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Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders
- Bartholomew Gregory
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1 Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders Anne-Marie Kermarrec, François Taïani, Juan Manuel Tirado Martin To cite this version: Anne-Marie Kermarrec, François Taïani, Juan Manuel Tirado Martin. Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders. Proceedings of the 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS-2015), Jun 2015, Grenoble, France. Springer International Publishing, pp.14, 2015, <http://link.springer.com/book/ / >. < / >. <hal > HAL Id: hal https://hal.inria.fr/hal Submitted on 2 Jul 2015 HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
2 Cheap and Cheerful: Trading Speed and Quality for Scalable Social Recommenders Anne-Marie Kermarrec 1 François Taïani 2 Juan M. Tirado 1 1 INRIA Rennes 2 University of Rennes 1 - IRISA - ESIR Abstract. Recommending appropriate content and users is a critical feature of on-line social networks. Computing accurate recommendations on very large datasets can however be particularly costly in terms of resources, even on modern parallel and distributed infrastructures. As a result, modern recommenders must generally trade-off quality and computational cost to reach a practical solution. This trade-off has however so far been largely left unexplored by the research community, making it difficult for practitioners to reach informed design decisions. In this paper, we investigate to which extent the additional computing costs of advanced recommendation techniques based on supervised classifiers can be balanced by the gains they bring in terms of quality. In particular, we compare these recommenders against their unsupervised counterparts, which offer lightweight and highly scalable alternatives. We propose a thorough evaluation comparing 11 classifiers against 7 lightweight recommenders on a real Twitter dataset. Additionally, we explore data grouping as a method to reduce computational costs in a distributed setting while improving recommendation quality. We demonstrate how classifiers trained using data grouping can reduce their computing time by 6 while improving recommendations up to 22% when compared with lightweight solutions. 1 Introduction As web and on-line services continuously grow to encompass more facets of our lives, personalization and recommendation are emerging as key technologies to help users exploit the deluge of data they are submitted to. This is particularly true in social-networking applications (Facebook, Google+, Linkedin, Twitter), which receive, store, and, process a continuously growing mass of information produced for tens to hundreds of millions of users daily. Implementing a recommendation mechanism that works for such a large user base over terabytes of data is a highly challenging task: an ideal solution should be accurate, lightweight, and easily scale to the distributed and cloud environments in which modern recommenders are being deployed [1]. Traditional approaches to user recommendation in social networks have so far heavily relied
3 on topological metrics to identify new users or items that might be of interest to a user. These approaches, pioneered by Liben-Nowell and Kleinberg [16], can be unsupervised, in which a topological metric (e.g. number of common neighbors, length of shortest path) is used directly to predict links in the underlying social graph and thus derive recommendations. These approaches are typically lightweight, and scale well, but can provide sub-optimal recommendations, and tend to depend on the suitability of the chosen metrics for a particular dataset. In recent years, a second strand has therefore emerged that exploit classifiers developed for machine learning to improve on these earlier approaches [18, 17, 24, 19]. These classifiers often use as inputs the same topological metrics developed for unsupervised learning, and are trained on a part of the social-graph to construct an appropriate prediction model. Due to this training phase, these methods can better adapt to the specifics of individual datasets. They are also able to combine several metrics into one predictor [14], and thus offer a natural path to merge different types of information into a recommender, including topological data, semantic information based on the content consumed and produced by users [18, 24], or geographic information in geolocated social networks (Foursquare, Gowalla) [19]. Unfortunately, training such supervised models can require very large training sets (up to twice as large as the prediction set [17]), and be particularly costly in terms of computation time, even on today s highly distributed, highly parallel infrastructures found in datacenters and cloud providers. Computation costs are in turn a fundamental decision factor [13] used to select practical online recommenders, and has led companies as prominent as Netflix [1] to discard improved, but particularly costly solutions that were difficult to deploy in their target environment (in Netflix s case, Amazon s public cloud). Almost no information exists nowadays about this fundamental trade-off, balancing training s computation cost on modern infrastructures and the quality of the returned recommendation. This lack of analysis is highly problematic, as it leads to researchers to focus almost exclusively on quality metrics that ignore a decision factor that is key to practitioners. In this paper, we analyze this very trade-off, and present an extensive study that contrast the benefits brought by supervised classifiers against their computational costs on parallel architectures under a wide range of operational assumptions. First, we describe a method that combines topology-based and content-based information to improve the quality of recommendations. Second, we explore the utilization of data grouping methods to reduce the computation time required to train classifiers while improving recommendations. We carry out a thoroughly evaluation using a real dataset extracted from Twitter that demonstrates the benefits our approach can bring to scalable user-recommenders. This work is structured at follows. In Section 2 we state the problems of user recommendation. Section 3 briefly introduces the related work. Section 4 describes our approach. Section 5 describes the evaluation of this approach and finally we conclude in Section 6.
4 2 Problem statement The tremendous growth of users data in modern on-line services has made recommendation a key enabling technology in the last few years [4]. This is particularly true in on-line social networks such as Facebook, Twitter, or Weibo, which allow users to maintain an on-line web of social connections, where they produce and consume content. These networks serve up to hundreds of millions of users (for instance Facebook reported 1.15 billion monthly active users in June 2013 [8]), and must select recommendation techniques able to scale to their user base, while being amenable to the highly distributed infrastructures in which these services are typically deployed. The ability to scale and distribute recommendation algorithms has been shown in the past to play a key role in their acceptability: Netflix for instance revealed in 2012 that it had not adopted the winning algorithms of its own one million dollar Netflix prize, in part because of the engineering challenges raised to port the algorithm to their distributed infrastructure (hosted by Amazon) [1]. In this work, we focus on the problem of recommending users to other users. This problem can be compared to the link prediction problem [16] where we try to predict when the user u will create a link with another user v. In order to do this, we have to compute a recommendation score (score(u, v)) or score function indicating the interest of u to create a link with v. How to compute this score, depends on the taken approach. One common approach is to use unsupervised models consisting of a generic solution that is oblivious to the distinguishing features of the dataset. A second approach uses supervised models, consisting of classifiers trained with an excerpt of data extracted from the target dataset. Generally, this score is given by a pre-computed model that has been previously trained using the information available in the system. The general approach is to provide the system with a representative number of observations in order to compute an accurate model. Once a score function is chosen, we can compute a matrix of scores among the users in the system and chose the largest scored users as recommendations. Computing the score among all the users is not practical, therefore only a small subset is scored. Normally only a subset of close neighbors are score for each user during the recommendation. Apart from the time cost of computing the score of the neighbors, we have to consider the cost of training the supervised models. This aspect is generally ignored, although it is a major constraint in the design of distributed user-recommenders. 3 Related work The problem of link prediction has been addressed in two separated strands. A first approach follows the seminal work of Liben-Nowell and Kleinberg [16] using metrics based on the network topology [23, 2]. As observed by Yien et al. [25] these metrics only reflect changes in the network topology being oblivious to the creation of links regarding other aspects contained into the users metadata. In this sense, Schifanella et. al [21] find tags to be a good link predictor. However,
