text:
\section{Introduction}

In the framework of a video understanding system (figure~\ref{fig:subgraph}),
video sequences are \emph{abstracted} in physical objects: objects of interest
for a given application. Then the physical objects are used to recognize events.
In this paper, we are interested by the group behavior in public spaces. Given a
set of detected and tracked people, our task is finding associations of those
people into spatially and temporally coherent groups, and detecting events
describing group behavior.

\addtolength{\textfloatsep}{-5mm}

\begin{figure}[h]
  \centering
    \includegraphics[width=\linewidth]{images/schema.png}
 \caption{Description of the proposed video understanding system}
  \label{fig:subgraph}
\end{figure}

Tracking people, and especially groups of people in relatively an unconstrained,
cluttered environment is a challenging task for various reasons.
In~\cite{Ge09}, Ge \emph{et al.} propose a method to
discover small groups of people in a crowd based on a bottom-up hierarchical
clustering approach. Trajectories of pedestrians are clustered into groups based
on their closeness in terms of distance and velocity. The experiments of this
work have been made on videos taken from a very elevated viewpoint, providing
few occlusions. Haritaoglu \emph{et al.}~\cite{Haritaoglu2001} detect and track
groups of people as they shop in a store. Their method is based on searching
strongly connected components in a graph created from trajectories of individual
people, following the idea that people belonging to the same group have a lower
inter-distance. This method however does not allow group members to move away
and return to the group without being disconnected from it. Furthermore, the
application of a shopping queue lacks genericity (people are rather static and
have a structured behavior), it is not clear how well this method is adaptable
to another context of use. Other approaches, such as~\cite{Naturel2008}, aim at
detecting specific group-related events (\emph{e.g.} queues at vending machines)
without tracking. Here again, the method does not aim at consistently tracking a
group as its dynamics vary. In~\cite{Jacques2007}, an algorithm for group
detection and classification as voluntary or involuntary (\emph{e.g.} assembled
randomly due to lack of space) is proposed. A top-down camera is used to track
individuals, and Voronoi diagrams are used to quantify the sociological concept
of personal space. No occlusion handling is done in this work hence the
applicability to other points of view of the camera or to denser scenes is
questionable. Figure~\ref{fig:eindhoven} shows the result of our event
recognition on tracked groups on a dataset recorded at the Eindhoven airport.

\begin{figure}
 \centering
 \includegraphics[width=0.49\linewidth]{images/EindhovenSplit2bis.png}
 \includegraphics[width=0.49\linewidth]{images/EindhovenMerge2bis.png}
 \caption{Group event recognition on Eindhoven airport sequences. Left: The
group is detected as splitting into 2 sub-groups. Right: Two groups are detected
as merging into one.}
 \label{fig:eindhoven}
\end{figure}

Event recognition is a key task in automatic understanding of video sequences.
In this work we are mainly interested in group events, but the usual techniques
can be applied to different kinds of objects (person, vehicle, group,...). The
typical detection algorithm (figure~\ref{fig:subgraph}) takes as input a video
sequence and extracts interesting objects (physical objects). This
\textbf{abstraction stage} is the layer between the image and the semantic
worlds. Then, these objects of interest are used to \textbf{model} events.
Finally, the events are \textbf{recognized}. The abstraction stage determines
which modeling techniques can be applied.\\
The possible abstraction technique can be pixel based~\cite{Bobick01} or object
based~\cite{Vu03}. The first kind of techniques is not well adapted for groups.
Indeed, persons belonging to the same group are not necessary physically linked.
With object abstraction, a video sequence is represented thanks to the detected
objects (persons, vehicles,...) and their associated properties (speed,
trajectory,...). In the literature, several approaches use this abstraction
level~\cite{Romdhane2010}, because an activity can be naturally modeled based on
those properties.

Lavee \emph{et al.}~\cite{Lavee09} classify existing event modeling techniques
in three categories: the pattern recognition models, the state based models and
the semantic models.\\
The \textbf{pattern recognition models} are classical recognition techniques
using classifiers as the nearest neighbor method, boost techniques, support
vector machines and neural networks~\cite{Chen06}. These techniques are well
formalized. But adding new types of events implies the training of new
classifiers.\\
The \textbf{state based models} formalize the events in spatial and temporal
terms using semantic knowledge: Finite State Machines (FSM), Bayesian Networks
(BN), Hidden Markov Models (HMM), Dynamic Bayesian Networks (DBN) and
Conditional Random Fields (CRF). The HMMs and all their variants are heavily
used for event modeling~\cite{Duong05}. They take the advantages of the FSM
(temporal modeling) and of the BNs (probabilistic modeling). But due to the
nature of the HMMs (time sliced structure), the complex temporal relations
(\emph{e.g.} during) are not easily modeled. Lin \emph{et al.}~\cite{Lin10}
propose an asynchronous HMM to recognize group events. Brdiczka \emph{et al.}
construct in~\cite{Brdiczka05} HMMs upon conversational hypotheses to model
group events during a meeting. One drawback of the modified HMM methods is that
since the classical structure of HMMs is modified, efficient algorithms can not
be applied without approximation.\\
The \textbf{semantic models} define spatio-temporal relations between sub-events
to model complex events. Due to the nature of these models, the events must be
defined by an expert of the application domain. Moreover, these models are often
deterministic. Several techniques are studied: grammar based models, Petri nets
(PN), constraint solving models and logic based models.\\
As shown in this section, the quantity of techniques for abstraction and event
modeling is huge. In this paper, we propose a framework (\emph{ScReK}: Scenario
Recognition based on Knowledge) to easily model the semantic knowledge of the considered
application domains: the objects of interest and the scenario models, and to
recognize events associated to the detected group based on spatio-temporal
constraints.\\

In the rest of this paper, we first describe our technique to detect and track
groups of people (section~\ref{sec:groupTracking}), then we describe our event
detection method applied to tracked groups (section~\ref{sec:events}). Section~\ref{sec:res} presents evaluations.

\section{Group Tracking}
\label{sec:groupTracking}

Given a set of detected and tracked people, the proposed method focuses mainly
on the task of finding associations of those people into spatially and
temporally coherent groups. The human definition of a group is \emph{people
that know each other or interact with each other}. In fact,
according to McPhail~\cite{McPhail82}: \emph{Two defining criteria of a group
[are] \emph{proximity} and/or \emph{conversation} between two or more persons}.
It is quite difficult to directly detect people interactions and conversation in
a video or the fact that people know each other. For automatic recognition we
derive this definition: \emph{two or more people who are spatially and
temporally close to each other and have similar direction and speed of
movement}, or better: \emph{people having similar trajectories}.

Group tracking is based on people detection.
The people detection can be performed by various methods.
We have compared several methods and chosen the best one, it is based on
background-subtraction described in~\cite{Yao2007} because of the quality of
its results (see table~\ref{tb:resPeopleDetectionFiltered} for a comparison
of several methods).

Blobs of foreground pixels are grouped to form physical objects (also called
\emph{mobiles}) classified into predefined categories based on the 3D size of
objects (using a calibrated camera): {\small \verb+GROUP_OF_PERSONS+}, {\small
\verb+PERSON+} and {\small\verb+NOISE+}. When people overlap (which happens
quite often with a low viewpoint, such as in figure~\ref{fig:vanaheim}) or are
too close to each other, segmentation fails to split them and they are detected
as a single object classified as {\small \verb+GROUP_OF_PERSONS+} because its
size is bigger than the size of a single person. Thoses classes of objects are
specified using gaussian functions. Mean, sigma, min and max values are provided
for each class and a score is computed representing how well an object's
dimensions fit in each category. The category with the best score is assigned as
the class of the object. Detected objects at each frame are tracked consistently
on the long term using a multiple feature-based tracker~\cite{Chau2011REAL}.

Individual trajectories are the input of the group tracking algorithm, which is
divided into four parts: creation, update, split/merge and termination.
In order to detect temporally coherent groups, we observe people trajectories
over a \emph{time window}, denoted delay $T$. In the experiments presented
section~\ref{sec:res}, we used $T=20$ frames. Working at frame $t_c-T$, $t_c$
being the current frame of the video stream, we cluster trajectories of
individuals between frames $t_c-T$ and $t_c$ to find similar trajectories,
representative of groups. We choose the Mean-Shift clustering
algorithm~\cite{meanshift} because it does not require to set as input the
number of clusters. However, Mean-Shift does require a \emph{tolerance}
parameter determining the size of the neighborhood for creating clusters.
Figure~\ref{fig:actorCluster} shows the input prints and the clustering result.

