text:
\section{Conclusion and Future Work}
This paper presented a novel machine learning approach to evaluate creative artifacts. We used ideas from philosophies on creativity to build a prediction model which predicts user and critics ratings better. The improvements obtained from our experiments even with movie metadata instead of actual content confirms that this paper is in the right direction. From the correlation analysis, we found that the measures for different creative criteria are more aligned to the critic ratings as compared to the audience ratings. This is expected as critics will consider creativity more while rating a movie as compared to audiences who are also affected by other biases like favouritism towards a particular actor, director, genre and demography. We also observed that our unexpectedness scores were able to come up with reasonable movies at the top. We feel that having good similarity measures can make the \textit{creativity} scores more reliable and might result in better prediction model. We propose to use \textit{metric learning} to find better similarity measure as future extension to this paper. Inclusion of more domains like fashion and music also seem natural extensions as our framework is domain-independent.
\section{Experiments}
For our experiments we have considered a single dataset in the domain of movies but the techniques developed in this paper can be extended to any other creative domain. Experiments were done on 5000+ movies metadata from IMDb and Rottentomatoes (RT) website. Two sets of experiments were carried out. First one analyzes how various \textit{creativity} criteria-specific measures (\textit{novelty}, \textit{influence}, \textit{aggregate} and \textit{unexpectedness}) are correlated with popularity and ratings of these artifacts. Second one is to use these measures in a prediction model to get better accuracy in predicting popularity and rating.

Rest of this section is divided into two sub-sections. First sub-section explains data preparation and computation of \textit{creativity} criteria-specific measures. Second section explains details of the analysis carried out on the data including the model used to predict popularity of movie using these measures.

\subsection{Data Preparation and Computing \textit{Creativity} Measures}
\subsubsection{\textbf{Dataset Collection and Curation}}
The Internet Movie Database (IMDb) \footnote{www.imdb.com} and RottenTomatoes (RT) \footnote{www.rottentomatoes.com} are public websites with a large collection of metadata for movies, television programs and other visual media. IMDb users are invited to rate any film on a scale of 1 to 10, and the totals are converted into a weighted mean-rating that is displayed beside each movie title. Similarly in Rotten Tomatoes, each movie has critic ratings/scores and audience ratings/scores. The reviews from top movie critics are collated to form a critic rating/score.
We obtained 28 attributes for 5043 movies from Kaggle IMDb 5000 Movie Dataset \cite{chuansun2016}. In addition, we scraped additional attributes for these movies from the RT website and corresponding Wikipedia movie plots to get a total of 49 attributes. Out of these, some attributes like  `director\_name', `actor\_1\_name', `actor\_2\_name', `actor\_3\_name', `movie\_imdb\_link', 'movie\_release\_date', `movie\_writer\_name', etc. were dropped either because they conveyed no useful information or the information they conveyed can be expressed by some other attribute e.g. director\_facebook\_likes was more useful than the name of the director for our analysis. We also dropped attributes like box-office collection of a movie, number of audience/critics who rated the movie, number of facebook likes of the movie, etc. which are heavily correlated to the rating of the movie. Following attributes were used as output labels to be predicted by ML models:
\begin{enumerate}
    \item \textsc{IMDb rating} (1-10)
    \item \textsc{RT Audience rating} (1-5)
    \item \textsc{RT Audience score} (0-100)
    \item \textsc{RT Critics rating} (1-10)
    \item \textsc{RT Critics score} (0-100)
\end{enumerate}

Computation of measures for some criteria requires time when an artifact was created. We removed movies which had their release year missing. If either of IMDb year of release or RT year of release for the movie were present, we kept that movie entry. If both the entries were present, we take the IMDb year. If none were present, we discard that movie. After this elimination, we were left with 5021 movies to proceed further.

\subsubsection{\textbf{Attribute Representation}}
After the curation of the dataset, we finally had 21 unique attributes (Table \ref{t1}). In the Table, we also report the number of missing values for each attribute. The reason for missing values is either because they were not present in the original Kaggle-dataset or due to inconsistencies in matching movies between RT and IMDb during the web-scraping of movies from the RT website. We filled in the mean of the corresponding attributes and `NA' for missing numeric and string attribute values, respectively.

Next, we needed to convert categorical attributes to numerical values to be used further in our analysis. For attributes which consisted of single string and had semantic meaning associated with them ( e.g. plot\_keywords, genres, country, language), we used Word2Vec \cite{word2vec} to get vectors of dimension 300 each. The attributes which consisted of one or more sentences (like Wiki\_plot, RT\_plots) were converted to Skip-Thought vectors\cite{kiros15} of dimension 4800 each, by average pooling across sentences. For attribute values which did not have a word2vec or skip-thought representation,we took a zero vector of corresponding size. The vectors for attributes with multiple strings( like genres and plot\_keywords) or multiple sentences (like wiki\_plot) were obtained by taking the mean vector across all strings/ sentences which represent that attribute. Attributes such as color and rt\_studio were converted to one-hot encoded vectors.

To reduce the large dimensionality of the feature space(\textasciitilde20000) as compared to the number of movie samples(\textasciitilde5000), we applied PCA on individual skip-thought and word2vec vectors such that 90\% of the variance is captured by them. This resulted in 407 features(in total for 21 attributes) which were then used as input for calculation of similarity measures. These features will be called as \textbf{PCA features} in future references. These features have been computed in isolation for an artifact and depict the \emph{value} of the artifact as explained in Section \ref{sec:value}.

\begin{table}
    \centering

    \resizebox{0.45\textwidth}{!}
    {
    \begin{tabular}{|l|c|c|r|}
        \hline
    \textbf{Attribute} & \textbf{Representation} & \textbf{Similarity} & \textbf{Missing Values} \\ \hline
	IMDb plot & Skip-Thought & Cosine & 2484 \\ \hline
	Wiki plot & Skip-Thought & Cosine & 1339 \\ \hline
	Language & Word2Vec & Cosine & 12 \\ \hline
	Color & One-Hot & Cosine & 19\\ \hline
	Certification & One-Hot & Cosine & 303\\ \hline
	Why certification & Skip-Thought & Cosine & 1442\\ \hline
	RT plot & Skip-Thought  & Cosine & 513\\ \hline
	Studio & One-Hot Encoded  & Cosine& 566 \\ \hline
	Genres & Word2Vec  & Cosine & 0 \\ \hline
	Movie title & Word2Vec  & Cosine & 0 \\ \hline
	Plot keywords & Word2Vec  & Cosine & 153 \\ \hline
	Country & Word2Vec  & Cosine & 5 \\ \hline
	Actor-1 FB likes & Real Number & Linear, Exp & 7 \\ \hline
	Actor-2 FB likes & Real Number & Linear, Exp & 13 \\ \hline
	Actor-3 FB likes & Real Number & Linear, Exp & 23 \\ \hline
	Aspect ratio & Real Number & Linear, Exp & 329 \\ \hline
	Budget & Real Number & Linear, Exp & 492 \\ \hline
	Cast total  FB likes & Real Number & Linear, Exp & 0 \\ \hline
	Director FB likes & Real Number & Linear, Exp & 104 \\ \hline
	Runtime & Real Number & Linear, Exp & 15 \\ \hline
    \# of Faces in poster & Real Number & Linear, Exp & 13\\ \hline

 \hline
    \end{tabular}}
    \caption{Different Attributes used along with their representation, similarity type and number of missing values}
    \label{t1}
\end{table}

\subsubsection{\textbf{Computing similarity}}
We employed mainly three types of similarity measures depending on the representation of the attribute. For real-valued attributes, we used linear and exponential similarity measures (See Eqn \ref{eq:sim}) and for the rest, we used cosine similarity (Table \ref{t1}). For each attribute, we obtained a $N \times N$ similarity matrix (where $N$ = number of movies) by finding the similarity between all pairs of movie combinations. Before passing the numeric attributes for calculating linear and exponential similarities, we normalized them to avoid scaling issues.

\subsubsection{\textbf{Computing \textit{Creativity} Measures}}

To compute the novelty, influence and aggregate scores we implemented the graph network as discussed in Section \ref{sec:nov_inf} above. \textit{Aggregate} is combination of novelty and influence which is obtained from eqn(\ref{calc}). The movies formed the nodes of the graph. The similarity value between two movies was used as a weight of the edge connecting the two movies in the graph. We obtained one graph for each attribute and similarity type. We used $\alpha = 0.95$, $\beta = 0.2$ and $0.5$ in eqn(\ref{calc}) to compute novelty, influence and aggregate. Experimentally we found that using $\beta=0.2$ gives better results and hence we are only reporting results with $\beta=0.2$.

For calculating mean, max and inverse\_weighted measures of unexpectedness (See Section \ref{sec:unex} for details), we took movies in a 5 year window past the current movie year. Amongst the calculated unexpectedness measures, \emph{mean} was used for feeding into the prediction models.

\subsection{Analysis}
\subsubsection{\textbf{Correlation Analysis}}
We obtained the Pearson Correlation of our attribute-wise influence, novelty, aggregate (of novelty and influence) and unexpectedness scores with each of the output labels. Though we computed aggregate scores, novelty scores and influence scores with $\beta=0.2$ and $\beta=0.5$; we are only reporting results for $\beta = 0.2$. We obtained correlation values for mean, max and inverse\_weighted unexpectedness scores as well. We are only reporting results of max-unexpectedness in this paper for the interest of space.

\subsubsection{\textbf{Prediction Models}}

We experimented with different combinations of attributes and different models for predicting the values of the five output labels. Our aim was to show that using \textit{creativity} measures (novelty, influence, aggregate and unexpectedness) with PCA features gives better results than just using PCA features.

To make experiments across output labels comparable, we normalized them by dividing with their highest possible value (e.g. IMDb ratings were divided by 10) before giving to ML models for training. Experiments were carried out on the following combinations of features:
\begin{enumerate}
    \item \textbf{Baseline}: We consider PCA features as the baseline for our experiments.
    \item \textbf{PN}: PCA features + Novelty measure
    \item \textbf{PI}: PCA features + Influence measure
    \item \textbf{PU}: PCA features + Unexpectedness measure
    \item \textbf{PUN}: PCA features + Unexpectedness measure + Novelty measure
    \item \textbf{PUI}: PCA features + Unexpectedness measure + Influence measure
    \item \textbf{PUNI}: PCA features + Unexpectedness measure + Novelty measure + Influence measure
    \item \textbf{PUNIA}: PCA features + Unexpectedness measure + Novelty measure + Influence measure + Aggregate measure.
\end{enumerate}

We trained five different types of models: Support Vector Regressor, Random Forest Regressor, Ridge Regression, Bayesian Regression and K-Nearest Neighbour Regressor for each combinations above. We used the implementations of these models as present in scikit-learn version 0.18.1\cite{scikit-learn}. For KNN, we used n\_neighbours = 5; and for Random Forest Regressor we used n\_estimators=600 and min\_samples\_split=2. For other models, we used the default values of parameters. We converted the data to zero mean and unit standard deviation before giving to the models for training. We used a train-test split of 80-20 \%. After the training was done, root mean square error (RMSE) was used as a measure of test error.