5 in [7] authors find that tags are not very effective for link-prediction in their explored datasets. A second approach employs methods to combine different features in order to exploit all the available information in the systems. Rowe et al. [18] exploit Twitter semantics using logistic regression. Authors claim the need for topical affinity between users to create links. Although their work has some resemblance with the one we present, their work differs from our approach in the utilization of topics instead of natural language and the analysis of just one classifier. Scellato et al. [19] use supervised learning to predict links in a location-based social network. The authors train a set of classifiers with different location and social-based metrics finding that the combination of these metrics results into an improvement of recommendations. They find that a combination of location and social-based metrics does not significantly improve recommendations compared with only location-based recommenders. Wang et al. [24] present a framework for link-prediction based on an ensemble of classifiers trained with graph features and similarity metrics. They claim a 30% improvement of recommendations when compared with other approaches. However, they ignore the elapsed time in the training process using an exhaustive amount of information during training. Although the aforementioned works emphasize the combination of different data sources in order to improve recommendations, none of the mentioned works present any conclusion about computational costs. In practice, many of these systems are not scalable and only practical for centralized designs [13]. However, the utilization of methods exploiting different features in user-recommenders has not been analyzed from a computational perspective that may facilitate the design of distributed solutions. 4 Proposed approach User-recommendation is essentially a classification problem where we determine whether a candidate is relevant for a user or not. A score(u, v) function determines the probability of u to establish a link with the candidate v after comparing both users. In socially oriented systems for any user u we find his outgoing edges (social links) Γ (v) and part of the content u has consumed or generated. The content can have different formats such as tagged content (e.g. YouTube videos) or natural language text (e.g. posts, tweets). Both of them can be managed in bags of words [18] consisting of a vector containing all the words (or tags) employed by the user. Each user has an associated corpus C u containing all the employed words, where P u (i) is the probability of finding the word i into the corpus. Both Γ (v) and C v can be employed to compute similarity metrics that can be used as score functions (score(u, v)). These metrics have been widely used in distributed systems where the user has a partial vision of the network [22, 5] and also in graph-based solutions [16, 3]. The main reason for their utilization is that they are computationally light and summarize relevant features users may
6 have in common. Table 1 shows the score functions used in this work and their notation. Content-based score functions 1 Pu(i) Jensen-Shannon js 2 i Pu(i) log + 1 Pv(i) P v(i) 2 i Pv(i) log P u(i) with i Cu Cv Jaccard jacc C u C v / C u C v Adamic-Adar Cosine cos 1 aa i wu(i)wv(i) wu(i)2 i i wv(i)2, i Cu Cv i C u C v 1/ log P u(i) Graph-based score functions Jaccard jaccf Γ (u) Γ (v) / Γ (u) Γ (v) Adamic-Adar aaf i Γ (u) Γ (v) 1/ log Γ (i) Preferential pa Γ (u) Γ (v) attachment Table 1: score(u, v) functions based on content and graph information. The Jensen-Shannon divergence measures the distance between two corpuses using the probability distribution of the words used by each user. The Jaccard coefficient is a common metric that measures the probability of sharing items between users. Cosine distance considers the elements and their occurrences as the dimensions of two vectors. Adamic-Adar weights rare common features [16]. Finally, preferential attachment considers the probability of connecting two users proportionally to their connectivity degree. For the Jaccard and Adamic-Adar metrics we present a content-based and a social graph based version. The aforementioned score functions can be directly used as unsupervised score-based classifiers to compute the probability of u and v to become connected or not. However, in some scenarios computing recommendations remains a challenge. For example, cold-start recommendations [20] will probably fail as there is no available data. In other scenarios the score can be biased. For example, if u and v have similar content and a low number of common links, probably v is not relevant to u. They share similar topics but are distant neighbors. And similarly, a low content score wit a great social similarity indicates close users although they consume different contents. Combining both approaches (content-based and social-based) at the same time can improve recommendations in scenarios where only one approach may be insufficient. 4.1 Supervised multi-score recommenders There is an extensive literature in supervised classification algorithms [15] that take advantage of different statistic features. Supervised classifiers have to be trained with a given set of observations (training dataset) each one containing
7 a set of features and the class belonging to. Depending on the classifier and the training dataset, the classifier will come up with a different classification model. In our approach, we propose to use a training dataset with entries containing {s 1, s 2,..., s n, c} where s i is a score function (Table 1) and c indicates whether users u and v are connected (1 if connected, 0 otherwise). Ideally, for every user u in the social graph, we can create a training dataset and then train the corresponding classifier. However, this is not a scalable solution as we would need to compute as many classifiers as users in the system. Additionally, computing a single model for the whole dataset requires identifying a representative enough sample which is a difficult task. In order to cope with this problem, we split the graph into manageable groups and compute classifiers for these groups. Intuitively, groups composed of users with similar features should have similar classification models. This permits to train personalized recommendation models for samples of users. Additionally, by splitting the problem we can consider the parallelization through distribution using paradigms such as Map-reduce. Algorithm 1 Supervised score-based model training 1: for each group g do 2: T {} training set 3: U random sample of users belonging to g 4: //Fill the training dataset 5: for u U do 6: N neighborsselection(u, d) 7: for n N do 8: Compute each similarity metric i 9: s i score i(u, n) 10: c connected(u, n) 11: Add [s 1, s 2...s n, c] to T 12: end for 13: end for 14: //Find the best classification model 15: B i {} 16: for each classifier i do 17: Bi c {} 18: for each configuration c do 19: Train M c using configuration c and cross-fold validarion over T 20: Add Mc to Bi c 21: end for 22: M i model with largest value from Bi c 23: Save M i, discard the other models 24: end for 25: M g model with largest from B i 26: end for Algorithm 1 describes the steps carried out to train and find the most suitable model for each group of users g. First, we define the training set T for each group
