text:
\section{Introduction}

Nowadays, in automation industry humans and robots share a common workspace while collaborating on tasks in real-time simultaneously. In such workspaces, any risk of injuries to humans due to the human-robot collaboration should be fully eliminated. For that reason, the robots should have a clear holistic understanding of the scene including the most accurate and precise information related to the human activities in the workspace. This is achievable only if the robotic-system learns the possible context of human-object and object-object  arrangements, and the interactions among them. We therefore  propose a density function that captures the relationship between human-object and object-object interactions. This function can be effectively used to train methods to achieve improved segmentation performance on real-world data. Hence, showing its potential for research scenarios related to safe human-robot collaboration (SHRC) and interaction (SHRI) in the industrial domain.  Optimized modeling for scene understanding workflow eases to learn such contexts.  Objects in real-world interact with each other, so it is very meaningful to capture this interaction for accurate and precise predictions of actions. This is  only possible if the training algorithm (e.g. classifier)  has learnt well  the underlying physical interaction and reasoning for better predictions.  In order to analyse and estimate the scene space in real-world, it is essential to model a synthetic scene relevant to the real-world in a virtual environment for analysis and dataset creation.

\begin{figure}[t]
\centering

\includegraphics[width=0.58\columnwidth]{CRF.pdf}
} \vspace*{-0.15cm}
\caption{Top-view segmentation results for real-world depth data, obtained from KINECT sensor installed at a height of 3.5 meters. (\textit{From bottom:}) The first level of the hierarchy  shows the test depth map, the
second and third levels show the predictions obtained
from RDF classifier and the CRF modeling.} \label{fig:overview_snippet_i}
\end{figure}

In contrast to the past, now we have relatively cheap RGB-D cameras, publicly available free simulation softwares, free 3D CAD models,
low priced consumer hardware with computational resources to allow graphics support.
In this paper, we keep the cost of hardware and software to barely minimum and focus on achieving an appropriate and efficient real-time segmentation.
We use rendering simulation software (VREP~\cite{vrep}) which is freely available. We use a skeletal tracking system which is based on a multi-sensor KINECT setup instead of using a very expensive marker based motion capture system.  There is minimal manual intervention for generating very large and realistic synthetic scenes by using the rendering software.  Also by using the rendering software we can generate fully automatic ground-truth labeling for free.

In this paper, our goal is to learn the interaction between objects while recognizing objects. This way there is a possibility to infer the scene and predict the actions based on the object modeling. This modeling information is beneficial\footnote{Fraunhofer Institute for Factory Operation and Automation IFF. \url{http://www.iff.fraunhofer.de/en/business-units/robotic-systems/research/human-robot-interaction.html}, 2015}: in aviation/automobile industry for integrating aircraft/automobile components, in manufacturing operations carried hand-in-hand with humans,  for reliable collision detection,  and in healthcare and medical industry for facilitating minimally-invasive-surgery.

For demonstration, we install a RGB-D sensor on the ceiling at the height of 3.5 meters from the ground (see Fig.~\ref{fig:overview_snippet_i}). The camera observes our robotic workspace and the viewed scene is examined for analysis using depth data.  The depth measurements are directly processed to provide an accurate and spatially resolved information about the object classes in real-time from top-view. Our main contributions are as follows:
\begin{itemize}
\item While most previous works consider modeling human and object interactions to infer and predict actions. We propose  to learn  these interactions in RGB-D data in order to improve segmentation which can be useful for SHRI and SHRC scenarios.
\item We propose a  low cost solution composed by a publicly available free rendering simulation software, cheap RGB-D cameras, and free CAD models taken from the internet. With these resources we can create  synthetic scenes that can be used for training and help improve segmentation performance.
\item We show that synthetic data generated from a density function, that governs the human-object and object-object interactions, can be effectively used to train methods to achieve improved segmentation performance by ${\sim}$7\% in mAP and mAR over state-of-the-art methods on real-world data.
\end{itemize}

The remainder of the paper is structured as follows.  In Section II, the related work is given. Section III, describes the proposed density function for scene modeling\iffalse creation of synthetic data\fi. Section IV, explains the feature selection, and some basics on RDF classifier \& CRF modeling. In Section V-VI, implementation details, data collection and the experimental evaluation are given. Finally in Section VII, conclusions are drawn.

\section{Related Work}
For vision applications, we can acquire RGB-D data using real-world sensors or by generating synthetic data using computer graphics rendering techniques in a 3D environment. The real-world data is also possible to use for training the system, but researchers generally avoid this because of several reasons: real-world data generation usually requires expensive equipments (i.e. software and hardware gadgets). In addition manual hand labeling of objects is a very-time consuming and tedious task because precise measurement of objects is required. Also manual ground truth labeling leads to missing out several important information in the scene due to imperfect and error-prone human annotations. Therefore, some vision researchers avoid using real-world data for training vision systems. While by using the present precise rendering techniques and simulation frameworks with modeled environment, it is possible to synthesize accurate and realistic data, which is very close to real-world data. This provides the flexibility to tune the elements of scene for incorporating camera noise, lighting-effects, shadows, rotation/translation, etc. during rendering in order to make the synthetic data as consistent as possible with the physical real-world  data.

Here, we discuss some of the previous works related to vision problems. In~\cite{Neumann}, Neumann-Cosel~et al. use synthetic data obtained from the modeled Virtual Test Drive simulation for lane tracking in driver assistance and active safety systems. An identical work from Haltakov~et al. ~\cite{Haltakov} proposes a framework for generation of synthetic data from a realistic driving simulator to create  modeled traffic scenarios  similar to real-world scenarios.  A number of works uses synthetic human data for pose and activity recognition~\cite{Dittrich,Me,Ganapathi,Jiang,cvpr,Shotton,Sung}. In vision problems, human pose and activity recognition has received great attention in the last decade. In~\cite{Shotton}, Shotton~et al. synthesize a large and varied dataset of human actions using a motion capture technique.  In~\cite{Me,cvpr}, the authors use a simple synthetic human body representation in a virtual environment using a KINECT skeleton estimation for generating synthetic human pose data. In~\cite{Shotton}, the data is generated based on front, top, and side view, while in~\cite{Me,cvpr}, it is top view only. Both works are applied in real-world scenarios, but neither of the models incorporate the physical interaction of objects with other objects. Also no structural and modeled arrangements are dealt with. Thus, their algorithm fails to recognize  human body-parts, when human body-parts overlap other objects, or are partially occluded by other objects in the scene. In~\cite{Hoiem},  Hoiem~et al. discard the object candidates that are not well supported by proper occlusion boundaries. Yet, we know that reasoning about occlusion would definitely improve the recognition performance. A substantial little work has been done in the context of scene modeling.