\section{Proposed Framework}
In this section, we are going to elaborate the details of the learning based framework that can be used to evaluate creativity of artifacts in any domain. Let us consider a set of $m$ artifacts $\{M_i\}^{m}_{i=1}$ which we want to rate based on creativity. Based on the information $a_i$ available about the artifact $M_i$ , the artifact can be rated as:

\vspace*{-5mm}
\begin{align}
    y_i = \Phi (a_i) \label{eqn1}
\end{align}

Here, $\Phi$ is a function which takes all the information available about the artifact as input and produces the rating $y_i$. Here, $a_i = \{a_{i1},...,a_{iN}\}$ corresponds to a list of $N$ attributes. Examples of attributes can be \emph{genre, runtime, budget, plot}, etc in case of movies. For a fashion apparel, attributes can be \emph{gender, apparel type, brand, colour, texture, image of the apparel}, etc. Function $\Phi$ can be broken down further into a feature extraction step followed by prediction step as follows:
\vspace*{-4mm}
\begin{align}
    \textbf{x}_i = \mathscr{F} (a_i) \\ \textbf{x}_i = [\textbf{f}_
    ,\textbf{n}_i,\textbf{l}_i,\textbf{u}_i]\\
    y_i = \Psi (\textbf{x}_i)
\end{align}

The function $\mathscr{F}$ involves extraction of different kinds of features like \emph{value} features $\textbf{f}_i$,
\emph{novelty} features $\textbf{n}_i$, \emph{influence} features $\textbf{l}_i$ and \emph{unexpectedness} features $\textbf{u}_i$ from $a_i$. Thus, given a set of $m$ datapoints (or artifacts) in the form $S = \{a_i,y_i\}_{i=1}^{m} $, the function $\mathscr{F}$ is used to transform them to training data $D = \{\textbf{x}_i,y_i\}_{i=1}^{m} $ for the regression model $\Psi$, which takes input feature $\textbf{x}_i$ for artifact $M_i$ to produce creativity rating $y_i$.

\subsection{Value}
\label{sec:value}
Previous works like \cite{maher2010,regent16,maher2013,varshney13,varshneyicci2013} have highlighted that \emph{value} is an important criteria for creativity. In \cite{maher2010}, value is defined as \emph{a measure of how the artifact is valued by domain experts for this class of artifact or is a reflection of the acceptance of this artifact by society}. For example, a movie directed by `Christopher Nolan' will be perceived as highly valuable compared to a lesser known director. This fact is elucidated very well in \cite{regent16} for the domain of fashion, where certain colour combinations of apparels are valued higher based on a synergy list consisting of most prevalent colours.

Every artifact $M_i$ can have $N$ attributes $ \{a_{i1},...,a_{iN}\}$, which could be of various types, like numerical, categorical, textual and even different modality like image, etc. Numerical attributes need not have any encoding and their values can be used as is, whereas categorical attributes need one-hot encoding and textual or other modalities require embedding-based representations. Application of encoding schemes and dimensionality reduction technique like Principal Components Analysis (PCA) can be applied to produce a list of vectors $\textbf{f}_i = \{\textbf{f}_{i1},...,\textbf{f}_{iN}\}$. This list of vectors $\textbf{f}_i$ can be treated as an input vector (produced by feature extraction from $a_i$) to a regression model $\Theta$ to predict the output rating $y_i$:

\vspace*{-5mm}
\begin{align}
    y_i = \Theta (\textbf{f}_i) \label{eq2}
\end{align}

In the equation ~\ref{eq2} above, the learning model tries to assign higher weights to dimensions of $\textbf{f}_i$ which are highly correlated with the output rating. So, the model will consider a datapoint highly valuable if its combination of correlated dimensions leads to a better rating prediction. In our formulation, the \emph{value} aspect of creativity is inherently captured by the regression-based model $\Theta$ when the features for every datapoint $i$ is extracted in isolation without considering other datapoints.

\subsection{Novelty and Influence}\label{sec:nov_inf}
In our framework, we propose to include two important metrics computed from the similarity graph obtained from the data points : \emph{novelty} and \emph{influence}. According to \cite{maher2010}, novelty is \emph{a measure of how different the artifact is from known artifacts in its class}. In other words, if an artifact is far apart from other known artifacts, then it is considered highly novel. For example, the first ever time time-travel movie will have high novelty score compared to other time-travel movies which came much later. Similarly, there can be artifacts which can be completely different from other known artifacts, hence novel, yet missing out on the value aspect.

On the other hand, \emph{influence} measures if an artifact was an inspiration or if it had an impact on another artifact. For example, `The Godfather' movie had significant influence on its sequel. Similarly, the first time-travel movie would have had significant influence on similar movies (`The Terminator') appearing later in time. In the domain of fashion, designers tend to get inspired by works of other designers, which means the previous works had an influence on the later ones. It is also, however, possible that non-novel artifacts can also influence later works.

As is evident from the above definitions, novelty and influence measures for an artifact cannot be computed in isolation from other artifacts as they require similarity with other artifacts. It is to be noted that even though a design appearing later in time may not have been directly inspired by an earlier one, but in the eyes of the observer, the earlier one is influential if there is a high similarity. Considering the observer/critic point of view (in line with Boden's view of H-creativity \cite{boden91}), these measures are highly related to the chronological order of the artifacts, as the earlier artifacts tend to have influence over the later ones. Some works like \cite{gravino16,Wasserman03022015,andreas14} have already mentioned using chronological order of movies and measuring influence based on a movie citation network which includes explicit references between movies. Explicit references lead to very sparse connections and may have incomplete information as they are manually curated by IMDb. Hence, we suggest using a similarity based graph for artifacts as used in  \cite{Mariani2016IdentificationOM} for identifying milestone papers, in \cite{elgammal15} for quantifying creative paintings and in PageRank\cite{Pageetal98} for calculating importance of web documents.

The first step towards construction of a similarity graph based analysis is computation of the similarity matrices $\textbf{W}_1,...,\textbf{W}_N$ between all pairs of artifacts corresponding to each attribute $k$ of $N$ attributes. The type of attribute dictates the choice of similarity measure as follows:
\begin{align}
    Sim(M_i,M_j,k) =
    \begin{cases}
        lin(f_{ik}, f_{jk}) = \frac{1}{1+|f_{ik} - f_{jk}|}, \text{numerical}\ k.\\
        \\
        exp(f_{ik}, f_{jk}) = e^{-|f_{ik} - f_{jk}|}, \text{numerical}\ k.\\
        \\
        cos(\textbf{f}_{ik}, \textbf{f}_{jk}) = \frac {\textbf{f}_{ik} \cdot \textbf{f}_{jk}}{||\textbf{f}_{ik}|| \cdot ||\textbf{f}_{jk}||}, \text{others.}
    \end{cases} \label{eq:sim}
\end{align}

Every artifact $M_i$ also comes with a time of occurrence $t(i)$ based on which a selection matrix \textbf{S} is computed. The main idea here is to have a graph among the artifacts, the edges being related to the similarity between the nodes, in such a way that the novelty and influence scores of the artifacts can be computed using PageRank\cite{Pageetal98} algorithm. This follows from the logic that if there is a high similarity edge between two nodes, the source node is more influential and novel than the target and hence, more creative. The complete sequence of steps (refer \cite{elgammal15} for details) to be followed for computing the parameters of the graph are listed below:
\begin{align}
    w_k(i,j) = Sim(M_i,M_j,k),\ \forall\ i,j,k \label{sim}\\
    s(i,j) =
    \begin{cases}
        1, & \text{if}\ t(i)<t(j)  \\
        0, & \text{otherwise}
    \end{cases} \label{sel1}
    \\
    w_k(i,j) = w_k(i,j) * s(i,j),\ \forall\ i,j,k \label{sel2}\\
    \hat{w}_k(i,j) = w_k(i,j) - \tau_k,\ \forall\ i,j,k \label{thresholding}\\
    Reversal :
    \begin{cases}
        \hat{w}_k(i,j) \ge 0 \rightarrow \tilde{w}_k(i,j) = \hat{w}_k(i,j) \\
        \hat{w}_k(i,j) < 0 \rightarrow \tilde{w}_k(j,i) = -\hat{w}_k(i,j)
    \end{cases},\
    \forall\ i,j,k \label{rev}\\
    p(i,j) =
    \begin{cases}
        1, & \text{if}\ t(i)>t(j)  \\
        0, & \text{otherwise}
    \end{cases} \label{sel3}
    \\
    \tilde{w}^p_k(i,j) = \tilde{w}_k(i,j) * p(i,j)\ \forall\ i,j,k \label{sel4}
    \\
    q(i,j) =
    \begin{cases}
        1, & \text{if}\ t(i) \le t(j)  \\
        0, & \text{otherwise}
    \end{cases} \label{sel5}
    \\
    \tilde{w}^q_k(i,j) = \tilde{w}_k(i,j) * q(i,j)\ \forall\ i,j,k \label{sel6}
\end{align}

Eqn ~\ref{sim} is related to the similarity matrices $\textbf{W}_k$ created for every attribute $k$. Similarity matrices being symmetric, the graph formed by $\textbf{W}_k$ is an undirected graph. The next steps in equations ~\ref{sel1} and ~\ref{sel2} select only those edges in the graph which go forward in time, which is also the direction of influence, thus producing a directed graph. This is followed by a thresholding step in equation ~\ref{thresholding}, which mandates only high value edges eligible for score propagation.
This step leads to addition of more negative weighted edges to the graph, which can be corrected by an edge reversal step in equation ~\ref{rev}, resulting in creation of edges not going forward in time. However, to preserve the distinction between edges which were reversed and the others, the graph is split into two directed graphs, \emph{prior} graph and \emph{subsequent} graph in the remaining steps. As elaborated in \cite{elgammal15}, the former graph leads to computation of novelty scores whereas the latter leads to generation of influence scores. Equations ~\ref{sel4} and ~\ref{sel6} along with column normalization step produces two column-stochastic matrices $\tilde{\textbf{W}}^p_k$ and $\tilde{\textbf{W}}^q_k$ for every attibute $k$. These matrices together help in computing the \emph{novelty} scores $n_{ik}$, \emph{influence} scores $l_{ik}$ and \emph{aggregate} scores $c_{ik}$ based on the following equations:
\begin{align}
    \textbf{c}_k = \frac{1-\alpha}{m}\textbf{1} + \alpha [\beta \tilde{\textbf{W}}^p_k \textbf{c}_k + (1-\beta) \tilde{\textbf{W}}^q_k \textbf{c}_k] \label{calc}\\
    \Rightarrow \textbf{c}_k = [\frac{1-\alpha}{m}\textbf{E} + \alpha \beta \tilde{\textbf{W}}^p_k + \alpha (1-\beta) \tilde{\textbf{W}}^q_k] \textbf{c}_k,\\
    \text{where}\ \textbf{E}\ \text{is all 1's matrix.} \nonumber \\
    \Rightarrow \textbf{c}_k = \mathcal{M} \textbf{c}_k, \text{where} \mathcal{M}\ \text{is column stochastic.} \label{eigen}\\
    \Rightarrow \textbf{n}_k = \alpha \beta \tilde{\textbf{W}}^p_k \textbf{c}_k\ \text{,}\ \textbf{l}_k = \alpha (1-\beta) \tilde{\textbf{W}}^q_k \textbf{c}_k \label{novinf}
\end{align}
The power iteration method \cite{Pageetal98} can be used to solve the above equation ~\ref{eigen}. Now, substituting back the solution $\textbf{c}_k$ in equation ~\ref{calc}, we get the novelty scores $\textbf{n}_k$ and $\textbf{l}_k$ over all $m$ artifacts for attribute $k$ as in equation ~\ref{novinf}. On computation of above for all attributes results in instance specific scores $\textbf{n}_i$, $\textbf{l}_i$, and $\textbf{c}_i$ for artifact $M_i$, each of these vectors being of same dimension as number of attributes.