A trajectory is defined as $Traj=\{(x_i,y_i), i=0 \dotso T-1\} \cup
\{(s_{x_i},s_{y_i}), i=1 \dotso T-1\}$ where $(x_i,y_i), i \in [0;T-1]$ in each
trajectory is the position of a group in the same frame $i$, and
$(s_{x_i},s_{y_i})=speed(i-1,i), i \in [1;T-1]$ is the speed of the group
between frames $i-1$ and $i$. If $k$ positions on the trajectory are missing
because of lacking detections, we interpolate the $k$ missing positions between
known ones. Each trajectory is a point in a $2(2T-1)$-dimensional space.
Mean-Shift is applied on a set of such points.
To make the approach more generic and being able to add other features, we
normalize the values using minimum and maximum ranges. The range of positions on
the ground plane is determined by the field of view. The minimum speed is 0 and
the maximum speed is set to $\unit{10} \meter\per\second$, greatly exceeding all
observed values. From the raw value of $x$, $y$ and $s$ (the speed) denoted by
$r \in [min,max]$, we compute the corresponding normalized value $n \in [0,1]$
as: $n=\frac{r-min}{max-min}$, where $min$ and $max$ are the respective minimum
and maximum values.
We set the \emph{tolerance} to $0.1$, considering grouping trajectories distant
by less than 10\% of the maximum. This value is quite low because clustering is
used only to group very close people, the case where people temporarily split
being handled by the update step described below.

\begin{figure}
 \centering
 \includegraphics[width=0.49\linewidth]{images/actor.jpg}
 \includegraphics[width=0.49\linewidth]{images/00127_output.jpg}
 \caption{Example of 5 tracked people clustered into 3 trajectory clusters. A
group is created from cluster 2.}
 \label{fig:actorCluster}
\end{figure}

We characterize a group by three properties: the average over the frames in
which the group is detected of the inter-mobile distance and the average over
frames of standard deviations of speed and direction. These properties enable
the definition of a coherence criterion:
$groupIncoherence=\omega_{1}\cdot distanceAvg+\omega_{2}\cdot
speedStdDev+\omega_{3}\cdot directionStdDev$, where the weights $\omega_{1}$,
$\omega_{2}$ and $\omega_{3}$ are normalization parameters. We use
$\omega_{1}=7$ and $\omega_{2}=\omega_{3}=5$ to slightly favor distance over
speed and direction similarity which are quite noisy. With this definition, a
low value of $groupIncoherence$ is significative of a group.

Groups are created from clusters of more than one physical object.
In the case where one {\small \verb+GROUP_OF_PERSONS+} object is detected at
frame $t_c-T$, we analyze its trajectory through the time window. If this object
stays the size of a group, or is close to other objects, we can create a group
and compute its $groupIncoherence$. If the resulting value is low enough, we
keep the created group. In case of a single {\small \verb+GROUP_OF_PERSONS+}
object, the $groupIncoherence$ value is naturally very low because of a null
$distanceAvg$ component. The \textbf{creation} step is made up of these two
cases.

Group dynamics vary. Sometimes all group members do not have similar
trajectories, for example when the group is waiting while one member buys a
ticket at a vending machine. Clustering is not enough to correctly
\textbf{update} an existing group in that case.
First, we try to associate clusters with groups existing at the previous frame,
using the notion of \emph{probable group} of a mobile, defined hereafter. During
tracking, mobiles detected at different frames are connected by probabilistic
links in order to track consistently the same real objects. We use the term
\emph{father} and \emph{son} for the mobiles resp. in the oldest and most recent
frame of the link. If a father, within a window of $T$ frames, of the mobile $m$
was in a group $g$ and the link probability between father and son is above a
given threshold (a value of $0.6$ is usually used in the experiments
section~\ref{sec:res}), then the father's group $g$ is called the probable group
of the mobile $m$: $PG(m)=g$. Each cluster $c$ is associated with the probable
group of most mobiles in the cluster: $G(c)=\operatorname{argmax}_{g \in
\{g_i^c\}}|\{g_i^c|g_i^c=g\}|$, where $G(c)$ is the group associated with
cluster $c$ and $\{g_i^c\}=\{PG(m_i^c)\}$ the set of probable groups of mobiles
belonging to cluster $c$ ($\{m_i^c\}$ being the set of mobiles in cluster $c$).
Several clusters can be associated to the same group, ensuring that group
members having temporarily diverging trajectories will be kept in the group for
a minimal amount of time. Each mobile $m_i^c$ is added to the group $G(c)$ if
this group is really the probable group of the considered mobile:
$PG(m_i^c)=G(c)$. In fact, the update step aims at tracking existing members of
the group and not new comers. This procedure is summarized in
algorithm~\ref{update}.

\begin{algorithm}[h]
 \label{update}
 \Input{$\{groups_{t_c-T-1}\}$, $\{mobiles_{t_c-T}\}$}
 \Output{updated $\{groups_{t_c-T}\}$}

  $\{clusters_{t_c-T}\}=MeanShift(\{mobiles_{t_c-T}\})$\;

  \For{$c \in \{clusters_{t_c-T}\}$}
  {
    \For{$m_i^c \in \{m^c\}$}
    {
      $g_i^c=PG(m_i^c)$\;
    }
    $G(c)=\operatorname{argmax}_{g \in \{g_i^c\}}|\{g_i^c|g_i^c=g\}|$\;
  }

  \For{$m_i^c \in mobiles_{t_c-T}$}
  {
     \If{$PG(m_i^c)=G(c)$}
     {
      $G(c).add(m_i^c)$\;
     }
  }
 \caption{Update of groups.}
\end{algorithm}

The \textbf{split} of groups operates naturally. When a mobile from a group has
moved away for too many frames, its probable group becomes empty and it cannot
be added to an existing group during the update step, so it splits. It may be
part of a new group in the creation step, if it gets clustered together with
other mobiles.

Two groups $g_1$ and $g_2$ can be \textbf{merged} if two mobiles, one in each
group at frame $t_c-T+k$ ($k \in [0;T-1]$), have the same son at frame $t_c-T+l$
($l \in [k+1;T-1]$), meaning that the two mobiles will merge. The oldest group
among $g_1$ and $g_2$ is kept and all mobiles of the disappearing group are
added into the remaining group.

The group \textbf{termination} step erases old groups. Mobiles that have been
detected at a largely outdated frame (\emph{e.g.} $t_c-5T$) are deleted at frame
$t_c-T$ and empty groups are erased. As a consequence, groups having no new
mobiles for $5T$ frames are erased. All existing groups, even currently empty
ones, can potentially be updated.

Finally, the output of the group tracker, which is the input of the event
detection, is a set of tracked groups (keeping a consistent id through frames)
having properties (such as the intra-objects distance) and composed of detected
physical objects at each frame.

\begin{figure}[!htb]
  \centering
    \includegraphics[width=0.25\textwidth]{images/schema2.png}
 \caption{Knowledge modeling for video event recognition.}
\label{fig:know}
\end{figure}

\section{Event Recognition: a Generic Framework}
\label{sec:events}

In this work, a generic framework for event recognition is proposed
(\emph{ScReK}). The motivation of this framework is to use it within any video
understanding application. The genericity is obtained in terms of objects of
interest and event models.
We can identify two main parts in an event recognition process: the application
knowledge (what are the expected objects? what are the event models?) and the
event recognition algorithm.\\
Knowledge representation is a key issue for genericity. We believe that the
knowledge should be modeled with the collaboration of two different categories
of people (figure~\ref{fig:know}): vision experts (specialists in vision algorithms), and application domain
experts (specialists in the expected events of their domain). Vision experts are modeling the \textbf{objects of interest} (detected
by vision algorithms) and the \textbf{video primitives} (properties computed on the detected objects of interest). Domain experts have to model the
expected application events.\\
Usually, for video event recognition, knowledge is represented using OWL (Web
Ontology Language).
Even with
tools like Prot\'eg\'e, it is difficult for a non computer specialist to
create her/his own model without a long and tedious learning of the OWL
formalism.
The \emph{ScReK}
framework proposes its own declarative language to easily describe the application domain knowledge: the
\emph{ontology}.
\emph{ScReK} proposes a grammar description of the objects and events using the
extended BNF (Backus Naur Form) representation.
$O_i$, is described by its parent, $O_j$, and its attributes:
$O_i=\{a_k\}_{k=0,...O_i^n}$. The objects are defined using an inheritance
mechanism. The object $O_i$ inherits all the attributes of its parent $O_j$. The
attributes are described with the help of basic types. 11 basic types are
predefined: boolean, integer, double, timestamp, time interval, 2D point
(integer and double), 3D point (integer and double), and list of 3D points. The
user can contribute by adding new basic types. Moreover, a history of the values
of a basic type is automatically stored. It is useful for vision primitives
based on the evolution of a value in time (\emph{e.g.} trajectory).\\
For group behavior recognition, detected group objects within the video
sequence and scene context objects (zone, equipment) are described. The scene
context objects help to recognize specific events (\emph{e.g.} by defining
a forbidden access zone or a threshold). For instance, the class of group
objects is defined as follows in the ScReK language:

{\footnotesize
\begin{lstlisting}
class Group:Mobile {
   const false;
   CSInt NumberOfMobiles;
   CSDouble AverageDistMobiles;}
\end{lstlisting}
}

A \textit{Group} is a \textit{Mobile} and it inherits all the attributes of a
\textit{Mobile} object (3D size, 3D position,...). A \textit{Group} is not
constant (dynamic, \emph{i.e.} its attributes values can change throughout
time). One of its attributes, \textit{NumberOfMobiles} is the number of objects
which compose the group.\\
The second kind of knowledge to represent is the event models. They are
composed of 6 parts: (1) the \textbf{type} of the \textbf{scenario} can be one of the following:
  \emph{PrimitiveState}, \emph{CompositeState}, \emph{PrimitiveEvent},
  \emph{CompositeEvent}, from the simplest to the most complex events. (2) the \textbf{name} of the event model which can be referenced for more complex events. (3) the list of \textbf{physical objects} (\emph{i.e.} objects of interest)
  involved in the event. The type of the objects is depending on the application
  domain. (4) the list of \textbf{components} contains the sub-events composing the event model. (5) the list of \textbf{constraints} for the physical objects or the
  components. The constraints can be temporal (between the components) or
  symbolic (for physical objects). (6) the \textbf{alarm} information describes the importance of the scenario
  model in terms of urgency. Three values are possible, from less urgent to more
  urgent: {\small \verb+NOTURGENT+}, {\small \verb+URGENT+}, {\small
  \verb+VERYURGENT+}. The alarm level can be used
  to filter recognized events, for displaying only important events to the
  user. Hereafter is a sample event model:

{\footnotesize
\begin{lstlisting}
CompositeEvent(browsing,
  PhysicalObjects((g:Group),(e:Equipment))
  Components((c1:Group_Stop(g))
  	(c2:Group_Near_Equipment(g,e)))
  Constraints((e->Name = "shop_window"))
  Alarm ((Level : URGENT)))
\end{lstlisting}
}

The application domain expert models the event \textit{browsing} by ``a group is stopped in
front of the shop-window" with the model above. The vision expert models the sub-event
\textit{Group\_Near\_Equipment} (by measuring the distance between a group and
an equipment) and \textit{Group\_Stop} (by computing the speed of a group).\\

The last part of the event recognition framework is the recognition algorithm
itself. The proposed algorithm solves spatio-temporal constraints on the
detected groups. The usual algorithms to recognize such events can be time
consuming. The
\emph{ScReK} framework proposes
to define optimal event models: at most two components, at most one temporal
constraint (Allen's algebra) between these components. This property is not
restrictive since all event models can be optimized in this format. Thanks to
the optimal property, the event model tree is computed. The tree defines which
sub-event (component) triggers the recognition of which event: the sub-event
which happens last in time triggers the recognition of the global event. For
instance, the event A has two components B and C with constraint: B
\textit{before} C. The recognition of C triggers the recognition of A. The tree
triggers the recognition of the only events that can happen, decreasing the
computation time.\\
The first step of the event recognition process is to recognize all the possible
simple events (most of these events are based on the vision primitives) by
instantiating all the models with the detected objects (\emph{e.g.}
instantiating the model \verb?Group_Stays_Inside_Zone? (takes as input one group
and one zone) for all the detected groups and all the zones of the context). The
second step consists in recognizing complex events according to the event model
tree and the simple events previously recognized. The final step checks if the
recognized event at time $t$ has been already recognized previously to update
the event (end time) or create a new one.

 \begin{figure}[!htb]
  \centering
     \includegraphics[width=0.45\textwidth]{images/Picture7.png}
  \caption{Proposed group event ontology.}
 \label{fig:ontology}
 \end{figure}

\section{Results}
\label{sec:res}

People detection is an input to group detection. We compared several methods to
validate our choice of method. Table~\ref{tb:resPeopleDetectionFiltered} sums up
the results of an evaluation done on a 36006 frames sequence (approximately 2
hours of video) in which 37 ground truth (GT) objects (people) have been
annotated. \cite{Corvee2011} is a feature-based people detector
whereas~\cite{Nghiem2009} and~\cite{Yao2007} both perform motion segmentation
and classification of detected objects. The method C combines the first two
methods for a more robust detection than each one separately. The method
from~\cite{Yao2007} gives the best results and is used as input of the group
tracking process. This method learns a background model, resulting in better
motion segmentation and better detection of small objects (far from the camera)
and static objects. The drawback is the time necessary to learn the model and
the low speed of the background-subtraction.

\rowcolors{1}{Gainsboro}{White}

\begin{small}

\begin{table}
    \centering
   \begin{tabular}{|c|c|c|c|c|}
        \cline{2-5}
	\hiderowcolors
        \multicolumn{1}{c|}{} & \cite{Corvee2011} & \cite{Nghiem2009} & C &
\cite{Yao2007} \\
	\hline\\[-1.2em]\hline
	\showrowcolors
        True Positives (TP) & 3699 & 3897 & 4547 & 6559 \\ \hline
        False Positives (FP) & 1379 & 185 & 125 & 128 \\ \hline
        False Negatives (FN) & 3572 & 3374 & 2724 & 2598 \\ \hline
        Precision (global) & 0.73 & 0.95 & 0.97 & 0.98 \\ \hline
        Sensitivity (global) & 0.51 & 0.54 & 0.62 & 0.72 \\ \hline
    \end{tabular}
    \caption{Comparison several people detection methods.}
      \label{tb:resPeopleDetectionFiltered}
\end{table}
\end{small}

 \begin{figure}[!htb]
  \centering
     \includegraphics[width=0.46\textwidth]{images/livelyGettingOff.png}
  \caption{Event detection in Turin subway.}
 \label{fig:vanaheim}
 \end{figure}

We have performed evaluation of the group tracking algorithm using 4 different
views from 3 datasets: videos recorded for the european project
VANAHEIM\footnote{\url{https://www.vanaheim-project.eu/}} in the Turin subway
(figure~\ref{fig:vanaheim}), videos recorded for the european project
ViCoMo\footnote{\url{http://www.vicomo.org/}} at the Eindhoven airport
(figure~\ref{fig:eindhoven}) and videos from the benchmarking
CAVIAR\footnote{\url{http://homepages.inf.ed.ac.uk/rbf/CAVIARDATA1/} } dataset:
the INRIA entrance and the shopping center corridor. In
tables~\ref{tb:ResultsCAVIAR} and~\ref{tb:Results} the following metrics are
used. The \emph{fragmentation} metric computes throughout time how many tracked
objects are associated with one reference object (ground truth data). The
\emph{tracking time} metric measures the percentage of time during which a
reference object is correctly tracked. The \emph{purity} computes the number of
reference object IDs per tracked object. A value close to 1 is significative of
a good tracking.

Table~\ref{tb:ResultsCAVIAR} shows group detection and tracking results on 16
sequences from the CAVIAR dataset.
The first 9 are sequences from INRIA, and the remaining are from the shopping
center corridor. One can notice that the shopping view is far more challenging
than the hall because more people are visible and there are more occlusions due
to the low position of the camera. Table~\ref{tb:Results} contains the results
of this evaluation on 3 annotated sequences (resp. 128, 1373 and 17992 frames)
from the Turin subway dataset. In both tables, detection results are good for
almost all sequences. In the sequence c2ls1, ground truth groups in the far end
of the corridor fail to be detected because of the limitations of the
background-subtraction method. Tracking shows good results with a few
exceptions. For instance, sequence 2 of table~\ref{tb:Results} contains a main
group present in the foreground for the whole duration of the sequence. This
group is correctly tracked with only one id-switch, but many groups are
annotated far in the background and are difficult to detect for the motion
segmentation algorithm. Their sparse detection results in many id-switches for
group tracking. At the best of our knowledge, there is no possibility of
comparing our method to an existing one (no public results or code available).

\begin{footnotesize}

\setlength{\tabcolsep}{0.01cm}

\begin{table}
    \centering
   \begin{tabularx}{\linewidth}{|p{0.85cm}|X|X|X|X|X||X|X|X|}
	\hhline{~--------}
	\hiderowcolors
	\multicolumn{1}{c|}{} &
\multicolumn{5}{{>{\columncolor{Linen}}c||}}{Detection} &
\multicolumn{3}{{>{\columncolor{Linen}}c|}}{Tracking}\\
        \cline{1-9}
        \begin{sideways}{Seq}\end{sideways} &
	\begin{sideways}{TP}\end{sideways} &
	\begin{sideways}{FP}\end{sideways} &
	\begin{sideways}{FN}\end{sideways} &
	\begin{sideways}{Prec}\end{sideways} &
	\begin{sideways}{Sens}\end{sideways} &
	\begin{sideways}Frag\end{sideways} &
	\begin{sideways}TT\end{sideways} &
	\begin{sideways}Purity\end{sideways} \\
\hlinewd{1pt}
	\showrowcolors
	fc & 125 & 101 & 3 &
	0.55 & 0.98 & 1 & 0.97 & 1 \\
	\hline
	fomd & 159 & 0 & 8 &
	1 & 0.95 & 1 & 0.95 & 1 \\
	\hline
	fra1 & 139 & 0 & 67 &
	1 & 0.67 & 1 & 0.61 & 1 \\
	\hline
	fra2 & 141 & 0 & 55 &
	1 & 0.72 & 1 & 0.70 & 1 \\
	\hline
	mc1 & 231 & 0 & 82 &
	1 & 0.74 & 1 & 0.72 & 1 \\
	\hline
	ms3g & 145 & 37 & 37 &
	0.80 & 0.80 & 1 & 0.61 & 1 \\
	\hline
	mwt1 & 156 & 0 & 89 &
	1 & 0.64 & 1 & 0.28 & 1 \\
	\hline
	mwt2 & 336 & 0 & 268 &
	1 & 0.56 & 1 & 0.53 & 1 \\
	\hline
	sp & 165 & 4 & 36 &
	1 & 0.82 & 1 & 0.67 & 1 \\
	\hlinewd{1pt}
        c2es1 & 858 & 652 & 487 &
	0.57 & 0.64 & 0.58 & 0.41 & 0.81 \\
	\hline
        c2es3 & 1093 & 550 & 735 &
	0.66 & 0.60 & 0.66 & 0.38 & 0.83 \\
	\hline
        c2ls1 & 788 & 1664 & 655 &
	0.32 & 0.55 & 0.50 & 0.13 & 1 \\
	\hline
	c3ps1 & 1298 & 1135 & 210 &
	0.54 & 0.86 & 1 & 0.68 & 1 \\
	\hline
	cosme2 & 1119 & 852 & 35 &
	0.57 & 0.97 & 0.25 & 0.60 & 1 \\
	\hline
	csa1 & 269 & 163 & 0 &
	0.63 & 1 & 1 & 0.96 & 1 \\
	\hline
	cwbs1 & 2224 & 89 & 1090 &
	0.96 & 0.67 & 1 & 0.45 & 0.80 \\
	\hline
    \end{tabularx}