8 g. For each g, we select a sample of users U large enough to be representative but small enough to be computationally feasible. The users are chosen using the neighborsselection(u, d) function as explained in Section 4.2. For each of the users in U we compute the score functions and add them to the training set T. Once we have all the training sets, we can compute the best classification model. Considering the most adequate classifier for a given social graph a priori is a difficult task. Different classifiers show different performance depending on the incoming training set. We propose to simultaneously train several classifiers using cross-fold validation over T. For each classifier we train models M c for a set of pre-determined configurations (if the classifier accepts additional configuration), compute the obtained (Area Under the ROC Curve) for M c. The measures the predictive power of the model between 0 and 1. Values larger than 0.5 indicate better performance than a random classifier. The model with the largest will finally be selected as the classifier for the group (M g ). 4.2 Training set users selection The training set T described in Algorithm 1 must contain a proportional ratio of observations belonging to both classes (connected and non-connected) in order to get an accurate classifier. We select a random sample of users U from group g. Then for each user u in U we compute the features corresponding to a connected and a non-connected user. Selecting a connected user we just have to select one v belonging to Γ (u) and compute the different score(u, v) functions. However, the remaining users in the graph could be considered as non-connected examples. We propose a social distance approach to determine which users to consider as non-connected. We define the social distance d as the minimal number of links u has to traverse in order to find user v. Previous works observe that most of the new links in social networks are established for small values of d [25]. We use the social distance to determine when a user shall be considered as an example of connected or non-connected class in the training set. For d = 1 we consider the users to be connected as they are currently neighbors. Then for d > 1 we consider non-connected samples. According to this assumption, for large values of d a classifier must find easier to distinguish between connected and non-connected users during training. Figure 1 describes how the neighborsselection(u, d) function works. In Figure 1a we have the original directed social graph. In Figure 1b we use d = 2 selecting users 3, 7 and 8 (green) as connected examples with 5 and 6 as non-connected taking 4 as the origin. Similarly, in Figure 1c we use d = 3 being 9 the only non-connected candidate. 5 Evaluation We evaluate our approach using a Twitter dataset extracted using the public Twitter API 3. We have crawled Twitter s social graph for users in the London 3
9 (a) Social graph (b) d = 2 (c) d = 3 Fig. 1: Example of training set users selection using d = 2 and d = 3. Vertex 4 indicates the starting user. Vertices 3, 7 and 8 indicate examples of connected users. With d = 2 vertices 5 and 6 are non-connected vertices. With d = 3 the non-connected vertex is 9. area extracting their tweets and list of followings (users they follow) before July 28 th Our dataset accounts for 106,385 users with 11,111,386 following links and a total of 21 million tweets. The median of the distribution is 94 followings with 80% of the users having less than 265 followings. We aim to understand how a combination of graph and user content information can improve user recommendations and how we can reduce the computational overhead in order to facilitate the scalability of this process. We use the score functions defined in Table 1 in order to compare two users in terms of social and content similarities. We use the tweets to compute the content-based score functions. Tweets use natural language that may reduce the amount of information we can extract from them. For that reason, we first remove stop words and punctuation symbols (except hashtags). We use a set of representative classifiers available in the R Caret package [11]. This package offers a unified interface for a large number of classifiers, simplifying the implementation. Table 2 enumerates the classifiers we have used for our experiments. We choose these classifiers in order to have a representative set with various classifiers that may get some benefit from our approach. For each training we use cross-fold validation with 10 folds. Then we select a sample of random users with a variable social distance d and compute the to measure the quality of recommendations. In order to compare the computational cost of training we show the average elapsed time for 5 executions. All the experiments are carried out in a non-fully dedicated 8 Intel Xeon cores machine with 32 GBytes of memory. 5.1 Single-score recommenders In this section, we show the quality of recommendations simply using score functions as recommenders. For clarity, we analyze the recommendation power depending on the nature of the score (graph-based or content-based), the number of user followings and the social distance d of the users in the evaluation set. The results showed in Figure 2 indicate that increasing the social distance d improves the performance of recommenders. However, graph-based recommenders
10 Method Abbreviated name Bagging bagfda Gradient boosted models [9] gbm,blackboost Decision tree C5.0Tree Random forests [6] parrf K-nearest neighbors knn Multivariate adaptive regression splines [10] earth Logistic regression glm,glmnet General additive models gam Support Vector Machine svmlinear Table 2: Summary of employed classifiers used in the evaluation and their abbreviated names. are more accurate than content-based. In particular, we observe that the increases for users with a larger number of followings. This could be due to the cold-start effect that limits the amount of available information. However, in the case of content-based recommenders we observe that the number of followings do not substantially modify the recommendations. 5.2 Supervised multi-score recommenders After analyzing the recommendations obtained using score functions, we explore the recommendations obtained with multi-score recommenders. We use the same evaluation set employed in the previous section with d 2. First we only combine score functions from graph-based (Figure 3a) and content-based (Figure 3b) score functions. Then we combine both in order to check how by combining data sources we can improve recommendations (Figure 3c). For graph-based multi-score recommenders (Figure 3a) we observe a significant improvement for the users with less than 100 followings. This improvement is particularly relevant for users with less than 10 followings achieving a 22% improvement (0.71 compared with graph-based single-score that only achieved 0.58). However, we do not observe relevant improvements for contentbased multi-score recommenders (Figure 3b) compared with the single-score version. Finally, the combination of all the scores (Figure 3c) significantly improves the recommendations of some classifiers such as glm or svmlinear. In the other cases there is an improvement of the recommendations, although it is not very significant. 5.3 Train set grouping In the previous section we show how using multi-score classifiers improves recommendations. However, in order to facilitate the deployment of a distributed solution we have to consider the computation cost of training classifiers. The elapsed time training a classifier depends on the amount of data and the classifier itself. Additionally, as described in Algorithm 1 our approach considers
11 Graph-based Content-based aaf jaccf pa JS Cos jacc aa (a) d 2 (d) d aaf jaccf pa JS Cos jacc aa (b) d 3 (e) d aaf jaccf pa JS Cos jacc aa (c) d 4 (f) d 4 Fig. 2: according to the number of followings and the social distance for content-based similarity metrics. the training of several models in order to find the most accurate model. This operation may require a significant amount of time. In order to reduce this time while keeping the quality of recommendations, we split users into groups. In our experiments we split the dataset into five equally-sized groups depending on the number of outgoing edges. The reason behind this partition is to reduce the diversity of features found in each training set. Intuitively, users with the same number of outgoing links may have similar profiles, and therefore it would be easier to find a model to classify them. We assume that the
12 knn glm glmnet parrf blackboost gbm earth gam pda c50tree svmlinear knn glm glmnet parrf blackboost gbm earth gam pda c50tree svmlinear (a) Graph-based multi-score recommenders knn glm glmnet parrf (b) Content-based multi-score recommenders blackboost gbm earth gam pda c50tree svmlinear (c) Graph and content-based multi-score recommenders Fig. 3: for multi-score recommenders using graph-based scores, contentbased scores and both kind of scores. information regarding each user (followings and content) is fully available when computing the scores. In Figure 4 we plot the elapsed average time for training and the per classifier using a multi-score recommender combining graph and content-based scores. We classify the same set of users employed in the previous section with and without grouping (left and right highlighted areas respectively). We observe an increment of the for all the classifiers with a significant time reduction after grouping. In some cases like parrf the slightly improves while the training is 6 times faster. In other cases like the earth classifier, the increment is more significant than the saved time. This experiment shows how partition permits to reduce the training time while not affecting the quality of recommendations. 5.4 Discussion There are many aspects to be evaluated in a recommendation system. We know the evaluation presented in this paper is not complete. However, we think that many of the results presented in this work are promising and open the door for