\subsection{Unexpectedness}\label{sec:unex}
\emph{Unexpectedness}, also known as \emph{surprise} is defined as a criteria for creativity, which measures if an artifact is different from its expected attributes, the amount of difference may not be important \cite{maher2010}. Typically,  the evaluation of unexpectedness considers only the recent past to figure out if an artifact or its attributes is unexpected. For example, a black-and-white movie in 1920s will not be unexpected as colour movies were not yet the norm. However, the 2011 French movie `The Artist' would have been considered highly unexpected in the age of colour movies. Another curious example is that from wide-rimmed glasses, which were pretty much in fashion till 1990s and for a brief period went out of fashion. So, if such an artifact appeared in early 2000s, it would have been highly unexpected compared to current years as they are back in fashion again. There is a fine difference between novelty and unexpectedness as novelty implies unexpectedness but not the other way round. The unexpectedness defined here is different from Boden's notion of P-creativity \cite{boden91} as our notion considers if an artifact is unexpected at a certain time rather than if it is unexpected from its creator.

In this work, we measure unexpectedness by considering the recent past (say, 5 years) of an artifact $M_i$ and how different it is from its predecessors in the recent past, belonging to the set $\gamma = \{j: t(i-5) \le t(j) < t(i)\}$. Here, for each attribute $k$, the unexpectedness of the artifact is defined separately as $u_{ik}$ by the following different measures as below:

\begin{align}
    u_{ik} =
    \begin{cases}
        max\_unexp(i,k) = - \underset{j}{\max}\ Sim(M_i,M_j,k)\\
        avg\_unexp(i,k) = - \frac{1}{|\gamma|} \underset{j}{\sum}\ Sim(M_i,M_j,k)\\

        inv\_unexp(i,k) = - \frac{\underset{j}{\sum} v_{ij} * Sim(M_i,M_j,k)}{\underset{j}{\sum} v_{ij}},\\ \ \ \ \text{where}\ v_{ij} = \frac{1}{|t(i)-t(j)|}
    \end{cases} \label{unexp}
\end{align}

All of the measures rely on the fact that the more similar an artifact $M_i$ is to its recent predecessors, the less unexpected it will be. The different measures considered are maximum similarity, mean similarity and inverse weighted mean similarity, where the weights are higher for a recent movie compared to an earlier one. Thus, for every artifact $M_i$, unexpectedness scores $\textbf{u}_i$ can be generated by one of the above measures in equation ~\ref{unexp}, where the dimension of the score vector is the number of attributes.

\section{Introduction}

Humans have been creative since time unknown. The progress of civilizations has happened as our ancestors have found creative ways to solve problems. Creativity is evident in every aspect of human advancement ranging from science to various forms of art. In fact, creative intelligence is considered by many as the ultimate goal of artificial intelligence. The achievements of artificial intelligence in solving real-world problems has been nothing short of extraordinary. However, the ability to generate creative content is one area where much is left desired from machines. Availability of creative content in various domains like music, painting, dance, fashion, literature, movies, games and cooking has fueled research in an emerging field known as \emph{computational creativity}.

According to \cite{maher2010}, research in computational creativity is motivated by the following goals: first, modelling the creative generation process to enable machines to generate creative content; second, enabling machines to augment human creativity, and third, assessing and identifying creativity without any assumption of an artifact being produced by machines or humans. Understanding creativity entails the following: first, quantitative evaluation of creativity based on different criteria, and second, learning creative scoring models on top of above criteria. Following this idea, we formulate an automated learning based approach to evaluate creative artifacts.

According to the recent literature \cite{elgammal15,regent16,maher2010,maher2013,Mariani2016IdentificationOM,varshney13,varshneyicci2013}, the essential criteria for creativity are mainly the following: \emph{value}, \emph{novelty}, \emph{influence} and \emph{unexpectedness}.
\emph{Value} of an artifact is a measure of how well it is perceived by the society in isolation; without any comparison with other artifacts in its domain. That is, how appealing or useful or pleasant or performing the artifact is; the exact criterion being heavily dependent upon the domain and the users. As an example, one user can rate the movie `Silence of the Lambs' high because of good acting and direction, whereas another user can rate it low because of the presence of violent and disturbing stills in the movie. \emph{Novelty} of an artifact is a notion of how different it is from other artifacts in its domain. For example, `Honey Butter Pepper Lemon BBQ Chicken' is a novel recipe, as nobody has tried it out, even though it may taste weird and hence, not valuable. \emph{Influence} of an artifact quantifies how much impactful or inspiring it has been towards artifacts occurring later in time. For example, `Mona Lisa' is known to have been an inspiration for Raphael's painting `Young Woman with Unicorn' and many others. Another criteria is the \emph{unexpectedness} or \emph{surprise} which defines how different the artifact or some of its attributes are from expected behavior. For example, a black-and-white movie like `The American Astronaut' in year 2001 is unexpected, even though such movies are not novel as they have appeared in the past.

Convinced with the idea that evaluation of creative artifacts should involve all the above four criteria, we proceed to build a machine learning framework incorporating them; to come up with an objective and automatic way of evaluating creativity which will be applicable to all domains. In this approach, we first compute the criteria-specific measures (value, novelty, influence and unexpectedness), which can be then used as features for a regression-based model. From the above definitions, it is clear that all criteria except \emph{value} require comparison of an artifact with others with the help of some similarity function. The similarity function can vary based on the domain. Our framework assumes the availability of a similarity function to define the computation of measures for different creative criteria to be included in the regression model. Hence, this approach is applicable to all domains, conditional on the availability of similarity function for the corresponding domains.

Even though our approach can be used to evaluate creative artifacts in all domains, we choose to perform our experiments on the movies dataset due to the availability of ground truth ratings like critics score and audience score. According to \cite{lowagreement17}, \emph{audience ratings correlate only weakly with the judgment of professional movie critics}. Although the movie `Moonlight' was hailed by critics, it was not received very well by audience. On the other hand, `Interstellar' was slammed by critics even though it was a huge success. This is perhaps because the critics are well-versed with the landscape of movies, and hence give a higher importance to novelty and influence than the audience. We justify the above claim in our experiments by showing that measures for those criteria are highly correlated with critics score. In our prediction model, the inclusion of various creative criteria lead to improved performance in predictions in both audience and critics score. Previous work by \cite{debarun16} has tried to combine multiple criteria to evaluate artifacts using various heuristics. To the best of our knowledge, this is the first machine-learning approach which uses ideas from philosophies of creativity to come up with a rating prediction model.

In a nutshell, our contributions are primarily the following:
\begin{enumerate}
    \item A domain-independent learning based framework incorporating measures for novelty, influence, value and unexpectedness for evaluating creative artifacts.
    \item Empirical analysis showing that incorporating various creative criteria improves the rating prediction of movies.
    \item The above measures are highly correlated to objective measures like critics score compared to audience score.
\end{enumerate}

\section{Results and Discussion}
Though experiments were done on the metadata of the movies and not on the actual content of the movie (which is the video and audio of the movie), we were able to find promising observations and results. We will first discuss results from correlation analysis and later explain rating prediction findings.

\subsection{Correlation Analysis}
In the first set of experiments we computed the pearson correlation between
\textit{novelty}, \textit{influence}, \textit{aggregate} and \textit{unexpectedness} with popularity and ratings of the movies. These measures were computed separately for each attribute. Again \textit{aggregate} here is a combination of novelty and influence which is obtained from eqn(\ref{calc}).

Figures \ref{fig:creat1}, \ref{fig:inf1} and \ref{fig:unex1} shows the correlation heatmap obtained for \textit{aggregate}, \textit{influence} and \textit{unexpectedness} measures, respectively. We were able to make following observations.
\begin{enumerate}
    \item From the heatmaps we can see that \textit{creativity measures} are almost all positively correlated with the different ratings. This confirms the hypothesis that these \textit{measures} can indeed capture audience and critics interests.
    \item In figure \ref{fig:creat1} and \ref{fig:inf1} we can see that second and fourth column are more darker than other columns which implies that \textit{aggregate} and \textit{influence} measures are more correlated with critic rating and critic score compared to audience rating and score. This might point out that critics give more importance to influence and novelty of a movie compared to audience.
    \item \textit{Plot keywords} are more correlated to ratings compared to detailed plots of the movie: \textit{IMDb plot, wiki plot, RT plot}. Inability of skip-thought vectors to represent a large text content might be the reason for this. Among the plots, \textit{wiki plot} is the longest and detailed plot in our dataset. We could see that correlation values for \textit{wiki plot} is the least among plots. More texts might have diluted the main content of the plot in the representation.
    \item \textit{Language}, \textit{Color} and \textit{Aspect ratio} are least correlated attribute in all three \textit{measures}.
\end{enumerate}

From figure (\ref{fig:unex1}) one could see that unexpectedness on \textit{Genres} is highly correlated with the RT critic score. To further understand how unexpectedness scores were given to each movie in this case, we obtained a scatter plot in figure(\ref{fig:unex_scatter}). It is very interesting to see many famous movies getting higher scores in the figure. One could see that "Snow White and The Seven Dwarfs(1937)", first color animation movie with voice has got a high score. Similarly "Psycho(1960)" ranked among most greatest films has also got high score. These analyses further confirm that using \textit{unexpectedness} and other \textit{creativity} criteria on creative artifacts is the right direction to pursue.

