline is proposed to relieve overfitting during the collected information training. First, the singular value decomposition is used in pretreatment of data set which includes outlier detection and dimension reduction. Then, the genetic algorithm is introduced in the training process to find the proper initial parameters of network, and in this way, it can prevent the network from falling into the local minimum. A method of calculating the importance of students' features is also proposed. The experiment result shows that the new pipeline works well, and the predictor has high accuracy on predicting fresh samples. The design procedure and the prediction design will provide suggestions to deal with students' state of mind and the college's public opinion.},
  journal = {Complexity}
}

@article{zhangClusterPredictionOpinion2020,
  title = {Cluster {{Prediction}} for {{Opinion Dynamics}} from {{Partial Observations}}},
  author = {Zhang, Zehong and Lu, Fei},
  year = {2020},
  month = jul,
  abstract = {We present a Bayesian approach to predict the clustering of opinions for a system of interacting agents from partial observations. The Bayesian formulation overcomes the unobservability of the system and quantifies the uncertainty in the prediction. We characterize the clustering by the posterior of the clusters' sizes and centers, and we represent the posterior by samples. To overcome the challenge in sampling the high-dimensional posterior, we introduce an auxiliary implicit sampling (AIS) algorithm using two-step observations. Numerical results show that the AIS algorithm leads to accurate predictions of the sizes and centers for the leading clusters, in both cases of noiseless and noisy observations. In particular, the centers are predicted with high success rates, but the sizes exhibit a considerable uncertainty that is sensitive to observation noise and the observation ratio.},
  archivePrefix = {arXiv},
  eprint = {2007.02006},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Zhang_Lu/Zhang_Lu_2020_Cluster Prediction for Opinion Dynamics from Partial Observations.pdf;/home/aleksandar/Zotero/storage/QHC9LE4I/2007.html},
  journal = {arXiv:2007.02006 [stat]},
  primaryClass = {stat}
}

@article{zhangComputationLatentVariable2020,
  title = {Computation for {{Latent Variable Model Estimation}}: {{A Unified Stochastic Proximal Framework}}},
  shorttitle = {Computation for {{Latent Variable Model Estimation}}},
  author = {Zhang, Siliang and Chen, Yunxiao},
  year = {2020},
  month = aug,
  abstract = {Latent variable models have been playing a central role in psychometrics and related fields. In many modern applications, the inference based on latent variable models involves one or several of the following features: (1) the presence of many latent variables, (2) the observed and latent variables being continuous, discrete, or a combination of both, (3) constraints on parameters, and (4) penalties on parameters to impose model parsimony. The estimation often involves maximizing an objective function based on a marginal likelihood/pseudo-likelihood, possibly with constraints and/or penalties on parameters. Solving this optimization problem is highly non-trivial, due to the complexities brought by the features mentioned above. Although several efficient algorithms have been proposed, there lacks a unified computational framework that takes all these features into account. In this paper, we fill the gap. Specifically, we provide a unified formulation for the optimization problem and then propose a quasi-Newton stochastic proximal algorithm. Theoretical properties of the proposed algorithms are established. The computational efficiency and robustness are shown by simulation studies under various settings for latent variable model estimation.},
  archivePrefix = {arXiv},
  eprint = {2008.07214},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Zhang_Chen/Zhang_Chen_2020_Computation for Latent Variable Model Estimation.pdf;/home/aleksandar/Zotero/storage/KMKUEFIY/2008.html},
  journal = {arXiv:2008.07214 [stat]},
  primaryClass = {stat}
}

@article{zhangConditionalCooperatorEnhances2021,
  title = {Conditional Cooperator Enhances Institutional Punishment in Public Goods Game},
  author = {Zhang, Boyu and An, Xinmiao and Dong, Yali},
  year = {2021},
  month = feb,
  volume = {390},
  pages = {125600},
  issn = {0096-3003},
  doi = {10.1016/j.amc.2020.125600},
  abstract = {The role of incentive institutions on promoting cooperation in public goods game (PGG) has attracted much attention. Theoretical studies based on Nash equilibrium analysis predict that the punishment effect is often stronger than the reward effect. Although this result is confirmed by empirical studies, subjects do not always play these rational strategies. Recent experiments indicate that most subjects in PGGs are conditional cooperators who tend to contribute the group average. In this paper, we consider PGGs with three types of subjects, namely, cooperators, defectors, and conditional cooperators. Evolutionary game method is applied to investigate how conditional cooperators affect the effectiveness of different types of incentives. Overall, having conditional cooperators cannot lead to a higher contribution level in the standard PGG or the PGG with institutional rewards. However, they can enhance the effectiveness of institutional punishment, where a high contribution level can be maintained even for small punishments. As a consequence, in PGGs with conditional cooperators, punishment always leads to a higher contribution rate than do rewards. Numerical analysis indicates that this result is robust to errors in decision making.},
  file = {/home/aleksandar/Zotero/storage/V4L8MYYU/S0096300320305555.html},
  journal = {Applied Mathematics and Computation},
  language = {en}
}

@article{zhangDetectingCommunityStructure2019,
  title = {Detecting Community Structure in Complex Networks via Resistance Distance},
  author = {Zhang, Teng and Bu, Changjiang},
  year = {2019},
  month = jul,
  volume = {526},
  pages = {120782},
  issn = {03784371},
  doi = {10.1016/j.physa.2019.04.018},
  journal = {Physica A: Statistical Mechanics and its Applications},
  language = {en}
}

@article{zhangDisentangledDynamicGraph2020,
  title = {Disentangled {{Dynamic Graph Deep Generation}}},
  author = {Zhang, Wenbin and Zhang, Liming and Pfoser, Dieter and Zhao, Liang},
  year = {2020},
  month = oct,
  abstract = {Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of proteins (i.e., graph of amino acids). Existing work typically focuses on static rather than dynamic graphs, which are actually very important in the applications such as protein folding, molecule reactions, and human mobility. Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns. Here, this paper proposes a novel framework of factorized deep generative models to achieve interpretable dynamic graph generation. Various generative models are proposed to characterize conditional independence among node, edge, static, and dynamic factors. Then, variational optimization strategies as well as dynamic graph decoders are proposed based on newly designed factorized variational autoencoders and recurrent graph deconvolutions. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed models.},
  archivePrefix = {arXiv},
  eprint = {2010.07276},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Zhang et al/Zhang et al_2020_Disentangled Dynamic Graph Deep Generation.pdf;/home/aleksandar/Zotero/storage/6G2ZEIM4/2010.html},
  journal = {arXiv:2010.07276 [cs]},
  primaryClass = {cs}
}

@article{zhangEmotionDetectionOnline2020,
  title = {Emotion {{Detection}} in {{Online Social Networks}}: {{A Multi}}-Label {{Learning Approach}}},
  shorttitle = {Emotion {{Detection}} in {{Online Social Networks}}},
  author = {Zhang, Xiao and Li, Wenzhong and Ying, Haochao and Li, Feng and Tang, Siyi and Lu, Sanglu},
  year = {2020},
  pages = {1--1},
  issn = {2327-4662},
  doi = {10.1109/JIOT.2020.3004376},
  abstract = {Emotion detection in online social networks (OSNs) can benefit kinds of applications such as personalized advertisement services, recommendation systems, etc. Conventionally, emotion analysis mainly focuses on the sentence level polarity prediction or single emotion label classification, however, ignoring the fact that emotions might co-exist from users' perspective. To this end, in this work, we address the multiple emotions detection in OSNs from user level view, and formulate this problem as a multi-label learning problem. First, we discover emotion labels correlations, social correlations, and temporal correlations from an annotated Twitter dataset. Second, based on the above observations, we adopt a factor graph based emotion recognition model to incorporate emotion labels correlations, social correlations and temporal correlations into a general framework, and detect the multiple emotions based on multi-label learning approach. Performance evaluation demonstrates that the factor graph based emotion detection model can outperform the existing baselines.},
  file = {/home/aleksandar/Zotero/storage/3CIRM2YI/9123365.html},
  journal = {IEEE Internet of Things Journal}
}

