text:
543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.34},
doi = {10.1109/ASONAM.2011.34},
abstract = {Link analysis has been an important tool in crime investigation. Explicit or implicit social links, such as kinship, financial exchange, telephone connection, links derived from modus operandi, time of day, and geographic relationship, are often used to construct links between criminals. This paper proposes an association model based on modus operandi mining to establish links among crime cases and chronic criminals. Two data sets of robbery and residential burglary crime records were collected from a local police department and were used for experiment to evaluate the performance of the proposed approach.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {548–550},
numpages = {3},
keywords = {Crime Association Model, Implicit Link Mining, Information Entropy, Modus Operandi},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.23,
author = {Hou, Jianhua and Chen, Han},
title = {Countries Co-Citation Network and Research Fronts of International Energy Technology},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.23},
doi = {10.1109/ASONAM.2011.23},
abstract = {In this article, we draw countries cooperation networks joint with their research fronts of international energy technology. We derived the countries co-citation networks by first using graph-theoretical approaches as implemented in Cite Space. We retrieved the documents by topic word "energy technolog*" between 2001 and 2010 which is published by article type in English from web of science which contains SCI and SSCI database index. Based the cluster information, we analysis the distribution and the research fronts of international energy technology.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {551–552},
numpages = {2},
keywords = {International energy technology, information visualization, CiteSpace, country co-citation network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.13,
author = {Aarthi, S. and Bharanidharan, S. and Saravanan, M. and Anand, V.},
title = {Predicting Customer Demographics in a Mobile Social Network},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.13},
doi = {10.1109/ASONAM.2011.13},
abstract = {A network made of individuals connected based on their communication behaviour using mobile phones can be called as a Mobile Social Network. Mapping and measuring of interactions and flows between people across mobile social networks are being performed extensively in an attempt to understand the intriguing patterns of human behaviour. Such analyses can help in arriving at useful inferences for improving the accuracy of 'Targeted Advertisements'.This paper makes one such attempt to extract the demographics (i.e. age, gender and economic status) of a person based on his/her connectivity in his/her respective social network(s) and mobile phone usage over a period of time. The need for prediction arises from the fact that, for prepaid users, the demographics are either unavailable or inaccurate. The results produced are evaluated and standardized based on proven statistics pertaining to the nation considered. We use candlestick charts to compare the experimental results.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {553–554},
numpages = {2},
keywords = {Data Mining Algorithm, Demographics Prediction, Mobile Social Network Analysis, Targeted Advertisement, Telecommunication data},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.62,
author = {Conghuan, Ye},
title = {Dense Subgroup Identifying in Social Network},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.62},
doi = {10.1109/ASONAM.2011.62},
abstract = {The densest coherent sub graphs can provide valuable knowledge about the underlying internal structure of a social network, and mining frequently occurring coherent sub graphs of a large network has been witnessed several applications and received considerable attention in the graph mining community recently. However, some key players are not always appeared in the clique, therefore, clique detection could not identify some core members in social networks. In this paper, we define a generalization of the dense sub graph problem by an additional distance restriction to the nodes of the dense sub graph which is a quasi-clique in fact. We propose a new quasi-clique detection algorithm based on the definition of dense sub graph, and a novel optimization techniques based on idea of synchronization, which can prune the unpromising and redundant alien from the dense sub graph. The proposed methods could discover quasi-cliques and core players that are not shown in clique.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {555–556},
numpages = {2},
keywords = {dense subgroup detection, influential nodes, social network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.18,
author = {Babu, Y. Kiran and Babu, T. Narendra and Ramesh, B.},
title = {Minimizing Interference through Channel Assignment in Multiradio Wireless Mesh Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.18},
doi = {10.1109/ASONAM.2011.18},
abstract = {Breadth First Search Channel Assignment(BFSCA) is a hybrid channel assignment algorithm that utilize multiple radio interfaces to improve the throughput and minimize the interference within the wireless mesh network and between the mesh network and co-located wireless mesh networks. This new channel assignment scheme allows different nodes in the same network to communicate with each other without causing too much interference to their neighbors. It is introducing Multiradio Conflict Graph(MCG) to model interference in the wireless mesh network. Breadth First Search Channel Assignment considers both the fixed channels (static) and the dynamic channels to reduce interference of the network. BFSCA will increase the network throughput greatly.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {557–558},
numpages = {2},
keywords = {interference, channel assignment, multiradio wireless mesh network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.124,
author = {Lee, JooYoung and Duan, Yue and Oh, Jae C. and Du, Wenliang and Blair, Howard and Wang, Lusha and Jin, Xing},
title = {Automatic Reputation Computation through Document Analysis: A Social Network Approach},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.124},
doi = {10.1109/ASONAM.2011.124},
abstract = {We develop and study two social network-based algorithms for automatically computing authors' reputations from a collection of textual documents. First, given a set of documents, both algorithms examine keyword reference behaviors of the authors to construct a social network. This social network represents the relationship among the authors in terms of information reference behavior. With the resulting network, the first algorithm computes each author's reputation value considering only direct referential activities while the second considers indirect activities as well. We discuss the reputation values computed by the two algorithms and compare them with the reputation ratings given by a human domain expert. We also analyze the social network through a community detection algorithm. We observed several interesting phenomena including the network being scale-free and having negative assortativity.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {559–560},
numpages = {2},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.115,
author = {Shim, Kyong Jin and Hsu, Kuo-Wei and Srivastava, Jaideep},
title = {Effects of Mentoring on Player Performance in Massively Multiplayer Online Role-Playing Games (MMORPGs)},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.115},
doi = {10.1109/ASONAM.2011.115},
abstract = {Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in Ever Quest II, a popular MMORPG developed by Sony Online Entertainment.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {561–562},
numpages = {2},
keywords = {massively multiplayer online games, video games, player performance, mentoring},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.39,
author = {Jung, Jason J.},
title = {Towards Named Entity Recognition Method for Microtexts in Online Social Networks: A Case Study of Twitter},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.39},
doi = {10.1109/ASONAM.2011.39},
abstract = {Given a certain question, named entity recognition (NER) methods can be an efficient strategy to extract relevant answers. The goal of this work is to extend NER methods for analyzing a set of micro texts, which are short text on online social media. To do so, we propose two contextual closure properties to discover contextual clusters of micro texts, which can be expected to improve the performance of NER tasks. Experimental results demonstrate the feasibility of the proposed method for extracting relevant information in online social network applications.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {563–564},
numpages = {2},
keywords = {Multiplex social network, Social networks, Named entity recognition, Network analysis},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.33,
author = {Chen, Kai and Zhou, Yi and Song, Li and Yang, Xiaokang},
title = {Building Artificial Identities in Social Network Using Semantic Information},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.33},
doi = {10.1109/ASONAM.2011.33},
abstract = {as the popularity of social networking sites increase, so does their attractiveness for criminals. In this work, we show how an adversary can build artificial identities using semantic information in social network. Our method make the identities look more like real people, therefore can be used to support many kinds of attacks, such as ASE [1], profile cloning[2]. A prototype of this method is implemented, includes following stages: Firstly, categories of virtual identity are predefined, and each category has multiple properties, such as geographical region, hobby, education, age, interested topic/keywords, etc. Secondly, based on category information, each identity will foster its own "life" semantically, such as edit profile and update status, find hot related news/topic from Google then post to wall, find related groups/networks then request to add in, and find/like/create/comment pages/posts, etc. Thirdly, artificial identity will evolve to multiple stages according to its status (for example, number of friends of real people), single identity with different evolutionary stages is linked together to a group that will help to ensure the number of attack edges [3].},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {565–566},
numpages = {2},
keywords = {Artifical Identity, Semantic Bot, Social Network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.87,
author = {Roy, Ram Babu and Sarkar, Uttam Kumar},
title = {A Social Network Approach to Examine the Role of Influential Stocks in Shaping Interdependence Structure in Global Stock Markets},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.87},
doi = {10.1109/ASONAM.2011.87},
abstract = {This paper investigates the role of influential stocks in shaping the emergent system-level interdependence in global stock markets using a large set of stocks selected from major stock market indices from across the globe. We have proposed a method to identify influential stocks using various centrality measures used in social network analysis literature. Our study shows how these influential stocks provide key linkages in integrating the global stock markets as an interconnected system. We have also shown that the regional influence dominates over the economic sector influence in shaping the topological structure of stock market network. The study also captures the change in the topology of this network following the collapse of Lehman Brothers.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {567–569},
numpages = {3},
keywords = {Centrality, Correlation, Financial Crisis, Network Approach, Minimum Spanning Tree, Stock Market},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.72,
author = {Wang, Qinna and Fleury, Eric},
title = {Community Detection with Fuzzy Community Structure},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.72},
doi = {10.1109/ASONAM.2011.72},
abstract = {In order to find a cover which allows nodes to be shared among several communities, we propose a simple fuzzy community detection algorithm, which is based on an existing partition detection technique. For the performance of overlapping nodes that makes the partition ambiguous, a new extended modularity is introduced to qualify covers. With modularity optimization, the cover can be found with a high quality. We applied our method to real networks. The results demonstrate that our method can discover meaningful fuzzy community structure, overlapping community structure and hierarchical structure.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {575–580},
numpages = {6},
keywords = {community detection, hierarchical organization, Fuzzy community structure, overlapping community structure},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.50,
author = {Juszczyszyn, Krzysztof and Budka, Marcin and Musial, Katarzyna},
title = {The Dynamic Structural Patterns of Social Networks Based on Triad Transitions},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.50},
doi = {10.1109/ASONAM.2011.50},