\subsection{Prediction Task}
Here we report results of experiments ran on three output labels: \textsc{IMDb rating}, \textsc{RT Critic Score} and \textsc{RT Audience Score} (skipping others for the interest of space as they have similar observations). In Tables \ref{t2}-\ref{t4}, we present the RMSE on the test data for different combinations of datasets and models with each output label. The best score for each model has been highlighted in red and the best percentage increase across all models and datasets has been highlighted with blue in the Tables. As can be clearly seen from the numbers, the inclusion of \textit{influence}, \textit{novelty}, \textit{aggregate} and \textit{unexpectedness} scores leads to better prediction performance for all models and output labels.
In all cases, better performance from the baseline is shown. This shows that creativity measures are important features for our prediction model. Tables show that we are getting improvement as high as 4.5\% in RMSE.

\begin{table*}
    \centering
    \resizebox{0.8\textwidth}{!}
    {
    \begin{tabular}{|l|r|r|r|r|r|}
        \hline
    \textbf{Combination} & \textbf{Ridge Regression} & \textbf{Bayesian Regression} & \textbf{SVR} & \textbf{KNN} & \textbf{Random Forest} \\ \hline
Baseline & 0.08983 & 0.08935 & 0.09402 & 0.11224 & 0.09042 \\ \hline
PN & 0.0891 & 0.0886 & 0.09274 & 0.11004 & 0.08929 \\ \hline
PI & 0.09182 & 0.08942 & 0.09365 & 0.11148 & 0.08989 \\ \hline
PU & 0.08931 & 0.08895 & 0.09308 & 0.10963 & 0.08953 \\ \hline
PUN &0.08896 & \textcolor{red}{\textbf{0.0882}} & \textcolor{red}{\textbf{0.09224}} & 0.10777 & 0.08908 \\ \hline
PUI &0.08968 & 0.0889 & 0.09289 & 0.10877 & 0.08922 \\ \hline
PUNI &\textcolor{red}{\textbf{0.08826}} & 0.08871 & 0.0931 & 0.10835 & 0.08936\\ \hline
PUNIA & 0.0898 & 0.08844 & 0.09232 & \textcolor{red}{\textbf{0.1071}} & \textcolor{red}{\textbf{0.08891}} \\ \hline
\textbf{Improvement\%} & \textbf{1.75309
} & \textbf{1.29546
} & \textbf{1.88974
} & \textcolor{blue}{\textbf{4.58040}
} & \textbf{1.67013
} \\ \hline

    \end{tabular}}
    \caption{Comparison of RMSE on different combinations and different models for output label - \textsc{IMDb rating}}
    \label{t2}
\end{table*}

\begin{table*}
    \centering
    \resizebox{0.8\textwidth}{!}
    {
    \begin{tabular}{|l|r|r|r|r|r|}
        \hline
    \textbf{Combination} & \textbf{Ridge Regression} & \textbf{Bayesian Regression} & \textbf{SVR} & \textbf{KNN} & \textbf{Random Forest} \\ \hline
Baseline & 0.24125 & 0.23533 & 0.23151 & 0.26919 & 0.23472 \\ \hline
PN & 0.24095 & 0.23518 & 0.23107 & 0.26899 & 0.23119 \\ \hline
PI & 0.24673 & 0.23615 & 0.22868 & 0.26955 & 0.23097 \\ \hline
PU & 0.24104 & 0.23546 & 0.23065 & 0.26639 & 0.23101 \\ \hline
PUN &\textcolor{red}{\textbf{0.24038}} & \textcolor{red}{\textbf{0.23453}} & 0.22989 & 0.26782 & \textcolor{red}{\textbf{0.23040}} \\ \hline
PUI &0.24742 & 0.23652 & 0.22838 & 0.26776 & 0.23042 \\ \hline
PUNI & 0.24350 & 0.23483 & 0.22806 & 0.26952 & 0.23136 \\ \hline
PUNIA & 0.24799 & 0.23609 & \textcolor{red}{\textbf{0.22765}} & \textcolor{red}{\textbf{0.26539}} & 0.23127 \\ \hline

\textbf{Improvement\%} & \textbf{0.35934
} & \textbf{0.34056
} & \textbf{1.66685
} & \textbf{1.41486
} & \textcolor{blue}{\textbf{1.8394}
} \\ \hline

    \end{tabular}}
    \caption{Comparison of RMSE on different combinations and different models for output label - \textsc{RT Critic Score}}
    \label{t3}
\end{table*}

\begin{table*}
    \centering
    \resizebox{0.8\textwidth}{!}
    {
    \begin{tabular}{|l|r|r|r|r|r|}
        \hline
    \textbf{Combination} & \textbf{Ridge Regression} & \textbf{Bayesian Regression} & \textbf{SVM} & \textbf{KNN} & \textbf{Random Forest} \\ \hline
Baseline & 0.1695 & 0.165 & 0.16259 & 0.20086 & 0.16583 \\ \hline
PN & 0.1684 & 0.16427 & 0.16114 & 0.19898 & 0.16439 \\ \hline
PI & 0.17877 & 0.165 & 0.1614 & 0.19889 & 0.16384 \\ \hline
PU & 0.16881 & 0.16411 & 0.16056 & 0.19659 & 0.16391 \\ \hline
PUN &\textcolor{red}{\textbf{0.16742}} & \textcolor{red}{\textbf{0.16306}} & 0.15954 & 0.19541 & 0.16410 \\ \hline
PUI &0.17528 & 0.16477 & 0.15973 & 0.19451 &\textcolor{red}{\textbf{ 0.16307}} \\ \hline
PUNI & 0.17251 & 0.16529 & 0.15947 & 0.19198 & 0.16342 \\ \hline
PUNIA & 0.17295 & 0.16422 & \textcolor{red}{\textbf{0.1591}} & 0.19332 & 0.16396 \\ \hline

\textbf{Improvement\%} & \textbf{1.22812
} & \textbf{1.17284
} & \textbf{2.1457
} & \textcolor{blue}{\textbf{4.4213}
} & \textbf{1.66459
} \\ \hline

    \end{tabular}}
    \caption{Comparison of RMSE on different combinations and different models for output label - \textsc{RT Audience Score}}
    \label{t4}
\end{table*}

Though this improvement were not as high as we expected, these results are encouraging. It is observed that the percentage improvement in performance over the baseline for audience-related output labels (\textsc{IMDb rating}, \textsc{RT Audience Score}) is better as compared to that for critics-related output labels(\textsc{RT Critic Score}). More investigation is required into this observation. As per the best performing feature combination and ML model are concerned, they vary depending on the output label. Though in most cases, the feature combination with PCA features, unexpectedness and one of the other creativity scores stands out to be the winner.

A thorough analysis is needed to understand how to extract the best out of these models. Choosing better similarity measures would also help in getting better accuracy. For predicting movie ratings and popularity we feel that there is a limit to which metadata can capture. Using main content of movies (video and audio) should give better accuracy as the ratings/scores are given by the audience and the critics by watching the main content. Among other domains, specifically fashion seems to be the right fit for these techniques as whole or most of the information about the artifact will be captured in an image.

\section{Introduction}

The \textit{proceedings} are the records of a conference.\footnote{This
  is a footnote}  ACM seeks
to give these conference by-products a uniform, high-quality
appearance.  To do this, ACM has some rigid requirements for the
format of the proceedings documents: there is a specified format
(balanced double columns), a specified set of fonts (Arial or
Helvetica and Times Roman) in certain specified sizes, a specified
live area, centered on the page, specified size of margins, specified
column width and gutter size.

\section{The Body of The Paper}
Typically, the body of a paper is organized into a hierarchical
structure, with numbered or unnumbered headings for sections,
subsections, sub-subsections, and even smaller sections.  The command
\texttt{{\char'134}section} that precedes this paragraph is part of
such a hierarchy.\footnote{This is a footnote.} \LaTeX\ handles the
numbering and placement of these headings for you, when you use the
appropriate heading commands around the titles of the headings.  If
you want a sub-subsection or smaller part to be unnumbered in your
output, simply append an asterisk to the command name.  Examples of
both numbered and unnumbered headings will appear throughout the
balance of this sample document.

Because the entire article is contained in the \textbf{document}
environment, you can indicate the start of a new paragraph with a
blank line in your input file; that is why this sentence forms a
separate paragraph.

\subsection{Type Changes and {\itshape Special} Characters}

We have already seen several typeface changes in this sample.  You can
indicate italicized words or phrases in your text with the command
\texttt{{\char'134}textit}; emboldening with the command
\texttt{{\char'134}textbf} and typewriter-style (for instance, for
computer code) with \texttt{{\char'134}texttt}.  But remember, you do
not have to indicate typestyle changes when such changes are part of
the \textit{structural} elements of your article; for instance, the
heading of this subsection will be in a sans serif\footnote{Another
  footnote here.  Let's make this a rather long one to see how it
  looks.} typeface, but that is handled by the document class file.
Take care with the use of\footnote{Another footnote.}  the
curly braces in typeface changes; they mark the beginning and end of
the text that is to be in the different typeface.

You can use whatever symbols, accented characters, or non-English
characters you need anywhere in your document; you can find a complete
list of what is available in the \textit{\LaTeX\ User's Guide}
\cite{Lamport:LaTeX}.

\subsection{Math Equations}
You may want to display math equations in three distinct styles:
inline, numbered or non-numbered display.  Each of
the three are discussed in the next sections.

\subsubsection{Inline (In-text) Equations}
A formula that appears in the running text is called an
inline or in-text formula.  It is produced by the
\textbf{math} environment, which can be
invoked with the usual \texttt{{\char'134}begin\,\ldots{\char'134}end}
construction or with the short form \texttt{\$\,\ldots\$}. You
can use any of the symbols and structures,
from $\alpha$ to $\omega$, available in
\LaTeX~\cite{Lamport:LaTeX}; this section will simply show a
few examples of in-text equations in context. Notice how
this equation:
\begin{math}
  \lim_{n\rightarrow \infty}x=0
\end{math},
set here in in-line math style, looks slightly different when
set in display style.  (See next section).

\subsubsection{Display Equations}
A numbered display equation---one set off by vertical space from the
text and centered horizontally---is produced by the \textbf{equation}
environment. An unnumbered display equation is produced by the
\textbf{displaymath} environment.

Again, in either environment, you can use any of the symbols
and structures available in \LaTeX\@; this section will just
give a couple of examples of display equations in context.
First, consider the equation, shown as an inline equation above:
\begin{equation}
  \lim_{n\rightarrow \infty}x=0
\end{equation}
Notice how it is formatted somewhat differently in
the \textbf{displaymath}
environment.  Now, we'll enter an unnumbered equation:
\begin{displaymath}
  \sum_{i=0}^{\infty} x + 1
\end{displaymath}
and follow it with another numbered equation:
\begin{equation}
  \sum_{i=0}^{\infty}x_i=\int_{0}^{\pi+2} f
\end{equation}
just to demonstrate \LaTeX's able handling of numbering.

\subsection{Citations}
Citations to articles~\cite{bowman:reasoning,
clark:pct, braams:babel, herlihy:methodology},
conference proceedings~\cite{clark:pct} or maybe
books \cite{Lamport:LaTeX, salas:calculus} listed
in the Bibliography section of your
article will occur throughout the text of your article.
You should use BibTeX to automatically produce this bibliography;
you simply need to insert one of several citation commands with
a key of the item cited in the proper location in
the \texttt{.tex} file~\cite{Lamport:LaTeX}.
The key is a short reference you invent to uniquely
identify each work; in this sample document, the key is
the first author's surname and a
word from the title.  This identifying key is included
with each item in the \texttt{.bib} file for your article.

The details of the construction of the \texttt{.bib} file
are beyond the scope of this sample document, but more
information can be found in the \textit{Author's Guide},
and exhaustive details in the \textit{\LaTeX\ User's
Guide} by Lamport~\shortcite{Lamport:LaTeX}.

This article shows only the plainest form
of the citation command, using \texttt{{\char'134}cite}.