    \caption{Results of group detection and tracking on 16 CAVIAR sequences.
(Seq -- official sequence name, Prec -- Precision, Sens -- Sensitivity, Frag --
Fragmentation, TT -- Tracking Time)}
      \label{tb:ResultsCAVIAR}
\end{table}

\end{footnotesize}

\rowcolors{1}{Gainsboro}{White}

\begin{small}

\begin{table}
    \centering

   \begin{tabularx}{\linewidth}{|X|X|X|X|X|X||X|X|X|}
	\hhline{~--------}
	\hiderowcolors
	\multicolumn{1}{c|}{} &
\multicolumn{5}{{>{\columncolor{Linen}}c||}}{Detection} &
\multicolumn{3}{{>{\columncolor{Linen}}c|}}{Tracking}\\
        \cline{1-9}
        \begin{sideways}{Seq}\end{sideways} &
	\begin{sideways}{TP}\end{sideways} &
	\begin{sideways}{FP}\end{sideways} &
	\begin{sideways}{FN}\end{sideways} &
	\begin{sideways}{Prec}\end{sideways} &
	\begin{sideways}{Sens}\end{sideways} &
	\begin{sideways}Frag\end{sideways} &
	\begin{sideways}TT\end{sideways} &
	\begin{sideways}Purity\end{sideways} \\
\hlinewd{1pt}
	\showrowcolors
        1 & 65 & 0 & 6 &
	1 & 0.91 & 1 & 0.86 & 1 \\
	\hline
        2 & 1346 & 69 & 318 &
	0.95 & 0.80 & 0.80 & 0.14 & 0.91 \\
	\hline
        3 & 6977 & 1677 & 4594 &
	0.80 & 0.60 & 0.40 & 0.32 & 0.98 \\
	\hline
    \end{tabularx}

    \caption{Results of group detection and tracking on 3 sequences from the
Turin subway. (Prec -- Precision, Sens -- Sensitivity, Frag -- Fragmentation, TT
-- Tracking Time)}
      \label{tb:Results}
\end{table}

\end{small}

\begin{table}
\begin{center}
\begin{tabular}{ | l | c || c | c | c | }
   \hhline{~----}
   \multicolumn{1}{c|}{} & GT & TP & FP & FN \\
   \hline
   fighting & 2 & 1 & 0 & 1 \\
   \hline
   split up & 3 & 3 & 0 & 0 \\
   \hline
   joining & 3 & 3 & 0 & 0 \\
   \hline
   shop enter & 5 & 5 & 0 & 0 \\
   \hline
   shop exit & 6 & 6 & 1 & 0 \\
   \hline
   browsing & 3 & 3 & 1 & 0 \\
   \hline
   getting off train & 10 & 9 & 8 & 1 \\
   \hline
 \end{tabular}
  \caption{Group event recognition for the 3 video datasets}
      \label{tb:ResultsEvents}
\end{center}
\end{table}

One major achievement of this paper is an ontology for group events based on video sensor (figure \ref{fig:ontology}). The ontology is composed
of 49 event models (45 models are generic and re-usable in any application with
groups (\textit{Group\_stop, Group\_lively,...}), 4 models are specifically
defined for the applications of this paper (the events depend on the application
context, \textit{enter shop},...)). The events have been modeled with help of metro surveillance staff.\\

The results of the group event recognition are given in
table~\ref{tb:ResultsEvents} for the interesting events. Examples of event
recognition are shown in figures~\ref{fig:eindhoven},~\ref{fig:vanaheim}
and~\ref{fig:fight}. There is only a few instances of each event because we
only focus on meaningful group events. The events are correctly recognized with
low false positive and false negative rates. Most of the false positive
detections for the event \textit{getting off train} are due to the fact that the
door in the foreground is detected as a person when open. The errors can be
corrected by adding a new video primitive: \textit{door detector}.

\begin{figure}[ht!]
 \centering
 \includegraphics[width=0.23\textwidth]{images/fomd3_fightsplit.jpg}
 \includegraphics[width=0.23\textwidth]{images/c2es3_exit.jpg}\\
 \includegraphics[width=0.23\textwidth]{images/c2es3Browsing.jpg}
 \includegraphics[width=0.23\textwidth]{images/c2ls1_browsingMiss.jpg}
 \caption{Group event recognition for the CAVIAR sequences. a. Fighting then splitting. b. Exit from the shop. c. Browsing. d.
The mis-detected group (ghost due to reflections) is browsing.}
 \label{fig:fight}
\end{figure}

\section{Conclusions}

We propose a generic, plug and play framework for event recognition from videos:
\emph{ScReK}. The scientific community can share a common ontology composed of
event models and vision primitives. We demonstrate this framework on 4 group
behavior recognition applications, using a novel group tracking approach. This
approach gives satisfying results even on very challenging datasets (numerous
occlusions and long duration sequences) such as in figure~\ref{fig:vanaheim}.
The vision primitives are based on global attributes of groups (position, speed,
size). The proposed event detection approach correctly recognizes events but
shows its limitation for some specific events (\emph{e.g.} fighting is best
characterized by internal group movement). Adapted vision primitives, such as
optical flow, solve specific limitations and are easy to plug into \emph{ScReK}.
Moreover, in this work the gap between video data and semantical events is
modeled manually by vision experts, the next step is to learn automatically the
vision primitives.\\

\textbf{Acknowledgment} This work was supported partly by the Video-Id, ViCoMo,
Vanaheim, and Support projects. However, the views and opinions expressed herein
do not necessarily reflect those of the financing institutions.

{\small
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


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China Now Has 2.22 Million 5G Base Stations
As shown by a recent report released by the Ministry of Industry and Information Technology (MIIT), China has made steady strides in building its communication capability. As of the end of September, China has built 10.72 million mobile communication base stations, a net increase of 754,000 from the end of the previous year. Among them, the total number of 5G base stations climbed by 795,000 to 2.22 million compared with the end of the previous year. Accounting for 20.7% of the total mobile base stations, the proportion of 5G base stations grows at a faster pace of 6.4 percentage than at the end of the previous year.
Additionally, China also witnessed steady growth in the length of the optical cable line. As of the end of September, China has built a 58.76 million kilometers optical cable line, up 6% year on year with a net increase of 3.956 million kilometers from the end of the previous year. The proportion of access network cable, local network relay cable and long-distance cable line is 62.2%, 36% and 1.8%, respectively.
China continues shoring up gigabit network construction. By the end of September, the number of Internet broadband access ports in China has reached 1.055 billion, a net increase of 36.98 million over the previous year. Among them, fiber access (FTTH/O) ports reached 1.008 billion, a net increase of 47.74 million compared with the end of the previous year. That contributes to 95.5% of internet broadband ports with a 1.2% rise from the end of last year. As of the end of September, the number of 10G PON ports able to provide gigabit network service reaches 12.68 million, mounting by 4.822 million from the end of the previous year.
Tags:MIIT|5G Base Stations|5G|Cable|broadband|FTTH|PON
CBN: 1.26 Million 5G Base Stations Accessed to the 5G Core Network(2023-1-5)
Total 5G Base Stations in China Hit 2.287 million(2022-12-28)
Number of 5G Mobile Phone Users Reaching 542 Million(2022-12-28)
Density of 5G Base Stations in China: 15.7 per 10,000 People(2022-12-20)
Over 80,000 5G Base Stations Built in Hubei(2022-11-23)
45,000 Additional 5G Base Stations Launched by Henan Province(2022-11-11)
MIIT Approves China Unicom to Re-farm the 900MHz Spectrum Resources for 5G Systems(2022-11-7)

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No chapter better corresponds to the author's original intention than that entitled "On the Christian Life." It offered a clear, balanced set of directions and encouragements to all who desired to live according to the gospel. Strong in its theological affirmation of God's righteousness and providential care, of the reconciliation won for us by Christ and of the Holy Spirit's work of sanctification, it was equally strong in its pastoral concern for believers who were beset by their own weakness and sin, who daily endured trial and temptation and who nevertheless, united to Christ by faith, shared in his life and tasted his power.
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One and a half minutes later and the final beep-beep from the F1 paddock gates chimes. 01.58. They're out. They're clear. 40+ hard-working Red Bull crew night-time curfew-running… again.
It's all down to him, Newey's to blame.
Formula 1 is excellence; ultra-competitive men driven to achieve, striving for performance, seeking speed in all that they do. No one, no one epitomizes this ethos more than Red Bull's Chief Technical Officer and greatest ever F1 car designer, Adrian Newey.
The man is relentless. Ultra, ultra-competitive to almost maniacal levels. Of course his record speaks for itself: eight constructors' title-winning cars for three different teams, over a hundred grand prix wins and counting.
1992's Williams FW14B, the 1993 FW15C, Red Bull's 2010 and '11 RB6 and RB7 – one can make a case for all to be the Englishman's greatest cars. But this year's might just be Newey's finest achievment yet.
Sure the RB8 hasn't set the world alight in quite the same way as its most recent siblings. Over the past year or so almost all of Adrian's ingenious engineering trickery has been outlawed, copied or cajoled away, giving McLaren, Ferrari, Mercedes, Williams, Lotus and Sauber their chance.
So what does Red Bull's genius do? What would you do? In the face of a widely perceived desire – by those in F1 power – to see an Alonso and Ferrari championship triumph, would you throw in the towel, scatter your toys all over the room and say to hell with it? Many would. Newey won't.
Striving ever harder to get his latest car back up to speed he's been working around the clock. Thinking, drawing, watching, learning, all in a hell-bent desire to show he's still the man to beat.
The Red Bull mechanics and engineers, ever tired but high on recent championship success, knew and know what to expect. With a never-ending stream of requests for new parts to be fettled, made or machined they're well aware of what's expected. Get it done, get it done well and get it done fast.
Last Sunday's field-crushing Sebastian Vettel Japanese Grand Prix win showed the world Red Bull are back. Back to hunt down another championship success.
And pulling the strings, pushing his guys ever harder, demanding absolute commitment is the quiet man, notepad ever present, scribbling performance-increasing ideas as he goes.
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The Voodoo Queen of New Orleans meets Sister Grimm in today's episode as we discuss our first NONFICTION witch, the legendary Marie Laveau, and compare her to Marvel's most recent on-screen magical lady, Nico Minoru from "Runaways." Also: questioning whether doctors are cool, this is not a paid advertisement, an actual rainbow overtakes Regina's physical form, what to expect when you visit Murder-hyphen-any-other-word, beignets and peach cobbler, and so much more! Audio samples: Dr. John – "Right Place, Wrong Time" Siddhartha Khosla – "Runaways Theme" Don't disappoint the gods by missing an episode! Subscribe on iTunes, Stitcher, Google Play, or SoundCloud!