@article{zhangEvolutionNetworkControllability,
  ids = {zhangEvolutionNetworkControllabilitya},
  title = {The Evolution of Network Controllability in Growing Networks},
  author = {Zhang, Rui and Wang, Xiaomeng and Cheng, Ming and Jia, Tao},
  doi = {S0378437119300433},
  abstract = {{$<$}p{$>$}Publication date: Available online 16 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Rui Zhang, Xiaomeng Wang, Ming Cheng, Tao Jia{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}The study of network structural controllability focuses on the minimum number of driver nodes needed to control a whole network. Despite intensive studies on this topic, most of them consider static networks only. It is well-known, however, that real networks are growing, with new nodes and links added to the system. Here, we analyze controllability of evolving networks and propose a general rule for the change of driver nodes. We further apply the rule to solve the problem of network augmentation subject to the controllability constraint. The findings strengthen our understandings of network controllability and shed light on controllability of real systems.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{zhangExperimentalStudyPedestrian,
  title = {Experimental Study on Pedestrian Behavior in a Mixed Crowd of Individuals and Groups},
  author = {Zhang, Bosi and Chen, Wenyan and Ma, Xian and Qiu, Ping and Liu, Fupeng},
  doi = {10.1016/j.physa.2020.124814},
  abstract = {{$<$}p{$>$}The influences of groups on pedestrian behavior in a mixed crowd that comprises individuals and groups were investigated experimentally. The crowd comprised 20 pedestrians: some were composed into groups of three or four pedestrians (3-pedestrian and 4-pedestrian groups), and the others walked as individuals The walking speed and the walking behavior of pedestrians in the crowds were the main focus. Results showed that the walking speed of pedestrians\textemdash the individuals and group members in a mixed crowd\textemdash basically decreased compared with their walking speed in none-group crowd, and the decreasing of pedestrians' walking speed became more obvious with the size of groups inside the mixed crowd. Basically, the decrease in the degree of walking speed was larger for female pedestrians than for the male pedestrians. However, in the mixed crowd conditions with the 3-pedestrian group, the walking speed of individuals with an initial location near the group might possibly be higher than that in the none-group crowd condition, which might be caused by the accelerating process for overtaking the group in front of them. This speed accelerating behavior was more remarkable among the male pedestrians in this study. In the mixed crowd conditions with the 4-pedestrian group, the overtaking of the group became more difficult for the individuals. The walking speed of a group in a crowd was lower than that in the free-walking environment because the group members had to pay more attention to maintain group relationships and not separate from each other in the crowd.{$<$}/p{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{zhangIndividualHeterogeneousLearning2020,
  title = {Individual {{Heterogeneous Learning}} with {{Global Centrality}} in {{Prisoner Dilemma Evolutionary Game}} on {{Complex Network}}},
  author = {Zhang, Zundong and Zhang, Yifang and Danziger, William},
  year = {2020},
  issn = {1875-6883},
  doi = {10.2991/ijcis.d.200603.002},
  abstract = {The influence of individual heterogeneity on the evolutionary game has been studied extensively in recent years. Whereas many theoretical studies have found that the heterogeneous learning ability effects cooperation rate, the individual learning ability in networks is still not well understood. It is known that an individual's learning ability is influenced not only by its first order neighbors, but also by higher order individuals, and even by the whole network. At present, existing methods to represent individual learning ability are based on degree centrality, resulting in ignoring the global centrality of nodes. In this paper, we design a method for describing the heterogeneous learning ability by taking advantage of a pre-factor \dbend\dbend\dbend\dbend\dbend\dbend x related to the node betweenness. And a parameter \dbend\dbend\dbend\dbend\dbend\dbend{} is used to tune \dbend\dbend\dbend\dbend\dbend\dbend x. Experiments show that individual heterogeneous learning ability is effected by global information. Our findings provide a new perspective to understand the important influence of the global attributes of nodes on the evolutionary game.},
  file = {/home/aleksandar/Dropbox/Zotero/Zhang et al/Zhang et al_2020_Individual Heterogeneous Learning with Global Centrality in Prisoner Dilemma.pdf},
  journal = {International Journal of Computational Intelligence Systems},
  language = {en}
}

@article{zhangInterclusterTransmissionControl2020,
  title = {Inter-Cluster {{Transmission Control Using Graph Modal Barriers}}},
  author = {Zhang, Leiming and Sadler, Brian M. and Blum, Rick S. and Bhattacharya, Subhrajit},
  year = {2020},
  month = oct,
  abstract = {In this paper we consider the problem of transmission across a graph and how to effectively control/restrict it with limited resources. Transmission can represent information transfer across a social network, spread of a malicious virus across a computer network, or spread of an infectious disease across communities. The key insight is to assign proper weights to bottleneck edges of the graph based on their role in reducing the connection between two or more strongly-connected clusters within the graph. Selectively reducing the weights (implying reduced transmission rate) on the critical edges helps limit the transmission from one cluster to another. We refer to these as barrier weights and their computation is based on the eigenvectors of the graph Laplacian. Unlike other work on graph partitioning and clustering, we completely circumvent the associated computational complexities by assigning weights to edges instead of performing discrete graph cuts. This allows us to provide strong theoretical results on our proposed methods. We also develop approximations that allow low complexity distributed computation of the barrier weights using only neighborhood communication on the graph.},
  archivePrefix = {arXiv},
  eprint = {2010.04790},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Documents/Zotero/Zhang et al/Zhang et al_2020_Inter-cluster Transmission Control Using Graph Modal Barriers.pdf;/home/aleksandar/Zotero/storage/LDY3K9RU/2010.html},
  journal = {arXiv:2010.04790 [cs]},
  primaryClass = {cs}
}

@article{zhangJointOptimizationFramework2020,
  title = {A Joint Optimization Framework for Better Community Detection Based on Link Prediction in Social Networks},
  author = {Zhang, Shu-Kai and Li, Cheng-Te and Lin, Shou-De},
  year = {2020},
  month = jul,
  issn = {0219-3116},
  doi = {10.1007/s10115-020-01490-z},
  abstract = {Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities, in this paper, a joint optimization framework, Communities detected on Predicted Edges, is proposed. Our goal to improve the quality of community detection through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Link prediction and community detection are mutually reinforced to generate better results of both tasks. Experiments conducted on a number of well-known network data show that the proposed COPE stably outperforms several state-of-the-art community detection algorithms.},
  journal = {Knowledge and Information Systems},
  language = {en}
}

@article{zhangNetworkAnalysisGlobal,
  title = {A {{Network Analysis}} of {{Global Trust Across}} 11 {{Democratic Countries}}},
  author = {Zhang, Robert Jiqi and Liu, James H. and Brown, Gary and {Gil de Z{\'u}{\~n}iga}, Homero},
  doi = {10.1093/ijpor/edaa002},
  abstract = {Trust is seen as a glue in modern societies that facilitates economic prosperity and political functioning (e.g., Fukuyama, 1995; Newton, 2001; Putnam, 2000; se},
  file = {/home/aleksandar/Dropbox/Zotero/Zhang et al/Zhang et al_A Network Analysis of Global Trust Across 11 Democratic Countries.pdf},
  journal = {International Journal of Public Opinion Research},
  language = {en}
}

@article{zhangNetworkReconstructionBinarystate2020,
  title = {Network Reconstruction from Binary-State Time Series in Presence of Time Delay and Hidden Nodes},
  author = {Zhang, Yuhao and Li, Huaxun and Zhang, Zhaoyang and Qian, Yu and Pandey, Vikas},
  year = {2020},
  month = jun,
  issn = {0577-9073},
  doi = {10.1016/j.cjph.2020.06.009},
  abstract = {Complex networks with binary-state dynamics represent many meaningful behaviors in a variety of contexts. Reconstruction of networked systems hosting delayed binary processes with hidden nodes becomes an outstanding challenge in this field. To address this issue, we extend the statistical inference method to complex networked systems with distinct binary-state dynamics in presence of time delay and missing data. By exploiting the expectation-maximization (EM) algorithm, we implement the statistical inference based approach to different (i.e., random, small world, and scale-free) networks hosting delayed-binary processes. Our framework is completely data driven, and does not require any a prior knowledge about the detailed dynamical process on the network; especially, our method can independently infer each physical connectivity and estimate the time delay solely from the data of a pair of nodes in this link. We provide a physical understanding of the underlying mechanism; and extensive numerical simulations validate the robustness, efficiency, and accuracy of our method.},
  file = {/home/aleksandar/Documents/Zotero/Zhang et al/Zhang et al_2020_Network reconstruction from binary-state time series in presence of time delay.pdf;/home/aleksandar/Zotero/storage/RYUR4RYU/S0577907320301556.html},
  journal = {Chinese Journal of Physics},
  keywords = {DONE},
  language = {en}
}

@article{zhangRevealingStrongStable2020,
  title = {Revealing the Strong and Stable Community Structure in Social Networks},
  author = {Zhang, Xue and Luo, Zhi-Hong and Wu, Hui-Dong and Yang, Zhuo-Xuan and Ma, Pei-Yuan},
  year = {2020},
  month = aug,
  pages = {2050351},
  publisher = {{World Scientific Publishing Co.}},
  issn = {0217-9849},
  doi = {10.1142/S0217984920503510},
  abstract = {In a complex network, the global similarity standard is of high complexity and local similarity standard has biased error on node similarity. This paper puts forward a community discovering algorithm OBS, based on the degree of contribution of shared nodes in complex network. It distributes the contribution, using the degree of shared nodes as the unit, equally, which is made by shared nodes. Compared with average commute time standard, Random Walk with Restart standard and Hierarchical Structure Method standard, OBS standard has a more veracious forecast to the similar relationship between nodes. The experimental results of artificial network and typical complex network show that OBS algorithm has a better stability than GN, Fast-Newman (FN), and N-cut algorithms.},
  file = {/home/aleksandar/Zotero/storage/LCXTGCFT/S0217984920503510.html},
  journal = {Modern Physics Letters B}
}