abstract = {In modern social networks built from the data collected in various computer systems we observe constant changes corresponding to external events or the evolution of underlying organizations. In this work we present a new approach to the description and quantifying evolutionary patterns of social networks illustrated with the data from the Enron email dataset. We propose the discovery of local network connection patterns (in this case: triads of nodes), measuring their transitions during network evolution and present the preliminary results of this approach. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads, then we show how it can help to discover the dynamic patterns of network evolution. Also, we analyse the roles performed by different triads in the network evolution by the creation of triad transition graph built from the TTM, which allows us to characterize the tendencies of structural changes in the investigated network. The future applications of our approach are also proposed and discussed.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {581–586},
numpages = {6},
keywords = {triad transitions, Social network, network evolution},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.106,
author = {Das, D. and Bandyopadhyay, S.},
title = {Emotions on Bengali Blog Texts: Role of Holder and Topic},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.106},
doi = {10.1109/ASONAM.2011.106},
abstract = {The paper presents an approach to identify the emotions of the bloggers on different topics provided in the Bengali blog documents. The rule based identification of emotion holders and topics along with their corresponding emotional expressions forms the baseline system. A Support Vector Machine (SVM) based supervised framework is also employed to identify the three components from the blog sentences and it outperforms the baseline system. As the topic of a document is not always conveyed at the sentence level, the similarity between a document's overall topic and sentential topic is measured through semantic clustering approach. Two different approaches are adopted to identify the many to many relationships among the holders and topics on Ekman's six emotions. One is based from the perspectives of the holders and other is with respect to topics. The two way evaluation of Ekman's six emotions achieves precision, recall and F-Score of 65.02%, 76.23% and 70.18% for 10 bloggers and 71.02%, 78.47% and 74.55% for 8 different topics on 512 test sentences respectively.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {587–592},
numpages = {6},
keywords = {Topic, Holder, Blogs, Emotions},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.71,
author = {Zygmunt, Anna and Brodka, Piotr and Kazienko, Przemyslaw and Kozlak, Jaroslaw},
title = {Different Approaches to Groups and Key Person Identification in Blogosphere},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.71},
doi = {10.1109/ASONAM.2011.71},
abstract = {Two approaches for identifying key persons in the blogosphere-based social network are analysed in the paper: discovery of the most important individuals either in persistent or in global social communities existing on web blogs. A new method for the separation of stable groups fulfilling given conditions is presented. Additionally, a new concept for extraction of user roles and key persons in such groups is proposed. It has been compared to the general clustering method and structural node position measure applied to rank users in the time-aggregated data. Experimental, comparative studies have been conducted on real blogosphere data gathered over one year.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {593–598},
numpages = {6},
keywords = {social network, persistent role identification, SNA, key person, social network analysis, fast modularity optimization, social group extraction, blogosphere, node position, CPM, stable group extraction},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.123,
author = {Jedrzejewski, Krzysztof and Morzy, Mikolaj},
title = {Opinion Mining and Social Networks: A Promising Match},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.123},
doi = {10.1109/ASONAM.2011.123},
abstract = {In this paper we discuss the role and importance of social networks as preferred environments for opinion mining and sentiment analysis especially. We begin by briefly describing selected properties of social networks that are relevant with respect to opinion mining and we outline the general relationships between the two disciplines. We present the related work and provide basic definitions used in opinion mining. Then, we introduce our original method of opinion classification and we test the presented algorithm on real world datasets acquired from popular Polish social networks, reporting on the results. The results are promising and soundly support the main thesis of the paper, namely, that social networks exhibit properties that make them very suitable for opinion mining activities.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {599–604},
numpages = {6},
keywords = {social networks, sentiment analysis, opinion mining, social computing},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.35,
author = {Lin, Fang-Ling and Chiou, Guey-Fa},
title = {Social Brokerage behind Knowledge Sharing},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.35},
doi = {10.1109/ASONAM.2011.35},
abstract = {This study explored the implicit brokerage positions of social network behind knowledge sharing and demonstrated applicability to researches of community of practices. A concept of knowledge brokerage was proposed on the basis of the theory of social brokerage, and hypotheses were developed to verify the relational properties of brokerage opportunities. Social network analysis (SNA) was applied to conduct the egocentric network surveys and related analyses. Several new ways of evaluating the performance of knowledge sharing have been developed. The results verified the usability of brokerage as indicators of knowledge sharing. The results also can reveal efficiencies and deficiencies in knowledge dissemination among members of communities. These findings hold relevance for studies on the information technology practices of other professionals.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {605–610},
numpages = {6},
keywords = {community of practice, social brokerage, knowledge sharing, social network analysis},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.26,
author = {Chang, Wei-Lun and Lin, Sian-Ting},
title = {Analyzing Personality Correlation of Social Network in Organizations},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.26},
doi = {10.1109/ASONAM.2011.26},
abstract = {The focus of this study is to find out the similarity among personalities of persons. This study investigates 203 significant personality adjectives to find out the stable intrinsic value and the connotation of every adjective in context is introduced for vectorization comparison. The 203 phrases create 41209 similar scores totally. This study takes students of Tamkang University in Taiwan as subjects for establishing adjective database. The valid samples received are 973, the recovery rate being 83.87%. The following important conclusions are received after analysis, discussion and outcome induction: (1) In naturally formed interpersonal groups, openness to experience will be taken as the most important formative indicator. (2) Groups under high task environment will seek to take conscientiousness as the primary formative indicator to converge into task groups. (3) Lover relationship takes partial personality similarity as the predominant phenomenon. The convergence of group personality will show comparatively obvious concentration.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {611–617},
numpages = {7},
keywords = {Big Five Mode, Co-relational Personality, Social Network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.85,
author = {Younus, Arjumand and Qureshi, M. Atif and Asar, Fiza Fatima and Azam, Muhammad and Saeed, Muhammad and Touheed, Nasir},
title = {What Do the Average Twitterers Say: A Twitter Model for Public Opinion Analysis in the Face of Major Political Events},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.85},
doi = {10.1109/ASONAM.2011.85},
abstract = {Social media platforms have become a forum for giving a voice to the masses with a significant proportion of those masses coming from the developing world. This was largely evidenced through the significant role played by social media platforms in the recent uprisings in the Arab world. In this paper, we take up a study of social media engagement patterns of users from the developing world through a study of Twitter's role during the recent Tunisian uprising. Motivated by the results of a user survey conducted mainly for users from the developing world who tweeted heavily during the uprisings in the Arab world, we propose a novel method for subjectivity analysis of tweets corresponding to political events in the developing world. Our proposed method differs from previous subjectivity analysis approaches in that it is the first method that takes into account social features of social media platforms for the subjectivity classification task. Through experimental evaluations, we observe the accuracy of the proposed method to be 83.3% which demonstrates a promising outcome for large-scale application of our proposed subjectivity analysis technique.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {618–623},
numpages = {6},
keywords = {social features, Twitter, subjectivity},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.31,
author = {Chen, Jun Ming and Chen, Ying Ying and Sun, Yeali S. and Chen, Meng Chang},
title = {A Novel Approach for Developing Automatic Knowledge Construction and Diagnostic System for Tag-Based Learning Environment},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.31},
doi = {10.1109/ASONAM.2011.31},
abstract = {With the advent of Web 2.0 technology, researchers have attempted to use Web 2.0 tools to develop adaptive and cooperative learning environments. However, in building learning and teaching diagnostic system, one of the major difficulties is the lack of prior knowledge to help learners read and understand what they read in articles. Moreover, because of the lack of a mechanism to assist teachers in monitoring the running activities and student progress, such that constructive suggestions can be given to the students and tutoring strategies can be improved accordingly. Therefore, this paper presents a framework for calculating semantically meaningful prior knowledge and generating spreading energy for discovering student s reading status in semantic networks using a modified version of Semantic Analysis and Social Network techniques. An application to the development of a Tag-based Collaborative reading learning system is very useful for teachers and students.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {624–625},
numpages = {2},
keywords = {Educational technology, Recommender system, Semantic Web, Tagging},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.38,
author = {Uesugi, Shiro},
title = {Effects of Personality Traits on Usage of Social Networking Service},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.38},
doi = {10.1109/ASONAM.2011.38},
abstract = {A number of social networking services (SNSs) have become increasingly popular in recent years, as characterized by the growing number of their users and the deepening penetration of their services. As the number of users increases, the variety of user profiles, serving as a reflection of the real world, also increases. Psychological aspects must be taken into account to better understand the behaviors of SNS users. This study investigated the relationships between personality traits and SNS usage, and the results of a preliminary survey using the Big Five model of personality traits are reported herein.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {629–634},
numpages = {6},
keywords = {Privacy Protection, SNS, Social Network Services, Big Five Personal Traits, Technology Acceptance},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.41,
author = {Wu, Yu-Lung and Tao, Yu-Hui and Li, Ching-Pu and Yang, Pei-Chi and Huang, Guo-Shin},
title = {The Moderating Role of Virtual Community Cohesion and Critical Mass on the Link between Online-Game Website Service Quality and Play Satisfaction},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.41},
doi = {10.1109/ASONAM.2011.41},
abstract = {The online game market in Taiwan is close to saturation. The sudden emergence of online community games has lead to a change in the type of game players, and online game providers are forced to deploy new strategies to cope with this change in trend. Meanwhile, literature research reveals a mixed result of the impact of virtual community cohesion and critical mass on online game platform service quality and satisfaction. Therefore, this research further investigates the moderating roles of online players' virtual community cohesion and critical mass on the link between website service quality and player satisfaction. The result indicates that virtual community cohesion does play a moderating role while critical mass does not. Discussions and implications are also provided in this paper.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {635–640},
numpages = {6},
keywords = {E-Service Quality, Virtual Community Cohesion, Critical Mass, Online Game},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.93,
author = {Chang, M. C. and Gregor, S. D.},
title = {Encouraging Participation in Virtual Community with Classified Facilitation Mechanisms: A Perspective from Online Communication Needs and Behaviors},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.93},
doi = {10.1109/ASONAM.2011.93},