The category of veracity, as well as objectivity, is declared to be the main principle in the theory and practice of journalism. The journalistic descriptions of categories have their own specificity as compared to philosophical considerations. The veracity of journalistic texts including the closely related categories of objectivity and accuracy are of great importance in contemporary research discourses. The object of the research is scientific concepts of veracity, objectivity, accuracy, facts, knowledge; we focus on their peculiarities and purpose in journalistic discourse. The article deals with approaches and ways of veracity interpretation in mass media. The specific features and purposes of veracity are analyzed. Attention is drawn to multi-dimensionality of the category which is revealed in journalistic texts in connection with informativity, accuracy, objectivity; indicators and criteria of veracity and accuracy in contemporary media texts. Researching of objectivity through sub-categories of informative value, accuracy and objectivity leads to the conclusion that it is more appropriate to take into consideration the interference between objectivity and subjectivity, accuracy and approximation, various types of information required for veracity evaluation.
Arutyunova, N.D., 1995. Ot redaktora [Editorial Column]. Logicheskij analiz jazyka. Istina i istinnost' v culture i jazyke. Moscow: Nauka: 3-7 (in Russian).
Bloor, D.,1991 . Knowledge and social imagery (2nd ed.). Chicago, Illinois: University of Chicago Press.
Couldry N., Hepp A., 2016. The mediated construction of reality. Polity Press, Cambridge, UK: 256.
Federation "On Law"]. http://docs.cntd.ru/document/9003299 (in Russian).
Filosofia: entsiklopedicheskij slovar' [Philosophy: Encyclopedia] ,2004. Moscow: Gardariki, 1072 p.(in Russian).
Ilyinova, E.Yu., 2012. O tochosti i tonalnosti predstavlenija sobytija v tekste novostej [On the Accuracy and Tonality of Event Representation in News Texts]. Diskurs kak socialnaja dejatelnost': prioritety i perspektivy. Ch. 1. In: Vestnik Moskovskogo lingvisticheskogo universiteta, 5 (638):162-172 (in Russian).
Ivin, A.A., Nikifiriv, A.L., 1997. Slovar' po logike [Dictionary of Logic] Moscow: Vlados : 384 (in Russian).
Jakovleva, E.S.,1994. Fragmenty russkoj jazykovoj kartiny mira [Fragments of Russian Language Worldview]. Moscow: Gnosis, 344 p.(in Russian).
Korkonsenko, S.G., 2004. Osnovy zhurnalistiki [Introduction to Journalism]. Moscow: AspektPress, 287 p. (in Russian).
Kozhemyakin, E.A., 2011. Objektivnost' kak filosofskaja kategorija v zhurnalistskom diskurse [Objectivity as a Philosophical Category in Journalistic Discourse]. Nauchnije vedomosti Belgorodskogo gosudarstvennogo universiteta. Seria: Gumanitarmije nauki, 18 (113), Iss.11: 187-194 (in Russian).
Kuraev, V.I.,2009. Tochnost' [Accuracy] . Enciklopedia epistemologii i filosofii nauki. Moscow: Kanon+, Reabilitatsia, 990 p. (in Russian).
Lazutina, G.V.,2004. Osnovy tvorcheskoj dejatelnosti zhurnalista [Introduction to Creative Activity of Journalists]. Moscow: AspektPress, 240 p. (in Russian).
Lektorskij, V.A., 2009. Realizm, anti-realizm, konstruktivizm i konstruktivnyj realism v sovremennoj epistemologii i naukah o cheloveke [Realism, Anti-Realism and Constructive Realism in Contemporary Epistemology and Humanities]. Konstruktivnyj podhod v epistemologii i naukah o cheloveke. Moscow: Kanon+:4-40 (in Russian).
Lektorskij, V.A., 2012. Transformatsija epistemologii: novaja zhizn' starykh problem [Transformation of Epistemology: New Life of Old Issues].
Epistemologija: perspektivy razvitija. Moscow: Kanon+, Reabilitatsia: 5-47 (in Russian).
Lozovskij, B.N., 2004. Zhurnalistika: kratkij slovar' [Journalism: Brief Dictionary]. Ekaterinburg: Izd. Ural. Un., 116 p. (in Russian).
Melnikova, E.A., 2014. Tochnost' i dostovernost' kak kriterii informativnosti teksta novostnogo soobschenija [Accuracy and Veracity as Criteria for Informativeness of News Texts]. Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija: Filologija. Zhurnalistika, 5: 94-100 (in Russian).
Olyanich, A.V., 2007. Prezentatsionnaja teorija diskursa [Presentational Theory of Discourse]. Moscow: Gnosis, 407p. (in Russian).
Panchenko, N.N., 2010. Dostovernost' kak kommunikativnaja kategorija [Veracity as a Communicative Category]. Volgograd; Peremena, 323 p. (in Russian).
Prokhorov, E.P., 2012. Vvedenije v teriju zhurnalistiki [Introduction to Journalism Theory]. Moscow: AspektPress, 351 p. (in Russian).
Pronin, E.P., 1980. Vyrazitelnije sredstva zhurnalistiki [Expressive Means of Journalism]. Moscow: MGU, 92 p. (in Russian).
Sarafannikova, E.V., 2006. Diskursivnije markery sootnesenija soobschaemogo s dejsctvitelnostju (na materale televisionnykh novostej na russkom i anglijskom jazykakh) [Discursive Markers of Correspondence of the Message with the Reality (on the Examples of Russian and English Language TV News)]. Voronezh, 20 p. (in Russian).
Sotsiologija zhurnalistiki [Sociology of Journalism], 2014. Moscow: Jurajt, 421 p. (in Russian).
Stilisticheskij Entsiklopedicheskij Slovar Russkogo Jazyka [Stylistic Encyclopedia of Russian Language] , 2006. Moscow: Flinta, 696 p.(in Russian).
Svitich, L.G., 2003. Professija zhurnalist [Profession of journalist]. Moscow: AspektPress, 255 p. (in Russian).
Tertychnyj, A.A., 2011. Zhanry periodicheskoj pechati [Genres of Periodical Print Media]. Moscow: AspektPress, 320 p. (in Russian).


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Prince Harry and Meghan Markle's Daughter's Name Is Adorable — See the Meaning Behind Lilibet
Jun 6, 2021 12:39 pm·
By Laura Rizzo
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She's here! Prince Harry and Meghan Markle welcomed baby No. 2, newborn daughter Lilibet "Lili" Diana Mountbatten-Windsor, on Friday, June 4, and her name has the sweetest meaning behind it.
See All the Glimpses Inside Prince Harry and Meghan Markle's New Mansion
"Lili is named after her great-grandmother, Her Majesty The Queen, whose family nickname is Lilibet," a statement from the press secretary for the Duke and Duchess of Sussex revealed on Sunday, June 6. "Her middle name, Diana, was chosen to honor her beloved late grandmother, The Princess of Wales."
Needless to say, the royal couple, who wed in May 2018, are over the moon about their new addition. They also share 2-year-old son Archie Harrison. Prince Harry, 36, gushed over feeling so "grateful" and "amazing" to have a second child after they revealed they were having a girl during their tell-all CBS interview in March.
"Like to have any child, any one or any two would have been amazing. But to have a boy and then a girl, what more could you ask for?" he said at the time. "We got our family — the four of us and our two dogs, it's great."
An insider told Life & Style Archie couldn't "wait to meet his baby sister" during Meghan's pregnancy. "[He] loves putting his hand on his mummy's tummy and feeling the baby move … He's such a caring, bright, loving little boy and is super advanced for his age," divulged the insider about the toddler in April.
The newly minted family of four had a lot of changes happen within the past year. In January 2020, the philanthropist and former Suits actress announced they were stepping back as senior royals. The two promptly left their U.K. home at Frogmore Cottage and relocated to Canada. They eventually made a permanent move to their current home in Montecito, California.
In February 2021, the palace released a statement confirming the Duke and Duchess Sussex's royal exit was permanent. Harry noted stepping down was a "desperate" move after having gone to "the institution" for help and not receiving any during their CBS interview.
"We never left the family," Meghan added, explaining they only stepped back from their professional roles. "We were saying, 'OK, if this isn't working for everyone, we're in a lot of pain, you can't provide us with the help that we need, we can just take a step back. We can do it in a commonwealth country.' We suggested New Zealand, South Africa."
Take a Look at Prince Harry and Meghan Markle's Relationship Timeline
Harry also pointed out they "never blindsided" his grandmother, Queen Elizabeth II. "It was like, 'I need to do this for my family.' This is not a surprise to anybody," he acknowledged. "It's really sad that it's gotten to this point, but I've got to do something for my own mental health, my wife's, and for Archie's, as well, because I could see where this was headed."
It looks like Harry and Meghan are happier than ever with their adorable children!
Baby annoucement
Marie Osmond Wants 'Darling' Disney Star to Play Her in a Biopic Film
closer Exclusives!
Barbara Walters Was 'Close' to Daughter Jackie Before Death at 93
Hot in Cleveland's Wendie Malick: Show Was a 'Delightful Surprise'
'Self-doubt': Inside Paul McCartney's Life After the Beatles Split
Behind-the-Scenes Filming Secrets of the Classic Movie 'The Way We Were'
Marie Osmond's Grandkids Watch Her Perform, Wave to Her on Stage
Inside Ava Gardner and Gregory Peck's Decades-Long Friendship
Marie Osmond Isn't Going to 'Worry' About Weight Loss in 2023
Sandra Dee Was 'Grateful' for Son Dodd After Facing Heartbreak
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text:
Das Kristofer-Randers-Denkmal ist ein Denkmal in der norwegischen Stadt Ålesund.