Our approach is driven by three key objectives namely computational efficiency, robustness and time efficiency (i.e. real-time) for industrial applications. It further differs from Shotton~et al.~\cite{Shotton} in the following aspects. (a) In~\cite{Shotton}, all training data were thereby synthetically generated by using marker based motion capture (Mo-Cap) technique. Such setups are very expensive and inaccessible to most users. On the other hand, we use a highly optimized virtual representation of 3D human skeleton in a virtual environment. We generate the synthetic data of human body-parts in a virtual environment using a multi-sensor KINECT setup for skeleton tracking in real-world~\cite{Me}. In addition to human data, we also generate synthetic data for objects as well. (b) Calibration of Mo-Cap is a tedious task in comparison to KINECT setup. (c) Processing Mo-Cap data requires sophisticated hardware in comparison to KINECT depth data which can be porcessed with consumer hardware. This way computational expense is reduced.

For learning and reasoning about the physical scene,  we need to capture the relevant and meaningful information (i.e. geometry, pose, shape, occlusion, and other attributes) and train the classifier system. The density functions capture  the relationship, physical interactions, and the context among objects as well as the geometry of the objects.  For physical scene understanding, occlusion boundaries should be well identified, since they form a very crucial component for depth ordering. Incorporating occlusion during synthetic scene modeling, helps to get a better understanding of the real-world scene.  In~\cite{Jiang}, Jiang~et al. model a density function  based on 3D spatial features to capture the object-object and human-object relationship  in a 3D scene. They use the density function to infer arrangements of object in a scene. In~\cite{Jiang}, a camera is installed in the virtual environment, in the corner of the room, where two walls and the ceiling meet, or where a wall and the ceiling meet. Their evaluation is only limited to the virtual environment for training and testing assessments. Similar is done in~\cite{Dittrich}, where Dittrich~et al. model the density function on depth features using a top-view RGB-D sensor. Their evaluation is also limited to their virtual environment for assessment.  Our work is inspired by top-view segmentation from Shotton~et al.~\cite{Shotton} and Sharma~et al.~\cite{cvpr}, and we define the density  function in VREP~\cite{vrep} in order to model object-object and human-object relationships. We use a top-view RGB-D sensor data. However, our evaluation is not just limited to virtual environment, but also to physical real-world scenes. In addition, our work focuses on modeling relationships between a group of multiple 3D objects in a scene, while Shotton~et al.~\cite{Shotton} and Sharma~et al.~\cite{cvpr} only focuses on a single 3D object in a scene. Moreover, they do not incorporate any form of interaction modeling (i.e. non-modeled) between humans and objects.

Some other work, similar to our work, Gupta et al.~\cite{gupta}, Aksoy et al.~\cite{aksoy}, and Pieropan et al.~\cite{pier} use the spatial relationship to perform activities recognition where human-object and object-object relationships are encoded. Also some other notable work, similar to ours that uses the same idea of output of a classifier as input of another model are like ``{Autocontext}" by Tu~et al.~\cite{tu}, ``{Decision Tree Fields}" by Nowotzin~et al.~\cite{now} or ``{Structured Class-Labels in Random Forests for Semantic Image Labelling}" by Kontschieder~et al.~\cite{kont}, among others.

One major problem that arises when using synthetic data, is how to determine if the synthesized data is realistic in comparison to real-world data. This could be determined by evaluating the statistics of object constellation in the synthetic and real world.  A simple approach could be to apply collision avoidance while modeling interactions between objects, but such a modeling is not realistic as occlusions never exist in the modeled scene. Therefore, a realistic scene with appropriate object interactions could be modeled using a density function in order to produce good prediction results, not just for synthetic data, but also for real-world data (see Section~\ref{sec:dataset} and Section~\ref{subsec:comp} for details).

\section{Dataset Generation using a Density Function}\label{sec:dataset}

\begin{figure}[t]
\centering

\includegraphics[width=0.85\columnwidth]{vrep.png}
} \vspace*{-0.15cm}
\caption{Demonstration of a virtual scene in VREP. Object classes from industrial domain is considered as the subset of object constellations  which are consistent to the real-world (Source~\cite{Dittrich})} \label{fig:vrep}
\end{figure}

We used a Virtual Robot Experimentation Platform (VREP) \cite{vrep} (Fig.~\ref{fig:vrep} and Fig.~\ref{fig:overview_snippet}) for modeling and dataset generation. VREP is a robotics simulator with an embedded application for a robot in an integrated development environment (IDE) with potential field tasks without depending physically on the actual robot.  VREP robot simulator has been used for 3D modeling and generation of the synthetic data. VREP simulator provides a virtual environment, which is capable of emulating realistic motion generation, translation, rotation of the actual objects in the IDE. Each object/model can be individually controlled via a remote API client, this makes it ambidextrous because of its distributed control architecture. It supports  C/C++, Python, Java, Lua, Matlab, Octave or Urbi. Also it is publicly available for hobbyists, academic and research purposes. We use 3D CAD models for object classes: chair, table, storage obtained from website\footnote{\url{http://www.hermanmiller.com/design-resources/3d-models-revit/3d-models-by-product/seating.html}, 2015}, and plant (domain: industrial office type) obtained from website\footnote{\url{http://xfrog.com/category/samples.html}, 2015}. These CAD models are also publicly available for free. We created our own 3D human model, and imported it to the virtual environment. The 3D models we use are of the file types *.3ds and *.obj. Refer to \cite{vrep} for detailed information about how to import CAD models into the scene.

Here, we define a density function in VREP for scene modeling, which includes: spatial layout of object, object pose, object orientation, object arrangement, object interaction and relationships between object classes i.e. none, partial, and full occlusion. The density function captures the relationships between  human-object ($H-O$) and object-object ($O-O$) arrangements and the interactions among them. This allows us to generate  a consistent synthetic dataset with physical real-world scenarios. Fig.~\ref{fig:dataset} shows our synthesized dataset using the density function.