abstract = {Based on a literature review and three focused group sessions involving a total of 30 experienced designers of virtual community, this research examines design principles of computer-mediated facilitation mechanisms from the perspective of meeting participants online communication needs. The design principles are classified into three categories based on members intrapersonal, interpersonal and group communication needs and behaviors. The findings address the importance of understanding the different communication needs for individual participants when selecting facilitation mechanisms to encourage member participation in virtual communities. These findings yield innovative implications for research and practice.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {641–646},
numpages = {6},
keywords = {faciliation mechanisms, communication, partcipation, virtual community},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.82,
author = {Orito, Yohko and Okada, Hitoshi and Sai, Hidenobu},
title = {Private Information Transmission on the Consumer Generated Media: Information Privacy in the Japanese Context},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.82},
doi = {10.1109/ASONAM.2011.82},
abstract = {This study examined private information transmissions on CGM/UGM websites from the perspective of the Japanese sense of information privacy. The characteristics of Japanese private information transmission on the CGM are described and discussed.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {647–649},
numpages = {3},
keywords = {Information Privacy, Consumer Generated Media (CGM), SNS (Social Networking Services), Blog},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.98,
author = {Chao, Chian-Hsueng},
title = {Reconceptualizing the Mechanism of Internet Human Flesh Search: A Review of the Literature},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.98},
doi = {10.1109/ASONAM.2011.98},
abstract = {The development of Internet technologies and popularity of social network enable the netizens to search, process, broadcast, and share information in a more efficient and collaborative way. Opinions of netizens in the cyberspace have emerged and become one of the major public opinions in the real world. One of the phenomenon rising recently over the Internet that most represent the influence between the virtual and real world is human flesh search (HFS). Usually, the HFS object is incompatible with social justice or violation of the mainstream moral characters, or events. This §violation of moral triggers the HFS process either in the cyberspace or in the real world. HFS has arose numerous controversies, especially the dilemmas between privacy violation and public opinion. Literatures and studies of HSF are mainly from China. International journals or periodical literatures target on this issue are very few. In addition, current studies are mainly qualitative studies lacking of quantitative and theoretical model building and empirical research. Therefore, it is necessary to acquire better understanding of HSF structure and evolving process. The purpose of this paper is to provide a broad discussion and summarized attributes of HFS. Based on reviews of more than twenty journal papers and articles regarding HFS, this study hopes to formulate a reference framework for future HFS studies.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {650–655},
numpages = {6},
keywords = {Social Participation Theory, Human Flesh Search, Deindividuation, Bandwagon effect},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.75,
author = {Chao, Chih Yang and Yen, Chia-Sung and Yang, Shih-Chun and Yeh, Tien-Hui and Sun, Yi-Inn},
title = {The Organizational Justice Strategies to Affect Learning Performance and Self-Efficacy: A Case Study in Campus Media News Gathering},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.75},
doi = {10.1109/ASONAM.2011.75},
abstract = {It is important to perform teamwork with high organizational justice. When some unfair events happen, it is counterproductive not only to the performance of the organization, but to organizational efficacy as well. Presses are time-conscious organizations, but they have to face the difficulty of resource distribution. The main purpose of this study is to examine organizational justice in presses. Because presses are so different from other organizations, there must be some kinds of important elements composing their organizational justice. In the procedure of news gathering, reporters always have to face the uncertainty of those events. If presses emphasize too much on the equity of distribution, it is possible that they may miss the deadline, or even lose the news. So presses seem to need another kind of evaluation to appraise their organizational justice. This paper would focus on the campus media, finding how students perform when they face the assignments of news gathering, and what strategies to do justice in this media could affect students self-efficacy.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {656–663},
numpages = {8},
keywords = {human resource management, self-efficacy, equity theory, organization behavior, organizational justice, learning performance},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.83,
author = {Lin, I-Mei and Hwang, Yuan-Chu},
title = {Exploring the Social Network Relationships on Hostility Behaviors},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.83},
doi = {10.1109/ASONAM.2011.83},
abstract = {How to determine who is the one that you are willing to express your emotion? Emotion expression and behavior responses are highly related to the subjective judgment of the target user, especially the negative emotions (e.g., anger/ hostility). This study is to explore how the hostility dimensions are distributed in various relationship types, including power-dependences, closeness distance, mutual expectations, and cooperation/competition. This study used semi-structured in-depth interviews for a six-minute anger recall task that asked the participants to recall and report an anger event that happened in the past six months. There were 75 participants recruited to attend this study. The results indicate the dynamic social network types of power-dependences, closeness distance, and cooperation/competition are key factors to impact negative emotional behaviors and determine the hostility behaviors significantly. Although mutual expectations could also leads to higher anger level, but there is no significant difference on statistic analysis.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {667–671},
numpages = {5},
keywords = {cooperation/competition, mutual expectations, hositility behavior, closeness distance, power-dependences},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.101,
author = {Chang, Pei-Shan and Ting, I-Hsien and Wang, Shyue-Liang},
title = {Towards Social Recommendation System Based on the Data from Microblogs},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.101},
doi = {10.1109/ASONAM.2011.101},
abstract = {With the rapid growth of Internet and social networking websites, there are various services that provided in these platforms. For instance, Face book focuses on social activities, Twitter and Plurk are both focus on the interaction of users through short messages (which are so-called microblogs). Therefore, there are more than millions of users registered in these websites and become places where full of marketing possibilities. Thus, it is an important issue to assist companies to understand the users in the social networking websites in order to enhance the accuracy and efficiency of target marketing. In this paper, we have proposed the architecture of a social recommendation system based on the data from microblogs. The social recommendation system is conducted according to the messages and social structure of target users. The similarity of the discovered features of users and products will then be calculated as the essence of the recommendation engine. A case study will be included to present how the recommendation system works based on real data that collected from Plurk.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {672–677},
numpages = {6},
keywords = {Microblogs, Target Marketing, Social Networks Analysis, Social Recommendation System},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.17,
author = {Tu, Yi-Ning and Seng, Jia-Lang},
title = {Evaluating the Impact Power of Authors via Bayesian Estimation of Authors' Social Connections},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.17},
doi = {10.1109/ASONAM.2011.17},
abstract = {This study tries to detect the impact research topics from impact authors with their connections, that is, who have larger impact in the same research field. These topics are impact research topics the pursuit of which would be very valuable for researchers, especially for new scholars or for researchers who want to combine their original field with other new domains but who may not have enough background knowledge about the new field. Bayesian estimation in our model uses subjective data (published volume) as the prior distribution and objective data as the likelihood function (citation frequency) to predict the posterior distribution of the target which we called impact power. After finding the impact power of each paper or topic then filtering these papers and topics, we can find impact research topics or papers.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {678–684},
numpages = {7},
keywords = {Bayesian estimations, topic detection, impact power, authors' social connections},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.99,
author = {Wang, Chen-Shu and Ting, I-Hsien and Li, Yu-Chieh},
title = {Taiwan Academic Network Discussion via Social Networks Analysis Perspective},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.99},
doi = {10.1109/ASONAM.2011.99},
abstract = {Academic network (AN) is quite different network and hard to acquire relative information for inexperience researchers. To realize AN is an important but difficult work because of the constituents of AN are complicated and dynamic variation. In this research, two Taiwan academic networks are established from the perspectives of academic conference topic and participant committee. Each academic conference is regarded as an AN node to establish the conference academic network. In addition, there are two connection types between AN nodes, including: the relationship among CFP topics (academic conference topic perspective) and the relationship between committee member (participant committee perspective). Finally, 15 IT/IM relative target conferences in Taiwan 2009 are analyzed via social network analysis methodology. According to experiment result, the values of degree and betweeness reveal some interesting findings, such as TANET, OOTA and IMP are top three important bridge conferences in Taiwan. Additionally, as the cluster analyzed result shown, academic network somehow represents domain expertise because of the committee are grouped according to their research domains. These finding is consistence with the reality academic network in Taiwan and helpful information for Taiwan academic network understanding.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {685–689},
numpages = {5},
keywords = {social network analyze, academic social network network, Taiwan academic network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.109,
author = {Wu, Qin and Duval, Robert and Fuller, Edgar and Qi, Xingqin and Zhang, Cun-Quan and Spahiu, Arian and Christensen, Kyle},
title = {Modeling Network Changes: Systemic Centrality in Foreign Policy Interaction Analysis},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.109},
doi = {10.1109/ASONAM.2011.109},
abstract = {The complex network of relationships between countries provides an abundance of data from which intelligence analysts must synthesize useful interpretations that effectively inform foreign policy and security decision making. In this work, a network of foreign policy event interactions is developed and then used to describe the state of the international system by estimating the behavioral distances from all relevant actors to the US. Using a graph theoretic approach, we develop a tool for estimating behavioral distances through direct network links when behavior is present, and indirect network paths when direct behavior is unobserved. The international system is examined in temporal aggregations of 60 and 30 days prior to and following the 1991 Gulf War and the 2003 invasion of Iraq. Both periods see distancing from the US as the wars approach, and a partial return to the prior state by 60 days after the initiation of conflict. The overall position of the US and the response of other nations towards the US indicates that the systemic leadership role of the US is diminished in behavioral relationships with a moderate number of actors.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {690–695},
numpages = {6},
keywords = {foreign policy, social networks, graph theory, clustering},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.96,
author = {Bhukya, Sreedhar},
title = {Discover Academic Experts in Novel Social Network Model},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.96},
doi = {10.1109/ASONAM.2011.96},