13 knn glm glmnet parrf blackboost gbm earth Time (seconds) Fig. 4: Comparison of and time used in model training. The right and left areas indicate the results not using and using groups respectively. new ideas in the development of scalable recommendation systems that can combine multiple sources of information. Our experiments demonstrate how training classifiers using classic user comparison metrics can improve recommendations. We observe that there is a significant improvement when dealing with users offering small amounts of data. Furthermore, we demonstrate how classifiers trained with metrics based on different sources of information we can get 22% better recommendations than the best value obtained simply using these metrics. The combination of graph and content-based slightly improves recommendations. However, this result may vary in other datasets. Group partitioning demonstrates to be a good approach to reduce the average elapsed time in classifiers training. Additionally, we observe how recommendation improves for all the evaluated classifiers after grouping. This result is particularly promising in order to develop distributed recommender systems using supervised classifiers. Grouping data with common features improves recommendations. This makes possible to reduce the amount of training data, and therefore the elapsed training time. In this work, we only provide one group partitioning strategy based on the number of outgoing links of the user. A large number of partition techniques based on topology features can be explored [12]. However, our main goal is to demonstrate the importance of group partitioning in terms of recommendations and time. Exploring other grouping techniques remains for future work. Finally, the reduction in the time needed to train the models makes possible to compute a larger number of models. And facilitates the parallelization of the recommendation process. Our experiments indicate that the gbm (Gradient Boosted Modelling) classifier gets the best results in almost every scenario. However, this result can differ depending on the dataset.
14 6 Conclusions In this work we explore the design of scalable solutions for social recommenders. First, we propose the utilization of supervised classification methods for user recommendation in social networks. We describe a method that combines topologybased and content-based similarity metrics to improve the quality of recommendations. Second, we explore the utilization of data grouping methods to reduce the computation time required to train classifiers and make easier the deployment of distributed solutions. We carry out a thoroughly evaluation using a real dataset extracted from Twitter that demonstrates the benefits of our approach. In particular, we find that our solution improves the quality of recommendations by 22% compared with unsupervised solutions. Additionally, we observe that data grouping permits to speedup the training of classifiers by 6. Our work shows promising results and opens several directions in the development of scalable social recommenders. We plan to extend our study about the effects of data grouping in the quality of recommendations and how it facilitates the deployment of scalable solutions. Additionally, we will extend our evaluation to other datasets. Acknowledgments This work was partially funded by the French ANR project SocioPlug (ANR- 13-INFR-0003), and by the DeSceNt project granted by the Labex CominLabs excellence laboratory (ANR- 10-LABX-07-01). References 1. X. Amatriain and J. Basilico. Netflix recommendations: Beyond the 5 stars. netflix-recommendations-beyond-5-stars.html, April (accessed 20/9/2013). 2. L. Backstrom and J. Leskovec. Supervised random walks: predicting and recommending links in social networks. WSDM 11, pages , A. L. Barabasi, J. De, P. Lettres, L. Al, N. Cimento, H. Jeong, H. Jeong, Z. Neda, Z. Neda, and A. L. Barabasi. Measuring preferential attachment in evolving networks. Europhysics Letters, 61(61): , J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. Recommender systems survey. Knowledge-Based Systems, 46(0): , A. Boutet, D. Frey, R. Guerraoui, A. Jégou, and A.-M. Kermarrec. WhatsUp Decentralized Instant News Recommender. In IPDPS 2013, May L. Breiman. Random forests. Machine Learning, 45(1):5 32, M. J. Brzozowski and D. M. Romero. Who should i follow? recommending people in directed social networks. In ICWSM, Facebook Inc. Key facts. https://newsroom.fb.com/key-facts, (accessed 2 Oct. 2013).
15 9. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1): , J. H. Friedman. Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1):1 67, M. K. C. from Jed Wing, S. Weston, A. Williams, C. Keefer, A. Engelhardt, and T. Cooper. caret: Classification and Regression Training, R package version J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. Powergraph: Distributed graph-parallel computation on natural graphs. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, OSDI 12, pages 17 30, Berkeley, CA, USA, USENIX Association. 13. P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R. Zadeh. Wtf: the who to follow service at twitter. In WWW, pages , M. Hasan and M. Zaki. A survey of link prediction in social networks. In C. C. Aggarwal, editor, Social Network Data Analytics, pages Springer US, S. B. Kotsiantis. Supervised machine learning: A review of classification techniques. In Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in ehealth, HCI, Information Retrieval and Pervasive Technologies, pages 3 24, Amsterdam, The Netherlands, The Netherlands, IOS Press. 16. D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. CIKM 03, pages ACM, R. N. Lichtenwalter, J. T. Lussier, and N. V. Chawla. New perspectives and methods in link prediction. KDD 10, pages , New York, NY, USA, ACM. 18. M. Rowe, M. Stankovic, and H. Alani. Who will follow whom? exploiting semantics for link prediction in attention-information networks. ISWC 12, pages , Berlin, Heidelberg, Springer-Verlag. 19. S. Scellato, A. Noulas, and C. Mascolo. Exploiting place features in link prediction on location-based social networks. KDD 11, pages , New York, NY, USA, ACM. 20. A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. SIGIR 02, pages , New York, NY, USA, ACM. 21. R. Schifanella, A. Barrat, C. Cattuto, B. Markines, and F. Menczer. Folks in folksonomies: social link prediction from shared metadata. WSDM 10, pages , J. M. Tirado, D. Higuero, F. Isaila, J. Carretero, and A. Iamnitchi. Affinity p2p: A self-organizing content-based locality-aware collaborative peer-to-peer network. Comput. Netw., 54(12): , Aug J. Valverde-Rebaza and A. de Andrade Lopes. Structural link prediction using community information on twitter. CASoN 12, pages , C. Wang, V. Satuluri, and S. Parthasarathy. Local probabilistic models for link prediction. In Data Mining, ICDM Seventh IEEE International Conference on, pages IEEE, D. Yin, L. Hong, and B. D. Davison. Structural link analysis and prediction in microblogs. In Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM 11, pages , New York, NY, USA, ACM.
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text:
Sweet Pickle Spears
Recipe by Ellen Brody
I got this recipe from a neighbor many years ago. It is very simple and very good and inexpensive. They are requested all the time from family and friends.
Top Review by deb elshoff
I used this recipe last weekend but I didn't do the spears. I cut the cucumbers into slices.Except for the ones that were to big to slice so those I skinned and cut out the seeds and chunked up. We haven't tried them yet but it was so easy to do . And didn't take me 2 weeks like some other recipes.
See Discussion:1 Review
Ingredients Nutrition
- 4 lbs cucumbers (3-4inch)
- 4 cups sugar
- 3 3⁄4 cups vinegar
- 3 tablespoons canning salt
- 4 teaspoons celery seeds
- 4 teaspoons turmeric
- 1 1⁄2 teaspoons mustard seeds
Directions See How It's Made
- slice ends from cucumbers and cut into spears.
- Cover cucumbers with boiling water; let stand 2 hours.
- Drain and pack into hot jars, leaving 1/4 inch head space.
- Bring remaining ingredients to a boil.
- Pour hot liquid over cucumbers, leaving 1/4 inch head space.
- Remove air bubbles.
- Adjust caps.
- Process l0 minutes in boiling water bath.