A scene is denoted by $S$, and it contains industrial objects $O = \{O_{1},O_{2},...,O_{n}\}$ placed in the scene. The density function captures the relationship of human and industrial objects in the scene that is similar to real-world. This helps  to improve the segmentation performance in  real-world scenarios.
The human object class is denoted by $H$ with variable poses, specified by spatial joint location and activity.  The density function $\Phi$ capturing the context of human-object and object-object relationships in a scene is defined as:
\begin{equation}  \Phi(S)=\Psi(H,O;\theta)\Psi(O,O;\theta), \end{equation}
where $\theta$ is threshold. We chose 4 industrial objects (i.e. chair, plant, table, and storage) based on an industrial environment, and 6 localized human body-parts of the human as object classes (i.e. head, body, upper-arm, lower-arm, hand and legs). In order to illustrate the scene well,  the density function describing the human-object and object-object relationships is defined as:
\begin{equation}
\begin{split}
 \Psi(H,O;\theta)= \psi(H_{h})\psi(H_{p})\psi(H_{pos})\psi(H_{ori})\psi(O_{h})\\\psi(O_{pos})\psi(O_{ori})\psi((H,O)_{\theta})\psi((H,O)_{rel})
\end{split}
 \label{eq:h-o}
\end{equation}
\begin{equation}
\begin{split}
 \Psi(O,O;\theta)= \psi(O_{h})\psi(O_{pos})\psi(O_{ori})\psi((O,O)_{\theta})\\\psi((O,O)_{rel})
\end{split} \label{eq:o-o}\end{equation}
The terms used in the equation~\ref{eq:h-o} and~\ref{eq:o-o} are explained below.

\begin{figure}[t]
\centering
{\includegraphics[width=0.82\columnwidth]{overview.pdf}
} \vspace*{-0.15cm}
\caption{Schematic layout of our proposed segmentation system incorporating: scene modeling using density function in a virtual environment for synthetic data generation; define feature space and correspondingly feature selection and extraction for RDF classifier training; finally CRF modeling.} \label{fig:overview_snippet}
\end{figure}

\paragraph*{\textbf{Height ($h$)}} $\psi(H_{h})$ is the candidate parameter for human height, as not all humans are of same body proportions i.e. height and breadth. We set a scaling factor for variable human heights $\{h_{1},h_{2},h_{3},...,h_{n}\}$, that maintains a uniform correlation between height and breadth
of 3D human model with the real-world human. Our 3D human model is modeled on a set of 173 spheres arranged according to the skeleton estimate. We record the real-world human choreographies via KINECT skeleton tracking from a calibrated multi-sensor setup. We use the stored skeletons for modeling the  3D human model in the virtual environment.  The simple scaling of recorded skeleton is a vector  based on body proportions applied as:
\begin{equation}  SC_{scaled} = \beta \cdot SC_{original} = \{\beta \cdot SC_{1}, \beta \cdot SC_{2},...,\beta \cdot SC_{m} \} \end{equation}
where $SC$ is the estimated skeleton setups, $\beta$ being a fixed scaling factor ranging between [$\beta_{min}$ , $\beta_{max}$ ], which maintains a huge variation in body proportions of the 3D human model. This measure facilitates a proper correspondence in the generation of the synthetic human data in approximate relevance to the real-world human. The human height ranges between 160-190 cm. $\psi(O_{h})$ is the candidate parameter for industrial objects height. For table, chair, plant, and storage, standard industrial based heights of object instances were chosen. Table, chair, storage range is between 70-90 cm, and plant ranges between 15-35 cm. The probability density function for   $\psi(H_{h})$  is uniformly distributed on the interval $h \in [h_{1}=160,h_{2}=190]$ and  $\psi(O_{h})$ is uniformly distributed on the interval $h \in [h_{1}=70,h_{2}=90]$.
\iffalse
and is given by:
\begin{equation} \psi(H_{h}) =   \frac{1}{h_{2}-h_{1}},  \enspace    h \in [h_{1}=160,h_{2}=190]   \end{equation}
and  $\psi(O_{h})$ is uniformly distributed on the interval $h \in [h_{1}=70,h_{2}=90]$.
\fi

\paragraph*{\textbf{Pose ($p$)}} $\psi(H_{p})$ is the candidate parameter for human pose. The human poses and appearances include: \textit{sitting, standing, stretching, walking, working, dancing, bending, bowing, swinging, boxing, and tilting} with combinations of single and both arms, angled arms, and other combinations.  The pre-defined set of poses have one-to-one correspondence to a set of candidate scaling parameter.

\paragraph*{\textbf{Position or translation ($pos$)}} $\psi(H_{pos})$ and  $\psi(O_{pos})$ are the candidate parameters for human and object positions in the scene respectively. There is a preference of human and objects to be placed with reference to each other and translated in the whole parent scene uniformly at random. We incorporate maximum randomization as is the case in real-world. The probability density function for   $\psi(H_{pos})$ and $\psi(O_{pos})$ is uniformly distributed on the interval $pos \in [w,h]$  and is given by $1/(w \times h)$, where $w$ resembles width and $h$ resembles  height of the  parent scene.

\paragraph*{\textbf{Orientation or rotation ($ori$)}} $\psi(H_{ori})$ and  $\psi(O_{ori})$ are the candidate parameters for human and object orientations  in the scene respectively. The rotation ranges between $0 - {360}^{^{\circ}}$ about the vertical axis at random. The probability density functions for  $\psi(H_{ori})$ and $\psi(O_{ori})$ are uniformly distributed on the interval $ori \in [ori_{1}=0,ori_{2}=360]$.

\begin{figure*}[htb]
\centering
{\includegraphics[width=1.79\columnwidth]{dataset.pdf}
} \vspace*{-0.15cm}
\caption{ Training dataset.  (\textit{Top}) Ground truth labels  of synthetic depth data (\textit{Bottom}) generated using a density function with a synthetic KINECT sensor. The captured dataset shows high variability of the spatial layout of object, object pose, object orientation, object arrangement, object interaction and relationships between object classes.  Synthetic depth frames are with additive white Gaussian noise.} \label{fig:dataset}
\end{figure*}

\paragraph*{\textbf{Threshold or distance function ($\theta$)}}  $\psi((H,O)_{\theta})$ and $\psi((O,O)_{\theta})$ are the candidate parameters for defining the threshold of preferred occlusion of boundaries for $H-O$ and $O-O$ classes respectively. This parameter encodes the distance upto what level the $H-O$ and $O-O$ classes would overlap based on the Euclidean thresholded distance ($\theta^{'}$). The probability density functions for   $\psi(H_{h})$ and $\psi(O_{h})$ are uniformly distributed on the interval $\theta \in [\theta_{1}=0,\theta_{2}=\theta^{'}] $.  $\theta^{'}$ can be set to different values for $H-O$ and $O-O$ interactions, though in our case  we use $\theta^{'}=30$ \% for both cases. See ``Comparison of Models'' in Section~\ref{subsec:demon} for more details.