abstract = {A number of recent studies on social networks are based on a characteristic which includes assortative mixing, high clustering, short average path lengths, broad degree distributions and the existence of community structure. Here, a model has been developed in the domain of\^{a} Academic collaboration' which satisfies all the above characteristics, based on some existing social network models. In addition, this model facilitates interaction between various communities (academic/research groups).This model gives very high clustering coefficient by retaining the asymptotically scale-free degree distribution. Here the community structure is raised from a mixture of random attachment and implicit preferential attachment. In addition to earlier works which only considered Neighbor of Initial Contact (NIC) as implicit preferential contact, we have considered Neighbor of Neighbor of Initial Contact (NNIC)also. This model supports the occurrence of a contact between two Initial contacts if the new vertex chooses more than one initial contacts. This ultimately will develop a complex social network rather than the one that was taken as basic reference.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {696–700},
numpages = {5},
keywords = {Academic collaboration, secondary contact, social networks, initial contact, tertiary contact},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.46,
author = {Ho, Yao Hua and Wu, Yao Chuan and Chen, Meng Chang and Wen, Tsun-Jui and Sun, Yeali S.},
title = {GPS Data Based Urban Guidance},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.46},
doi = {10.1109/ASONAM.2011.46},
abstract = {In many metropolitan areas, traffic congestion is an escalating problem which causes a significant waste of money and time. Nowadays, cars equipped with GPS devices become widespread. The location information of those cars is very useful for estimate traffic condition in the complex city road network. Using the accurate and real time traffic condition, we can provide dynamic route guidance to ease traffic congestion. In this paper, we proposed a speed pattern model, called two phase piecewise linear speed model (2PEED), to estimate traffic condition and represent speed pattern in a road network using GPS data collected vehicles. With the estimated traffic condition and speed pattern, a proposed classification-based route guidance approach using machine learning technique provides dynamic routing for drivers. Using both current traffic data and the experience learned from history data, our route guidance approach is able to accurately predict the future traffic condition and selects a best route. We give simulation results to show that the proposed approach is able to select and dynamically update a route to prove drivers a best (e.g., less traffic and shortest travel time) route to their destination.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {703–708},
numpages = {6},
keywords = {machine learning, GPS, speed pattern estimation, route guidance},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.68,
author = {Dai, Bi-Ru and Lee, Chang-Yi and Chung, Chih-Heng},
title = {A Framework of Recommendation System Based on Both Network Structure and Messages},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.68},
doi = {10.1109/ASONAM.2011.68},
abstract = {The evolving of Internet technology allows people to communicate even they are far away from each other. More and more people share information and exchange their thoughts via the communities on the websites and become friends. A larger community usually attracts more users, therefore, how to enhance the development of a social network on the website is an important issue for the survival of a website. In this paper, we combine the social network features into the recommendation system. In addition to messages between nodes, the features of network structure are taken into consideration. Experimental results show that the recommendation accuracy of our method is higher than the existing method which is based on the message ratio.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {709–714},
numpages = {6},
keywords = {network structure, recommendation system, social network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.51,
author = {Saravanan, M. and Buveneswari, S. and Divya, S. and Ramya, V.},
title = {Bayesian Filters for Mobile Recommender Systems},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.51},
doi = {10.1109/ASONAM.2011.51},
abstract = {With millions of registered users and a phenomenal increase in the amount of information exchange between them, it is clear that social networks are becoming increasingly popular on the Internet. Due to this popularity, they have become an important platform for business and marketing activities. In this paper, we understand the availability and the need of social networking sites with the mobile environment. The existing mobile recommender systems focus on targeting the users based on predicted demographics rather than the interests of the user. By extracting users' information from social networking sites, we design four novel filters to improve the targeting accuracy of the mobile service providers. Finally we have applied the Bayesian approach on the filters to arrive at a proper response percentage of the targeted customers. In essence, we ensure that based on applied strategy of understanding the customer's unique preferences, mobile service providers can launch specific campaigns on selected groups of users to get useful feedback. Moreover, it improves customer loyalty towards the operator. We have evaluated our findings through standard techniques on real world data.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {715–721},
numpages = {7},
keywords = {Mobile Recommender System, Social Networks, Natural Language Processing, Bayesian Approach, K-Means Clustering},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.122,
author = {Yang, Cheng-Ta and Chang, Wen-Sheng and Cheng, Fan-Ning and Teng, Wei-Guang},
title = {Assessing Media Relevance via Eye Tracking},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.122},
doi = {10.1109/ASONAM.2011.122},
abstract = {To ease the problem of data overloading, it is crucial to understand the user behavior when s/he interacts with online contents, or more specifically, a web page containing several entries for further exploration. We devise to estimate the relevance of an entry to the user goal by observing eye movements as implicit feedback. Specifically, this study proposes a framework that assumes eye movement measures can be used to infer a user s cognition. A rating task was conducted in which subjects were required to judge whether an image was relevant to a word. Results showed that the total fixation duration and the fixation count can be used to discriminate between the relevant and irrelevant conditions, in contrast, the first fixation duration cannot. In addition, the subjective rating and relevancy manipulation interacted on the total fixation duration. Converging evidence verifies the assumption we have proposed.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {722–727},
numpages = {6},
keywords = {social media, relevance feedback, eye movement},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.15,
author = {Shao, Jianhua},
title = {Mobile Engagement},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.15},
doi = {10.1109/ASONAM.2011.15},
abstract = {Web of Things is a practice towards Internet of Things. It enables physical digital objects to be organised as web resources. Mobility enables mobile phones as a great media to engage people with operations to those extended web resources in Web of Things. Mobile engagements include three scopes with differences on networking and purposing. We illustrate each engagement with specific examples. Each engagement could complement each other to improve overall performance. In practice, apps are the actual implementations for mobile engagement. Meanwhile, privacy control of digital footprint needs to be explored further for mobile engagement because of data explosion in the Web of Things.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {728–731},
numpages = {4},
keywords = {Applications, Web of Things, Mobile, Services, HCI, Future, Engagement},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.25,
author = {Ting, I-Hsien and Lin, Chia-Hung and Wang, Chen-Shu},
title = {Constructing a Cloud Computing Based Social Networks Data Warehousing and Analyzing System},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.25},
doi = {10.1109/ASONAM.2011.25},
abstract = {The research area of Social networks analysis has been recognized as extremely time-consuming tasks as well as large storage space is always necessary in order to store the social data, especially to deal with the data in the World Wide Web. Therefore, how to design an architecture and environment for performing social networks analysis is very essential. In this paper, we proposed a data warehousing and analyzing system which is based on the concept of cloud computing. The system has also been implemented and evaluated under the proposed environment with different cloud computing approaches.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {735–740},
numpages = {6},
keywords = {Hama, BSP, Social Networks Analysis, Hadoop, Cloud Computing, MapReduce},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.79,
author = {Yang, Bo-Wen and Tsai, Wen-Chih and Chen, An-Pin and Ramandeep, Singh},
title = {Cloud Computing Architecture for Social Computing - A Comparison Study of Facebook and Google},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.79},
doi = {10.1109/ASONAM.2011.79},
abstract = {As far as we know, cloud computing is a new business model and the cloud computing architecture is the famous topic recently. Today, everyone enjoy the innovative search engine or social network application for new Internet services no longer require the large capital outlays in hardware to access those service or the human expense to operate it. Currently, Google is the largest search engine and Face book is the largest social network in the Software as a Service (SaaS). But how them can support the huge requests from world thought each personal computer, mobile device, and smart phone. In this paper, we will try to analysis their backend cloud computing architecture to support future SaaS especially in large social network.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {741–745},
numpages = {5},
keywords = {Software as a Service, Cloud Computing, social network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.125,
author = {Tu, Chien-Chih and Chen, An-Pin},
title = {Building a Learning Games Network in Cloud Learning Platform Based on Immigrant Education},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.125},
doi = {10.1109/ASONAM.2011.125},
abstract = {As with rapid growth of the computer technology, E-learning systems usually require many hardware and software resources, There are numerous educational institutions that cannot offer such investments, and cloud learning platform is the best solution for them. This paper proposes a model of using cloud computing and learning network upon cloud-learning solutions development. The cloud-learning platform combined with different types of games is gradually noticed by people because it can enhance user learning motivation. This research has developed a Chinese language cloud-learning system for the new immigrant based on games mode, and investigated the properties of game-based cloud-learning system, expecting to help more and more new immigrants in Taiwan. The basic concept for designing the system is developing the learning games network using digital materials, which is applied to the cloud learning platform to attract the immigrant residents and assist them to improve Chinese language skills.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {746–750},
numpages = {5},
keywords = {Cloud-Learning Platform, Social Networks, Game-Based Learning, Immigrant Education},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.97,
author = {Wen, Mao-Ping and Lin, Hsio-Yi and Chen, An-Pin and Yang, Chyan},
title = {An Integrated Home Financial Investment Learning Environment Applying Cloud Computing in Social Network Analysis},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.97},
doi = {10.1109/ASONAM.2011.97},
abstract = {This paper tried to apply cloud computing technology in social network analysis for a comprehensive home financial learning environment that individual investors may use as a reference in establishing web-based learning and investment platforms. The major contributions were described in three parts. First, this paper advanced the social network analysis technology to be able to handle millions of nodes and links. Second, we demonstrate how cloud computing can be applied to advanced computing in social network. Third, we performed several intelligent analyses on a very popular social network, IHFILE, to identify some interesting and important features of it. In addition to analyzing a homogeneous social network such as IHFILE, we also propose direction of how cloud computing can be performed on a social network analysis as our future work.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {751–754},
numpages = {4},
keywords = {Social network analysis, Integrated home financial investment learning environment, Cloud computing, Micro-blogging},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.127,
title = {Author Index},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.127},
doi = {10.1109/ASONAM.2011.127},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {755–758},
numpages = {4},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.128,
title = {Publisher's Information},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.128},
doi = {10.1109/ASONAM.2011.128},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {760},
numpages = {1},
series = {ASONAM '11}
}