@article{zhangStatisticalInferenceAssortative2020,
  title = {Statistical Inference of Assortative Community Structures},
  author = {Zhang, Lizhi and Peixoto, Tiago P.},
  year = {2020},
  month = jun,
  abstract = {We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model. We show that this approach succeeds in finding statistically significant assortative modules in networks, unlike alternatives such as modularity maximization, which systematically overfits both in artificial as well as in empirical examples. In addition, we show that our method is not subject to a resolution limit, and can uncover an arbitrarily large number of communities, as long as there is statistical evidence for them. Our formulation is amenable to model selection procedures, which allow us to compare it to more general approaches based on the stochastic block model, and in this way reveal whether assortativity is in fact the dominating large-scale mixing pattern. We perform this comparison with several empirical networks, and identify numerous cases where the network's assortativity is exaggerated by traditional community detection methods, and we show how a more faithful degree of assortativity can be identified.},
  archivePrefix = {arXiv},
  eprint = {2006.14493},
  eprinttype = {arxiv},
  file = {/home/aleksandar/Dropbox/Zotero/Zhang_Peixoto/Zhang_Peixoto_2020_Statistical inference of assortative community structures.pdf;/home/aleksandar/Zotero/storage/D6FMYCFM/2006.html},
  journal = {arXiv:2006.14493 [cond-mat, physics:physics, stat]},
  primaryClass = {cond-mat, physics:physics, stat}
}

@article{zhaoBayesianEmpiricalLikelihood,
  ids = {zhaoBayesianEmpiricalLikelihooda},
  title = {Bayesian Empirical Likelihood Inference with Complex Survey Data},
  author = {Zhao, Puying and Ghosh, Malay and Rao, J. N. K. and Wu, Changbao},
  doi = {10.1111/rssb.12342},
  abstract = {{$<$}p{$>$}We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically valid design-based frequentist properties under single-stage unequal probability sampling designs with small sampling fractions. Large sample properties of the Bayesian inference proposed are established for both non-informative and informative priors under the design-based framework. We also propose a Bayesian model selection procedure with complex survey data and show that it works for general sampling designs. An efficient Markov chain Monte Carlo procedure is described for the required computation of the posterior distribution for general vector parameters. Simulation studies and an application to a real survey data set are included to examine the finite sample performances of the methods proposed as well as the effect of different types of prior and different types of sampling design.{$<$}/p{$>$}},
  journal = {Journal of the Royal Statistical Society. Series B: Statistical Methodology}
}

@misc{zhaoRankingInfluentialNodes2020,
  title = {Ranking {{Influential Nodes}} in {{Complex Networks}} with {{Information Entropy Method}}},
  author = {Zhao, Nan and Bao, Jingjing and Chen, Nan},
  year = {2020},
  month = jun,
  volume = {2020},
  pages = {e5903798},
  publisher = {{Hindawi}},
  issn = {1076-2787},
  doi = {10.1155/2020/5903798},
  abstract = {The ranking of influential nodes in networks is of great significance. Influential nodes play an enormous role during the evolution process of information dissemination, viral marketing, and public opinion control. The sorting method of multiple attributes is an effective way to identify the influential nodes. However, these methods offer a limited improvement in algorithm performance because diversity between different attributes is not properly considered. On the basis of the k-shell method, we propose an improved multiattribute k-shell method by using the iterative information in the decomposition process. Our work combines sigmod function and iteration information to obtain the position index. The position attribute is obtained by combining the shell value and the location index. The local information of the node is adopted to obtain the neighbor property. Finally, the position attribute and neighbor attribute are weighted by the method of information entropy weighting. The experimental simulations in six real networks combined with the SIR model and other evaluation measure fully verify the correctness and effectiveness of the proposed method.},
  file = {/home/aleksandar/Documents/Zotero/Zhao et al/Zhao et al_2020_Ranking Influential Nodes in Complex Networks with Information Entropy Method.pdf;/home/aleksandar/Zotero/storage/NVK6ENS5/5903798.html},
  howpublished = {https://www.hindawi.com/journals/complexity/2020/5903798/},
  journal = {Complexity},
  keywords = {DONE},
  language = {en},
  type = {Research {{Article}}}
}

@article{zhouFindingLargeDiverse2020,
  title = {Finding Large Diverse Communities on Networks: The Edge Maximum k {\textsuperscript{*}} -Partite Clique},
  shorttitle = {Finding Large Diverse Communities on Networks},
  author = {Zhou, Alexander and Wang, Yue and Chen, Lei},
  year = {2020},
  month = aug,
  volume = {13},
  pages = {2576--2589},
  issn = {2150-8097},
  doi = {10.14778/3407790.3407846},
  abstract = {In this work we examine the problem of finding large, diverse communities on graphs where the users are separated into distinct groups. More specifically, this work considers diversity to be the inclusion of users from multiple groups as opposed to homogeneous communities in which the majority of users are from one group. We design and propose the k{${_\ast}$}-Partite Clique (and the edge-maximum k{${_\ast}$}-Partite Clique Problem) which modifies the k-Partite Clique structure as a means to capture these large, diverse communities in a way that does not currently exist. We then design a non-trivial baseline enumeration algorithm, which is further improved via heuristics to significantly reduce the running time whilst avoiding excessive memory requirements. Moreover, we propose a core as well as a truss structure for the kPartite environment aimed at finding the edge-maximum k{${_\ast}$}Partite Clique structure on the network. Comprehensive experiments on real-world datasets verify both the effectiveness of the k{${_\ast}$}-Partite Clique at finding diverse communities as well as the efficiency of the proposed heuristics to our algorithms compared to reasonable baselines.},
  file = {/home/aleksandar/Zotero/storage/ECFPAPNM/Zhou et al. - 2020 - Finding large diverse communities on networks the.pdf},
  journal = {Proceedings of the VLDB Endowment},
  language = {en},
  number = {12}
}

@article{zhouHierarchicalItemResponse2019,
  ids = {zhouHierarchicalItemResponse,zhouHierarchicalItemResponse2019a,zhouHierarchicalItemResponsea,zhouHierarchicalItemResponseb},
  title = {Hierarchical {{Item Response Models}} for {{Analyzing Public Opinion}}},
  author = {Zhou, Xiang},
  year = {2019},
  month = oct,
  volume = {27},
  pages = {481--502},
  issn = {1047-1987, 1476-4989},
  doi = {10.1017/pan.2018.63},
  abstract = {Opinion surveys often employ multiple items to measure the respondent's underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package,~hIRT, is available for fitting the proposed models.},
  journal = {Political Analysis},
  language = {en},
  number = {4}
}

@article{zhouPowerlawDistributionDegree2020,
  title = {Power-Law Distribution of Degree\textendash Degree Distance: {{A}} Better Representation of the Scale-Free Property of Complex Networks},
  shorttitle = {Power-Law Distribution of Degree\textendash Degree Distance},
  author = {Zhou, Bin and Meng, Xiangyi and Stanley, H. Eugene},
  year = {2020},
  month = jun,
  publisher = {{National Academy of Sciences}},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1918901117},
  abstract = {Whether real-world complex networks are scale free or not has long been controversial. Recently, in Broido and Clauset [A. D. Broido, A. Clauset, Nat. Commun. 10, 1017 (2019)], it was claimed that the degree distributions of real-world networks are rarely power law under statistical tests. Here, we attempt to address this issue by defining a fundamental property possessed by each link, the degree\textendash degree distance, the distribution of which also shows signs of being power law by our empirical study. Surprisingly, although full-range statistical tests show that degree distributions are not often power law in real-world networks, we find that in more than half of the cases the degree\textendash degree distance distributions can still be described by power laws. To explain these findings, we introduce a bidirectional preferential selection model where the link configuration is a randomly weighted, two-way selection process. The model does not always produce solid power-law distributions but predicts that the degree\textendash degree distance distribution exhibits stronger power-law behavior than the degree distribution of a finite-size network, especially when the network is dense. We test the strength of our model and its predictive power by examining how real-world networks evolve into an overly dense stage and how the corresponding distributions change. We propose that being scale free is a property of a complex network that should be determined by its underlying mechanism (e.g., preferential attachment) rather than by apparent distribution statistics of finite size. We thus conclude that the degree\textendash degree distance distribution better represents the scale-free property of a complex network.},
  chapter = {Physical Sciences},
  copyright = {\textcopyright{} 2020 . https://www.pnas.org/site/aboutpnas/licenses.xhtmlPublished under the PNAS license.},
  file = {/home/aleksandar/Documents/Zotero/Zhou et al/Zhou et al_2020_Power-law distribution of degree–degree distance.pdf;/home/aleksandar/Zotero/storage/WGAXGWR5/1918901117.html},
  journal = {Proceedings of the National Academy of Sciences},
  keywords = {DONE},
  language = {en},
  pmid = {32541015}
}