Es befindet sich auf dem Berg Aksla, nahe dem Aussichtspunkt Fjellstua. Errichtet wurde es 1951 zu Ehren des Schriftstellers Kristofer Randers. Das von Arnold Haukeland geschaffene Denkmal stand zunächst am Rønneberghaugen in Ålesund und kam dann später an seinen heutigen Standort.

Das Denkmal besteht aus einer Kristofer Randers darstellenden Statue, die gestützt auf einen Wanderstock ihren rechten Fuß auf einen erhöhten Vorsprung setzt. Die Statue steht auf einem steinernen Sockel.

Auf der Vorderseite des Sockels befindet sich die Inschrift:

Kristofer Randers
1851 - 1917

Weblinks
 Informationen zum Kristofer-Randers-Denkmal auf www.alesund.kommune.no (norwegisch)

Personendenkmal (Autor)
Skulptur (1951)
Erbaut in den 1950er Jahren
Kunst (Ålesund)
Metallskulptur
Denkmal in Norwegen
Skulptur (Norwegen)

redpajama_set_name: RedPajamaWikipedia


text:
Congratulations in order!
By jhankins,
April 20, 2011 in Order of the Arrow
jhankins 10
jhankins
Just have to say a big "Congratulations" to the boys and members of Cahuilla Lodge #127, now the 7 time winners of the E. Urner Goodman Camping Award!
pchadbo 60
pchadbo
Very cool and congratulations!!
What IS the E. Urner Goodman Camping Award?
a non-arrowman is curious.
click23 66
click23
"The E. Urner Goodman Award was established in 1969 as a tribute and testimonial to the Order's founder, E. Urner Goodman. Its purpose is to encourage and challenge Order of the Arrow members and lodges to increase their effectiveness in promoting and increasing Scout camping in each council. Awards are presented to two outstanding lodges in each region annually. For a lodge to be eligible for consideration, the E. Urner Goodman Camping Award petition should be completed and forwarded with the lodge's charter renewal application."


text:
Lagos teacher, Ogun generator repairer arrested for defiling minors
•Afuape
Afeez Hanafi and Daud Olatunji
A teacher at a private primary school in the Ago Okota area of Lagos, Moses Fabusola, has been arrested by the police for alleged defilement of his seven-year-old pupil.
Fabusola, who is said to also be the girl's lesson teacher, reportedly molested her in February 2020 by inserting his finger into her private parts.
Sunday PUNCH learnt that the girl had an infection sometime in June which made her mum to be curious.
After questioning, the girl was said to have opened up on the act to her mum, who reported it at the Ago Palace Way Police Station.
It was gathered that attempts by the police to apprehend the suspect proved futile until recently when the school resumed.
"A case of defilement perpetrated by the class and lesson teacher of the girl was reported at the station. The teacher allegedly molested the child in February before schools were shut down due to the coronavirus pandemic.
"She had an infection in the private parts which aroused her mum's suspicion. After much pestering, she told her mother that her teacher inserted his hand into her private parts. The police launched a manhunt for the teacher. He was eventually arrested last week," a source told our correspondent.
The police spokesperson in Lagos, SP Muyiwa Adejobi, confirmed the alleged defilement and the teacher's arrest, adding that the case had been transferred to the State Criminal Investigation and Intelligence Department, Yaba, for further investigation.
He said, "A case of defilement was reported by the mother of the girl against Moses Fabusola, who is a teacher at a private school in Ago Okota. He was alleged to have defiled the seven-year-old girl who is his pupil.
"The case has been transferred to SCIID Panti on the order of the Commissioner of Police, CP Hakeem Odumosu. The command will not relent on its efforts to rid the state of indecency, crimes and social vices."
Meanwhile, men of Ogun State Police Command have arrested a 21-year-old generator repairer, Julius Afuape, for allegedly defiling a 13-year-old girl.
The suspect, who lives in the same neighborhood in Ayetoro with the victim's aunt, reportedly sneaked into their apartment and defiled her.
The Police Public Relations Officer in the state, Abimbola Oyeyemi, disclosed this in a statement on Saturday.
Oyeyemi explained that the suspect was arrested following a report by the victim's aunt who reported the incident at the Ayetoro Divisional Headquarters.
The PPRO said the victim's aunt complained that she returned from work on September 30, 2020 at 3:45pm only to discover that her niece had been sexually abused by the suspect.
Oyeyemi added, "The victim explained that she was alone in the house when the suspect came around 1:30 pm and claimed that he was sent by her aunt to service a generator.
"It was on that excuse that he was allowed to gain entrance into the apartment. But on getting inside, he forcefully dragged the victim into her aunt's bedroom where he had carnal knowledge of her.
"The suspect took to his heels immediately after the incident consequent upon which the DPO, Ayetoro Division, CSP Mobolaji Jimoh, directed detectives to go after him and bring him to justice."
Oyeyemi said the suspect was apprehended three days after the incident at his hideout and made a confessional statement.
"The victim was taken to the general hospital where it was medically confirmed that the victim's virginity had been broken," he said, adding that the state CP, Edward Ajogun, had directed that the suspect be transferred to the Anti-Human Trafficking and Child labour unit of the State Criminal Investigation and Intelligence Department.
Tags: Lagos teacher rape minor Ogun generator repairer rape minor


text:
E. M. Delafield
Late and Soon (NOVEL & 8 SHORT STORIES): From the Renowned Author of The Diary of a Provincial Lady and The Way Things Are, Including The Bond of Union, Lost in Transmission & Time Work Wonders (eBook, ePUB)
Bisher 18,99 €**
**Preis der gedruckten Ausgabe (Broschiertes Buch)
This carefully crafted ebook: "Late and Soon (NOVEL & 8 SHORT STORIES)" is formatted for your eReader with a functional and detailed table of contents.
"The rain, slanting and silver, drove lightly across the terrace and down the grassy hollows of the park where nettles and docks and bramble bushes grew freely amongst the clumps of yellow gorse." (Late and Soon)
"Late and Soon" (1943) - It is the story of Valentine Arbell, a widowed chatelaine and her two daughters living in a large country house. One of her daughters, Primrose has an affair with Valentine's former admirer…mehr
Late and Soon (A Novel & 8 Short Stories) (eBook, ePUB)
LATE AND SOON: A NOVEL & 8 SHORT STORIES (eBook, ePUB)
LATE AND SOON (eBook, ePUB)
The War-Workers & Consequences: Two Novels From the Renowned Author of The Diary of a Provincial Lady, Thank Heaven Fasting, Faster! Faster! & The Way Things Are (eBook, ePUB)
The Messalina of the Suburbs (Based on a Real-Life Murder Case): Thriller Based on a True Story From the Renowned Author of The Diary of a Provincial Lady, Thank Heaven Fasting, Faster! Faster! & The Way Things Are (eBook, ePUB)
M. P. Artzybashev
The Best Russian Plays and Short Stories by Dostoevsky, Tolstoy, Chekhov, Gorky, Gogol and many more (Unabridged): An All Time Favorite Collection from the Renowned Russian dramatists and Writers (Including Essays and Lectures on Russian Novelists) (eBook, ePUB)
"Late and Soon" (1943) - It is the story of Valentine Arbell, a widowed chatelaine and her two daughters living in a large country house. One of her daughters, Primrose has an affair with Valentine's former admirer Rory, but Rory rekindles his passion for Valentine. Set against the backdrop of imminent second World War in 1942, each character has to face a difficult decision that will change their lives forever.
Along with 8 other short stories this edition shows the more evolved side of the author E. M. Delafield.
E. M. Delafield (1890-1943) was a prolific English author who is best known for her largely autobiographical works like Zella Sees Herself, The Provincial Lady Series etc. which look at the lives of upper-middle class Englishwomen.
Late and Soon
The Bond of Union
Lost in Transmission
Time Work Wonders
The Hotel Child
The Gallant Little Lady
The Philistine: A Story
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GB, GR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Verlag: e-artnow
Born in 1890 to Count Henry de la Pasture and his novelist wife, E.M. Delafield was brought up according to strict Late Victorian precepts; upon failing to ensnare a husband at a young age, she entered a convent in Belgium the moment she turned 21. Having recovered from this experience, she later became a VAD (voluntary nursing for the war effort) and wrote her first novel. Delafield began publishing her writing in her mid-twenties; the year her fourth novel, Consequences, was published, she married Paul Dashwood, a civil engineer turned land agent.The pair spent three years in Malaya, followed by a country life in rural Devon; many of Delafield's novels and short stories are semi-autobiographical or stem from her experiences living abroad and in the rural countryside. Delafield died in 1943.
Mehr aus der Kategorie eBook > Belletristik > Romane & Erzählungen > Fremdsprachig
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text:
/*global crash */

/**
 * @requires crash.timer
 */

if (!this.crash.test) {

    /**
     * @namespace Specialised functions for unit testing.
     */
    crash.test = {};
}

// I know, I know - yet another unit testing framework. Well, the world is large, so there's always space for one more.
// Did I know about JSUnit (http://www.jsunit.net/), yes I did.
// Did I know about RhinoUnit (http://code.google.com/p/rhinounit/), yes I did.
// Did I know about Dojo Doh (http://dojotoolkit.org/book/dojo-book-0-9/part-4-meta-dojo/d-o-h-unit-testing), yes I did.
// Did I know about Prototype testing (http://www.prototypejs.org/ it's in the svn repository), yes I did.
// Did I know about testcase (http://rubyforge.org/projects/testcase/), oops, no - damn.
// And I still did my own. There's tenacity for you. Ok, maybe not tenacity...