license_type: permissive

extension: bib


text:
DEFAULT_PREFERENCE = "-1"

require qt4-tools-native.inc
LICENSE = "LGPLv2.1 GPLv3"
PR = "${INC_PR}.0"

EXTRA_OECONF += " -fast"

TOBUILD := "src/tools/bootstrap ${TOBUILD}"

SRC_URI[md5sum] = "2a7b5126f2450d8525af355fc4c12ad6"
SRC_URI[sha256sum] = "55259c813324f6383cbd441aa2f23e01c320b6d63fbe3b5d52a7715055d28589"

license: MIT

repository: libo/openembedded

repo_name: libo/openembedded

branch_name: HEAD

extension: bb


text:
require bug-app.inc

DESCRIPTION = "A demonstration that takes a picture with the camera when you hold it still for a little while. Image is shown on the LCD (not saved)."
HOMEPAGE = "http://buglabs.net/applications/hellocamera"

DEPENDS += "service-tracker com.buglabs.bug.module.motion com.buglabs.bug.module.lcd com.buglabs.bug.module.camera com.buglabs.common com.buglabs.osgi com.buglabs.bug.jni.accelerometer com.buglabs.bug.module.motion "

PV = "1"

SRC_LINK = "http://buglabs.net/program_version/download/658"

APIVERSION = ""

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty


text:
DESCRIPTION = "Net::IP - Perl extension for manipulating IPv4/IPv6 addresses"
SECTION = "libs"
LICENSE = "Artistic|GPLv1+"
DEPENDS += "libio-zlib-perl-native"
RDEPENDS_${PN} += "libio-zlib-perl perl-module-math-bigint"
PR = "r8"

SRC_URI = "http://search.cpan.org/CPAN/authors/id/M/MA/MANU/Net-IP-${PV}.tar.gz"

S = "${WORKDIR}/Net-IP-${PV}"

inherit cpan

BBCLASSEXTEND="native"

SRC_URI[md5sum] = "a49c0b02a9b793ff60191cdafc0c202e"
SRC_URI[sha256sum] = "4cd6e2202fd88c46d5458d19bbea80e257b6ba0ec57ea3ac8ae94ed48c9a60c7"


text:
var NODE = NODE || false;
// check for node.js
if ((NODE)) {
  var ss = {
    ready: require('../../dist/ready.node.js')
  };
}

module('ss.ready', {
  setup: function(){
    ss.ready.reset();
  },
  teardown: function() {
  }
});

// Parameters fixture
var ParamsFixture = function()
{
  return {};
};

/**
 * Get the params fixture array
 *
 * @param {boolean=} opt_JSON set to true to get a JSON string
 * @return {Array|string}
 */
var getParams = function(opt_JSON)
{
  if (opt_JSON) {
    return JSON.stringify(new ParamsFixture());
  } else {
    return new ParamsFixture();
  }
};

test('Core functionality', function() {
  expect( 9 );

  stop();

  function hookOne()
  {
    ok(true, 'Listener One executed');
  }
  function hookTwo()
  {
    ok(true, 'Listener Two executed');
  }

  function hookThree()
  {
    ok(true, 'Listener Three executed');
  }
  function hookFour()
  {
    ok(true, 'Listener Four executed');
  }

  function hookFive()
  {
    ok(true, 'Listener Five executed');
  }

  ss.ready(hookOne);
  ss.ready(hookTwo);

  var main = ss.ready('main');

  // add 2 checks
  main.addCheck('task1');
  main.addCheck('task2');

  // add our listeners - test no params call
  ss.ready().addListener(hookThree);

  ok(!main.isDone(), 'Ready watch is not done yet');

  // complete the checks
  ok(!main.isDoneCheck('task1'), 'task 1 is not done yet');
  main.check('task1');
  ok(main.isDoneCheck('task1'), 'task 1 is now done');

  // get main watch in another variable
  var niam = ss.ready('main');
  niam.addListener(hookFour);

  niam.check('task2');

  ok(niam.isDone(), 'Ready watch is now done');

  // a done ready watch should execute synchronously the listener
  niam.addListener(hookFive);

  // try to trick the lib with dummy check call
  niam.check('not exist');

  start();

});

test('Sequence of execution', function() {
  expect( 12 );
  stop();

  var seq = 1;

  function addHook(testSeq, title, opt_done)
  {
    return function(){
      equal(seq, testSeq, title);
      seq++;
      if (opt_done) {
        start();
      }
    };
  }

  var r = ss.ready('a-ready-watch');

  // add checks
  r.addCheck('task1');
  r.addCheck('task2');
  r.addCheck('task3');

  // add listeners
  r.addListener(addHook(7, 'First main hook executed'));
  r.addListener(addHook(8, 'Second main hook executed'));
  r.addListener(addHook(9, 'Lazy hook executed'));
  r.addListener(addHook(10, 'Very lazy hook executed', true));
  r.addCheckListener('task1', addHook(1, 'Task1 check hook executed'));
  r.addCheckListener('task2', addHook(3, 'Task2 check hook executed'));
  r.addCheckListener('task2', addHook(4, 'Task2 check lazy hook executed'));
  r.addCheckListener('task3', addHook(6, 'Task3 check hook executed'));

  ok(!r.isDoneCheck('task1'), 'task1 is not done yet');

  // complete the checks
  r.check('task1');
  ok(r.isDoneCheck('task1'), 'task1 is now done');

  equal(seq, 2, 'After task1 is done');
  seq++;

  r.check('task2');

  equal(seq, 5, 'After task2 is done');
  seq++;

  // execute after 100ms
  setTimeout(function(){
    r.check('task3');
  }, 80);
});

test('Multiple Ready Watches', function() {

  expect( 28 );

  var seq = 1;
  function addHook(testSeq, title, opt_done)
  {
    return function(){
      equal(seq, testSeq, title);
      seq++;
      if (opt_done) {
        start();
      }
    };
  }

  // 14 names
  var readyNames = ['apple', 'oragne', 'banana', 'alpha', 'bravo', 'charlie', 'delta',
      'echo', 'foxtrot', 'golf', 'hotel', 'India', 'JulieT', 'Kilo'];

  // create ready watches and add checks
  var readyWatches = [];
  var r = null;
  for(var i = 0, l = readyNames.length; i < l ; i++) {
    r = ss.ready(readyNames[i]);
    r.addCheck('task1');
    r.addCheck('task2');
    r.addCheck('task3');
  }

  // add listeners to ready watches
  for(i = 0 ; i < l ; i++) {
    r = ss.ready(readyNames[i]);
    r.addListener(addHook(i + i + 1, readyNames[i] + ' hook executed No1'));
    r.addListener(addHook(i + i + 2, readyNames[i] + ' hook executed No2'));
  }

  // Complete checks
  for(i = 0 ; i < l ; i++) {
    r = ss.ready(readyNames[i]);
    r.check('task1');
    r.check('task2');
    r.check('task3');
  }

});

test('Lazy hooks and force init', function(){
  expect( 3 );
  function readyOne() {
    ok(true, 'Lazy ready watch should be executed');
  }

  function checkOne() {
    ok(true, 'Lazy check should be executed');
  }

  var r = ss.ready('a-watch');
  r.addCheck('a-check');

  // finish the ready watch
  r.check('a-check');

  // now add listeners after the watch is done
  r.addListener(readyOne);
  r.addCheckListener('a-check', checkOne);