@article{zhouRegressionwithResidualsMethodEstimating,
  ids = {zhouRegressionwithResidualsMethodEstimatinga},
  title = {A {{Regression}}-with-{{Residuals Method}} for {{Estimating Controlled Direct Effects}}},
  author = {Zhou, Xiang and Wodtke, Geoffrey T.},
  doi = {10.1017/pan.2018.53},
  abstract = {Political scientists are increasingly interested in causal mediation, and to this end, recent studies focus on estimating a quantity called the controlled direct effect (CDE). The CDE measures the strength of the causal relationship between a treatment and outcome when a mediator is fixed at a given value. To estimate the CDE, Joffe and Greene ({$<$}a href="\#r7"{$>$}2009{$<$}/a{$>$}) and Vansteelandt ({$<$}a href="\#r15"{$>$}2009{$<$}/a{$>$}) developed the method of sequential g-estimation, which was introduced to political science by Acharya, Blackwell, and Sen ({$<$}a href="\#r1"{$>$}2016{$<$}/a{$>$}). In this letter, we propose an alternative method called "regression-with-residuals" (RWR) for estimating the CDE. In special cases, we show that these two methods are algebraically equivalent. Yet, unlike sequential g-estimation, RWR can easily accommodate several types of effect moderation, including cases in which the effect of the mediator on the outcome is moderated by a posttreatment confounder. Although common in the social sciences, this type of effect moderation is typically assumed away in applications of sequential g-estimation, which may lead to bias if effect moderation is in fact present. We illustrate RWR by estimating the CDE of negative media framing on public support for immigration, controlling for respondent anxiety.},
  journal = {Political Analysis}
}

@article{zhuCooperationCompetitionInformation2020,
  title = {Cooperation and {{Competition}} among Information on Social Networks},
  author = {Zhu, Zhiqiang and Gao, Chang and Zhang, Yumeng and Li, Hainan and Xu, Jin and Zan, Yongli and Li, Zhi},
  year = {2020},
  month = jul,
  volume = {10},
  pages = {12160},
  publisher = {{Nature Publishing Group}},
  issn = {2045-2322},
  doi = {10.1038/s41598-020-69098-5},
  abstract = {When multiple information are spread on social networks, there may be Cooperation and Competition among these information. Based on a new spreading model of multiple information, we studied Cooperation and Competition in information spreading, and analyzed the influence of different factors on Cooperation and Competition. Through a large number of computer simulation experiments, we found that: (1) when multiple information are spread on social networks, there is Cooperation and Competition among these information; (2) the smaller the distance between two information sources is, the stronger the Cooperation and Competition among these information are; (3) the greater the value of social reinforcement is, the stronger the Cooperation and Competition among these information are; (4) the weaker the human heterogeneity of one information is, the stronger the Cooperation and Competition among this information and other information are.},
  copyright = {2020 The Author(s)},
  file = {/home/aleksandar/Dropbox/Zotero/Zhu et al/Zhu et al_2020_Cooperation and Competition among information on social networks.pdf;/home/aleksandar/Zotero/storage/9Q3958PQ/s41598-020-69098-5.html},
  journal = {Scientific Reports},
  language = {en},
  number = {1}
}

@article{zhuDynamicsAnalysisRumor,
  ids = {zhuDynamicsAnalysisRumora},
  title = {The Dynamics Analysis of a Rumor Propagation Model in Online Social Networks},
  author = {Zhu, Linhe and Liu, Mengxue and Li, Yimin},
  doi = {S037843711930007X},
  abstract = {{$<$}p{$>$}Publication date: Available online 11 January 2019{$<$}/p{$><$}p{$>$}\textbf{Source:} Physica A: Statistical Mechanics and its Applications{$<$}/p{$><$}p{$>$}Author(s): Linhe Zhu, Mengxue Liu, Yimin Li{$<$}/p{$><$}h5{$>$}Abstract{$<$}/h5{$><$}div{$><$}p{$>$}With rapid emergence of online social networks, the rumor transition has a growing significant impact on social stability and human lives. This paper investigates the local stability and optimal control of a rumor propagation model in online social network with changing number of total users, which is proposed in Ref. Dong et~al. (2018). In addition, we set up a model with a delay based on the reality, and analyse the sufficient conditions for Hopf bifurcation. Finally, the rumor spreading model is simulated numerically. The results of simulation are shown the process of rumor propagation and prove the correctness of the theoretical analysis.{$<$}/p{$><$}/div{$>$}},
  journal = {Physica A: Statistical Mechanics and its Applications}
}

@article{zhuUnderstandingStructureRisk2020,
  title = {Understanding the Structure of Risk Belief Systems Concerning Drone Delivery: {{A}} Network Analysis},
  shorttitle = {Understanding the Structure of Risk Belief Systems Concerning Drone Delivery},
  author = {Zhu, Xun and Pasch, Timothy and Bergstrom, Aaron},
  year = {2020},
  month = apr,
  pages = {101262},
  issn = {0160-791X},
  doi = {10.1016/j.techsoc.2020.101262},
  abstract = {While e-commerce industries envision drones as a promising solution to the challenges associated with last-mile logistics of product delivery, there is increasing public concern over the risks of these airborne innovations. A growing number of studies seek to gauge the public's risk beliefs about drone delivery and inform policy development before its widespread uptake. Complicating these efforts is the fact that beliefs are interconnected and embedded in a cognitive system. This article argues that public outcomes are not based on atomized and isolated beliefs about risks involving in drone delivery, but instead emerge from the patterns in the relationships among these expressed beliefs. However, little is known about the structural characteristics of risk belief systems related to drone delivery, and the implications for risk communication and management. In an effort to fill this void, we conducted a network analysis of risk beliefs about drone delivery based on nationally representative data (N = 1465). The results revealed structural connections among 11 risk beliefs and explored structural variations in the risk belief system between people with different attitudes toward drone delivery. The simulation results showed that risk-mitigating messages instigated greater changes to the public's risk perceptions when they targeted structurally central, as opposed to peripheral, risk beliefs. The reported evidence adds new insights into how risks about drone delivery are configured in the public consciousness, and provides guidance into how practitioners may mobilize structural leverage to optimize the effectiveness and efficiency of risk communication strategies.},
  journal = {Technology in Society},
  language = {en}
}

@article{zieglerPropagationModelsTrust2005,
  title = {Propagation {{Models}} for {{Trust}} and {{Distrust}} in {{Social Networks}}},
  author = {Ziegler, C. and Lausen, G.},
  year = {2005},
  doi = {10.1007/s10796-005-4807-3},
  abstract = {Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements.},
  file = {/home/aleksandar/Zotero/storage/3TGKEDIT/Ziegler_Lausen_2005_Propagation Models for Trust and Distrust in Social Networks.pdf},
  journal = {Inf. Syst. Frontiers}
}

@article{zielinskiTranslatingSocialCleavages2002,
  title = {Translating Social Cleavages into Party Systems: {{The}} Significance of New Democracies},
  shorttitle = {Translating {{Social Cleavages}} into {{Party Systems}}},
  author = {Zielinski, Jakub},
  year = {2002},
  month = jan,
  volume = {54},
  pages = {184--211},
  issn = {0043-8871, 1086-3338},
  doi = {10.1353/wp.2002.0005},
  journal = {World Politics},
  language = {en},
  number = {02}
}

@article{zielonkaQualityDemocracyJoining2007,
  title = {The Quality of Democracy after Joining the {{European Union}}},
  author = {Zielonka, J.},
  year = {2007},
  month = feb,
  volume = {21},
  pages = {162--180},
  issn = {0888-3254},
  doi = {10.1177/0888325406297133},
  journal = {East European Politics \& Societies},
  language = {en},
  number = {1}
}