subset: commoncrawl


text:
Contortions Among the Trees
We are an independent source of generic information.
Sustainability is one of the guiding principles in the use of timber across a range of structural, exterior and interior applications in the buildings that are featured on WoodSolutions. More than anything, these projects and those behind them recognise the potential of timber as the only truly renewable and sustainable building material.
So what makes building with timber sustainable?
A large part of the answer lies in timber's low embodied energy - that is, minimal non-renewable energy is consumed in the production of and construction with timber. Timber also has inherent thermal properties, meaning timber structures rely less on carbon-emitting heating and cooling devices. And timber has natural durability, with many Australian hardwoods having an above ground life of 50+ years, meaning less energy is consumed in maintenance, demolition and new construction. Add to this timber's recyclability, the fact that it stores carbon for life, and a high aesthetic quality, and you have a building material that begs the question - why not specify timber for your next design or building project?
For up-to-date information about new case studies and other developments in building with timber, register your details here.
If you would like to submit a case study to be featured on WoodSolutions, please register your interest here.
Designed for its small carbon footprint, the 52 Cubic Wood office was created through a partnership of two design firms.
The Weberhaus conference centre uses a huge amount of wood in its sustainable construction.
The Ballarat University Building Construction Training Centre was built before the embodied energy requirements were instituted, but still is very sustainable.
This home just off the coast from Melbourne has many unique features – not least its history as a chicory kiln and the reuse of an artwork as ceiling lining.
Hovering like a caterpillar's cocoon, the Yellow Treehouse is a beautiful redwood structure.
The Aratoi Centre is designed as a space to exhibit and to inhabit by its community.
The Residential Care Home in Graz, Austria, creates a bright, natural atmosphere through extensive wood construction.
The Korkeasaari Zoo Lookout Tower provides a 360-degree view of Helsinki and its surroundings.
The free form, wide span wooden cupola with its multiple layers was made possible through parametric programming that included design facilities and options, statics, production, CNC machining, transport and assembly parameters
A new pavilion in an existing complex, the Mountain Home has a warm interior to encourage family life.
The Birkenhead Library houses a large quantity of public services for its community.
The sales pavilion for Fraser Papamoa uses mostly wood throughout, including the moveable louvres of the exterior.


text:
Dcotor Who star will be materialising in Lowestoft
PUBLISHED: 17:31 09 January 2014 | UPDATED: 17:31 09 January 2014
©Archant 2013
Fans of Doctor Who can meet one of the programme's most popular characters at a shop in Lowestoft dedicated to the long running BBC science fiction show.
Sophie Aldred, who played the Doctor's companion Ace, will be materialising at the Dawn of Time coffee and comic shop in the High Street from noon on Saturday.
Ace was companion to the seventh Doctor, who was played by Sylvester McCoy, and she proved a match for daleks and cybermen with her baseball bat and homemade explosives.
Sophie has also starred in Corners, Melvin and Maureen's Music-a-grams and Words and Pictures.
Also appearing at Saturday's signing session will be Andrew Cartmel, who was a script editor on Doctor Who during Sylvester McCoy's time. He has also written several Doctor who novels.
The signing session is the first of as series of regular of Doctor Who-themed events lined up at Dawn of Time with Davros actor Terry Molloy due to appear on Saturday, February 8.
The shop opened in November.
Its owner is Jim Poole, whose daughter Bea also works there and dresses up as Ace in the shop.


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Rafael Nadal vs David Ferrer final match Monte Carlo Masters | Nadal vs Ferrer final Monte Carlo
Rafael Nadal vs David Ferrer final match Monte Carlo Masters: Rafael Nadal needed to produce one of his most stunning performances on clay on Saturday to edge an injured Andy Murray 6-4, 2-6, 6-1 and book a place in the final of the Monte Carlo Masters.
For the 15th time in the last two decades, a Spaniard will figure in the title match at the Country club, with six-time winner Nadal bidding for a seventh straight title here against compatriot David Ferrer.
The Spanish fourth seed earlier ended the surprise run of Jurgen Melzer 6-3, 6-2, a day after the Austrian had put out Roger Federer.
Nadal needed just shy of three hours to go past Murray, who was reportedly nursing an injured right elbow and who received treatment in the third set after putting up a titanic struggle in the first two sets.
The Spanish world number one did lose his first set in Monte Carlo since the 2009 final against Novak Djokovic, however, as Murray turned in a fighting effort.
Nadal has now won 36 straight matches in the principality heading into his clash with Ferrer, who he has beaten 11 times in 15 encounters.
Murray fought a 65-minute opening set, going down in the end but rallying well in the second with an attacking game that left Nadal puzzled as to the best response.
Murray won a marathon 19-minute game, saving five break points for a 3-1 lead and finally levelling the match after a huge effort.
But as what was reported as a pre-match pain-killing injection began to wear off, Murray's level dropped, with Nadal able to close out a hard-fought win with little resistance in the deciding set.
Ferrer doused cold water on Melzer's dream run after the Austrian had stunned Federer in the quarter-finals.
"Maybe the key was in my serve," said Ferrer.
"I played very consistent and didn't make mistakes. I was very regular and very focused all the match. I'm very pleased to be in the finals of a Masters 1000."
Ferrer was playing a Monte Carlo semi-final for the second straight year.
Melzer, seventh on the ATP, was the first Austrian to reach the semis here since Thomas Muster won the title 15 years ago.
"I think out of the first five games, I should have won all five of them," said Melzer. "You could see in the beginning he didn't feel comfortable at all.
"The longer the match went on, the more comfortable he felt and the discomfort I felt," said the Austrian, who said the neck pain that forced him off court for treatment against Federer was not a problem on Saturday.
"If you're in the semi-finals, you want to win no matter who you beat the day before."
The Austrian began strongly with a break but lost his way as Ferrer's steady game paid dividends to frustrate his opponent.
Melzer lost the first set in 38 minutes and went down two breaks in the second.
He saved a match point before Ferrer advanced on his second chance from a Melzer unforced error.


text:
Thanks for everyone's opinion about our family pictures! You guys are the best. I decided to go with #1--I agree with my friend Katy--I chose the one I thought I looked best in...yes, that sounds vain, but the kids and Jared also look good in that one. Anyways, thanks!
Pretty soon I'm going to have to change this to the "Pictures of Adalynn blog." Believe me, I only post about 1/10 of what I take (or less). Most of the time she is either a. In a bad mood b. Won't smile or ever look at the camera c. Looks like a little bum or d. Has a huge scratch or bruise on her face (right now she looks like she was in a fight or something--she feel out of the swing Saturday morning and has a few big scratches on her face). Sooo....when she looks cute and is cooperating I post.
And I know her headband is ginormous, but I think it is so cute. Don't be surprised if you see me wearing it around.
Even when she is cooperating I still get the next two looks a lot...
I'm not sure if I have mentioned this or not, but she loves, loves, LOVES Little Einsteins. Jared and I call them her morning coffee because the first thing she wants in the morning is to watch her show. She is totally dramatic if I don't let her. We have the little action figures and she carries them with her everywhere--if you ask her who her favorite is, she tells you "Leo."