  // now force init of our ready watch
  var newR = ss.ready('a-watch', true);
  newR.addListener(readyOne);
  // again, force init to overwrite previous addListener call
  var moreR = ss.ready('a-watch', true);
  moreR.addListener(readyOne);
  moreR.addCheck('one');
  moreR.one();
  // our basic assertion here is that we expect 3  asserts
});

test('Checks as function and chaining', function(){
  expect( 2 );
  function readyOne() {
    ok(true, 'Watch should be executed');
  }

  function checkOne() {
    ok(true, 'Check should be executed');
  }

  var r = ss.ready('aWatch');
  var oddName = 'dashes-not-allowed-as-func-names';
  r.addCheck('aCheck')
    .addCheck(oddName)
    .addListener(readyOne)
    .addCheckListener('aCheck', checkOne)
    // execution on an instance
    .aCheck();
  // execution from builder
  ss.ready('aWatch')[oddName]();
});

test('Early listeners and heavy chaining', function(){
  expect( 3 );

  function hookOne()
  {
    ok(true, 'Listener One executed');
  }
  function hookTwo()
  {
    ok(true, 'Check Listener Two executed');
  }
  function hookThree()
  {
    ok(true, 'Listener Three executed');
  }

  var r = ss.ready('a silly name');
  r.addListener(hookOne);

  var checkTwo = 'check Two';

  r.addCheck('check One') // add check
    .addCheckListener(checkTwo, hookTwo) // chain addCheckListener
    .addCheck(checkTwo);
  ss.ready('a silly name')[checkTwo]() // chain check execution (check done)
    .addListener(hookThree) // chain addListener
    .check('check One');
});

test('reset and Dispose method', function(){
  expect( 1 );

  function hookOne()
  {
    ok(false, 'Listener One should not be executed');
  }
  function hookTwo()
  {
    ok(true, 'Check Listener Two executed');
  }
  function hookThree()
  {
    ok(false, 'Listener Three should not be executed');
  }

  ss.ready.reset();

  var r = ss.ready('a silly name');
  r.addListener(hookOne);

  var checkTwo = 'check Two';

  r.addCheck('check One')
    .addCheckListener(checkTwo, hookTwo) // chain addCheckListener
    .addCheck(checkTwo); // addCheck
  ss.ready('a silly name')[checkTwo]() // chain check execution (check done)
    .addListener(hookThree); // chain addListener

  // dispose
  ss.ready('a silly name').dispose();

  // try to run the last check...
  r.check('check One');

});

test('arguments and callbacks', function(){

  expect( 28 );

  var pass = 1;

  var mockArgs = [4, 'foo', {a:1, b:{c:2}, d:'go'}];

  function hookCheckOne(arg1, arg2, arg3)
  {
    equal(arg1, mockArgs[0], 'Comparing arg1. Pass:' + pass);
    equal(arg2, mockArgs[1], 'Comparing arg2. Pass:' + pass);
    deepEqual(arg3, mockArgs[2], 'Comparing arg3. Pass:' + pass);
    equal(arguments.length, 3, 'checkOne hook must have 3 arguments. Pass:' + pass);
  }

  function hookOne(ready)
  {
    var newReady = ss.ready('go');

    ok(ready instanceof ss.ready.C, 'ready argument is instance of ready class. Pass:' + pass);
    deepEqual(ready, newReady, 'Deep comparing ready instances. Pass:' + pass);

    deepEqual(ready.getArgs('checkOne'), mockArgs, 'deep comparing checkOne arguments. Pass:' + pass);

    equal(ready.getArgs('checkOne').length, 3, 'checkOne args must be 3. Pass:' + pass);

    var checkTwoArgs = ready.getArgs('checkTwo');
    strictEqual(checkTwoArgs[1], null, 'Second arg of checkTwo is null. Pass:' + pass);
    equal(checkTwoArgs.length, 2, 'checkTwo has 2 total args. Pass:' + pass);

    var checkThreeArgs = ready.getArgs('checkThree');
    equal(checkThreeArgs.length, 4, 'checkThree has 4 total args. Pass:' + pass);
    equal(checkThreeArgs[3], 100, 'checkThree last arg is 100. Pass:' + pass);

    var checkFourArgs = ready.getArgs('checkFour');
    equal(checkFourArgs.length, 0, 'Check four has no arguments. Pass:' + pass);
    equal(Object.prototype.toString.call(checkFourArgs), '[object Array]', 'checkFourArgs must be of type array. Pass:' + pass);
  }

  var r = ss.ready('go')
    .addCheck('checkOne')
    .addCheck('checkTwo')
    .addCheck('checkThree')
    .addCheck('checkFour');

  r.addCheckListener('checkOne', hookCheckOne);
  r.addListener(hookOne);

  r.check.apply(r, Array.prototype.concat('checkOne', mockArgs));

  r.check('checkTwo', false, null);

  r.checkThree('foo', 'bar', 'baz', 100);

  r.checkFour();

  // call a lazy hook
  pass++;
  r.addListener(hookOne);

  // and a lazy check hook
  r.addCheckListener('checkOne', hookCheckOne);

});

test('Check callback binding', function(){
  expect( 12 );

  // create a pseudo class
  var AwesomeClass = function(opt_val) {
    this.val = opt_val || 1;
  };
  AwesomeClass.prototype.add = function(val) {
    this.val += val;
  };
  AwesomeClass.prototype.get = function() {
    return this.val;
  };

  var ac = new AwesomeClass(5);
  ac.add(4);

  var acTwo = new AwesomeClass(3);
  acTwo.add(3);

  function hookCheckOne()
  {
    ok(this instanceof AwesomeClass, 'this object is an instance of AwesomeClass');
    equal(this.get(), 9, '.get() method of this should return 9');
    this.add(3);
  }

  function hookTwo()
  {
    ok(this instanceof AwesomeClass, 'this object is an instance of AwesomeClass -two');
    equal(this.get(), 6, '.get() method of this should return 6');
  }

  function hookThree()
  {
    ok(this instanceof AwesomeClass, 'this object is an instance of AwesomeClass -three');
    equal(this.get(), 12, '.get() method of this should return 12');
  }

  var r = ss.ready('bind')
    .addCheck('one')
    .addCheck('two');

  r.addCheckListener('one', hookCheckOne, ac);
  r.addListener(hookTwo, acTwo);
  r.addListener(hookThree, ac);

  r.check('one');
  r.two();

  // call everything lazily
  ac.add(-3);
  r.addCheckListener('one', hookCheckOne, ac);
  r.addListener(hookTwo, acTwo);
  r.addListener(hookThree, ac);

});

branch_name: refs/heads/master

gha_language: JavaScript

extension: js


text:
package chapter13

import (
	"fmt"
	"testing"
)

func TestCountOccurrencesInSentence(t *testing.T) {
	s := "bcdacebe"
	fmt.Printf("CountOccurrencesInSentence(%q)\n", s)
	CountOccurrencesInSentence(s)
	fmt.Println()
}

extension: go


text:
@article{doi:10.1002/rob.20093,
author = {Emanuel Todorov and Weiwei Li and Xiuchuan Pan},
title = {From task parameters to motor synergies: A hierarchical framework for approximately optimal control of redundant manipulators},
journal = {Journal of Robotic Systems},
volume = {22},
number = {11},
pages = {691-710},
doi = {10.1002/rob.20093},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.20093},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.20093},
abstract = {Abstract We present a hierarchical framework for approximately optimal control of redundant manipulators. The plant is augmented with a low‐level feedback controller, designed to yield input‐output behavior that captures the task‐relevant aspects of plant dynamics but has reduced dimensionality. This makes it possible to reformulate the optimal control problem in terms of the augmented dynamics, and optimize a high‐level feedback controller without running into the curse of dimensionality. The resulting control hierarchy compares favorably to existing methods in robotics. Furthermore, we demonstrate a number of similarities to (nonhierarchical) optimal feedback control. Besides its engineering applications, the new framework addresses a key unresolved problem in the neural control of movement. It has long been hypothesized that coordination involves selective control of task parameters via muscle synergies, but the link between these parameters and the synergies capable of controlling them has remained elusive. Our framework provides this missing link. © 2005 Wiley Periodicals, Inc.}
}

repository: MaxRobinson/CALispCritique

repo_name: MaxRobinson/CALispCritique


text:
DESCRIPTION = "The Openmoko Application Manager"
SECTION = "openmoko/applications"
DEPENDS = "libmokoui2 libmokojournal2 startup-notification dbus-glib libice libsm opkg"
PV = "0.1.0+svnr${SRCREV}"
PR = "r2"

inherit openmoko2

FILES_${PN} += "/usr/share/openmoko-appmanager"


text:
package handlers

import (
	"fmt"
	"github.com/go-kit/kit/log/level"
	"github.com/gorilla/mux"
	dc "github.com/skoona/homie-service/pkg/deviceCore"
	"net/http"
	"strings"
)

// swagger:route GET /networks Network-Operations networks
// List all SiteNetworks
// responses:
//	202: networksResponse
//  500: genericError

// AllNetworks  List all SiteNetworks
func (c *Controller) AllNetworks(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "AllNetworks() called")

	body := c.service.AllNetworks()

	rw.WriteHeader(http.StatusAccepted)
	err := ToJSON(body, rw)
	if err != nil {
		rw.WriteHeader(http.StatusInternalServerError)
		level.Error(c.logger).Log( "error", err.Error())
	}
}

// swagger:route GET /devices/{networkName} Network-Operations devices
// List devices on the named network
// responses:
//	202: devicesResponse
//  404: genericError
//  406: validationError

// NetworkDevices (networkName string) (map[string]Device)
func (c *Controller) NetworkDevices(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "NetworkDevices() called")

	vars := mux.Vars(r)

	body, err := c.service.NetworkDevices(vars["networkName"])
	if err != nil {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusNotFound)
		ToJSON(&GenericError{Message: fmt.Sprintf("Network with id: %s not found", vars["networkName"])}, rw)
		return
	}

	rw.WriteHeader(http.StatusAccepted)
	err = ToJSON(body, rw)
	if err != nil {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusNotFound)
		ToJSON(&GenericError{Message: err.Error()}, rw)
	}
}