\subsection{Tables}
Because tables cannot be split across pages, the best
placement for them is typically the top of the page
nearest their initial cite.  To
ensure this proper ``floating'' placement of tables, use the
environment \textbf{table} to enclose the table's contents and
the table caption.  The contents of the table itself must go
in the \textbf{tabular} environment, to
be aligned properly in rows and columns, with the desired
horizontal and vertical rules.  Again, detailed instructions
on \textbf{tabular} material
are found in the \textit{\LaTeX\ User's Guide}.

Immediately following this sentence is the point at which
Table~\ref{tab:freq} is included in the input file; compare the
placement of the table here with the table in the printed
output of this document.

\begin{table}
  \caption{Frequency of Special Characters}
  \label{tab:freq}
  \begin{tabular}{ccl}
    \toprule
    Non-English or Math&Frequency&Comments\\
    \midrule
    \O & 1 in 1,000& For Swedish names\\
    $\pi$ & 1 in 5& Common in math\\
    \$ & 4 in 5 & Used in business\\
    $\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
  \bottomrule
\end{tabular}
\end{table}

To set a wider table, which takes up the whole width of the page's
live area, use the environment \textbf{table*} to enclose the table's
contents and the table caption.  As with a single-column table, this
wide table will ``float'' to a location deemed more desirable.
Immediately following this sentence is the point at which
Table~\ref{tab:commands} is included in the input file; again, it is
instructive to compare the placement of the table here with the table
in the printed output of this document.

\begin{table*}
  \caption{Some Typical Commands}
  \label{tab:commands}
  \begin{tabular}{ccl}
    \toprule
    Command &A Number & Comments\\
    \midrule
    \texttt{{\char'134}author} & 100& Author \\
    \texttt{{\char'134}table}& 300 & For tables\\
    \texttt{{\char'134}table*}& 400& For wider tables\\
    \bottomrule
  \end{tabular}
\end{table*}

It is strongly recommended to use the package booktabs~\cite{Fear05}
and follow its main principles of typography with respect to tables:
\begin{enumerate}
\item Never, ever use vertical rules.
\item Never use double rules.
\end{enumerate}
It is also a good idea not to overuse horizontal rules.

\subsection{Figures}

Like tables, figures cannot be split across pages; the best placement
for them is typically the top or the bottom of the page nearest their
initial cite.  To ensure this proper ``floating'' placement of
figures, use the environment \textbf{figure} to enclose the figure and
its caption.

This sample document contains examples of \texttt{.eps} files to be
displayable with \LaTeX.  If you work with pdf\LaTeX, use files in the
\texttt{.pdf} format.  Note that most modern \TeX\ systems will convert
\texttt{.eps} to \texttt{.pdf} for you on the fly.  More details on
each of these are found in the \textit{Author's Guide}.

\begin{figure}
\includegraphics{fly}
\caption{A sample black and white graphic.}
\end{figure}

\begin{figure}
\includegraphics[height=1in, width=1in]{fly}
\caption{A sample black and white graphic
that has been resized with the \texttt{includegraphics} command.}
\end{figure}

As was the case with tables, you may want a figure that spans two
columns.  To do this, and still to ensure proper ``floating''
placement of tables, use the environment \textbf{figure*} to enclose
the figure and its caption.  And don't forget to end the environment
with \textbf{figure*}, not \textbf{figure}!

\begin{figure*}
\includegraphics{flies}
\caption{A sample black and white graphic
that needs to span two columns of text.}
\end{figure*}

\begin{figure}
\includegraphics[height=1in, width=1in]{rosette}
\caption{A sample black and white graphic that has
been resized with the \texttt{includegraphics} command.}
\end{figure}

\subsection{Theorem-like Constructs}

Other common constructs that may occur in your article are the forms
for logical constructs like theorems, axioms, corollaries and proofs.
ACM uses two types of these constructs:  theorem-like and
definition-like.

Here is a theorem:
\begin{theorem}
  Let $f$ be continuous on $[a,b]$.  If $G$ is
  an antiderivative for $f$ on $[a,b]$, then
  \begin{displaymath}
    \int^b_af(t)\,dt = G(b) - G(a).
  \end{displaymath}
\end{theorem}

Here is a definition:
\begin{definition}
  If $z$ is irrational, then by $e^z$ we mean the
  unique number that has
  logarithm $z$:
  \begin{displaymath}
    \log e^z = z.
  \end{displaymath}
\end{definition}

The pre-defined theorem-like constructs are \textbf{theorem},
\textbf{conjecture}, \textbf{proposition}, \textbf{lemma} and
\textbf{corollary}.  The pre-defined de\-fi\-ni\-ti\-on-like constructs are
\textbf{example} and \textbf{definition}.  You can add your own
constructs using the \textsl{amsthm} interface~\cite{Amsthm15}.  The
styles used in the \verb|\theoremstyle| command are \textbf{acmplain}
and \textbf{acmdefinition}.

Another construct is \textbf{proof}, for example,

\begin{proof}
  Suppose on the contrary there exists a real number $L$ such that
  \begin{displaymath}
    \lim_{x\rightarrow\infty} \frac{f(x)}{g(x)} = L.
  \end{displaymath}
  Then
  \begin{displaymath}
    l=\lim_{x\rightarrow c} f(x)
    = \lim_{x\rightarrow c}
    \left[ g{x} \cdot \frac{f(x)}{g(x)} \right ]
    = \lim_{x\rightarrow c} g(x) \cdot \lim_{x\rightarrow c}
    \frac{f(x)}{g(x)} = 0\cdot L = 0,
  \end{displaymath}
  which contradicts our assumption that $l\neq 0$.
\end{proof}

\section{Conclusions}
This paragraph will end the body of this sample document.
Remember that you might still have Acknowledgments or
Appendices; brief samples of these
follow.  There is still the Bibliography to deal with; and
we will make a disclaimer about that here: with the exception
of the reference to the \LaTeX\ book, the citations in
this paper are to articles which have nothing to
do with the present subject and are used as
examples only.

redpajama_set_name: RedPajamaArXiv


text:
Home›Golf News›Brett Rumford: PGA Tour pros lack slow play awareness
Brett Rumford: PGA Tour pros lack slow play awareness
By Martin Inglis — 07 February, 2018
Brett Rumford ISPS Handa World Super 6 Perth Slow play
Event: ISPS Handa World Super 6 Perth
Date: February 8-11
Location: Lake Karrinyup CC, Perth, Australia
TV: Sky Sports Golf – Thu, Fri & Sat: 5am-9am; Sun: 4am-9am
Brett Rumford thinks PGA Tour pros have a different attitude towards slow play than their European Tour counterparts because the fines are so small in comparison to prize money on the line.
The Aussie, who is the defending champion at this week's ISPS Handa World Super 6 Perth, has experienced first-hand the difference in pace of play across golf's major tours, playing regularly on the Asian Tour and PGA Tour of Australasia as well as spending close to 20 years on the European Tour.
He also spent a full season on the PGA Tour back in 2008 and, given the PGA Tour has only dished out one slow play penalty in the last 20 years despite round times continuing to rise, he is of the opinion that golf's two main tours are poles apart in how the deal with the problem.
"Pace of play is something players [in Europe] are very aware of and are continuously trying to improve on," he told bunkered.co.uk. "I just don't think they have that awareness in America.
"If they fall behind a hole, I don't think players are proactive and thinking, 'Shit, we're a hole behind, we need to speed up here'.
"In Europe, because it's monitored so closely, it just takes half a hole before you're timed. There are big penalties too – thousands of pounds that start to accumulate very quickly through getting bad times."
Referring to the J.B. Holmes (below) situation at the Farmers Insurance Open, where the American was castigated for taking four minutes and ten seconds to play a shot while Alex Noren - predominantly a European Tour - was left waiting.
Read more - J.B. Holmes 'shocked' at reaction to slow play storm
"That was a classic example - a European player playing in America," he continued. "He's in the routine of playing at a reasonably fast pace and when you're out of it and waiting around, it's quite off-putting. What happened there was just a classic case and point."
While monetary fines do have an effect for the vast majority of European Tour players, Rumford believes that due to the sheer amount of money on the line on the PGA Tour, stroke penalties may be the only deterrent.
"On the European Tour, we've spoken about how to deal with slow play in terms of penalties and maybe that's something America just hasn't got correct yet because there's so much money there," he added.
"I'm not saying this is the case but if fines are $2,500-$5,000, the plus-minus gained scenario when they could earn $1m lends itself to players taking their time.
Read more - Justin Thomas has Holmes' back in slow play storm
"Maybe shot penalties can be brought in in America. I don't know what the scenario is where the wages are so high that they don't care about the fine. But in Europe, it's taken more seriously.
"I'm a fan of the stroke penalty because you can actually lose a tournament through strokes. I'd rather that than it be a monetary fine – or both would be even better."
Rumford's victory at Lake Karrinyup last year in the unique event, which combines strokeplay and matchplay, came less than two years after major surgery to remove a blockage in his stomach and three months after losing his full European Tour playing rights.
It's little wonder, then, that the 40-year-old refers to his triumph as the most important of his six European Tour wins.
Join us in wishing Brett Rumford a speedy recovery from his recent medical problems. See on the course soon Rummy! pic.twitter.com/vLa7igRv3K
— DP World Tour (@DPWorldTour) March 30, 2015
"The surgery - it affected me massively, hugely," added the Aussie, who spent 15 days in a South African hospital. "Behind closed doors, not one knows what I went through. Very few people understand the complications of having your abdominal wall cut into.
"It's your stability, strength, coordination - everything. My whole swing DNA was cut to pieces. At times, I thought I was never going to get back to playing the golf that I'd played in the past.
"So there was so much pressure - it was securing the win to get my card back," he added. "If I hadn't won that event, I was in a predicament with not having a tour to play on. So it was 100% the most important - after the surgery, the tour card on the line and also playing in front of a home crowd."
About World Super 6 Perth
The innovative tournament is the only event in European Tour history which has featured a combination of matchplay and strokeplay.
For the first 36 holes, 156 players will compete, with the top 65 and ties progressing after a cut. A further cut after round three will see the field size reduced to 24, with a sudden-death play-off taking place if required to reach this number.
The fourth and final round is a six-hole matchplay knockout format – the 24 players are eventually reduced down to two, from which the winner will be decided.
The big names in the field include Danny Willett, Lee Westwood, Thorbjorn Olesen and Andrew 'Beef' Johnston, who was a late replacement for the injured Tyrrell Hatton.
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redpajama_set_name: RedPajamaCommonCrawl