@techreport{zmigrodPsychologicalSociopoliticalConsequences2020,
  title = {The {{Psychological}} and {{Socio}}-Political {{Consequences}} of {{Infectious Diseases}}},
  author = {Zmigrod, Leor and Ebert, Tobias and G{\"o}tz, Friedrich Martin and Rentfrow, Jason},
  year = {2020},
  month = apr,
  institution = {{PsyArXiv}},
  doi = {10.31234/osf.io/84qcm},
  abstract = {What are the socio-political consequences of infectious diseases? Humans have evolved to avoid disease and infection, resulting in a set of psychological mechanisms that promote disease-avoidance, referred to as the behavioural immune system (BIS). One manifestation of the BIS is the cautious avoidance of unfamiliar, foreign, or potentially contaminating stimuli. Specifically, when disease infection risk is salient or prevalent, authoritarian attitudes can emerge that seek to avoid and reject foreign outgroups while favoring homogenous, familiar ingroups. In the largest study conducted on the topic to date (N{$>$}240,000), elevated regional levels of infectious pathogens were related to more authoritarian attitudes on three geographical levels: across US metropolitan regions, US states, and cross-culturally across 47 countries. The link between pathogen prevalence and authoritarian psychological dispositions predicted conservative voting behavior in the 2016 US Presidential Election as well as more authoritarian governance and state laws, in which one group of people imposes asymmetrical laws on others in a hierarchical structure. Furthermore, cross-cultural analysis illustrated that the relationship between infectious diseases and authoritarianism was pronounced for infectious diseases that can be acquired from other humans (nonzoonotic), and does not generalize to other infectious diseases that can only be acquired from non-human species (zoonotic diseases). At a time of heightened awareness of infectious diseases, these findings are important reminders that public health and ecology can have ramifications for socio-political attitudes, and this can shape how citizens vote as well as how they govern and are governed.},
  file = {/home/aleksandar/Dropbox/Zotero/Zmigrod et al/Zmigrod et al_2020_The Psychological and Socio-political Consequences of Infectious Diseases.pdf},
  language = {en},
  type = {Preprint}
}

@article{zolaInteractionStrengthAnalysis2020,
  title = {Interaction Strength Analysis to Model Retweet Cascade Graphs},
  author = {Zola, Paola and Cola, Guglielmo and Mazza, Michele and Tesconi, Maurizio},
  year = {2020},
  pages = {17},
  abstract = {Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph, which is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users' interactions. Initial findings show the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.},
  file = {/home/aleksandar/Zotero/storage/25C3TG4F/Zola et al. - 2020 - Interaction strength analysis to model retweet cas.pdf},
  language = {en}
}

@article{zouAdaptiveLassoIts2006,
  title = {The Adaptive {{Lasso}} and Its Oracle Properties},
  author = {Zou, Hui},
  year = {2006},
  month = dec,
  volume = {101},
  pages = {1418--1429},
  issn = {0162-1459, 1537-274X},
  doi = {10.1198/016214506000000735},
  journal = {Journal of the American Statistical Association},
  language = {en},
  number = {476}
}

@article{zschalerFamilyLanguagesTrustworthy2020,
  title = {A {{Family}} of {{Languages}} for {{Trustworthy Agent}}-{{Based Simulation}}},
  author = {Zschaler, Steffen and Polack, Fiona A C},
  year = {2020},
  pages = {6},
  abstract = {Simulation is a key tool for researching complex system behaviour. Agent-based simulation has been applied across domains, such as biology, health, economics and urban sciences. However, engineering robust, efficient, maintainable, and reliable agent-based simulations is challenging. We present a vision for engineering agent simulations comprising a family of domain-specific modelling languages (DSMLs) that integrates core software engineering, validation and simulation experimentation. We relate the vision to examples of principled simulation, to show how the DSMLs would improve robustness, efficiency, and maintainability of simulations. Focusing on how to demonstrate the fitness for purpose of a simulator, the envisaged approach supports bi-directional transparency and traceability between the original domain understanding to the implementation, interpretation of results and evaluation of hypotheses.},
  file = {/home/aleksandar/Zotero/storage/FEJAG8QV/Zschaler and Polack - 2020 - A Family of Languages for Trustworthy Agent-Based .pdf},
  language = {en}
}

license: MIT

repository: atomashevic/website-at

license_type: permissive

repo_name: atomashevic/website-at

branch_name: refs/heads/master

extension: bib


text:
package brandhub

// TaobaoBrandhubSpecialshowRptAdgroupGetResult 结构体
type TaobaoBrandhubSpecialshowRptAdgroupGetResult struct {
	// 日期
	Thedate string `json:"thedate,omitempty" xml:"thedate,omitempty"`
	// 计划名称
	SolutionName string `json:"solution_name,omitempty" xml:"solution_name,omitempty"`
	// 定向条件
	TargetName string `json:"target_name,omitempty" xml:"target_name,omitempty"`
	// 单元名称
	TaskName string `json:"task_name,omitempty" xml:"task_name,omitempty"`
	// 展现
	Impression string `json:"impression,omitempty" xml:"impression,omitempty"`
	// 点击
	Click string `json:"click,omitempty" xml:"click,omitempty"`
	// 访客数
	Uv string `json:"uv,omitempty" xml:"uv,omitempty"`
	// 点击访客数
	ClickUv string `json:"click_uv,omitempty" xml:"click_uv,omitempty"`
	// 点击率
	Ctr string `json:"ctr,omitempty" xml:"ctr,omitempty"`
	// 访客点击率
	UvCtr string `json:"uv_ctr,omitempty" xml:"uv_ctr,omitempty"`
	// 计划id
	SolutionId int64 `json:"solution_id,omitempty" xml:"solution_id,omitempty"`
	// 单元id
	TaskId int64 `json:"task_id,omitempty" xml:"task_id,omitempty"`
}

repository: bububa/opentaobao

repo_name: bububa/opentaobao

branch_name: refs/heads/main

gha_language: Go

extension: go


text:
SUMMARY = "A Python implementation of Recursive Length Prefix encoding (RLP)."
HOMEPAGE = "https://github.com/ethereum/pyrlp"
SECTION = "devel/python"
LICENSE = "MIT"
LIC_FILES_CHKSUM = "file://LICENSE;md5=00854fa80a84236706b11f47f23e61e7"

SRC_URI[sha256sum] = "63b0465d2948cd9f01de449d7adfb92d207c1aef3982f20310f8009be4a507e8"
SRC_URI += "file://0001-setup-don-t-use-setuptools-markdown.patch"

inherit pypi setuptools3

DEPENDS += "python3-pip-native"

RDEPENDS:${PN} += "python3-eth-utils"

repository: openembedded/meta-openembedded

repo_name: openembedded/meta-openembedded

gha_license_id: MIT

gha_language: BitBake

extension: bb


text:
require firmware.inc
require firmiszip.inc

ARM_INSTRUCTION_SET = "arm"

PR = "r0"
# T-CHL7DEUC
DL_URI = "http://downloadcenter.samsung.com/content/FM/200911/20091127100816015/${MACHINE}.exe"

# S = "${WORKDIR}
P_OFFSET = "3158558"
P_LINE = ";/mtd_rwarea/rcSGO     &"
require firmware-package.bbclass

repository: metalsky/SamyGO

repo_name: metalsky/SamyGO


text:
package main

import (
	"fmt"
)

/*
要求：

解题思路：
回溯算法,当某一次形成长度=k的组合后,回溯到之前k-1的长度并继续迭代形成新的组合,当此次k-1的迭代完成后,回溯到长度=k-2继续迭代

关键点：

*/

func combine(n int, k int) [][]int { // faster 65.64% less 100%
	var res [][]int
	c(&res, []int{}, 1, n, k)
	return res
}

func c(res *[][]int, coms []int, start int, n int, k int) {
	if k == 0 {
		tmp := []int{}
		tmp = append(tmp, coms...)
		*res = append(*res, tmp)
		return
	}
	for i := start; i <= n; i++ {
		coms = append(coms, i)
		fmt.Println("1111111111 coms = ", coms)
		c(res, coms, i+1, n, k-1)
		// 回溯
		coms = coms[:len(coms)-1]
		fmt.Println("22222 coms = ", coms)
	}
}

func main() {
	n := 4
	k := 2
	result := combine(n, k)
	fmt.Println(result)
}


text:
require update-alternatives-cworth.inc
inherit native

PROVIDES += "virtual/update-alternatives-native"

PR = "${INC_PR}.0"

do_stage () {
    install -d ${sbindir} \
               ${libdir}/opkg/alternatives

    install -m 0755 update-alternatives ${sbindir}/update-alternatives
}

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD


text:
# Copyright 2017 saehie.park@gmail.com
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

#
# option for building
#

# platform in lower case
string(TOLOWER ${CMAKE_SYSTEM_NAME} PLATFORM_NAME_L)

# redirect if user sets output folder to specific place
if(DEFINED TARGET_OUTPUT)
  set(PROJ_LIB_OUT ${TARGET_OUTPUT})
  set(PROJ_BIN_OUT ${TARGET_OUTPUT})
else()
  set(_PROJ_BIN_ROOT ${CMAKE_BINARY_DIR})
  set(PROJ_LIB_OUT "${_PROJ_BIN_ROOT}/../.")
  set(PROJ_BIN_OUT "${_PROJ_BIN_ROOT}/../.")
endif()

# test
enable_testing()

# google test
set(GTEST_INC "${CMAKE_SOURCE_DIR}/external/googletest/googletest/include")
set(GTEST_LIBS gtest gtest_main)