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Parmesan Herb Doggie Treats
Be the first to review
READY IN: 1hr 40mins
Recipe by BLUE ROSE
Always looking for a new treat for Killer. I am guessing on how many you get out of this. I got 15
See Discussion:
Ingredients Nutrition
- 1 (1/4 ounce) package active dry yeast (2 1/2 teaspoons)
- 1⁄4 cup water, lukewarm
- 2 cups chicken broth
- 2 tablespoons vegetable oil or 2 tablespoons olive oil
- 1⁄2 cup grated parmesan cheese
- 1⁄2 cup powdered milk
- 2 tablespoons dried parsley
- 1 teaspoon dried oregano leaves
- 1 cup whole wheat flour
- 1⁄2 cup rye flour
- 1⁄2 cup rice flour
- 1 cup cracked wheat
- 1 large egg
- 1 tablespoon milk
Directions See How It's Made
- In a large bowl, dissolve yeast in water. Add the chicken broth, oil, cheese, powdered milk, and herbs. Gradually stir in the flours and cracked wheat. (If needed, add enough wheat flour to form a stiff dough.)
- Transfer dough to a floured work surface and knead until smooth, about 3 to 5 minutes. Shape the dough into a ball and roll to 1/2-inch thickness. Cut into desired shapes and place on ungreased baking sheets.
- Bake at 325*F (160*C) for 45 minutes. Make glaze mixture by mixing egg with milk. Remove from oven. Brush the treats on both sides with the glaze mixture. Bake for an additional 30 minutes. Cool on wire racks.


text:
Here's the Deal
Slant is written by a community helping you be informed. Tell us what you're passionate about to get an awesome personalized feed.
#1 Kanban Tool
Kanban Tool is a visual project management application based on Kanban that helps companies visualize workflow, track project progress and analyze and significantly improve busin...
Pro Customizable and reassuring
Thanks to the visualization of work, it's much easier to see the progress of work. Boosts the confidence in what you endeavour upon and reassures each step of the process. The customization allows to fit a system right to the process you've planned, so that we don't have to match our w...
#2 Taiga.io
Free. Open Source. Simple to use. Taiga is a project management platform for agile developers & designers and project managers who want a beautiful tool that makes work truly
Pro Supports both Kanban and Scrum modes
User stories can be organized in both Kanban and Scrum task management systems.
Con Too much functionality for small projects
While it's possible to disable any unwanted features (modules), the amount of functionality that's present might be more than a small, short-term project needs.
Con Can be overwhelming at first
Taiga presents users with a lot of information and functionality right from the beginning with little guidance. Figuring how stuff works might take a bit.
#3 Trello
Trello is a task manager built around the concept of 'Kanban' which is essentially a scheduling system that allows you to move your tasks from one state to the next. The interfa...
Pro Highly flexible workflow
Trello calls items used in the workflow "cards." Cards are double-sided and can contain subtasks as well as notes and other details. Card columns can be used to simulate a workflow by moving cards from left to right as they are completed, or as a way of indicating task priority. The workflow is...
Pro Simple and easy to use interface
There are two primary columns: the board is shown on the left and all the other controls on the right. The main means of interaction is dragging and dropping to-do cards into the various lists. The board structure is very customizable, and includes a variety of features that help along the way: c...
Con Managing large projects may be difficult
Trello works best with medium to small projects and with very high-level overviews. It is less effective for projects that require very granular management due to the fact that it becomes considerably more difficult to keep track of various cards and priorities as they are pushed off the screen.
Pro Highly customisable and powerful workflows
You can provide custom workflows for all the different types of issues. For example you can make features go through a flow of "Backlog -> Needs design -> Built -> Needs QA -> done" with bugs going through a different flow. These workflows are very powerful as you can configu...
Con Slow to use
Every view switch and action takes a second or two. Doesn't seem too bad when you first start using it, but the UI is complicated enough that you need to manipulate it a lot and all that time adds up.
#5 Asana
Asana is a web and mobile application designed to enable teamwork without email. Asana puts conversations and tasks together, with the hope that teams can get more done with less...
Pro Simple workflow
The Asana task workflow within a project is broken down into "Today", "Upcoming" and "Later". The tasks themselves have the status of either done or not done. There is also a sub-task feature to group tasks together. Tasks can be assigned to each other and are stored
Pro Very polished interface
Teams can have private (only visible to project members) and public (visible to anyone in the team) projects. Each member can also have their own personal projects. Tasks can be viewed in list and calendar views. It's possible to display only the tasks assigned to the user or tasks organized...
Con No task dependencies in free version
Dependencies is only available only to Teams and Organizations on Asana Premium.
Con UX Design is overly opinionated
Asana forces a user to conform to their worldview of what the optimal workflow is. Workflow's are constantly changing. As such, an opinionated design is not always ideal for project management depending on how you like to organise your projects.
#6 Kantree
Kantree is a flexible and powerful collaborative tool which helps you manage any kind of tasks, projects and processes. It offers multiple ways to visualize and organize your wor...
Pro Easy card customization
In Kantree the most simple item that you can create is a "card". It can represent anything (like an idea, a task, an event, a person, etc). Each card comes with a description and other fields like files, assignee and labels. You can add more of them (from a list of 15 different types...
Pro Kanban + table view + calendar
Switch between multiple visualization modes Kanban for processing cards easily and get the big picture Table to review cards and their attributes Calendar to plan and track card with due dates and milestones All modes are interactive (drag and drop to move/assign)
#8 Worksection
Web-based project management tool which helps modern companies to organize. Work with you team: • Projects• Tasks • Checklists • Comments • Regular tasks • Templates Work with...
Pro Low prices and free options
Prices are really low as paid plans start at $9 per month and they don't depend on users quantity. There is a free plan for up to 2 projects and a premium plan if free for social and educational projects.
Pro Great features list
There are lots of features for tasks and subtasks management, for reports and billing, for costs and time control, handy calendar and Gantt chart, etc.
#9 Clubhouse
A powerful, modern tool for managing agile software development.
#11 parolu
All-In-One collaboration tool for designers, developers & project managers
#12 AgileZen
Cloud Kanban style board with lean interface and collaboration built-in. Just focus on tasks.
#13 Blimp
Blimp is a team management app that takes more of a goal-oriented approach and allows teams to view the progress on each project as a whole, even going as far as implementing a r...
#16 ProofHub
ProofHub is a cloud based project management software with features like gantt charts, proofing, reporting and custom roles. It's available for iOS and Android.
Pro 5 pricing plans that should fit anyone's needs
There are five different pricing plans for ProofHub, including a free plan, making for a flexable structure in which users can find the plan they need without spending over what they want.
Pro Slack integration
An update is posted to the Slack channel every time a task is created, completed, moved, or commented upon.
Con Slack integration is really basic
There is no way to interact with tasks in any way - new tasks can't be added, completed tasks can't be removed, tasks can't be assigned, etc. The integration only sends updates to the Slack channel. There is also no way to fine-tune what updates get posted to Slack.
Welcome to the Slant Community
Stop spending hours researching
Slant is written by a community helping you be informed. Let us know what you're passionate about to get an awesome personalized feed.