1 00:00:05,20 --> 00:00:09,860 Across there is the Royal London Borough of Greenwich and 2 00:00:09,860 --> 00:00:14,720 it's called the Royal Borough because it was the seat of royalty for 200 years 3 00:00:14,720 --> 00:00:18,380 and this view was said to be Christopher Wren's favourite view in the whole of London 4 00:00:18,380 --> 00:00:22,340 probably because he designed those two buildings over there 5 00:00:22,340 --> 00:00:27,940 The reason I'm here is because a few months ago, L&Q who are this company that build houses 6 00:00:27,940 --> 00:00:29,540 They asked me to make a film 7 00:00:29,540 --> 00:00:32,740 and they liked it so much that they asked me to make another one 8 00:00:32,740 --> 00:00:35,340 because they've got this big housing development going on down in North Greenwich 9 00:00:36,760 --> 00:00:38,840 My favourite way to go is by boat 10 00:00:40,160 --> 00:00:46,580 It's such a lovely way to travel down the Thames like this; it's excellent 11 00:00:46,580 --> 00:00:49,480 It takes about 20 minutes to get to North Greenwich by boat from London Bridge 12 00:00:49,480 --> 00:00:54,320 and I've got plenty of time so that's a nice way to go but if you want it's only 8 minutes on the Jubilee line 13 00:00:55,580 --> 00:00:56,340 and that runs 24 hours a day 14 00:00:57,580 --> 00:00:58,20 I love it in Greenwich 15 00:00:58,20 --> 00:01:00,100 "-wich" actually means "port" 16 00:01:01,700 --> 00:01:05,780 in anglo-saxon, so places like Greenwich, Harwich, Norwich 17 00:01:06,900 --> 00:01:10,980 L&Q have a lot of their homes around here so they asked me to come and check out the area 18 00:01:10,980 --> 00:01:13,180 and I think it's going to be quite exciting actually; I'm quite looking forward to it 19 00:01:13,260 --> 00:01:16,820 This piece of art is by Anthony Gormley 20 00:01:16,860 --> 00:01:18,620 it's called Quantum Cloud 21 00:01:18,620 --> 00:01:22,60 And you can see that it's an Anthony Gormley because if you look inside it you can see the form of his 22 00:01:22,60 --> 00:01:24,380 famous sculptures that he did of himself just standing there 23 00:01:24,380 --> 00:01:28,320 These stretch all the way up towards the Queen Elizabeth Olympic Park 24 00:01:28,320 --> 00:01:30,500 it's called "The Line" 25 00:01:30,500 --> 00:01:32,880 I'm just talking about this amazing piece of art here 26 00:01:32,880 --> 00:01:36,620 The human form caught between representation and abstraction 27 00:01:36,620 --> 00:01:38,260 I could have told you that. I wouldn't have to look it up 28 00:01:38,260 --> 00:01:43,280 Sliced in half. It's supposed to represent maritime history. The meridian 29 00:01:43,280 --> 00:01:46,100 slicing through the peninsula of Greenwich 30 00:01:46,100 --> 00:01:48,520 do you see what they've done there? 31 00:01:48,520 --> 00:01:49,340 What's this piece of art work here, Julian ? 32 00:01:50,820 --> 00:01:51,260 "Umbrella" by Joolzy 33 00:01:52,0 --> 00:01:56,0 I quite like that one actually. It looks really nice at night 34 00:01:56,0 --> 00:01:57,440 This is by Alex Chinnek 35 00:01:57,440 --> 00:02:00,760 It's called "Bullet from a shooting star" 36 00:02:00,800 --> 00:02:01,400 You'd make a good Dr. Who 37 00:02:01,400 --> 00:02:04,460 Something about that artwork behind you and the silhouette 38 00:02:04,460 --> 00:02:08,120 cheap BBC "oh we'll just make it out to be another planet" 39 00:02:08,120 --> 00:02:10,140 Calling my productions cheap? 40 00:02:10,140 --> 00:02:13,180 These art installation stretch all the way down Millennium Way 41 00:02:13,360 --> 00:02:15,320 There's graffiti and modern art 42 00:02:15,320 --> 00:02:19,440 the route even looks a bit like they've spelt out "The Line" there 43 00:02:19,440 --> 00:02:24,300 I love being by the river, I've just got an obsession with boats and rivers and 44 00:02:24,300 --> 00:02:27,980 sea-faring. That's why I love Greenwich so much 45 00:02:27,60 --> 00:02:29,660 Yes, The Millennium Dome 46 00:02:29,140 --> 00:02:30,460 It's now called the O2 Centre 47 00:02:30,460 --> 00:02:34,0 They have loads of restaurants, bars, gigs, massive sporting events 48 00:02:34,0 --> 00:02:37,60 The distance from the bottom to the top is 52 metres 49 00:02:37,220 --> 00:02:41,660 which corresponds to the amount of weeks in the year 50 00:02:42,800 --> 00:02:48,720 and those yellow spikes, there's 12 of those which is supposed to be the months in the year 51 00:02:48,720 --> 00:02:49,0 or the hours on a clock face 52 00:02:50,700 --> 00:02:51,660 as if you cared 53 00:02:52,380 --> 00:02:55,620 opposite the O2 is The Now Gallery 54 00:02:55,620 --> 00:03:00,600 which is a public art space and they short-list artists so if you have an idea they do take a few per year 55 00:03:00,600 --> 00:03:04,260 this one is by Rebecca Louise Law with 10 000 irises hanging from the ceiling 56 00:03:04,480 --> 00:03:08,480 We are building a new neighbourhood at Greenwich Peninsula 57 00:03:08,480 --> 00:03:12,320 the art gallery is part of the place-making agenda that we're doing here 58 00:03:12,500 --> 00:03:15,900 so it's fine art, fashion and design 59 00:03:15,900 --> 00:03:16,240 there's a golf range as well 60 00:03:16,240 --> 00:03:22,540 Oh, if you're wondering, L&Q are one of the largest providers of quality shared ownership homes in London 61 00:03:22,240 --> 00:03:24,600 now watch this drive 62 00:03:24,320 --> 00:03:27,560 You play a Slazenger 4, don't you? 63 00:03:27,560 --> 00:03:29,920 You must have played the wrong ball somewhere on the last fairway 64 00:03:30,680 --> 00:03:31,120 I own the club Mr. Bond 65 00:03:31,120 --> 00:03:35,800 So over there in the distance you can see the Thames Barrier 66 00:03:37,160 --> 00:03:41,920 it was built in 1982. It's got 10 flood gates, all abreast 67 00:03:41,920 --> 00:03:44,620 Do you think they're supposed to look like the sails of ships? 68 00:03:44,620 --> 00:03:47,100 Is that why they're designed like that? A bit like the Sydney Opera House. 69 00:03:47,100 --> 00:03:48,180 It's a bit further than it looks though. 70 00:03:48,180 --> 00:03:51,240 I did walk it yesterday and I'm not doing that again 71 00:03:52,280 --> 00:03:55,240 In order to stop the urban sprawl taking over 72 00:03:55,360 --> 00:03:58,840 they've got this little ecology park 73 00:03:58,840 --> 00:04:00,840 It's supposed to help with the flourishment of 74 00:04:00,180 --> 00:04:02,180 species like dragon flies... 75 00:04:03,680 --> 00:04:04,40 Bats, cleavers.... 76 00:04:04,40 --> 00:04:05,400 I thought it said cleavage for a second 77 00:04:05,400 --> 00:04:07,740 It's really serene actually 78 00:04:07,740 --> 00:04:12,760 L&Q wanted me to mention they've got more shared ownership homes coming up in 2018 in London and the southeast 79 00:04:12,80 --> 00:04:15,560 one of which is over towards the old town of Greenwich 80 00:04:15,560 --> 00:04:18,300 and I'm really looking forward to going over there because it's one of my favourite parts of London 81 00:04:18,300 --> 00:04:22,900 the staff on here are so much more jolly than on on the underground 82 00:04:24,120 --> 00:04:25,240 This is great fun 83 00:04:25,240 --> 00:04:28,960 I mean, it doesn't look quite like Venice but 84 00:04:29,940 --> 00:04:31,780 These are known as gondolas 85 00:04:35,560 --> 00:04:35,280 Excellent 86 00:04:35,280 --> 00:04:37,780 That took about 10 minutes 87 00:04:38,740 --> 00:04:39,780 That was so good 88 00:04:39,780 --> 00:04:41,740 a romantic trip on a gondola.... 89 00:04:41,440 --> 00:04:42,160 with Simon 90 00:04:42,160 --> 00:04:45,980 I really love the docklands light railway. It's like a toy train 91 00:04:45,980 --> 00:04:49,440 If you want to get to the Cutty Sark another way you can just go underneath this Greenwich foot tunnel 92 00:04:50,980 --> 00:04:52,580 which was built in 1899. 93 00:04:52,420 --> 00:04:54,260 Down into the Mines of Morea 94 00:04:55,860 --> 00:04:56,300 Through the mountain 95 00:04:57,780 --> 00:04:57,280 Ah, 96 00:04:58,740 --> 00:05:00,820 Her cutty sark, o' paisley harn 97 00:05:00,820 --> 00:05:03,860 that while a lassie she had worn...or something 98 00:05:03,440 --> 00:05:06,400 it's a poem by the Scottish poet Robbie Burns 99 00:05:08,400 --> 00:05:12,760 and the cutty sark referred to a short sort of shirt 100 00:05:12,760 --> 00:05:13,760 that the witch, Nannie, wore in his poem 101 00:05:13,760 --> 00:05:15,80 That's actually supposed to be her on the figure head 102 00:05:15,80 --> 00:05:20,0 it was the fastest vessel in the world, it managed to get to Australia in 73 days! 103 00:05:20,0 --> 00:05:21,480 Lunch at The Moth, Simon? 104 00:05:22,180 --> 00:05:26,180 This is the kind of quality you bump into in a pub in Greenwich 105 00:05:27,120 --> 00:05:28,400 I was just at the bar 106 00:05:28,400 --> 00:05:33,580 and who do I meet but Patrick Godfrey. He is a very well known actor 107 00:05:33,280 --> 00:05:36,0 I saw you in Les Miserables recently on TV 108 00:05:36,0 --> 00:05:37,600 You know we did it here. 109 00:05:38,900 --> 00:05:42,740 We filmed Les Mis, I think it was in the Royal Naval College 110 00:05:42,240 --> 00:05:44,320 and they made it look like Paris 111 00:05:45,460 --> 00:05:47,460 Her Cutty Sark o' paisley harn 112 00:05:47,460 --> 00:05:49,480 that while a lassie she had worn 113 00:05:50,120 --> 00:05:52,360 wi' twa pun Scots it was her riches 114 00:05:54,660 --> 00:05:56,980 Whatever graced a dance of witches 115 00:05:56,980 --> 00:05:59,560 The royal connection with Greenwich started in the 15th century when 116 00:05:59,560 --> 00:06:04,740 King Henry V....his brother, the Duke of Gloucester inherited some Land around here and he built 117 00:06:04,740 --> 00:06:06,980 what was regarded as the finest house in England 118 00:06:06,980 --> 00:06:09,420 and it was King Henry Vii who turned it into an actual palace 119 00:06:09,420 --> 00:06:13,820 King Henry VIII was born here, so was Queen Elizabeth I 120 00:06:13,820 --> 00:06:15,40 Henry Viii also married Anne of Cleves here 121 00:06:15,40 --> 00:06:16,680 She was the 4th wife. 122 00:06:16,320 --> 00:06:18,320 The ugly German one, so he said 123 00:06:18,320 --> 00:06:20,900 or am I thinking of my wives 124 00:06:21,520 --> 00:06:25,920 King James I had this house built for his wife Queen Anne of Denmark 125 00:06:25,920 --> 00:06:27,860 and it's now a part of the National Maritime museum 126 00:06:27,860 --> 00:06:30,940 but it fell into disrepair during the civil war 127 00:06:30,60 --> 00:06:35,700 and then Queen Mary insisted that this be part of a hospital for seamen 128 00:06:35,700 --> 00:06:37,880 No sniggering in the back row please 129 00:06:37,880 --> 00:06:38,960 when I said seamen 130 00:06:39,820 --> 00:06:43,220 but she insisted that the view of the Queen's House not be obscured 131 00:06:43,220 --> 00:06:48,380 so they got Christopher Wren to design these two rather unique wings of the Royal Naval Hospital 132 00:06:49,400 --> 00:06:52,120 So this is where, in 1805 on Christmas Eve 133 00:06:52,120 --> 00:06:56,140 They brought the body of Admiral Lord Nelson after he was killed at the battle of Trafalgar 134 00:06:56,140 --> 00:06:58,840 He arrived here pickled in rum 135 00:06:58,380 --> 00:07:01,260 and they say that some of the sailors used to 136 00:07:01,260 --> 00:07:04,500 drink some of the rum that he was pickled it 137 00:07:04,500 --> 00:07:06,640 and that's where we get the expression "Tapping the Admiral" 138 00:07:06,120 --> 00:07:11,840 You can actually see his blood stained trousers in the maritime museum 139 00:07:11,920 --> 00:07:14,640 These are the actual pants he was wearing 140 00:07:14,720 --> 00:07:15,480 full with perished gusset 141 00:07:15,480 --> 00:07:18,680 reminds me of some of my own underpants actually 142 00:07:18,920 --> 00:07:21,120 This is Greenwich park where Sir Walter Raleigh 143 00:07:22,540 --> 00:07:24,260 famously laid down his cloak over puddle 144 00:07:25,680 --> 00:07:27,0 for Queen Elizabeth to step over it 145 00:07:27,0 --> 00:07:29,820 Oh Sir Walter.....really! 146 00:07:29,280 --> 00:07:35,880 Famously this is where Anne Boleyn dropped her handkerchief as a signal to her lover 147 00:07:35,380 --> 00:07:40,780 and apparently King Henry VIII spotted it and that spelt her doom! 148 00:07:42,680 --> 00:07:43,280 And at the top of the hill 149 00:07:43,280 --> 00:07:46,660 is the first ever observatory in England 150 00:07:46,660 --> 00:07:49,620 This is where Edmund Halley charted the course of Halley's comet 151 00:07:49,620 --> 00:07:51,740 but unfortunately he died before he could be proven right 152 00:07:51,740 --> 00:07:56,960 it's a pity that they're renovating that building because that ball on top was installed in 1833 and 153 00:07:57,160 --> 00:08:01,600 every day since they've raised it at 5 minutes to 1pm 154 00:08:01,600 --> 00:08:06,780 and then lowered it at 1pm so that the ships on the river could set their clocks by it 155 00:08:06,480 --> 00:08:08,320 One yard, two feet, one foot 156 00:08:09,20 --> 00:08:10,740 six inches 157 00:08:10,740 --> 00:08:12,360 Give them an inch and they'll take a foot 158 00:08:12,360 --> 00:08:15,920 and the next thing you know you haven't got a leg to stand on 159 00:08:15,60 --> 00:08:17,980 This is where east meets west 160 00:08:18,660 --> 00:08:19,860 where time begins 161 00:08:19,860 --> 00:08:23,240 I am now half in the western hemisphere and half in the eastern hemisphere 162 00:08:23,240 --> 00:08:27,920 in 1884 there was a conference in Washington where they decided where 163 00:08:27,920 --> 00:08:28,320 to set the prime meridian 164 00:08:28,320 --> 00:08:31,900 and they came to the conclusion that Greenwich the best place 165 00:08:31,900 --> 00:08:34,400 because so many ships from around the world used to come here to Greenwich 166 00:08:35,520 --> 00:08:37,680 However, the French, typically 167 00:08:37,240 --> 00:08:42,960 decided not to accept it until 1911 so they kept their meridian in Paris 168 00:08:42,960 --> 00:08:45,920 but everyone else in the whole world decided to use Greenwich 169 00:08:45,480 --> 00:08:49,80 I can see why the Kings and Queens decided to move here . 170 00:08:49,80 --> 00:08:52,260 This is where Queen Elizabeth I used to play as a child 171 00:08:52,260 --> 00:08:55,80 This ancient tree known as Queen Elizabeth's oak 172 00:08:55,80 --> 00:08:58,340 is thought to have been planted in the 12th century 173 00:08:58,340 --> 00:09:03,720 The shadows grow tall, the sky bruises and we must be forced to camp 174 00:09:03,880 --> 00:09:05,360 A pint at the Plume of Feathers I think 175 00:09:05,360 --> 00:09:08,700 The oldest pub in Greenwich, built in 1691 176 00:09:08,700 --> 00:09:11,820 Seems like a reasonable place to finish the day 177 00:09:11,380 --> 00:09:17,380 Now if you enjoy my films please hit the subscription button or leave a comment underneath 178 00:09:17,380 --> 00:09:21,980 and if you want to know more about L&Q and their shared home ownership schemes 179 00:09:21,980 --> 00:09:23,60 then click the link in the description below 180 00:09:25,720 --> 00:09:25,280 Cheers!