# platform specific options
include("cmake/option/option_${TARGET_PLATFORM}.cmake")

# add user libsomething
set(PROJ_INCLUDES ${PROJ_INCLUDES} ${PROJ_USER_INCDIR})
set(PROJ_LIBPATHS ${PROJ_LIBPATHS} ${PROJ_USER_LIBDIR})
set(PROJ_LIBS     ${PROJ_LIBS}     ${PROJ_USER_LIBNAME})

# add common flags
foreach(FLAG ${FLAGS_COMMON})
  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${FLAG}")
  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAG}")
endforeach()

# add c flags
foreach(FLAG ${FLAGS_CONLY})
  set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${FLAG}")
endforeach()

# add cxx flags
foreach(FLAG ${FLAGS_CXXONLY})
  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAG}")
endforeach()

# ignore warings
set(ignoreUnusedWarnings "${CMAKE_TOOLCHAIN_FILE}")

repository: seanshpark/cmaketemplate

repo_name: seanshpark/cmaketemplate

extension: cmake


text:
package core

import (
	"context"
	"fmt"
	"net"
	"reflect"
	"strings"
	"time"

	"github.com/zdnscloud/zke/core/authz"
	"github.com/zdnscloud/zke/core/pki"
	"github.com/zdnscloud/zke/core/pki/cert"
	"github.com/zdnscloud/zke/core/services"
	"github.com/zdnscloud/zke/pkg/docker"
	"github.com/zdnscloud/zke/pkg/hosts"
	"github.com/zdnscloud/zke/pkg/k8s"
	"github.com/zdnscloud/zke/pkg/log"
	"github.com/zdnscloud/zke/pkg/util"
	"github.com/zdnscloud/zke/types"

	dockertypes "github.com/docker/docker/api/types"
	"github.com/zdnscloud/cement/errgroup"
	"github.com/zdnscloud/gok8s/client/config"
	"gopkg.in/yaml.v2"
	"k8s.io/api/core/v1"
	"k8s.io/client-go/kubernetes"
)

type Cluster struct {
	types.ZKEConfig      `yaml:",inline"`
	KubeConfig           string
	AuthnStrategies      map[string]bool
	Certificates         map[string]pki.CertificatePKI
	DockerDialerFactory  hosts.DialerFactory
	K8sWrapTransport     k8s.WrapTransport
	KubeClient           *kubernetes.Clientset
	KubernetesServiceIP  net.IP
	PrivateRegistriesMap map[string]types.PrivateRegistry
	ControlPlaneHosts    []*hosts.Host
	EtcdHosts            []*hosts.Host
	EtcdReadyHosts       []*hosts.Host
	InactiveHosts        []*hosts.Host
	WorkerHosts          []*hosts.Host
	EdgeHosts            []*hosts.Host
}

const (
	AuthnX509Provider       = "x509"
	AuthnWebhookProvider    = "webhook"
	StateConfigMapName      = "cluster-state"
	ClusterConfigMapName    = "cluster-config"
	UpdateStateTimeout      = 30
	GetStateTimeout         = 30
	KubernetesClientTimeOut = 30
	NoneAuthorizationMode   = "none"
	LocalNodeAddress        = "127.0.0.1"
	LocalNodeHostname       = "localhost"
	LocalNodeUser           = "root"
	ControlPlane            = "controlPlane"
	WorkerPlane             = "workerPlan"
	EtcdPlane               = "etcd"

	KubeAppLabel = "k8s-app"
	AppLabel     = "app"
	NameLabel    = "name"
)

func (c *Cluster) DeployControlPlane(ctx context.Context) error {
	select {
	case <-ctx.Done():
		return util.CancelErr
	default:
		// Deploy Etcd Plane
		etcdNodePlanMap := make(map[string]types.ZKENodePlan)
		// Build etcd node plan map
		for _, etcdHost := range c.EtcdHosts {
			etcdNodePlanMap[etcdHost.Address] = BuildZKEConfigNodePlan(ctx, c, etcdHost, etcdHost.DockerInfo)
		}
		if len(c.Core.Etcd.ExternalURLs) > 0 {
			log.Infof(ctx, "[etcd] External etcd connection string has been specified, skipping etcd plane")
		} else {
			if err := services.RunEtcdPlane(ctx, c.EtcdHosts, etcdNodePlanMap, c.PrivateRegistriesMap, c.Image.Alpine, c.Core.Etcd, c.Certificates); err != nil {
				return fmt.Errorf("[etcd] Failed to bring up Etcd Plane: %v", err)
			}
		}
		// Deploy Control plane
		cpNodePlanMap := make(map[string]types.ZKENodePlan)
		// Build cp node plan map
		for _, cpHost := range c.ControlPlaneHosts {
			cpNodePlanMap[cpHost.Address] = BuildZKEConfigNodePlan(ctx, c, cpHost, cpHost.DockerInfo)
		}
		if err := services.RunControlPlane(ctx, c.ControlPlaneHosts,
			c.PrivateRegistriesMap,
			cpNodePlanMap,
			c.Image.Alpine,
			c.Certificates); err != nil {
			return fmt.Errorf("[controlPlane] Failed to bring up Control Plane: %v", err)
		}
		return nil
	}
}

func (c *Cluster) DeployWorkerPlane(ctx context.Context) error {
	select {
	case <-ctx.Done():
		return util.CancelErr
	default:
		// Deploy Worker plane
		workerNodePlanMap := make(map[string]types.ZKENodePlan)
		// Build cp node plan map
		allHosts := hosts.GetUniqueHostList(c.EtcdHosts, c.ControlPlaneHosts, c.WorkerHosts, c.EdgeHosts)
		for _, workerHost := range allHosts {
			workerNodePlanMap[workerHost.Address] = BuildZKEConfigNodePlan(ctx, c, workerHost, workerHost.DockerInfo)
		}
		if err := services.RunWorkerPlane(ctx, allHosts,
			c.PrivateRegistriesMap,
			workerNodePlanMap,
			c.Certificates,
			c.Image.Alpine); err != nil {
			return fmt.Errorf("[workerPlane] Failed to bring up Worker Plane: %v", err)
		}
		return nil
	}
}

func ParseConfig(ctx context.Context, clusterFile string) (*types.ZKEConfig, error) {
	log.Debugf(ctx, "Parsing cluster file [%v]", clusterFile)
	var zkeConfig types.ZKEConfig
	if err := yaml.Unmarshal([]byte(clusterFile), &zkeConfig); err != nil {
		return nil, err
	}
	return &zkeConfig, nil
}

func InitClusterObject(ctx context.Context, zkeConfig *types.ZKEConfig) (*Cluster, error) {
	select {
	case <-ctx.Done():
		return nil, util.CancelErr
	default:
		// basic cluster object from zkeConfig
		c := &Cluster{
			AuthnStrategies:      make(map[string]bool),
			ZKEConfig:            *zkeConfig,
			PrivateRegistriesMap: make(map[string]types.PrivateRegistry),
		}

		// Setting cluster Defaults
		err := c.setClusterDefaults(ctx)
		if err != nil {
			return nil, err
		}
		// set cluster kubernetesService ip
		c.KubernetesServiceIP, err = pki.GetKubernetesServiceIP(c.Core.KubeAPI.ServiceClusterIPRange)
		if err != nil {
			return c, fmt.Errorf("Failed to get Kubernetes Service IP: %v", err)
		}

		// set hosts groups
		if err := c.InvertIndexHosts(ctx); err != nil {
			return nil, fmt.Errorf("Failed to classify hosts from config file: %v", err)
		}
		// validate cluster configuration
		if err := c.ValidateCluster(); err != nil {
			return nil, fmt.Errorf("Failed to validate cluster: %v", err)
		}
		return c, nil
	}
}

func (c *Cluster) SetupDialers(ctx context.Context, dailersOptions hosts.DialersOptions) error {
	select {
	case <-ctx.Done():
		return util.CancelErr
	default:
		c.DockerDialerFactory = dailersOptions.DockerDialerFactory
		c.K8sWrapTransport = dailersOptions.K8sWrapTransport
		return nil
	}
}

func RebuildKubeconfig(ctx context.Context, kubeCluster *Cluster, clusterState *FullState) error {
	return rebuildLocalAdminConfig(ctx, kubeCluster)
}