text:
Oracle knew about Java vulnerabilities
Security Explorations, a security startup, claims that Oracle knew about the current most dangerous flaw that made users disabling Java plug-in in their web browsers.
Microsoft exposes TechEd delegates' passwords
Software used by Microsoft's New Zealand outpost to register attendees for TechEd conference has exposed delegates' passwords.
New version of the "Blackhole"exploit kit
The developer of the infamous Blackhole exploit kit has released a new version that makes it more difficult to blacklist URLs pointing to websites containing malware.
Draft of Browser Web Cryptography
The W3C web standards consortium has presented a first Public Working Draft for integrating a Web Cryptography API (Application programming interface) into browsers.


text:
Bangkok Bar and Restaurant
40 - 44 Princess St?reet, Manchester, M1 6DD +44 1619377503
Showcasing the colourful and vivacious Thai culture through its cuisine, Bangkok Bar & Restaurant offers a real slice of the carnival action in the heart of Manchester. Its unique dining space plays host to world-class stage acts, full moon parties and street food events, ensuring diners get a very authentic taste of Thailand.
The Bangkok Bar & Restaurant menu is brimming with aromatic and fragrant Thai dishes, offering a fantastic selection of curries, stir fries and fish dishes, including sea bass marinated with chilli and garlic, and king prawn curry with coconut milk. Dessert highlights include coconut pancakes, served with a side of vanilla ice cream, and mango and bananas, served with sticky rice.
No true Thai experience would be complete without cocktails, and thankfully Bangkok Bar & Restaurant delivers. Enjoy the cleansing Singha Red Martini, infused with cranberry and orange juice, or the refreshing Blue Moon, blending Bombay Sapphire gin with Blue Bols and lemon syrup.
A taste of traditional Thailand in the heart of Manchester, Bangkok Bar & Restaurant in Manchester sits 10 minutes' walk from Manchester Piccadilly Rail Station.
Bangkok Bar and Restaurant
40 - 44 Princess St?reet, Manchester, M1 6DD
See all restaurants in Greater Manchester
See all restaurants in Manchester
Customer reviews for Bangkok Bar and Restaurant
- Food
- Value
- Ambience
- Location
- Service
- Drinks
((*** 15 October 2014 by David Wright from rochdale
The website shows a very plush restaurant and bar area so its reasonable to assume that's what we good get...not a chance. The reality was being ushered into a separate room, crowded and not very clean while a comedy show went on in the main area. Certainly not what it said on the tin and would never have booked if we had known. Where else to go by that time on a Saturday night ? Food was good though and staff were polite and attentive but was it good value ? Not really. Would we go back? Definitely not.
(**** 7 October 2014 by Melanie Lamb from Manchester
The service was very poor.
Unfortunately, you can't book Bangkok Bar and Restaurant online on this site
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text:
Discount Verb Synonyms: diminish, lessen, minimize, detract from
Discount Adjective Synonyms: reduce, mark down, deduct, lower, take or knock off
Period Noun Synonyms: interval, time, term, span, duration, spell, space, stretch, while, patch
Business / Accounting / Discount: The amount charged by a financial institution when a note receivable is discounted: calculated as maturity value times discount rate times discount period. MORE
Business / Finance / Net Period: The period of time between the end of the discount period and the date payment is due. MORE
Business / Real Estate / Periodic Tenancy: A leasehold interest from period to period that renews automatically unless a notice of termination is given. MORE
Science / Chemistry / Periodic Trend: A regular variation in element properties with increasing atomic number that is ultimately due to regular variations in atomic structure. MORE
Science / Chemistry / Periodic Table: An arrangement of the elements according to increasing atomic number that shows relationships between element properties. MORE
Entertainment / Literature / Periodic Style: A style of writing in which the sentences tend to be periodic. See discussion under periodic sentence, above. Periodic style in English is usually considered indirect or artificially 'artsy' in compar MORE


text:
- Trending Now: More Trending:
- transgender beleaguer scaramouch non sequitur admiral's mast
metallic paper
Definition of metallic paper
1 : paper so coated (as with lime, whiting, and size) that marks made on it with a metal point are indelible
2 : paper coated with finely flaked metal to give the effect of a metallic surface
3 : paper to which a metallic foil has been laminated —called also foil, foil paper
There's more!
Love words? You must — there are over 200,000 words in our free online dictionary, but you are looking for one that's only in the Merriam-Webster Unabridged Dictionary.
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text:
Pinewood Motels Fairlie
Our comfortable, friendly, family-owned and operated motel is situated a short walk from the centre of town and is the perfect base for travellers wishing to explore Fairlie, Tekapo and the many attractions that the Mackenzie district has to offer.
- Por noche
- $120 - $150
Comunícate con nosotros
1 / 11
Acerca de
We have a wide range of rooms available, all with fast free unlimited Wi-Fi, cooking facilities, bathrooms and outdoor seating areas.
Your hosts, Chris and Leanne, are on hand to ensure you have everything you need to make your stay comfortable and we can provide information and bookings for most local attractions. Continental breakfast is available for a small extra charge.
Children will love the playground! DVD players, DVDs and board games are available on loan from reception. Enjoy outdoor dining with our complimentary gas BBQ.
A guest laundry with washer, dryer and a drying room for ski gear are available for a small charge. Cot and highchair are provided on request (no charge).
Dirección física
Pinewood Motels Fairlie, 25 Mount Cook Road, Fairlie, Christchurch - Canterbury, 7925, New Zealand.
Horario de apertura
- Horario comercial:
- Checkin 2pm - 8.30pm
- Meses de funcionamiento:
- Todos los meses del año
Instalaciones y características
- BBQ Facility
- Children Welcome
- Disabled Access
- Internet (Public Use)
- Laundry
- Playground
- Sky - communal
- Proximidad con el aeropuerto:
- 40 minute drive to Timaru airport
- Proximidad con el terminal de autobuses turísticos:
- 5 minute walk to bus terminal for Christchurch and Queenstown connections
- Proximidad con el pueblo:
- 5 minute walk to town centre
- Cantidad de habitaciones:
- 10
Precios y condiciones
|Por noche
|$120.00 - $150.00
- Otros cargos:
- Rates are for 2 people. An additional $20 for each extra adult, child or infant, up to the maximum occupancy for the unit.
- Más información sobre precios:
- Studio units are for 1-2 adults only. Twin Studio units are for 1-2 adults and a child over 5 years old. Children and infants are permitted in the 2-bedroom apartments, and each child or infant is counted as one guest.