// swagger:route GET /network/{networkName} Network-Operations network
// Get specific network by name
// responses:
//	202: networkResponse
//  404: genericError
//  406: validationError

// NetworkByName (networkName string) Network
func (c *Controller) NetworkByName(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "NetworkByName() called")

	vars := mux.Vars(r)

	body := c.service.NetworkByName(vars["networkName"])
	if body.ElementType != dc.CoreTypeNetwork {
		level.Error(c.logger).Log( "error", fmt.Sprintf("Network with id: %s not found", vars["networkName"]))
		rw.WriteHeader(http.StatusNotAcceptable)
		ToJSON(&GenericError{Message: fmt.Sprintf("Network with id: %s not found", vars["networkName"])}, rw)
		return
	}

	rw.WriteHeader(http.StatusAccepted)
	err := ToJSON(body, rw)
	if err != nil {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusNotFound)
		ToJSON(&GenericError{Message: err.Error()}, rw)
	}
}

// swagger:route GET /deviceByName/{networkName}/{deviceName} Network-Operations deviceByName
// Get a specific device on the named network
// responses:
//	202: deviceResponse
//  404: genericError
//  500: genericError

// DeviceByName (deviceName, networkName string) (Device, error)
func (c *Controller) DeviceByName(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "DeviceByName() called")

	vars := mux.Vars(r)

	body, err := c.service.DeviceByName(vars["deviceName"], vars["networkName"])
	if err == nil {
		rw.WriteHeader(http.StatusAccepted)
		err := ToJSON(body, rw)
		if err != nil {
			rw.WriteHeader(http.StatusInternalServerError)
			level.Error(c.logger).Log( "error", err.Error())
		}
	} else {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusNotFound)
		ToJSON(&GenericError{Message: err.Error()}, rw)
	}
}

// swagger:route GET /deviceById/{networkName}/{deviceID} Network-Operations  deviceById
// Get a specific device using its unique deviceID the named network
// responses:
//	202: deviceResponse
//  404: genericError
//  406: validationError
//  500: genericError

// DeviceByID (deviceID string, networkName string) (Device, error)
func (c *Controller) DeviceByID(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "DeviceByID() called")

	vars := mux.Vars(r)
	emsg := c.validation.ValidateParam(vars["deviceID"], "eid")
	if len(emsg.Messages) != 0 {
		rw.WriteHeader(http.StatusNotAcceptable)
		level.Error(c.logger).Log( "validation", emsg.Messages)
		ToJSON(&emsg, rw)
		return
	}

	body, err := c.service.DeviceByID(vars["deviceID"], vars["networkName"])
	if err == nil {
		rw.WriteHeader(http.StatusAccepted)
		err := ToJSON(body, rw)
		if err != nil {
			rw.WriteHeader(http.StatusInternalServerError)
			level.Error(c.logger).Log( "error", err.Error())
		}
	} else {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusNotFound)
		ToJSON(&GenericError{Message: err.Error()}, rw)
	}
}

// swagger:route DELETE /removeDeviceId/{networkName}/{deviceID} Network-Operations removeDeviceId
// Remove a device from the named network
// responses:
//	204: noContentResponse
//  404: genericError
//  406: validationError

// RemoveDeviceID (deviceID string, networkName string) error
func (c *Controller) RemoveDeviceID(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "RemoveDeviceID() called")

	vars := mux.Vars(r)
	emsg := c.validation.ValidateParam(vars["deviceID"], "eid")
	if len(emsg.Messages) != 0 {
		rw.WriteHeader(http.StatusNotAcceptable)
		level.Error(c.logger).Log( "validation", emsg.Messages)
		ToJSON(&emsg, rw)
		return
	}

	err := c.service.RemoveDeviceByID(vars["deviceID"], vars["networkName"])
	if err == nil {
		rw.WriteHeader(http.StatusNoContent)
	} else {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusNotFound)
		ToJSON(&GenericError{Message: err.Error()}, rw)
	}
}

// NetworkMessageRequest send any message over MQTT
type NetworkMessageRequest struct {
	Topic    string `json:"topic" validate:"required"`
	Qos      int    `json:"qos" validate:"required"`
	Retained string   `json:"retained" validate:"required"`
	Value    string `json:"value"`
}

// swagger:route POST /publishNetworkMessage Network-Operations publishNetworkMessage
// Publish MQTT Message
// responses:
//	204: noContentResponse
//  404: genericError
//  406: validationError
//  422: genericError

// PublishNetworkMessage (dm DeviceMessage) topic, payload, qos, bRetained
func (c *Controller) PublishNetworkMessage(rw http.ResponseWriter, r *http.Request) {
	level.Debug(c.logger).Log( "api-method", "RemoveDeviceID() called")

	nmr := NetworkMessageRequest{}
	err := FromJSON(&nmr, r.Body)
	if err != nil {
		level.Error(c.logger).Log( "error", err.Error())
		rw.WriteHeader(http.StatusUnprocessableEntity)
		ToJSON(&GenericError{Message: err.Error()}, rw)
		return
	}

	// validate the request
	errs := c.validation.ValidateStruct(nmr)
	if len(errs.Messages) != 0 {
		c.logger.Log("validation", errs)

		// return the validation messages as an array
		rw.WriteHeader(http.StatusNotAcceptable)
		ToJSON(&errs, rw)
		return
	}

	tParts := strings.Split(nmr.Topic, "/")

	dm := dc.DeviceMessage{
		NetworkID: []byte(tParts[0]),
		DeviceID: []byte(tParts[1]),
		HomieType: dc.CoreTypePublishMessage,
		TopicS: nmr.Topic,
		Value: []byte(nmr.Value),
		Qosb: byte(nmr.Qos),
		RetainedB: strings.EqualFold(nmr.Retained, "true"),
	}

	c.service.PublishNetworkMessage(dm)
	rw.WriteHeader(http.StatusNoContent)
}

repository: skoona/homie-service

repo_name: skoona/homie-service

branch_name: refs/heads/main


text:
@article{Brown2017a,
 abstract = {BACKGROUND: Depressive disorder is the largest contributor to years lived with disability in the Caribbean, adding 948 per 100,000 in 2013. Depression is also a major risk factor for suicidal behaviour. Social inequalities influence the occurrence of depression, yet little is known about the social inequalities of this condition in the Caribbean. In support of the 2011 Rio Political Declaration on addressing health inequities, this article presents a systematic review of the role of social determinants on depression and its suicidal behaviours in the Caribbean. METHODS: Eight databases were searched for observational studies reporting associations between social determinants and depression frequency, severity, or outcomes. Based on the PROGRESS-plus checklist, we considered 9 social determinant groups (of 15 endpoints) for 6 depression endpoints, totalling 90 possible ways ('relationship groups') to explore the role of social determinants on depression. Studies with ≥50 participants conducted in Caribbean territories between 2004 and 2014 were eligible. The review was conducted according to STROBE and PRISMA guidelines. Results were planned as a narrative synthesis, with meta-analysis if possible. RESULTS: From 3951 citations, 55 articles from 45 studies were included. Most were classified as serious risk of bias. Fifty-seven relationship groups were reported by the 55 included articles, leaving 33 relationship groups (37%) without an evidence base. Most associations were reported for gender, age, residence, marital status, and education. Depression, its severity, and its outcomes were more common among females (except suicide which was more common among males), early and middle adolescents (among youth), and those with lower levels of education. Marriage emerged as both a risk and protective factor for depression score and prevalence, while several inequality relationships in Haiti were in contrast to typical trends. CONCLUSION: The risk of bias and few numbers of studies within relationship groups restricted the synthesis of Caribbean evidence on social inequalities of depression. Along with more research focusing on regional social inequalities, attempts at standardizing reporting guidelines for observational studies of inequality and studies examining depression is necessitated. This review offers as a benchmark to prioritize future research into the social determinants of depression frequency and outcomes in the Caribbean.},
 article-pii = {4371},
 author = {Brown, Catherine R. and Hambleton, Ian R. and Sobers-Grannum, Natasha and Hercules, Shawn M. and Unwin, Nigel and Nigel Harris, E. and Wilks, Rainford and MacLeish, Marlene and Sullivan, Louis and Murphy, Madhuvanti M.},
 completed = {20171127},
 corporate = {U.S. Caribbean Alliance for Health Disparities Research Group (USCAHDR)},
 doi = {10.1186/s12889-017-4371-z},
 electronic-issn = {1471-2458},
 electronic-publication = {20170615},
 grantno = {U24 MD006959/MD/NIMHD NIH HHS/United States},
 history = {2017/11/29 06:00 [medline]},
 investigator = {Alvarado, M, Alvarado M, Bennett, N, Bennett N, Bidulescu, A, Bidulescu A, Brown, C R, Brown CR, Ferguson, T, Ferguson T, Francis, D, Francis D, Hambleton, I R, Hambleton IR, Harris, E N, Harris EN, Hassell, C, Hassell C, Hennis, Ajm, Hennis A, Hercules, S M, Hercules SM, Howitt, C, Howitt C, Mac Leish, M, Mac Leish M, Murphy, M M, Murphy MM, Samuels, T A, Samuels TA, Sobers-Grannum, N, Sobers-Grannum N, Sullivan, L, Sullivan L, Unwin, N, Unwin N, Wilks, R, Wilks R, Williams, L, Williams L, Younger-Coleman, N, Younger-Coleman N},
 issue = {1},
 journal = {BMC public health},
 keywords = {Age Distribution, Caribbean Region/epidemiology, Depressive Disorder/*epidemiology, Humans, Observational Studies as Topic, Prevalence, Risk Factors, Severity of Illness Index, Sex Distribution, Social Determinants of Health/*statistics & numerical data, Suicide/*statistics & numerical data},
 language = {eng},
 linking-issn = {1471-2458},
 location-id = {577},
 month = {Jun},
 nlm-unique-id = {100968562},
 owner = {NLM},
 pages = {577},
 publication-status = {epublish},
 revised = {20181202},
 source = {BMC Public Health. 2017 Jun 15;17(1):577. doi: 10.1186/s12889-017-4371-z.},
 status = {MEDLINE},
 subset = {IM},
 title = {Social determinants of depression and suicidal behaviour in the Caribbean: a systematic review.},
 title-abbreviation = {BMC Public Health},
 volume = {17},
 year = {2017}
}