func rebuildLocalAdminConfig(ctx context.Context, kubeCluster *Cluster) error {
	if len(kubeCluster.ControlPlaneHosts) == 0 {
		return nil
	}
	log.Infof(ctx, "[reconcile] Rebuilding and updating local kube config")
	var workingConfig, newConfig string
	currentKubeConfig := kubeCluster.Certificates[pki.KubeAdminCertName]
	caCrt := kubeCluster.Certificates[pki.CACertName].Certificate
	for _, cpHost := range kubeCluster.ControlPlaneHosts {
		kubeURL := fmt.Sprintf("https://%s:6443", cpHost.Address)
		caData := string(cert.EncodeCertPEM(caCrt))
		crtData := string(cert.EncodeCertPEM(currentKubeConfig.Certificate))
		keyData := string(cert.EncodePrivateKeyPEM(currentKubeConfig.Key))
		newConfig = pki.GetKubeConfigX509WithData(kubeURL, kubeCluster.ClusterName, pki.KubeAdminCertName, caData, crtData, keyData)

		workingConfig = newConfig
		kubeConfig, err := config.BuildConfig([]byte(newConfig))
		if err != nil {
			return err
		}
		kubeClientSet, err := kubernetes.NewForConfig(kubeConfig)
		if err != nil {
			return err
		}
		kubeCluster.KubeClient = kubeClientSet
		if _, err := GetK8sVersion(ctx, kubeClientSet); err == nil {
			log.Infof(ctx, "[reconcile] host [%s] is active master on the cluster", cpHost.Address)
			break
		}
	}
	currentKubeConfig.Config = workingConfig
	kubeCluster.Certificates[pki.KubeAdminCertName] = currentKubeConfig

	return nil
}

func getLocalConfigAddress(kubeConfigYaml string) (string, error) {
	config, err := config.BuildConfig([]byte(kubeConfigYaml))
	if err != nil {
		return "", err
	}
	splittedAdress := strings.Split(config.Host, ":")
	address := splittedAdress[1]
	return address[2:], nil
}

func getLocalAdminConfigWithNewAddress(kubeConfigYaml, cpAddress string, clusterName string) string {
	config, err := config.BuildConfig([]byte(kubeConfigYaml))
	if err != nil {
		return ""
	}
	config.Host = fmt.Sprintf("https://%s:6443", cpAddress)
	return pki.GetKubeConfigX509WithData(
		"https://"+cpAddress+":6443",
		clusterName,
		pki.KubeAdminCertName,
		string(config.CAData),
		string(config.CertData),
		string(config.KeyData))
}

func ApplyAuthzResources(ctx context.Context, zkeConfig types.ZKEConfig, k8sClient *kubernetes.Clientset, dailersOptions hosts.DialersOptions) error {
	select {
	case <-ctx.Done():
		return util.CancelErr
	default:
		// dialer factories are not needed here since we are not uses docker only k8s jobs
		kubeCluster, err := InitClusterObject(ctx, &zkeConfig)
		if err != nil {
			return err
		}
		if err := kubeCluster.SetupDialers(ctx, dailersOptions); err != nil {
			return err
		}
		if len(kubeCluster.ControlPlaneHosts) == 0 {
			return nil
		}
		if err := authz.ApplyJobDeployerServiceAccount(ctx, k8sClient); err != nil {
			return fmt.Errorf("Failed to apply the ServiceAccount needed for job execution: %v", err)
		}
		if kubeCluster.Authorization.Mode == NoneAuthorizationMode {
			return nil
		}
		if kubeCluster.Authorization.Mode == services.RBACAuthorizationMode {
			if err := authz.ApplySystemNodeClusterRoleBinding(ctx, k8sClient); err != nil {
				return fmt.Errorf("Failed to apply the ClusterRoleBinding needed for node authorization: %v", err)
			}
		}
		if kubeCluster.Authorization.Mode == services.RBACAuthorizationMode && kubeCluster.Core.KubeAPI.PodSecurityPolicy {
			if err := authz.ApplyDefaultPodSecurityPolicy(ctx, k8sClient); err != nil {
				return fmt.Errorf("Failed to apply default PodSecurityPolicy: %v", err)
			}
			if err := authz.ApplyDefaultPodSecurityPolicyRole(ctx, k8sClient); err != nil {
				return fmt.Errorf("Failed to apply default PodSecurityPolicy ClusterRole and ClusterRoleBinding: %v", err)
			}
		}
		return nil
	}
}

func (c *Cluster) SyncLabelsAndTaints(ctx context.Context, currentCluster *Cluster) error {
	select {
	case <-ctx.Done():
		return util.CancelErr
	default:
		if currentCluster != nil {
			cpToDelete := hosts.GetToDeleteHosts(currentCluster.ControlPlaneHosts, c.ControlPlaneHosts, c.InactiveHosts)
			if len(cpToDelete) == len(currentCluster.ControlPlaneHosts) {
				log.Infof(ctx, "[sync] Cleaning left control plane nodes from reconcilation")
				for _, toDeleteHost := range cpToDelete {
					if err := cleanControlNode(ctx, c, currentCluster, toDeleteHost); err != nil {
						return err
					}
				}
			}
		}
	}

	if len(c.ControlPlaneHosts) > 0 {
		log.Infof(ctx, "[sync] Syncing nodes Labels and Taints")
		hostList := hosts.GetUniqueHostList(c.EtcdHosts, c.ControlPlaneHosts, c.WorkerHosts, c.EdgeHosts)
		_, err := errgroup.Batch(hostList, func(h interface{}) (interface{}, error) {
			log.Debugf(ctx, "worker starting sync for node [%s]", h.(*hosts.Host).NodeName)
			return nil, setNodeAnnotationsLabelsTaints(ctx, c.KubeClient, h.(*hosts.Host))
		})

		if err != nil {
			return err
		}
		log.Infof(ctx, "[sync] Successfully synced nodes Labels and Taints")
	}
	return nil
}

func setNodeAnnotationsLabelsTaints(ctx context.Context, k8sClient *kubernetes.Clientset, host *hosts.Host) error {
	node := &v1.Node{}
	var err error
	for retries := 0; retries <= 5; retries++ {
		node, err = k8s.GetNode(k8sClient, host.NodeName)
		if err != nil {
			log.Debugf(ctx, "[hosts] Can't find node by name [%s], retrying..", host.NodeName)
			time.Sleep(2 * time.Second)
			continue
		}

		oldNode := node.DeepCopy()
		k8s.SetNodeAddressesAnnotations(node, host.InternalAddress, host.Address)
		k8s.SyncNodeLabels(node, host.ToAddLabels, host.ToDelLabels)
		k8s.SyncNodeTaints(node, host.ToAddTaints, host.ToDelTaints)

		if reflect.DeepEqual(oldNode, node) {
			log.Debugf(ctx, "skipping syncing labels for node [%s]", node.Name)
			return nil
		}
		_, err = k8sClient.CoreV1().Nodes().Update(node)
		if err != nil {
			log.Debugf(ctx, "Error syncing labels for node [%s]: %v", node.Name, err)
			time.Sleep(5 * time.Second)
			continue
		}
		return nil
	}
	return err
}

func (c *Cluster) PrePullK8sImages(ctx context.Context) error {
	select {
	case <-ctx.Done():
		return util.CancelErr
	default:
		log.Infof(ctx, "Pre-pulling kubernetes images")
		hostList := hosts.GetUniqueHostList(c.EtcdHosts, c.ControlPlaneHosts, c.WorkerHosts, c.EdgeHosts)
		_, err := errgroup.Batch(hostList, func(h interface{}) (interface{}, error) {
			runHost := h.(*hosts.Host)
			return nil, docker.UseLocalOrPull(ctx, runHost.DClient, runHost.Address, c.Image.Kubernetes, "pre-deploy", c.PrivateRegistriesMap)
		})
		if err != nil {
			return err
		}
		log.Infof(ctx, "Kubernetes images pulled successfully")
		return nil
	}
}

func RestartClusterPods(ctx context.Context, kubeCluster *Cluster) error {
	log.Infof(ctx, "Restarting network, ingress, and metrics pods")
	// this will remove the pods created by ZKE and let the controller creates them again

	labelsList := []string{
		fmt.Sprintf("%s=%s", KubeAppLabel, CalicoNetworkPlugin),
		fmt.Sprintf("%s=%s", KubeAppLabel, FlannelNetworkPlugin),
		fmt.Sprintf("%s=%s", AppLabel, NginxIngressAddonAppName),
		fmt.Sprintf("%s=%s", KubeAppLabel, DefaultMonitorMetricsProvider),
		fmt.Sprintf("%s=%s", KubeAppLabel, CoreDNSAddonAppName),
	}