\paragraph*{\textbf{Relationship ($rel$)}} $\psi((H,O)_{rel})$ and $\psi((O,O)_{rel})$ are the terms that model the relationships for $H-O$ and $O-O$ classes. Relationships like: collision between objects (e.g. 2 tables attached together), objects occluded by one another (e.g. a storage placed below a table), objects  partially occluded (e.g. partial occlusion of a chair by table), objects placed over one-another (e.g. a plant pot placed on top of a table).  Other interactions such as  a person may have his hand placed on the table, holding the chair handle,  sitting on a chair and so on.

\section{Algorithm}

Fig.~\ref{fig:overview_snippet} shows the schematic layout of our proposed hierarchical segmentation system. The synthesized  RGB-D  training dataset incorporates modeled $H-O$ and  $O-O$ relationships and interactions obtained using a density function based scene modeling. The first step performed is sampling i.e. number of frames and features-per-object-class are chosen at random for training an RDF classifier.  Next, individual extracted  features  corresponding to an object class are passed to the RDF classifier. RDF returns a trained classification forest in which each leaf node represents the class prediction of a tree. Now a test depth map of the scene obtained from the real-world KINECT sensor is given as an input to the trained classification forest. The result obtained is a pixelwise object class labeling. The likelihood of an object label assigned to a pixel obtained from the classifier is integrated as a unary term in the pairwise CRF. The pairwise term is obtained from the Potts model~\cite{Potts}. Thus, this labeling problem modeled on pairwise CRF is optimized using $\alpha$-expansion built on graph cuts~\cite{GraphCuts} for finding a global optimal labeling (i.e. segmentation).

\subsection{Additive white gaussian noise in synthetic depth data}
The synthetic depth data from the virtual KINECT sensor in the scene usually contains no noise. The capturing of the real-world with sensors usually is a combination of ideal signal and noise. The generation of noise may be because of vast number of sources, variations in the detector sensitivity, environmental variations, the discrete nature of radiation, transmission or quantization errors, geometry dependant missing data due to shadows in the IR image cast by the object, that there are discretization errors, and noise increases with increasing depth,   all of which add up to a noise model.  Because of the noise in the real-world
data, and to cope with unseen data samples in the testing step
more robustly, meaning to increase the generalization ability of
the trained classifier, we add additive Gaussian white noise to
the depth values (Fig.~\ref{fig:dataset}).

\subsection{Feature Selection}
We adopt the same depth features as specified in~\cite{Me}. For a given pixel location $s$ of an object sample $O$ from its depth map, we denote its depth value as a mapping $d_{O}(s)$,  and design a feature $f_{O}(s)$ by using two 2D offsets positions $u$, $v$ from $s$:
\begin{equation}  f_{O}(s)= d_{O}(s+u)-d_{O}(s+v)\end{equation}
The  feature is  depth-invariant. We use a rectangular patch for extraction of depth values from an object sample. We compute a fixed number of 300 features for each object sample.

\subsection{Classifier}

The choice of selecting a good discriminative classifier is independent of the preceding sections, because our goal is to show that modeling using density function helps to improve the real-world segmentation performance.  Here,  we use a Random Decision Forests (RDF) classifier for pixelwise object classification. RDF is an ensemble of $t$ binary decision trees, $t \in \{1,...,T\}$. In a decision tree, each of the nodes is associated with a feature response function (i.e. weak learner or split function). The feature response function plays the most critical and crucial role in both training and testing of random forests. In the internal node, selection of parameters of the split function takes place associated with each split node by optimizing a chosen objective function defined on training data set. The objective function is based on maximizing the information gain. The geometric primitives of the split function are used to partition the data points. We employ two geometric primitives, axis-aligned and  linear feature response function. We also use bagging and randomized node optimization, that injects randomness into the trees during the tree training. The stopping criteria used for tree growth were maximum number of levels that a tree could reach, minimum information gain that the nodes could have, child nodes having the same entropy value.

\iffalse
The feature vector is injected at the root node of the decision tree and the first step is randomly choosing a subset of training samples for each training tree (i.e. bagging) in the forest, and randomly proposing a fixed sized subset of feature space dimensions out of $h \times w$ dimensions, i.e. candidate features ($\phi$) and thresholds ($\tau$) per feature for decision in the split node.  The feature vector is split into two subsets, left and right child nodes for each $(\phi, \tau)$ based on the chosen weak learner for the decision tree. Information gain for each ($\phi, \tau$) is computed. The largest information gain $argmax$ for ($\phi, \tau$) is selected and then the feature vector is split. These steps are repeated recursively for all newly constructed child nodes until a stopping criterion for the tree growth is met.  Refer to \cite{me} for detailed explanation. Fig.\ref{fig:RDF} shows a simple pixelwise object class labeling using RDF classifier.
\fi

\subsection{Conditional Random Fields}

The energy of the pairwise CRFs used for object class segmentation  can be defined as a sum of unary  and pairwise  potential terms, given as:
\begin{equation} E(\mathbf{x}) = \sum_{i \in \upsilon} \varphi_{i}(p_{i}) + \sum_{i \in \upsilon, j \in \eta} \varphi_{i,j}(p_{i},p_{j}), \label{eq:CRF_pair_en} \end{equation}  where $\upsilon$ corresponds to the vertex set of a 2D grid with each vertex corresponding  to pixel $p$ in the image and $\eta$ neighborhood of the pixels $\forall  i, j  \in \upsilon$. $\mathbf{x}$ is an arbitrary labeling. The unary  potential $\varphi_{i}(p_{i})$ term in the CRF energy is a data term. It is the likelihood of the object label assigned to pixel $i$. Here, the unary term is obtained from the RDF classifier for each pixel belonging to the object class.  The pairwise potential $\varphi_{i,j}(p_{i},p_{j})$ term in the CRF energy encodes a smoothness prior and encourages smooth segmentation by favoring neighboring pixels in a 2D grid having the same label~\cite{GraphCuts}. It takes the form of a Potts model \cite{Potts}, which is efficiently minimized by $\alpha$-expansion~\cite{GraphCuts} built on graph cuts.