// But why? I mean I really don't like writing reams of code.
// ...

// Set of utility functions for the unit test report information.
crash.test.utils = {

    createTotaliser: function () {
        return {
            complete: false,
            tests: 0,
            logs: 0,
            failures: 0,
            errors: 0,
            time: 0,
            abend: null,
            messages: []
        };
    },

    sumTotaliser: function (from, to) {
        to.tests += from.tests;
        to.logs += from.logs;
        to.failures += from.failures;
        to.errors += from.errors;
        to.time += from.time;
    },

    addTotaliserProperties: function (totaliser, name, testObject, func, callback, callbacks) {
        totaliser.name = name;
        totaliser.testObject = testObject;
        totaliser.func = func;
        totaliser.callback = callback;
        totaliser.callbacks = callbacks;
    },

    removeTotaliserProperties: function (totaliser) {
        delete totaliser.complete;
        delete totaliser.name;
        delete totaliser.testObject;
        // delete totaliser.func;
        // delete totaliser.callback;
        delete totaliser.callbacks;
    },

    createProfile: function () {
        return {
            complete: false,
            index: 0,
            total: 0,
            abend: null,
            tests: []
        };
    }
};

/**
 * The testing assertions. These functions are injected into the test object.
 *
 * @param totaliser the unit test totaliser (report).
 */
crash.test.assertions = function (totaliser) {

    var assertions = {

        log: function (message) {
            totaliser.logs += 1;
            totaliser.messages.push({ type: 'log', message: message });
        },

        pass: function () {
            totaliser.tests += 1;
        },

        fail: function (message) {
            totaliser.tests += 1;
            totaliser.failures += 1;
            totaliser.messages.push({ type: "fail", message: message });
        },

        error: function (error) {
            totaliser.tests += 1;
            totaliser.errors += 1;
            var message = error.name + ': ' + error.message;
            if (error.filename && error.lineNumber && error.stack) {
                message = error.filename + '(' + error.lineNumber + ') ' + message + '\n' + error.stack;
            }
            else if (error.filename && error.lineNumber) {
                message = error.filename + '(' + error.lineNumber + ') ' + message;
            }
            totaliser.messages.push({ type: "error", message: message });
        },

        assert: function (condition, message) {
            try {
                if (condition) {
                    assertions.pass();
                }
                else {
                    message = message || "assert: " + condition;
                    assertions.fail(message);
                }
            }
            catch(e) {
                assertions.error(e);
            }
        }
    };
    return assertions;
};

/**
 * This monster runs the unit tests. Since introducing asynchronous unit testing this piece of code has
 * grown exponentially. I'd really like to get it back down to a humane size, before it implodes under
 * its own weight.
 *
 * @param profile the testing report.
 * @param timeout the maximum time in milliseconds for all tests to be executed.
 */
crash.test.runner = function (profile, timeout) {
    var timerId = null;
    var intervalId = null;
    var functionAssertions = null;
    var callbackAssertions = null;
    var callbacks = null;

    function setTestTimeout(code, millis) {
        return crash.timer.setTimeout(code, millis);
    }
    function setTestInterval(code, millis) {
        return crash.timer.setInterval(code, millis);
    }
    function clearTestTimeout(timerId) {
        crash.timer.clearTimeout(timerId);
    }
    function clearTestInterval(timerId) {
        crash.timer.clearInterval(timerId);
    }

    function cleanUp() {
        var i = 0;
        var total = profile.total;
        var removeProps = crash.test.utils.removeTotaliserProperties;
        for (; i < total; i += 1) {
            removeProps(profile.tests[i]);
        }
    }

    function abend(reason) {
        if (timerId) {
            clearTestTimeout(timerId);
        }
        if (intervalId) {
            clearTestInterval(intervalId);
        }

        reason = reason || "Terminated by User";
        profile.abend = reason;
        var i = profile.index;
        var total = profile.total;
        for (; i < total; i += 1) {
            profile.tests[i].abend = reason;
        }

        cleanUp();
        profile.index = profile.total;
        profile.complete = true;
    }

    function injectAssertions(testObject, assertions) {
        var prop = null;
        for (prop in assertions) {
            if (assertions.hasOwnProperty(prop)) {
                testObject[prop] = assertions[prop];
            }
        }
    }

    function wrapCallback(testObject, callbackFunc, func, callbackIndex, index) {
        return function () {
            var test = profile.tests[index];
            injectAssertions(testObject, callbackAssertions);
            test.func = callbackFunc + " <- " + func;
            test.callback = callbackFunc;

            var startAt = new Date().getTime();
            try {
                try {
                    startAt = new Date().getTime();
                    callbacks[callbackIndex].apply(testObject, arguments);
                }
                finally {
                    test.time += (new Date().getTime() - startAt);
                }
            }
            catch (e1) {
                testObject.error(e1);
                try {
                    testObject.tearDown();
                }
                catch (e2) {
                    testObject.error(e2);
                }
             }

            injectAssertions(testObject, functionAssertions);
            test.complete = true;
        };
    }

    function testFunctionAndCallbacks(test, next) {
        var testObject = test.testObject;
        var callback = null;
        var length = test.callbacks.length;
        var i = 0;
        var index = profile.index + 1;
        callbackAssertions = crash.test.assertions(profile.tests[index]);
        callbacks = [];
        for (; i < length; i += 1) {
            callback = test.callbacks[i];
            callbacks.push(testObject[callback]);
            testObject[callback] = wrapCallback(testObject, callback, test.func, i, index);
        }

        function waitForCallback() {
            if (profile.complete) {
                return;
            }

            var testFunction = profile.tests[profile.index];
            var testCallback = profile.tests[profile.index + 1];
            if (testCallback.complete) {
                var testObject = testFunction.testObject;
                var length = testFunction.callbacks.length;
                var i = 0;
                for (; i < length; i += 1) {
                    testObject[testFunction.callbacks[i]] = callbacks[i];
                }

                profile.index += 2;
                clearTestInterval(intervalId);
                intervalId = null;
                setTestTimeout(next, 0);
            }
        }

        var startAt = new Date().getTime();
        try {
            try {
                testObject.setUp();
                startAt = new Date().getTime();
                testObject[test.func]();
            }
            finally {
                test.time += (new Date().getTime() - startAt);
            }
        }
        catch (e1) {
            testObject.error(e1);
            try {
                testObject.tearDown();
            }
            catch (e2) {
                testObject.error(e2);
            }
        }

        intervalId = setTestInterval(waitForCallback, 100);
    }

    function testFunction(test, next) {
        var testObject = test.testObject;
        var startAt = new Date().getTime();

        try {
            try {
                testObject.setUp();
                startAt = new Date().getTime();
                testObject[test.func]();
            }
            finally {
                test.time += (new Date().getTime() - startAt);
                testObject.tearDown();
            }
        }
        catch (e1) {
            testObject.error(e1);
        }

        profile.index += 1;
        setTestTimeout(next, 0);
    }

    function next() {
        if (profile.index >= profile.total) {
            if (timerId) {
                clearTestTimeout(timerId);
            }
            if (intervalId) {
                clearTestInterval(intervalId);
            }
            cleanUp();
            profile.complete = true;
            return;
        }

        var test = profile.tests[profile.index];
        var testObject = test.testObject;
        functionAssertions = crash.test.assertions(test);
        injectAssertions(testObject, functionAssertions);

        if (test.callbacks) {
            testFunctionAndCallbacks(test, next);
        }
        else {
            testFunction(test, next);
        }
    }

    function timedOut() {
        abend("Testing timeout (" + timeout + " ms) expired");
    }

    var runner = {

        run: function () {
            profile.complete = false;
            profile.index = 0;
            profile.total = profile.tests.length;

            if (timeout > 0) {
                timerId = setTestTimeout(timedOut, timeout);
            }
            setTestTimeout(next, 0);
        },

        abort: function (reason) {
            abend(reason);
        },

        check: function () {
            return { complete: profile.complete, abend: profile.abend, index: profile.index, total: profile.total };
        }
    };
    return runner;
};