text:
Bunni and Teddi L'angour
Smashwords Edition
Copyright 2012 Bunni and Teddi L'angour
Smashwords Edition, License Notes
This ebook is licensed for your personal enjoyment only. This ebook may not be re-sold or given away to other people. If you would like to share this book with another person, please purchase an additional copy for each recipient. If you're reading this book and did not purchase it, or it was not purchased for your use only, then please return to Smashwords.com and purchase your own copy. Thank you for respecting the hard work of this author.
Teacher's Little Cum Bucket
By Bunni and Teddi L'angour
Light streamed through the ivy that framed the old fashioned multiple pane windows of the classroom. The air in the classroom was full of those little tiny dust particles that inhabit the halls of centuries old Ivy League universities, no matter how well they are ever dusted. A shaft of light showed clear and crisp edges, illuminating Ellie's flaxen hair as she twirled a strand idly. Her breasts heaved out a sigh, and threatened to burst out of her pink midriff sweater as she looked around the room, desperate for distraction from the drone of Mr Lowe's voice.
Ellie was bored. Monumentally bored. Stupendously bored. She was bored not because she found school boring, but because she already knew this shit. Everybody, except apparently the morons in her class, knew this shit. She had heard a variation of this exact lecture two years previously in this very classroom, and two years before that in a very similar classroom in high school. Yet her class, seemingly, did not know this very basic shit.


text:
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text:
The fire that swept through Gateway Middle School Sept. 22 was labeled an arson by authorities.
A news release from the Killeen Police and Fire Department Monday stated the fire was the result of arson.
"Looking at the (fire's) area of origin and its behavior indicated that it was a suspicious fire," said J.D. Gardner, Killeen's fire chief.
Gardner also said the department brought in a special K-9 unit, which was trained to recognize the scent of accelerants.
The release also stated that investigators are looking to speak with a "person of interest" who was last seen climbing a fence behind the school at the time of the fire. The person is described as a Hispanic male possibly in his late 20s or early 30s.
Witnesses described the man as about 6 feet tall and weighing over 200 pounds. He was seen wearing a yellow shirt with dark-colored pants, according to the release.
Gardner said the description of the man was taken from witnesses, as well as video.
The fire, which took firefighters nearly two hours to contain, destroyed the school's offices. Other buildings sustained some fire, smoke and water damage.
Despite the weekend blaze, classes for Gateway's students resumed Monday without interruption. According to a statement from the Killeen Independent School District, Gateway High School students attended classes as usual while Gateway Middle School students were relocated to the district's old Career Center/Pathways complex located at 3004 Atkinson Ave. in Killeen.
According to a statement on the district's website, the Gateway Middle School facility will not be operational for the foreseeable future. The website also indicated that the district's board of trustees will receive an update on Gateway Middle School at a workshop meeting scheduled for 8 a.m. Thursday.


text:
Chanel Preston – IR Superstar
Released: November 25, 2016
When two legends collide! Chanel Preston is legendary. With her amazing body and intense sexual appetite, it was only fitting Dark X paired her with Mandingo. Watch as Chanel takes on the biggest BBC in the industry. Featuring multiple anal and vaginal positions that are so intense, she makes Mandingo come twice!
Size: 984 MB | Duration: 27 min | Video: 1280×720, 5025 kbits/s | Audio: 44100 Hz, 192 kbits/s
Spare link: (try when other links are dead)


text:
Our group actively investigates the development and application of immunological methods for the determination of chemical compounds such as pesticides, polycyclic aromatic hydrocarbons, pharmaceuticals, and mycotoxins. Applications range from human biomonitoring, screening of environmental samples (water, soil, air) and food samples to the preparation of new nanostructured materials for use in immunoaffinity chromatography.
Immunochemical techniques include the entire process of synthesis of reagents (hapten, immunogen, coating antigen, enzyme tracer), antibody production (pAb, mAb, rAb), assay development (direct or indirect format on microtiter plates and tubes, lateral flow devices) and validation (comparison with traditional reference methods).
Further projects deal with molecular imprinting, i.e. preparation of synthetic antibodies (MIPs), multi-analyte determination using antibody arrays, and synthesis of biofunctionalized nanoparticles as new molecular tags for assay labelling.
(c) 2017 Institute of
Technische Universität München, Marchioninistr. 17, D-81377 München
phone: +49 89 2180-78231, fax: +49 89 2180-78255, Impressum


text:
The Hockey shooting Pad is the largest on the market,and is great
for working on puck control and shooting. Use the extra size to
practice wide-angle toe-drages,double moves and whatever else you
can think of to build your skills.
Synthetic Ice is the favorite choice of operators who want to provide ice skating excitement and activities without the cost and heavy equipment for refrigeration.
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Skates glide, edges hold, precision spins happen, pucks slide, and everyone is delighted.
game shooting pad for team Images


text:
Actor Ajith New Lamborghini Car Cost Rs 34 Crore
Actor Ajith New Lamborghini Car Cost Rs 34 Crore
Kollywood Movie star Ajith Kumar is India's very own Steve Mcqueen. Ajith's first love was motorsport, for which he had to take many off track jobs to support. Shooting ad films was one of them, and this eventually led him to become a movie star. Even while being engrossed in the movie business, Ajith Kumar has managed to find time for motor racing, competing in top draw events such as Formula BMW and more recently, Formula2, which is a budding ground for F1 racers. Today, we take a look at Ajith Kumar's cars and superbikes that he's seen in, off the track.
The Honda Accord V6 engined luxury sedan is Ajith's daily drive machine. The car is a Japanese luxury barge that is a best seller globally. While the Accord has been taken off the Indian car market temporarily, it was a top seller until it was pulled off. The one that Ajith owns features a 3.5 liter V6 petrol engine, which puts out 275 Bhp and 339 Nm. The engine powered the front wheel of the car through a five speed automatic gearbox. When it was still sold here, the car commanded a price tag close to the 30 lakh rupees mark.
Another daily drive car that Ajith is seen in is the BMW 7-Series 740 Li luxury saloon. The 7er is BMW's top end luxury offering in India, and globally. The car is powered by a 3 liter, twin turbo, inline 6 petrol engine with 326 Bhp and 450 Nm on tap. An 8 speed automatic gearbox transmits all that torque to the rear wheels of the car. The 740 Li variant is no longer sold in India and the version that Ajith owns is the pre-facelift model. It used to carry a price tag of around 87 lakh rupees.
More than cars, Ajith's speed thrills come from motorcycles. Here's a quick look at the movie star and racer's motorcycle fleet.
The Aprilia Caponard is the actor's latest acquisition. The Italian steed is a dual purpose motorcycle that can take the rough with the smooth. Essentially an SUV among the motorcycle lot, the Caponard sold in India is a CBU import and is priced at 19 lakh rupees. The motorcycle uses a 1,200cc V-Twin petrol engine with 128 Bhp and 116 Nm on tap. The motorcycle can hit 100 Kph from standstill in 5.4 seconds while top speed is a very fast 225 Kph. The real way this motorcycle is to be enjoyed is by going on intercontinental tours though.