The predictions obtained from pixelwise labeling using RDF is very efficient and when modeled on a CRF to minimize the misclassification of labels assigned to the depth measurements, makes the algorithm more ``robust" and even contributes to the good results.

\begin{figure*}[htb]
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    \addlegendentry{Linear}

    \addplot[smooth,color=red,mark=x]
        plot coordinates {
	(75,0.564)
	(150,0.578)
	(300,0.592)
	(400,0.597)
	(500,0.602)
	(600,0.605)
        };
    \addlegendentry{Axis-Aligned}
    \end{axis}
    \end{tikzpicture}}
\resizebox{3.8cm}{!}{
\begin{tikzpicture}
    \begin{axis}[
	x label style={below=5mm},
   	y label style={anchor=south east},
        xlabel={$F$},
        ylabel=$mAP$,
	legend style={shift={(0.1,-0.40)}}]
    \addplot[smooth,mark=*,blue] plot coordinates {
        (40,0.5443)
        (200,0.6177)
        (400,0.6459)
	(1200,0.6836)
        (1600,0.6904)
        (2400,0.6939)
	(4800,0.7117)
    };
    \addlegendentry{Ours-Modeled}

    \addplot[smooth,color=red,mark=x]
        plot coordinates {
        (40,0.4603)
        (200,0.5623)
        (400,0.5770)
	(1200,0.6023)
        (1600,0.6117)
        (2400,0.6236)
	(4800,0.6289)
        };
    \addlegendentry{SOA-Non-Modeled}
    \end{axis}
    \end{tikzpicture}} \vspace*{-0.15cm}
\caption{ Evaluation results. (\textit{Column 1:}) effect of the additive white Gaussian noise ($\sigma$) to the synthetic training depth data in order to exhibit real-world camera noises on the average recall ($mAR$) and precision ($mAP$) measures of pixelwise object class segmentation. (\textit{Column 2:}) comparison of the linear and axis-aligned feature response function, using $mAR$ and $mAP$ as a function of the number of features extracted ($PC$). \iffalse (\textit{Column 3:}) effect of the number of trees ($T$) in a forest on $mAR$ and $mAP$. \fi (\textit{Column 3:}) comparison of the modeled and non-modeled training dataset, using $mAR$ and $mAP$ as a function of number of training synthetic depth frames ($F$). For the evaluation,  a random number of 65 real-world test depth maps were used.}
\label{fig:evaluation}
\end{figure*}

\section{Implementation Details}

For our implementation, we used  a desktop with Intel i7-2600K CPU at 3.40GHZ, 4GB RAM with an operating system installed on a SSD. A fixed rectangular feature patch of size ($w,h$)=($64,64$) is used for the whole training process. Each frame generated from a KINECT sensor is of size $640\times480$.

For training our RDF classifier, we used the following parameter setup: (a) tree depth (D=19); (b) number of trees in a forest (T=5); (c) number of features extracted from a rectangular patch (PC=300); (d) number of synthetic scenes (i.e. depth frames) used for training (F=1600/tree); (e) 100 thresholds per feature and 100 feature response samples in the node optimization, along with bagging approach.

\section{Experimental Evaluation}

The goal of this work is to improve the segmentation performance in real-world scenarios, for this reason we use only real-world data, a random number of 65 test depth maps for evaluation. We acquire RGB-D data from a real-world RGB-D sensor, which is placed on the ceiling in the center of our shared workspace. See Fig.~\ref{fig:dataset_noise_evaluation} (\textit{column 1}) for real-world depth maps.

We evaluate the impact on the segmentation performance  for three cases: (Section \ref{subsec:noise_eval}) what amount of noise should be added to synthetic data to make the synthetic data consistent with real-world; (Section \ref{subsec:feat_eval}) effect of the split function of RDF on classification performance; (Section \ref{subsec:comp}) comparison between modeled and non-modeled training dataset (i.e. with and without density function) through training an RDF classifier, when evaluated on real-world.

In order to evaluate the effect of modeling human \& object interactions, we show two variants of our method: a modeled version that takes into account these interactions and non-modeled version that ignores them. In addition, we compare against state-of-the-art approaches~\cite{cvpr,Shotton}. Note that, all our experiments are conducted on real-world test data (see Section~\ref{subsec:comp} and Section~\ref{subsec:demon}). For the performance measures, we generate mean average of recall (mAR) and precision (mAP) for each single object class and a combined average of all classes.

\subsection{Data Collection} A scene is a single frame where there is a single object or a combination of multiple 3D objects arranged and oriented in a particular configuration based on the density function. The scenes are composed of complex configurations of humans and objects. We generated an extensive synthetic dataset of RGB-D data using the 3D scene models of 4 different rooms based on industrial workspace and office domain with a total of 10 object classes in the virtual environment~\cite{vrep} (see Fig.~\ref{fig:dataset}). In case of training we use solely synthetic depth data, and in case of the testing data for the evaluation step, we use synthetic and real world depth data. The object classes are: human body-parts (head, body, upper-arm, lower-arm, hand, and legs), table, chair, plant, and storage. We generated the synthetic human body-parts data via using a real-world KINECT multi-sensor setup with a 3D multi-color human model in the virtual environment. For more detailed information about human data generation, refer to previous work~\cite{Me,icml}. We synthesized a dataset of 20,000 frames using density function for $H-O$ and $O-O$ relationships. The synthesized scene has a depth map ranging between $0-3.5$ meters. Each scene is assigned  a set of human, chair, plant, storage, and table object class. Each frame is a stationary image, having no kinematic or temporal information. We use the 3D models of objects based on industrial workspace, with 4 instances for each object class: \textit{chair with and without handles; rectangular and round table; flowers and plants within pot; shelves and wardrobes}.  The modeled scenes are commonly seen in the real-world. In Section~\ref{subsec:comp}, we show that training with synthetic modeled data is sufficient for the generalization of the classifier in regard to real-world data.

\subsection{Noise Evaluation} \label{subsec:noise_eval}
Additive white Gaussian noise using a standard deviation ($\sigma$) was added to synthetic training depth data for compensating with noise in real-world, by matching the camera output as close as possible, and have an approximately good segmentation.  The results in Fig.~\ref{fig:evaluation} (\textit{column 1})  implies  that using this setting for generating
synthetic data results in the best performance, when the synthetic depth frames with additive white Gaussian noise using a standard deviation of 15 cm is used.

\subsection{Feature Response Evaluation} \label{subsec:feat_eval}
The results in Fig.~\ref{fig:evaluation} (\textit{column 2}) show that  most of the gains occur for linear feature response and is  $\sim$10\% more than the axis-aligned. The qualitative results obtained from axis-aligned feature response produced overconfident predictions, while for linear feature response it was also more visually pleasing results.