	_, err := errgroup.Batch(labelsList, func(l interface{}) (interface{}, error) {
		runLabel := l.(string)
		// list pods to be deleted
		pods, err := k8s.ListPodsByLabel(kubeCluster.KubeClient, runLabel)
		if err != nil {
			return nil, err
		}
		// delete pods
		err = k8s.DeletePods(kubeCluster.KubeClient, pods)
		return nil, err
	})
	return err
}

func (c *Cluster) GetHostInfoMap() map[string]dockertypes.Info {
	hostsInfoMap := make(map[string]dockertypes.Info)
	allHosts := hosts.GetUniqueHostList(c.EtcdHosts, c.ControlPlaneHosts, c.WorkerHosts, c.EdgeHosts)
	for _, host := range allHosts {
		hostsInfoMap[host.Address] = host.DockerInfo
	}
	return hostsInfoMap
}

repository: zdnscloud/zke

repo_name: zdnscloud/zke


text:
DESCRIPTION = "GNU Common C++"
HOMEPAGE = "http://freshmeat.net/projects/ccrtp"
LICENSE = "GPL"
DEPENDS = "commoncpp2"
PR = "r0"

SRC_URI = "ftp://ftp.gnu.org/gnu/ccrtp/ccrtp-${PV}.tar.gz;name=archive \
           file://configure.ac.patch"
SRC_URI[archive.md5sum] = "3410d2f43a6a28679bd091ed8b2ed228"
SRC_URI[archive.sha256sum] = "365feddd276c78104600204ae6db4e76c66036df1e5b905e855239daac6a2473"

inherit pkgconfig autotools

do_configure_prepend () {
        sed -i -e 's#COMMON_LIBPATH=`pkg-config --variable=libdir libccgnu2`#COMMON_LIBPATH=${STAGING_LIBDIR}#' configure.ac
}


text:
SUMMARY = "GNU cpio -- a program to manage archives of files"
DESCRIPTION = "\
	GNU cpio is a tool for creating and extracting archives, or copying  \
	files from one place to another.  It handles a number of cpio formats\
	as well as reading and writing tar files. \
"
HOMEPAGE = "http://www.gnu.org/software/cpio/"
PR = "r0"
inherit debian-package

LICENSE = "GPLv3+"
LIC_FILES_CHKSUM = "\
	file://COPYING;md5=f27defe1e96c2e1ecd4e0c9be8967949"
inherit autotools-brokensep gettext
EXTRA_OECONF += "CPIO_MT_PROG=mt --bindir=${base_bindir}"

#remove info_TEXINFOS variable, because cpio.texi is not existed in ${S}/doc/
SRC_URI += "file://remove-info_TEXINFOS_debian.patch"

#install follow Debian jessie
do_install_append() {
	oe_runmake -C ${S}/po install-data-yes DESTDIR=${D}
	mv ${D}${base_bindir}/mt ${D}${base_bindir}/mt-gnu
	rm -r ${D}${libdir}
}

repository: nghiaht-tsdv/meta-debian

repo_name: nghiaht-tsdv/meta-debian

branch_name: refs/heads/jethro


text:
SUMMARY = "MyProject Additional files"
LICENSE = "CLOSED"
PR = "r1"

SRC_URI = "file://my-rule1.rules \
"

do_install[nostamp] = "1"
do_unpack[nostamp] = "1"

do_install () {
    echo "my-project install task invoked"
    install -d ${D}/etc/udev/rules.d
    install -m 0666 ${WORKDIR}/my-rule1.rules           ${D}/etc/udev/rules.d/my-rule1.rules
}

FILES_${PN} += " \
     /etc/udev/rules.d/my-rule1.rules \
     "

PACKAGES = "${PN}"
PROVIDES = "rfs-my-project"

repository: denverwilliams/meta-resin

repo_name: denverwilliams/meta-resin


text:
@proceedings{ws-2014-eacl,
    title = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    editor = {Dalmas, Tiphaine  and
      G{\"o}tze, Jana  and
      Gustafson, Joakim  and
      Janarthanam, Srinivasan  and
      Kleindienst, Jan  and
      Mueller, Christian  and
      Stent, Amanda  and
      Vlachos, Andreas},
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0200",
    doi = "10.3115/v1/W14-02",
}
@inproceedings{reichel-etal-2014-car,
    title = "In-Car Multi-Domain Spoken Dialogs: A Wizard of {O}z Study",
    author = "Reichel, Sven  and
      Ehrlich, Ute  and
      Berton, Andr{\'e}  and
      Weber, Michael",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0201",
    doi = "10.3115/v1/W14-0201",
    pages = "1--9",
}
@inproceedings{kadlec-etal-2014-ibms,
    title = "{IBM}{'}s Belief Tracker: Results On Dialog State Tracking Challenge Datasets",
    author = "Kadlec, Rudolf  and
      Libovick{\'y}, Jind{\v{r}}ich  and
      Macek, Jan  and
      Kleindienst, Jan",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0202",
    doi = "10.3115/v1/W14-0202",
    pages = "10--18",
}
@inproceedings{janarthanam-etal-2014-click,
    title = "Click or Type: An Analysis of Wizard{'}s Interaction for Future Wizard Interface Design",
    author = "Janarthanam, Srinivasan  and
      Hill, Robin  and
      Dickinson, Anna  and
      Fredriksson, Morgan",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0203",
    doi = "10.3115/v1/W14-0203",
    pages = "19--27",
}
@inproceedings{labsky-etal-2014-recipes,
    title = "Recipes for building voice search {UI}s for automotive",
    author = "Labsky, Martin  and
      Kunc, Ladislav  and
      Macek, Tomas  and
      Kleindienst, Jan  and
      Vystrcil, Jan",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0204",
    doi = "10.3115/v1/W14-0204",
    pages = "28--32",
}
@inproceedings{avalos-benotti-2014-natural,
    title = "A Natural Language Instructor for pedestrian navigation based in generation by selection",
    author = "Avalos, Santiago  and
      Benotti, Luciana",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0205",
    doi = "10.3115/v1/W14-0205",
    pages = "33--37",
}
@inproceedings{luna-benotti-2014-mining,
    title = "Mining human interactions to construct a virtual guide for a virtual fair",
    author = "Luna, Andr{\'e}s  and
      Benotti, Luciana",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0206",
    doi = "10.3115/v1/W14-0206",
    pages = "38--42",
}
@inproceedings{voss-etal-2014-collaborative,
    title = "Collaborative Exploration in Human-Robot Teams: What{'}s in their Corpora of Dialog, Video, {\&} {LIDAR} Messages?",
    author = "Voss, Clare  and
      Cassidy, Taylor  and
      Summers-Stay, Douglas",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0207",
    doi = "10.3115/v1/W14-0207",
    pages = "43--47",
}
@inproceedings{janarthanam-lemon-2014-multi,
    title = "Multi-threaded Interaction Management for Dynamic Spatial Applications",
    author = "Janarthanam, Srinivasan  and
      Lemon, Oliver",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0208",
    doi = "10.3115/v1/W14-0208",
    pages = "48--52",
}
@inproceedings{vystrcil-etal-2014-mostly,
    title = "Mostly Passive Information Delivery {--} a Prototype",
    author = "Vystr{\v{c}}il, Jan  and
      Macek, Tomas  and
      Luksch, David  and
      Labsk{\'y}, Martin  and
      Kunc, Ladislav  and
      Kleindienst, Jan  and
      Ka{\v{s}}parov{\'a}, Tereza",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0209",
    doi = "10.3115/v1/W14-0209",
    pages = "53--57",
}
@inproceedings{vystrcil-etal-2014-navigation,
    title = "Navigation Dialog of Blind People: Recovery from Getting Lost",
    author = "Vystrcil, Jan  and
      Maly, Ivo  and
      Balata, Jan  and
      Mikovec, Zdenek",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0210",
    doi = "10.3115/v1/W14-0210",
    pages = "58--62",
}
@inproceedings{pappu-etal-2014-conversational,
    title = "Conversational Strategies for Robustly Managing Dialog in Public Spaces",
    author = "Pappu, Aasish  and
      Sun, Ming  and
      Sridharan, Seshadri  and
      Rudnicky, Alexander",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0211",
    doi = "10.3115/v1/W14-0211",
    pages = "63--67",
}
@inproceedings{kousidis-etal-2014-situationally,
    title = "Situationally Aware In-Car Information Presentation Using Incremental Speech Generation: Safer, and More Effective",
    author = "Kousidis, Spyros  and
      Kennington, Casey  and
      Baumann, Timo  and
      Buschmeier, Hendrik  and
      Kopp, Stefan  and
      Schlangen, David",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0212",
    doi = "10.3115/v1/W14-0212",
    pages = "68--72",
}
@inproceedings{edlund-etal-2014-human,
    title = "Human pause and resume behaviours for unobtrusive humanlike in-car spoken dialogue systems",
    author = "Edlund, Jens  and
      Edelstam, Fredrik  and
      Gustafson, Joakim",
    booktitle = "Proceedings of the {EACL} 2014 Workshop on Dialogue in Motion",
    month = apr,
    year = "2014",
    address = "Gothenburg, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W14-0213",
    doi = "10.3115/v1/W14-0213",
    pages = "73--77",
}

repository: emorynlp/nlprankings

repo_name: emorynlp/nlprankings

gha_license_id: NOASSERTION

gha_language: TeX