text:
Tribute to Colonel Kontagora: The administrator who loved Kano
Dear Sam
Slander: Kaduna APC leaders rebuke Speaker Zailani, others
50th birthday surprise: Uba Sani attacks hunger in 4 Kaduna Orphanages
Gratuties:When Zullum wipes retirees tears with N12bn
Nigerian Political News Media
Politalk
Gang-up against Senator Shettima from failed security quarters – Arewa group declares
December 18, 2020 December 18, 2020 Editor News
…Arewa Group Protest politicization of security issues
By Hadiza Mohammed in Abuja
The Arewa Rapid Response Network (ARRN) has kicked against a gang up by people it describes as failed senior security chiefs to demonize and tar the former Borno state Governor Senator Kashim Shettima over his widely publicized stance both in the Senate and in the media against the glaring failure of security service chiefs, who all Nigerians from all walks of life are unanimous of their failure to stem the tide of insurgency in the North East and banditry and other forms of violent crimes all over the country.
The ARRN, a non-political pressure group based in Birnin Kebbi, Kebbi state said in a statement signed by its National Coordinator Alhaji Yakubu Ahmed-BK that the calculated and obnoxious media attacks launched against the person of Kashim Shettima are no doubt tied to his position on the unpopular retention of service chiefs whose failure to contain violent crimes despite the support they have been getting, since 2015 in terms of adequate resources, equipment and prayers from the federal government, all the state governments in the theatre of insurgency and banditry and Nigerians at large is unmistakably clear.
The Association noted that the position taken by Kashim Shettima, both in the Senate where he sponsored a resolution calling on President Muhammadu Buhari to dispense with the services of the security chiefs and in the media where he has been trenchant was nothing new but echoing the popular demand of almost all Nigerians who have become increasingly uncomfortable with the lack of any meaningful progress in the fight against violent crimes that have made all corners of Nigeria seemingly ungovernable due to the audacity of a group of rat tag gun men prowling the streets and bushes of the country making life hellish for everyone.
The Association recalled that a countless number of professional groups, trade unions, community development associations, student associations and religious leaders of all faith have, have at various times, come to the same position and are in fact insisting that retaining the service chiefs by President Buhari has not only inflame the violent crimes in the North East due to their leadership deficits and failures, but has completely demoralized the troops prosecuting different security operations across the country. In fact, the recent upsurge of anti-Buhari positions by even those who are, hitherto his diehard supporters, the ARRN remarked, is based on his rigid stance of keeping them in office despite the avalanche of opinions to the contrary by all Nigerians. Nigerians, ARRN stated, have never been so unanimous and agreeable than on the issue of sacking the service chiefs.
The unpopularity of the President in recent times, the Association further said, can be traced squarely on keeping faith with service chiefs whose failure has become legendary and the butt of jokes on the African continent and on the world stage.
The ARRN said it will amount to standing truth on its heads and a complete disservice to the fatherland for anybody, no matter what he or she stands to gain out of keeping non performing service chiefs in office, to take offense from the patriotic and courageous pro-Nigeria position as encapsulated by Kashim Shettima to the point of equating his stance as a coup against the very President who Kashim Shettima worked hard and tirelessly to install at a time it was very risky politically to do so.
The Association wondered where any of the service chiefs or their hangers on were when, since 2003 Buhari was almost a leprous loner who did all he could to advance his political pursuits to become President without success. Where were they when the battle to sell Buhari's candidature raged on until Kashim Shettima and the dozens of relentless and tireless progressive foot-soldiers from all parts of the country succeeded in strategizing to eventually make Buhari electable? It recalled that it was people like Shettima and other progressive elements who, against political common sense mobilized their states, their people and their resources to fight in order to sustain and coordinate peoples' support until he eventually got elected in 2015.
Kashim Shettima's support for the President dated back to the days very few people were ready to touch him because it never looked like, at any point in his struggles, that he was going to make it, not even in 2015 when the so-called Buhari hurricane surprisingly and against all political permutations, got not just many unlikely people into top political positions, but gave Buhari himself the presidency. When the fight was on to achieve this, the so-called later-day Buhari converts including those who today are bandying all manner of ''laughable coup scares'' in order to stampede and blackmail the President into retaining them in office, were, at best, on the sidelines waiting for the Buhari project to succeed so they could profit from it even when they were unworthy and undeserving of it as the facts are today showing, to the chagrin of the entire country and to a sad era of Nigeria's worse form of insecurity ever.
The Arewa Rapid Response Network was unequivocal that there is no basis for comparison between the kind of development brought about during the eight years of Kashim Shettima stewardship in Borno and the stewardship of perfidy and the outright failure by a bunch of service chiefs who, instead of throwing in the towel on account of overstaying in office which is against the law or to just resign in response to a groundswell of call for such by the entire country, have chosen to, against sound reasons, stay put regardless of the fact that under their watch, Nigeria sinks further into the abyss and is looking like the country is surrendering to criminals.
The ARRN said it is adding its voice and is joining virtually the entire country to call on the President to, not just sack the service chiefs without further delay, but to cause a probe to be instituted into widespread allegations of corruption that have repeatedly been leveled that the billions of dollars that were appropriated to purchase state of the art armament and equipment as well as the billions earmarked for troop's welfare have grossly been misappropriated. The way and manner the former Lafiya Dole Theatre Commander General Olusegun Adeniyi was dragged to a military tribunal on account of revealing the rot in the way the war against insurgency was being undermined through inadequate and substandard fighting equipment needs to also be revisited in order to stem the dangerous tide the country is sadly witnessing currently. Unless the country treads the path of encouraging officers to blow the whistle on undercurrent corrupt tendencies in the prosecution of the war, the truth of the matter which will help right wrongs and ultimately overwhelm criminals will continue to elude the Nigerian leadership and the country will be the worse for it.
"The ARRN therefore condemn, in strongest terms, the attempt by enemies of the country to deviate attention from their failure and shift attention to a pan-Nigerian message which was just re-echoed by Senator Kashim Shettima.
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Home > Product Catalogue > HYDRAULIC CYLINDERS
BALL END TOP MOUNT CYLINDERS
HOLE END TOP MOUNT CYLINDERS
HOLE END BOTTOM MOUNT CYLINDERS
HJ600 HYDRAULIC JACK
Download Catalogue PDF
Aline Trading P/L present a range of quality Italian made multi stage hydraulic cylinders designed for use on tipping trailers, ute backs and many other applications.
Our cylinders are made from hot-rolled tubes (without welding), having a maximum tensile stress R= N/mm2 590, yield point Rs= N/mm2 460. The last extension, on the contrary, in C 40 hot-rolled. The pressed cap and bottom disc are made with Fe 50 hot-pressed.
The external surface of all the extensions have a chromium-plated film applied with an approximate thickness of mm. 0,010 - 0,015, with a medium superficial ruggedness Ra= 0, 15- 0, 20.
The extension guide is secured by delrin rings, for both the external and internal sliding.
The seals used are highly resistant to permanent deformation and wear, with a working temperature from -40 Deg to +110 Deg C.
It is recommended, for better functioning and durability of the cylinder itself, to use hydraulic mineral oils and mount appropriate filters to prevent the possibility of infiltration of impurities inside the cylinder.
Rated pressure 160 Bar reaching maximum point of 180 Bar.
Absolutely avoid the cylinders reaching the end of their stroke and touching the body when in rest position. Over extending of the cylinders should be prevented by use of a physical, electric or hydraulic limiting device.
Characteristics and dimensions are not binding.
Note: Sizes listed herin are our normal stocked range. Other sizes are available against volume OEM orders.
Thrust in Kilograms for single extension
Pressure (Bar) 46 61 76 91 107 127 146
100 1.660 2.920 4.530 6.500 8.980 12.660 16.730
125 2.070 3.650 5.660 8.120 11.230 15.820 20.910
160 2.650 4.670 7.250 10.400 14.370 20.250 26.770
Body tilting tables.
RM = Jack stroke.
DP = Distance between pins in mm
I = Box tilting.
Note: Make sure that the cylinder does not bottom or top out and the box does not rest on it when in the home position.
Body tilting table based on the distance between pins and cylinder stroke.
Dimensions shown are the angles in Degrees.
DP/RM 480 500 600 680 700 800 850 900 950 1000 1050 1150 1200 1250 1300 1500 1700 1850 1900 2150 2300 2600
800 - - 44 50 52 60 - - - - - - - - - - - - - - - -
900 - - - 44 45 53 60 - - - - - - - - - - - - - -
1000 - - - 40 41 47 50 53 57 60 - - - - - - - - - - - -
1100 - - - - - 43 45 48 51 54 57 - - - - - - - - - - -