\subsection{Comparison of Models} \label{subsec:comp} In order to encode a consistent modeling of $H-O$ and $O-O$ interactions, we preferred occlusion of boundaries for maintaining as accurate as possible relevance with the real-world. This relationship between objects employ an euclidean thresholded distance $\theta^{'}$. We used a rank based approach for up to what level the occlusion should be allowed, $\theta^{'}$=\{0, 5, 10, 15, 20, 30, 50\} in \%. We found out that as $\theta^{'}$ is increased, the testing predictions increases monotonically  up to $\theta^{'}$=30, and just after that there is a sharp descent in both quantitative and qualitative performance. Therefore, we preferred $\theta^{'}$=30\%  for overlapping of boundaries, and our dataset was synthesized.

As a baseline, we implemented the same  state-of-the-art (SOA) pipeline used in \cite{cvpr,Shotton} for the generation of the non-modeled dataset, based on top-view (see Fig.~\ref{fig:dataset_non}). In a non-modeled scene, there is only a single 3D object in a scene with a particular configuration. In our case, there are multiple 3D objects in a scene which are modeled using a density function. We compare the results obtained with our modeled dataset against the results obtained by their non-modeled dataset using a number of frames for training the classifier. The results depicted in Fig.~\ref{fig:evaluation} (\textit{column 3}) thereby show  a steep ascent with a higher $mAR$ and $mAP$  for our proposed modeling. The density function based modeling, substantially improves the performance by  $\sim$7\% in  $mAR$ and $mAP$ over state-of-the-art~\cite{cvpr,Shotton}. Fig.~\ref{fig:dataset_noise_evaluation} (\textit{row 1}) shows the reduced misclassification around the border of the human body with better classification of the human-hand placed on the table and the chair for modeled case.

\begin{figure}[htb]
\centering
{\includegraphics[width=0.832\columnwidth]{data_collection.pdf}
\includegraphics[width=0.832\columnwidth]{plant_storage.pdf}
\includegraphics[width=0.83\columnwidth]{table_chair.pdf}
} \vspace*{-0.15cm}
\caption{ Non modeled training dataset.  Ground truth labels  and its corresponding synthetic depth data generated using the same pipeline used in \cite{Me,Shotton}.} \label{fig:dataset_non}
\end{figure}

\begin{table*}[t]
\caption{Confusion matrix-based mean  average F1-measure} \vspace*{-0.15cm}
\centering
\resizebox{16cm}{!}{
\begin{tabular}{|r|l l l l l l l l l l l|}
  \hline
 F1-measure & Avg  & Head& Body & UArm & LArm & Hand & Legs & Chair & Plant & Storage & Table \\
  \hline
$CRF_{Non-Modeled}$~\cite{cvpr,Shotton} &{0.76} &0.90 &0.71 &0.73 &0.65 &0.69 &0.48 &0.85 &0.78 &0.90 &0.91 \\
$CRF_{Modeled}$ &\textbf{0.84} &\textbf{0.96} &\textbf{0.84} &\textbf{0.79} &\textbf{0.70} &\textbf{0.79} &\textbf{0.52} &\textbf{0.93} &\textbf{0.90} &\textbf{0.98} &\textbf{0.97}\\
  \hline
\end{tabular}}
 \label{table:optimization_precision}
\end{table*}

\begin{figure}[b]
\centering
{
\includegraphics[width=0.87\columnwidth]{rdf_crf.pdf}
} \vspace*{-0.15cm}
\caption{ (\textit{Row 1:}) prediction results for real-world test depth data using the modeled and non-modeled training dataset. The segmentation improvements can be seen in the modeled case: the misclassification around the border of the human has diminished significantly; the human hand placed on the table and the chair are classified well with reduced mislabeling. (\textit{Row 2-3:}) shows the predictions obtained from the RDF classifier and the CRF modeling.} \label{fig:dataset_noise_evaluation}
\end{figure}

\subsection{Demonstration} \label{subsec:demon}
We use the integrated system for segmentation in our shared robotic workspace in real-time using real-world depth data. Fig.~\ref{fig:dataset_noise_evaluation} (\textit{row 2-3}) shows the prediction results for the proposed CRF model using real-world test data.  Table~\ref{table:optimization_precision} shows that the  F1-measure for the CRF  modeled  improves by  $\sim$8\% over the non-modeled ones~\cite{cvpr,Shotton}. For training the model with our optimized RDF parameter setup takes 43 minutes, and testing the model takes 34 ms.  Each block of our pipeline (i.e. scene modeling using a density function, fine tuning the additive noise in the synthetic data, selection of  the linear feature response for the RDF tree training, modeling the labeling problem on a CRF to minimise the misclassification of labels)  contributes  to the good results, and plays a crucial role in improving the segmentation performance. The resulting system is computationally efficient, robust and supports real-time for our targeted application.

\section{Conclusion}

In this paper we described an inexpensive and effective way to model the human-object and object-object interactions in order to improve  segmentation performance. For this purpose, we proposed a density function to model a 3D scene in a virtual environment for synthesizing a dataset incorporating human-object and object-object interactions, that is consistent with the real-world scenarios. Our proposed density function models spatial layout of object, object pose, object orientation, object arrangement, object interaction and relationships between the object classes i.e. none, partial, and full occlusion by each-other. Our experiments are based on industrial workspace and office domain with a total of 10 object classes. Our goal of modeling scene using density function improves the real-world scene segmentation performance measures by  $\sim$7\% in mean average precision and recall over state-of-the-art non-modeling based segmentation methods.  Our integrated resulting  system is computationally efficient, robust and supports real-time for our targeted application. In future work, we would like to extend this work towards  the semantic image understanding and image-sentence generation~\cite{Fei,Farhadi} for manufacturing and automation industry of challenging environments for safe human-robot interaction and collaboration.

\section*{Acknowledgment}

We would like to thank Jos{\'e} {Oramas M}, Michele Fenzi, Frank Dittrich and Stephan Irgenfried for their valuable suggestions.

\normalsize
\bibliographystyle{ieee}

redpajama_set_name: RedPajamaArXiv


Romantic Loft in the heart of Porto in a very quiet area , but at the same time , also in the center.
Hello, my name is Jorge and I'm passionate about Porto, I'm often a Tourist in my own city, I know a lot of night spots, Restaurants, panoramic views. I like Trailrunning, Surfing, photography and dining out whenever I can.