/**
 * Creates a unit test.
 *
 * @param {String} name the unit test name.
 * @param {Object} testObject the test object.
 * @param {Number} timeout the maximum time in milliseconds for all the tests to be executed.
 */
crash.test.unit = function (name, testObject, timeout) {
    var utils = crash.test.utils;
    var profile = null;
    var tests = [];
    var runner = null;

    return {

        prepare: function (parentProfile) {
            if (parentProfile) {
                profile = parentProfile;
            }
            else {
                profile = utils.createProfile();
            }

            var i = null;
            var length = null;
            var testArray = null;
            var test = null;
            var prop = null;
            var totaliser = null;
            if (testObject.crashTests) {
                testArray = testObject.crashTests;
                length = testArray.length;
                for (i = 0; i < length; i += 1) {
                    test = testArray[i];
                    totaliser = utils.createTotaliser();
                    utils.addTotaliserProperties(totaliser, name, testObject, test.func, null, test.callbacks);
                    profile.tests.push(totaliser);
                    tests.push(totaliser);

                    if (totaliser.callbacks) {
                        totaliser = utils.createTotaliser();
                        utils.addTotaliserProperties(totaliser, name, testObject, 'callback <- ' + test.func, null, null);
                        profile.tests.push(totaliser);
                        tests.push(totaliser);
                    }
                }
            }
            else {
                for (prop in testObject) {
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                            typeof testObject[prop] === 'function' &&
                            prop.indexOf("test") === 0) {
                        totaliser = utils.createTotaliser();
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                        profile.tests.push(totaliser);
                        tests.push(totaliser);
                    }
                }
            }

            if (!testObject.setUp) {
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            if (!testObject.tearDown) {
                testObject.tearDown = function () {};
            }
        },

        run : function () {
            if (!profile) {
                this.prepare();
            }
            runner = crash.test.runner(profile, timeout);
            runner.run();
        },

        abort: function () {
            runner.abort();
        },

        check: function () {
            return runner.check();
        },

        summarise: function () {
            var results = [];
            var total = utils.createTotaliser();
            var length = tests.length;
            var test = null;
            var i = null;
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                test = tests[i];
                utils.sumTotaliser(test, total);
                results.push({ name: test.func, summary: test });
            }
            utils.removeTotaliserProperties(total);
            return { name: name, abend: profile.abend, summary: total, tests: results };
        }
    };
};

/**
 * Creates a group of unit tests.
 *
 * @param {Array} tests the unit test array.
 * @param {Number} timeout the maximum time in milliseconds for all unit tests to be executed.
 */
crash.test.group = function (tests, timeout) {
    var utils = crash.test.utils;
    var profile = null;
    var runner = null;

    return {

        prepare: function () {
            profile = utils.createProfile();
            var length = tests.length;
            var unitTest = null;
            var i = null;
            for (i = 0; i < length; i += 1) {
                unitTest = tests[i];
                unitTest.prepare(profile);
            }
        },

        run: function () {
            if (!profile) {
                this.prepare();
            }
            runner = crash.test.runner(profile, timeout);
            runner.run();
        },

        abort: function () {
            runner.abort();
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        check: function () {
            return runner.check();
        },

        summarise: function () {
            var total = utils.createTotaliser();
            var results = [];
            var length = tests.length;
            var unitTest = null;
            var unitSummary = null;
            var i = null;
            for (i = 0; i < length; i += 1) {
                unitTest = tests[i];
                unitSummary = unitTest.summarise();
                utils.sumTotaliser(unitSummary.summary, total);
                results.push(unitSummary);
            }
            utils.removeTotaliserProperties(total);
            return { abend: profile.abend, summary: total, tests: results };
        }
    };
};

redpajama_set_name: RedPajamaGithub


text:
Q: Why can't I import this Haskell module? I put this in ~/Desktop/Shapes.hs:
module Shapes
( Shape(Rectangle)
) where

data Shape = Circle | Rectangle deriving (Show)

I then do this:
cd ~/Desktop
ghci

ghci> :m +Shapes

<no location info>:
    Could not find module `Shapes'
    It is not a module in the current program, or in any known package.

ghci> import Shapes

<no location info>:
    Could not find module `Shapes'
    It is not a module in the current program, or in any known package.

Why do I get this error?
I also tried compiling first with ghc -c Shapes.hs. It still doesn't work.
I installed the "Haskell Platform 2013.2.0.0 for Mac OS X, 64 bit" from haskell.org on my OS X 10.9.2 Mavericks. I also followed their ghc-clang-wrapper instructions.
Update:
Someone suggested doing :l Shapes.hs first. The problem is that :l Shapes.hs loads the entire Shapes file, meaning I have access to the Circle value constructor even if I did not export it. See my earlier question: Why can I use this "private" value constructor? I want to load only the module. Is this possible?

A: You need to load your Shapes.hs first by :l Shapes.hs.

*

*Because your Shapes have not been loaded, so :m Shapes will not work.

*Because your Shapes does not exist in a compiled package that ghci can find, so import Shapes will not work.
If you only want the exported symbols in scope, after :load a module, you could use :module or import to only import those symbols. For example, after :load Shapes.hs and :module Shapes, Rectangle will in scope but Circle will not.
See:
What's really in scope at the prompt?
:module and :load

redpajama_set_name: RedPajamaStackExchange


text:
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Home » Meet the MidAtlantic Best Projects Jury
MidAtlanticMid Atlantic Construction Projects2017MidAtlantic FeaturesBest Projects
ENR MidAtlantic's 2017 Best Projects
Meet the MidAtlantic Best Projects Jury
KEYWORDS ENR MidAtlantic Best Projects
Mark Lambert
Corporate Safety Director
KBS Inc.
Lambert has strategic and day-to-day responsibility for leading KBS's safety department. He heads the effort to promote and maintain safe, clean and efficient jobsites. Lambert and his team carry out weekly project site visits, providing oversight and training for superintendents and subcontractors.
Matthew Marquardt
Senior Vice President and COO for Vertical Services
Urban Engineers
Marquardt oversees the firm's facilities construction; site development; structural and building systems design; and the airport, transit and rail departments. Marquardt is or has been a board member of American Council of Engineering Companies of Pennsylvania, president of American Society of Highway Engineers and chairman of The PENJERDEL Council and Villanova University Engineering Alumni.
Robert Monser
Heery International Inc.
Monser, area manager of Heery's Philadelphia office, is in charge of the company's project-management services delivery in Pennsylvania, New Jersey, New York and Delaware. His background covers many industries and types of clients as well as management and oversight of a wide range of building design and construction services.
Helen Robinson
GEI Consultants Inc.
Robinson joined GEI's Exton, Pa., office as a senior project manager in April. She has experience in design, analysis and construction management services for geostructural projects. Robinson specializes in design of micropiles; tiebacks; soil nails; drilled shafts; sheet pile cofferdams; retaining walls; braced excavation support and grouting; and ground-improvement methods. Her experience also includes site investigations; soil classification; laboratory testing; load-testing field support and interpretation; and instrumentation.
Robinson chairs the Deep Foundation Institute's Women in Deep Foundations Committee. She has authored or co-authored more than 20 publications on practical technological applications and design in geotechnical engineering.
Bob Robidoux
Purchasing Director
Balfour Beatty Construction D.C.
Robidoux joined Balfour Beatty 37 years ago and spent nearly the entire first half of his career managing the day-to-day construction activities and safety initiatives on numerous projects as a project manager and superintendent. For the last 17 years, he has overseen subcontractor purchasing and contract negotiations. He has extensive experience in subcontractor procurement and is a specialist in trade packages and scopes of work. His résumé includes some of the Washington, D.C., area's most recognizable projects, including the Air Force Memorial, National Museum of the Marine Corps and, most recently, the Capitol Crossing project.
Mark Ulmer
Regional Safety Manager
The Walsh Group
Ulmer currently serves as safety manager of The Walsh Group's national building group-east. He manages the safety activities on 16 projects in 10 states and Puerto Rico. Working in the private and federal sectors, Ulmer is a Certified Safety Professional through the Board of Certified Safety Professionals and holds several training credentials and certifications pertaining to hazards associated with the construction industry.
Ken Umstead Jr.
IMC Construction
Umstead is responsible for implementing IMC's safety management program. He has experience in methodologies of safety assessment, measurement and developing preventative action plans. Umstead studies and refines existing safety management programs and develops core measures for new projects. He is responsible for assisting in the overall administration of project safety and accident- and fire-protection programs.
Raj Vora
Project Executive
DPR Construction
During his 17-year career, Vora has focused on life sciences projects, including complex engineering and construction projects ranging from laboratory fit-outs to ground-up current Good Manufacturing Practices (cGMP) manufacturing facilities. He has experience in pursuit, planning, design, construction and start-up of a variety of projects, focusing on biotechnology and pharmaceutical manufacturing facilities, bio-containment and R&D facilities.
He is past president of the International Society for Pharmacy Engineering's Chesapeake Bay Chapter and a founding member of the Design-Build Institute of America's Maryland Chapter.
Meet The Jury: ENR Mid-Atlantic Best Projects
Best Projects 2013: Meet the Jury
Designers Post Increased Revenue, but Outlook Is Cloudy
Regional Builders Grow Revenue at a Steady Pace
Region's Specialty Firms Stay Busy During Building Boom
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This blog, is written in english but also in Spanish.
First of all I'd like to explain all about the language why is it in english? Why also in Spanish?
Well.. there are some reasons.. first, I'd like my blog to arrange the widest amount of people and everyone knows that nowadays english is the 'Global Language'.
I'd also like to improve my english, and as you may have already noticed I'm not english and I have a lot to learn and improve. Now that we are hear I would like to apologize for my mistakes which can be sometimes a little bit funny, you'll noticed.
The Spanish version is the link on the right: Mi rincón chic.
So.. to finish, welcome to my blog! I hope that this blog will end up being your blog, a place where you can feel comfortable and find some useful things.