1200 - - - - - - 41 44 47 49 52 57 - - - - - - - - - -
1300 - - - - - - - 40 43 45 48 52 55 58 - - - - - - - -
1400 - - - - - - - - 40 42 44 48 51 53 55 - - - - - -
1500 - - - - - - - - - - 41 45 47 49 51 60 - - - - - -
1600 - - - - - - - - - - - 42 44 46 48 56 - - - - - -
1700 - - - - - - - - - - - - 41 43 45 52 60 - - - - -
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Retire in Argentina
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Retiring in Argentina Comes With a Lot of Positives
By Kevin Casey
It's no longer a secret: Argentina is one of the most welcoming retirement destinations you'll find… anywhere. Boasting stunning natural landscapes, a surprisingly European feel, and a cost of living that can be up to 75% lower than the U.S., it offers retirement-friendly visa options and is one of the least restrictive countries for foreigners to buy property.
As the planet's eighth-largest nation, Argentina has something for everyone: bustling cities alive with cosmopolitan swagger, verdant jungles, snow-capped mountains, sun-drenched wine country, chic beach resorts, and mouth-watering regional cuisines. And best of all, your dollar will go much further here than in the States… and you won't have to sacrifice comfort, liveability, or access to all the essential ingredients you need to start an amazing new life.
Where else on the planet can you find a forested lake district reminiscent of Switzerland, one of the most spectacular jungle waterfalls in the world, endless stretches of Atlantic beach, superb fishing, kayaking and hiking in beautiful Patagonia… and accommodation rental prices up to three times cheaper than what you're used to paying at home?
Argentina's hardy gauchos—the cowboys of the Pampas—still break horses, put cattle through their paces and sip mate (a traditional herbal tea, pronounced 'mottay') beneath the shade of carob trees. Afternoon siestas and Sunday asados (barbecues) are an integral part of the laidback lifestyle here and the tango is as popular now as it was in the 1880s, when this sensuous dance originated in the country's north.
But right alongside Argentina's rich history and honoured traditions is a clear vision for a bright future: Argentina boasts solid infrastructure, ultra-modern technology, and a quality healthcare system. According to the Global Peace Index, Argentina is also one of the three safest countries in South America—a place of incredible natural beauty where the friendly locals are keen to show off the hospitality and charms of their diverse homeland.
For those who might be worried about culture shock in moving to Latin America, retirement in Argentina offers a less daunting prospect—in many ways, it exudes an almost European vibe. Its capital, Buenos Aires, is often nicknamed 'The Paris of South America'… with inviting sidewalk cafes, a world-class fashion scene, striking architecture, a unique cultural identity, and sprawling green parks. Although Spanish is the official language, you'll find a healthy percentage of the population—especially the younger generation—speak a little English and are keen to learn more. The country is full of affordable language schools where you can brush up on your own Spanish too.
Affordable living in Argentina
©iStock.com/fotoquique
Argentina isn't South America's cheapest country but it's still extremely affordable. Depending on the area you choose to live in, prices for everyday expenses can be pleasantly low.
Obviously, living in one of the trendier Buenos Aires suburbs (like Palermo, Recoleta, or San Telmo) or in an alpine chalet in the Andes town of Bariloche is going to cost you more than settling in a smaller regional centre… but here there's a lifestyle, a budget, and a perfect location for all tastes.
One thing to remember about accessing money in Argentina is that daily ATM withdrawal limits can be as low as $100 per transaction, with a three-transactions-per-day limit at some machines and a $10-per-transaction ATM fee. Many expats get around this by bringing a good supply of U.S. dollars (cash) into the country and exchanging that for pesos as needed. A safer option is to use a money transfer service (such as TransferWise or Xoom) which allows you to wire cash to an Argentina bank account with low fees. Credit cards are also widely accepted in most parts of the country.
Average monthly expenditure in Argentina comes down to your location and lifestyle but it's possible to live quite comfortably as a single person on $1,200 to $1,500 per month here, or $2,000 to $2,500 a month for a couple. And this is living comfortably—many long-term expats spend less. Conversely, a couple adopting a luxurious lifestyle in an up-scale area could easily go through $4,000 a month.
Some retirees supplement their pension incomes with English-teaching work or by running online businesses. Your financial health as a retiree in Argentina will largely depend on whether your income is in pesos or U.S. dollars, and whether you adopt an 'Argentinian lifestyle' that prioritizes family, community, and social activities over the accumulation of 'more stuff'.
Where should you live in Argentina?
©iStock.com/saiko3p
Anyone who has spent any length of time in Argentina has their own favorite region or city. Here are just a handful of Argentina's most intriguing places for adventurous expats:
Argentina's most popular beach destination is a hive of tourist activity in the summer but slows down considerably in the off-season. Enjoy a meal of fresh seafood from a cliff-top restaurant overlooking the Atlantic, spend the day at the beach, go deep-sea fishing, or take in a tango show at Teatro Colon.
If you're the outdoor type, this Patagonian town is the perfect gateway to some of the most mesmerizing scenery in Santa Cruz Province, including the Perito Moreno Glacier.
Prefer a subtropical climate? Check out this small town just a stone's throw from one of the world's most spectacular natural wonders: the mighty Iguazu Falls, straddling the Brazilian border. If Puerto Iguazu is too small for you, the riverside city of Posadas to the west offers an attractive urban alternative with the same balmy weather.
Looking more like a Swiss mountain village than an Argentinian town, Bariloche offers snow-capped peaks, transparent blue lakes, some of the world's finest ski slopes, trout fishing, and plenty of other outdoor activities to keep you busy—all in one of most breathtaking locations in the country.
Mendoza is one of the most vibrant regional cities in Argentina, renowned for its award-winning wines and scenic location at the foot of the Andes. It's a supremely liveable city with everything you'll need including inspiring views, horseback riding, hiking, mountain biking, rafting, and an excellent restaurant scene.
Some retirees never seem to make it past Argentina's capital… falling instantly for the pulsating energy, quaint old neighborhoods, towering colonial buildings, gastronomic delights, bustling outdoor markets, and all-hours nightlife. Buenos Aires is arguably the most 'European' of all the South American capitals, combining the old-world charm of cobblestoned streets and tiny, café-lined squares with a thriving modern culture and top-notch amenities.
When it comes to sheer friendliness, Salta has a well-earned reputation for relaxed hospitality. Located in Argentina's scenic north-west, it's not too big, not too small and equipped with all you need for a pleasant retirement. From Salta, you can quickly reach the Andes mountains, deserts and even rainforest—and you'll find the living here considerably cheaper than Buenos Aires.
Life in Argentina
©iStock.com/mfron
Retiring in Argentina comes with a lot of positives. The healthcare system is extensive and affordable; there's a good range of quality pharmacies and trained specialists throughout the country. Public hospital care is excellent, though some expats opt for private health cover so they can access private hospitals and reduce waiting times.
Reliable Internet, good shopping, decent transport, recreational and social opportunities, and a strong sense of community are the norm in Argentina. Here, you can live well on a fraction of the income you need in the U.S.—and enjoy a more relaxed lifestyle to boot.
Visas and residency requirements can be a major issue in many of the world's premier retirement destinations but Argentina has made an effort to simplify things for foreigners. U.S. citizens can stay for up to 90 days without a visa—and it's easy to take a quick day trip across the Uruguayan border and then re-enter Argentina for another 90-day period. This isn't a long-term residency solution, of course, but it can be handy during an initial 'reconnaissance mission' to explore Argentina's potential for future retirement.
Argentina offers a number of visa options aside from the standard 90-day tourist stamp. Depending on your plans and circumstances, you might consider their Visa of Temporary Residence, Student Visa, Work Visa, Financier (a.k.a. Rentista) Visa, and Pensioner Visa (the last two are the most useful to retirees). If your ultimate goal is permanent residency, you must first be a temporary resident for at least two years.
Because Argentina places so few restrictions on the ability of foreigners to own property, you may decide it's more sensible to buy instead of rent when you've reached the stage of serious accommodation-hunting. Either way, you'll be paying a lot less than you're used to for a comfortable home in one of the most appealing nations on earth.
Featured Image Copyright:©iStock.com/diegograndi
Travel in Argentina
Move to Argentina
Healthcare in Argentina
Argentina Visa and Residency Information
Cost of Living in Argentina
Where is Argentina? / Map of Argentina
Why Argentina?
Taxes in Argentina
Rolodex: Contacts in Argentina
Argentina Fact File
Invest in Argentina
Live in Argentina
Argentina Real Estate
Learn more about Argentina and other countries in our daily postcard e-letter. Simply enter your email address below to sign up for these free postcards and we'll also send you a FREE REPORT – What Nobody will Tell You About Argentina.


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