The underhanded C Contest is an annual contest where developers are asked to write code which is clear, readable and as straight forward as possible while doing something evil in a very subtle manner. The goal behind the contest is to make people aware that even code that looks completely benign can be doing something bad either intentionally or accidentally. The contest has been around for 7 years and the solutions people have come up with are amazing. This years winner was Karen Pease and trying to understand their code made my head hurt.
The year (or rather, the number 0) is written to the audit record three times. This overfills the audit buffer.
Thus the final AUDIT call zeroes out a user's created time, if the user was surveilled.
Looking at this code, it explains how difficult it is to prevent a skilled programmer from adding backdoors or Easter eggs in software and why code reviews are so important.


Here's how a wet, cold, miserable Memorial Day weekend might make you happier than the one you're imagining.
I know what Memorial Day is supposed to look like.
And maybe–bear with me for a minute–that could actually make you happier.
Here's the thing about vacations and holidays: they're weighed down by the burden of expectations. That means times we really really feel like need to be happy have to struggle even harder to get there (birthdays and holidays merit a whole chapter in How to Be a Happier Parent).
So when we've got the sunshine and the barbecue and the waving flags, we're often not appreciating it, because we're too focused on what we don't have—the red-and-white striped straws, say, or the sister-in-law who is still speaking to us, or the child who isn't having a tantrum or buried in her iPhone.
But if what we've got instead is rain and cold and the depressing prospect of boiled hot dogs instead of hot off the grill, we can let those expectations go–and more importantly, we've got a set-up for a Memorial Day you'll actually remember. The more epic the so-called fail, the more likely you and your family are to hold that day forever in your minds–the wind that flips over the picnic table, the kids who were brave enough to go in the water in spite of blue lips and goose bumps, the way Grandpa's truck looked after that hailstorm.
Of course, I don't wish any of that for any of you. But if it happens, try to revel in the horror and the humor, and know that you're making memories for the long haul.
If you're fortunate enough to get the blue skies and the sweetest watermelon and the picture-perfect picnic, you'll have to work harder to remember it. Not that you'll be complaining.
Think back to a time when you really wanted this, or forward to a time when it will be gone.
Slow down. Take deep breaths. Look around, and flag details: smells, sounds, the way your body feels right now.
Tell those you are sharing the experience with how much you value it, and them, and your time together.
Set out to savor the moment, and take time to cement those memories as the day ends.
It's Memorial Day, after all. Remember those who serve and served—and make some memories, too.


Lusaka, Zambia, Nov 20 2015 (IPS) - When health officer Kennedy Mulenga was faced with a male patient developing breasts at the remote Ngwerere Clinic 30km north of the Zambian capital, Lusaka, he logged onto Virtual Doctors to get help solving the medical mystery.
After taking notes and creating a patient file he took a photo with the camera in his computer and then uploaded all the information to the Internet cloud.
A UK-based doctor then reviewed the file and offered diagnostic advice: the HIV-positive patient was experiencing gynecomastia, the swelling of breast tissue and a side effect of antiretroviral medicine, but was in no danger.
Virtual Doctors uses so-called telemedicine software to improve local primary healthcare in rural areas lacking doctors. It was started by former safari guide Huw Jones, now based in the UK developing the technology.
Zambia has roughly 1,600 doctors for a population of about 14 million, of whom about 9 million live rurally, and relies heavily on remote healthcare workers like Mulenga. He has worked for six years at Ngwerere Clinic, using Virtual Doctors for three years.
Mulenga said he remembered another patient arriving at the clinic with a growth at the base of the spine. Mulenga debated whether the abscess just needed piercing and draining or whether that might risk paralysis, so he consulted a virtual doctor who confirmed specialist surgery was required. The patient had the surgery and then recovered.
In many developing countries, including parts of Africa and Asia, communication infrastructure has not followed the same path as in the West. There, countries began with fixed networks and moved to mobile networks, but developing economies have skipped a phase and gone straight to mobile.
This is having a particular impact in healthcare where innovative practitioners are using connectivity to change how care is delivered to patients. Virtual Doctors is not alone in using the power of mobile Internet to bring healthcare solutions to Africa.
In South Africa, telemedicine has gained wide acceptance with numerous projects springing up, such as one by Telemedicine Africa, to establish private telehealth centres across the country to reduce patient waiting times in an efficient and cost-effective manner.
A telemedicine project by the University of Lagos in Nigeria, sponsored by the ministries of education, health, and science and technology, enables a patient accompanied by a Nigerian doctor to see and speak with an Indian medical consultant via an interactive screen for additional advice to diagnose an ailment.
While in Monrovia, to help slow the spread of Ebola in West Africa, a robot created by Vecna Technologies using a two-way communication system, basically a tablet computer on wheels, enabled doctors to assess incoming patients without physically examining them, reducing potential infections in healthcare workers.
The idea for Virtual Doctors followed a life-changing experience for UK-born Jones while a safari guide in 1998. Driving through the Zambian bush, he came across a blood trail on the ground. He followed it for 5km and caught up with a heavily pregnant woman slumped on the bicycle her husband was pedalling frantically. The woman and her unborn baby died in Jones's jeep on route to hospital.
Now supporting six clinics, the plan is to include another 13 by early 2016 and serve between 12,000 and 17,000 patients each. Jones spent this September in Zambia visiting health officers and handing out new tablet computers to replace the original children's netbooks issued because they were easy to use and robust.
He also showed health officers how to use new software, developed during the previous 9 months by a team of nine volunteer software engineers from UK-based Landmark Information Group, a company specialising in digital mapping, property and environmental risk information.
Something simple can have a significant impact. A common problem in rural Zambia is people with suspected tuberculosis must travel to typically distant hospitals to see radiographers for X-rays. But often a radiologist isn't available to interpret an X-ray and a patient must wait, paying for accommodation after already forking out for transport.
This can total about 50 kwacha about 5 dollars, a significant sum for those earning about 1 dollar a day. Through Virtual Doctors patients can immediately return to villages with X-rays sent to international radiologists who confirm the presence of TB or not.
Mwewa explained how the clinic only has a simple laboratory and before Virtual Doctors he was limited to simple tests. "Now I can think outside the box, " he said.
With so few doctors, Zambia needs all the medical expertise it can get.
Chizema is also encouraged, she said, by the opportunity through Virtual Doctors to involve overseas Zambian doctors, many of whom get in touch and say they want to help their homeland.
Currently run as a charity, Jones runs endless rounds of fund raising and seeking donor patronage. He also pointed out how the times are changing.
Hence Jones is liaising with the Zambian government, working toward it taking more ownership of the service provided by Virtual Doctors.
Jones said he appreciates such vocal support but cannot help wondering when, or even if, it will turn into tangible action.
If Virtual Doctors became self-sustainable in Zambia that would enable Jones to set his sights elsewhere. "We've proved the concept, now we could take this anywhere in Africa," he said.