text:
#
# base recipe: http://git.yoctoproject.org/cgit/cgit.cgi/meta-maker/tree/recipes-python/monotonic/python-monotonic_1.3.bb?h=master
#
SUMMARY = "implementation of time.monotonic() - Python 2.x"
DESCRIPTION= "This module provides a monotonic() function which returns the value (in \
fractional seconds) of a clock which never goes backwards. On Python 3.3 or \
newer, monotonic will be an alias of time.monotonic from the standard library. \
On older versions, it will fall back to an equivalent implementation: \
GetTickCount64 on Windows, mach_absolute_time on OS X, and clock_gettime(3) \
on Linux/BSD."

inherit debian-package
require recipes-debian/sources/python-monotonic.inc

LICENSE = "Apache-2.0"
LIC_FILES_CHKSUM = "file://LICENSE;md5=d2794c0df5b907fdace235a619d80314"

DEBIAN_QUILT_PATCHES = ""

inherit setuptools

RDEPENDS_${PN} += "${PYTHON_PN}-ctypes ${PYTHON_PN}-io ${PYTHON_PN}-re ${PYTHON_PN}-threading"

repository: meta-debian/meta-debian

repo_name: meta-debian/meta-debian

branch_name: refs/heads/warrior

gha_license_id: MIT

gha_language: BitBake


text:
import getCoordinates from './getCoordinates'
import clamp from './clamp'

export default function (action) {
  const {
    y, x, target, originX = 50, strength = 10, event = null, minX, minY, maxX, maxY,
  } = action

  let { originY = 50, } = action

  if (event === 'scroll') {
    originY = -originY / 2
  }

  const movementX = clamp(
    (x - originX / 50) * strength,
    minX,
    maxX
  )
  const movementY = clamp(
    (y - originY / 50) * strength,
    minY,
    maxY
  )

  return {
    ...getCoordinates(movementX, movementY),
    target,
  }
}

repository: Aminerman/vue-kinesis

repo_name: Aminerman/vue-kinesis


text:
require recipes-image/console/console-net-image.bb
require recipes-image/include/x11-base.inc

export IMAGE_BASENAME = "gnome-base-image"

GNOME_DM ?= "gdm"

IMAGE_INSTALL += " \
    ${X11_BASE_INSTALL} \
    ${GNOME_DM} \
    \
    packagegroup-gnome-desktop \
    packagegroup-gui-base \
    \
    network-manager-applet \
"

# reenable graphical target
IMAGE_FEATURES += "x11-base"

repository: ZeraGmbH/meta-mortsgna

repo_name: ZeraGmbH/meta-mortsgna


text:
@article{cl-2017-linguistics-43,
    title = "Computational Linguistics, Volume 43, Issue 2 - June 2017",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
}
@article{irvine-callison-burch-2017-comprehensive,
    title = "A Comprehensive Analysis of Bilingual Lexicon Induction",
    author = "Irvine, Ann  and
      Callison-Burch, Chris",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2001",
    doi = "10.1162/COLI_a_00284",
    pages = "273--310",
}
@article{ballesteros-etal-2017-greedy,
    title = "Greedy Transition-Based Dependency Parsing with Stack {LSTM}s",
    author = "Ballesteros, Miguel  and
      Dyer, Chris  and
      Goldberg, Yoav  and
      Smith, Noah A.",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2002",
    doi = "10.1162/COLI_a_00285",
    pages = "311--347",
}
@article{sajjad-etal-2017-statistical,
    title = "Statistical Models for Unsupervised, Semi-Supervised Supervised Transliteration Mining",
    author = {Sajjad, Hassan  and
      Schmid, Helmut  and
      Fraser, Alexander  and
      Sch{\"u}tze, Hinrich},
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2003",
    doi = "10.1162/COLI_a_00286",
    pages = "349--375",
}
@article{chinaei-etal-2017-identifying,
    title = "Identifying and Avoiding Confusion in Dialogue with People with {A}lzheimer{'}s Disease",
    author = "Chinaei, Hamidreza  and
      Currie, Leila Chan  and
      Danks, Andrew  and
      Lin, Hubert  and
      Mehta, Tejas  and
      Rudzicz, Frank",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2004",
    doi = "10.1162/COLI_a_00290",
    pages = "377--406",
}
@article{jansen-etal-2017-framing,
    title = "Framing {QA} as Building and Ranking Intersentence Answer Justifications",
    author = "Jansen, Peter  and
      Sharp, Rebecca  and
      Surdeanu, Mihai  and
      Clark, Peter",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2005",
    doi = "10.1162/COLI_a_00287",
    pages = "407--449",
}
@article{paraboni-etal-2017-squib,
    title = "{S}quib: Effects of Cognitive Effort on the Resolution of Overspecified Descriptions",
    author = "Paraboni, Ivandr{\'e}  and
      Lan, Alex Gwo Jen  and
      Sant{'}Ana, Matheus Mendes de  and
      Coutinho, Fl{\'a}vio Luiz",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2006",
    doi = "10.1162/COLI_a_00288",
    pages = "451--459",
}
@article{ehrmann-2017-book,
    title = "Book Review: Linked Lexical Knowledge Bases Foundations and Applications by Iryna {G}urevych, Judith Eckle-er and {M}ichael Matuschek",
    author = "Ehrmann, Maud",
    journal = "Computational Linguistics",
    volume = "43",
    number = "2",
    month = jun,
    year = "2017",
    url = "https://www.aclweb.org/anthology/J17-2007",
    doi = "10.1162/COLI_r_00289",
    pages = "461--463",
}

repository: emorynlp/nlprankings

repo_name: emorynlp/nlprankings

gha_license_id: NOASSERTION

gha_language: TeX


text:
SUMMARY = "Base applications for Hawaii"
LICENSE = "GPLv2 & LGPLv2.1"
LIC_FILES_CHKSUM = " \
	file://LICENSE.GPLv2;md5=b234ee4d69f5fce4486a80fdaf4a4263 \
	file://LICENSE.LGPLv2.1;md5=4fbd65380cdd255951079008b364516c \
"

inherit hawaii systemd

DEPENDS += " \
    libhawaii \
    greenisland \
    polkit-qt-1 \
    qt-gstreamer \
"

SRCREV = "c882b526c131d860d584197d8141120535be324d"
PV = "0.6.90+git${SRCPV}"
S = "${WORKDIR}/git"

EXTRA_OECMAKE += " \
    -DSYSTEMD_USER_UNIT_DIR=${systemd_system_unitdir} \
"

FILES_${PN} += " \
    ${datadir}/glib-2.0 \
    ${datadir}/desktop-directories \
    ${OE_QMAKE_PATH_PLUGINS} \
"

RREPLACES_${PN} = "hawaii-baseapps"
RPROVIDES_${PN} = "hawaii-baseapps"
RCONFLICTS_${PN} = "hawaii-baseapps"


text:
SECTION = "devel/chicken"
DESCRIPTION = "Sorting and merging"
LICENSE = "PD"
LIC_FILES_CHKSUM = "file://srfi-95.meta;md5=f8e5c7c392af783957e7a4d28706e9d6"
DEPENDS_append_class-cross = " chicken-egg-srfi-63-cross chicken-egg-setup-helper-cross"
DEPENDS_append_class-crosssdk = " chicken-egg-srfi-63-crosssdk chicken-egg-setup-helper-crosssdk"
DEPENDS_append_class-nativesdk = " nativesdk-chicken-egg-srfi-63 nativesdk-chicken-egg-setup-helper chicken-egg-srfi-63-crosssdk chicken-egg-setup-helper-crosssdk"
DEPENDS = " chicken-egg-srfi-63 chicken-egg-setup-helper chicken-egg-srfi-63-cross chicken-egg-setup-helper-cross"
RDEPENDS_${PN} = " chicken-egg-srfi-63 chicken-egg-setup-helper"

SRC_URI = "http://code.call-cc.org/egg-tarballs/4/srfi-95/srfi-95-${PV}.tar.gz"

SRC_URI[md5sum] = "2dee3c53cedb63213b35db1193c7d49b"
SRC_URI[sha256sum] = "3286afa63c49af5e951552735c99a75e665863253a0aa898d8df0b38bf14aad9"

S = "${WORKDIR}/srfi-95-${PV}"

inherit chicken_install

BBCLASSEXTEND = "cross crosssdk nativesdk"

repository: OSSystems/meta-chicken

repo_name: OSSystems/meta-chicken


text:
SRCREV = "e868971c7fc3a1829abda893075e3fd3d034e08e"

require libiio.inc

repository: kyoungblee/meta-adi

repo_name: kyoungblee/meta-adi