At the core of The Fibre Company's philosophy is the commitment to sharing a heartfelt passion and creating yarns that are, at heart, as nature intended. Using a combination of a variety of natural fibres, they recognise the fragility of the landscapes that inspire their yarns whilst paying particular attention to the footprint from producer to mill to warehouse and everthing in between. Their sublime range of gorgeous yarns mirror the company's commitment to produce product of the highest qualtity and to bring authenticity to everything they do.


text:
Q: signalr with .net core 2.0 I'm trying to use SignalR with core 2.0. If I set transport to LongPolling it works, but I get warnings about timeouts. I'm trying to use it with WebSockets, but I get

WebSocket is not in the OPEN state

I checked lots of similar questions, in most of them it's due to system restrictions, but I'm using Windows 10 and Chrome v.67.
I think the problem is in Startup.cs:
public void Configure(IApplicationBuilder app, IHostingEnvironment env)
{
    app.UseCors("CorsPolicy");
    app.UseSignalR(routes =>
    {
        routes.MapHub<NotifyHub>("/notify");
    });
}

public void ConfigureServices(IServiceCollection services)
{
    services.AddCors(o => o.AddPolicy("CorsPolicy", builder =>
    {
        builder.AllowAnyMethod()
               .AllowAnyHeader()
               .WithOrigins("http://localhost:52170/");
    }));
    services.AddSignalR();
}

Front(TypeScript):
ngOnInit() {
        let builder = new HubConnectionBuilder();
        this.hubConnection = builder.withUrl("http://localhost:52170/notify", HttpTransportType.WebSockets).build();//'http://localhost:1874/notify').build();
        this.hubConnection.serverTimeoutInMilliseconds = 5000;
        this.hubConnection
            .start()
            .then(() => console.log('Connection started!'))
            .catch(err => console.log(err)); //'Error while establishing connection :('));
        console.log(this.hubConnection);
        this.hubConnection.onclose = e => {
            this.hubConnection.start();
        }
   }

I get these two messages from browser:

Information: WebSocket connected to
  ws://localhost:52170/notify?id=SJ4cb49oWZtL6lIt4cqhKg WebSocket is not
  in the OPEN state

A: @Jamil, best on your code i have created on project and it is working. Can you provide more details or compare with below code. Let me also know if there is any different.
-- In Startup.cs
public void ConfigureServices(IServiceCollection services)
{
    services.AddMvc();

    //DKS: Makes the SignalR services available to the dependency injection system.
    services.AddCors(options => options.AddPolicy("CorsPolicy",
    builder =>
    {
        builder.AllowAnyMethod().AllowAnyHeader()
               .WithOrigins("http://localhost:53249")
               .AllowCredentials();
    }));

    services.AddSignalR();
}

// This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
public void Configure(IApplicationBuilder app, IHostingEnvironment env)
{
    if (env.IsDevelopment())
    {
        app.UseDeveloperExceptionPage();
        app.UseWebpackDevMiddleware(new WebpackDevMiddlewareOptions
        {
            HotModuleReplacement = true
        });
    }
    else
    {
        app.UseExceptionHandler("/Home/Error");
    }

    app.UseStaticFiles();

    app.UseCors("CorsPolicy");
    app.UseSignalR(routes =>
    {
        routes.MapHub<ChatHub>("/chatHub");
    });

    app.UseMvc(routes =>
    {
        routes.MapRoute(
            name: "default",
            template: "{controller=Home}/{action=Index}/{id?}");
    });
}
}
}

--- home.component
import { Component, OnInit } from '@angular/core';
import { HubConnection } from '@aspnet/signalr';
import * as signalR from '@aspnet/signalr';

@Component({
    selector: 'app-home-component',
    templateUrl: './home.component.html'
})

export class HomeComponent implements OnInit {
    private _hubConnection: HubConnection | undefined;
    public async: any;
    message = '';
    messages: string[] = [];

    constructor() {
    }

    public sendMessage(): void {
        const data = `Sent by you: ${this.message}`;

        if (this._hubConnection) {
            this._hubConnection.invoke('SendAngular', data);
        }

        this.messages.push(data);
    }
    ngOnInit() {
        let builder = new signalR.HubConnectionBuilder();
        this._hubConnection = builder.withUrl("http://localhost:53249/chatHub", signalR.HttpTransportType.WebSockets).build();//'http://localhost:1874/notify').build();
        this._hubConnection.serverTimeoutInMilliseconds = 9999999999999;
        this._hubConnection
            .start()
            .then(() => console.log('Connection started!'))
            .catch(err => console.log(err)); //'Error while establishing connection :('));
        console.log(this._hubConnection);

        this._hubConnection.on('Send', (data: any) => {
            const received = `Received by Friend: ${data}`;
            this.messages.push(received);
        });

        //this._hubConnection.onclose = e => {
        //    this._hubConnection.start();
        //}

    }
}

using Microsoft.AspNetCore.SignalR;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading.Tasks;

namespace SignalRExample.Hubs
{
    public class ChatHub : Hub
    {
        public Task SendAngular(string data)
        {
            return Clients.All.SendAsync("Send", data);
        }
    }
}

A: comment this line "this.hubConnection.serverTimeoutInMilliseconds = 5000;" and check again.
serverTimeoutInMilliseconds is too small.

redpajama_set_name: RedPajamaStackExchange


The goals are surely similar. And some of the skills, responsibilities and aptitude demanded of each, including the ability to conduct a deep analyzation of their "team" abilities, talents, and options, are much the same.
At initial contact, many of my Borrowers, especially those that are First-Time Home Buyers, have a preconceived notion about the type of loan they're seeking. This is usually a result of their having performed an internet search ... or friends and family members have shared their financing experiences with them.
While both can prove insightful, it can also lead to my clients having a pretty narrow or myopic focus during the early stages of their mortgage search. When that occurs, I remind them that an open mind and willingness to consider all mortgage options (even unknown options or those previously dismissed) is important at this crucial stage in the mortgage process.
Originators, just as coaches, are capable of offering their clients the best options available only after gathering and analyzing the specific attributes and strengths of their "players" in the game. And often those options end up not being what the Borrower initially thought they'd be.
No-Money Down Loan Program guaranteed by the United States Department of Veterans Affairs (VA) for Military Personnel (Active and Former Military Veterans, Reservists, and National Guard).
The Spouses of military members that died while on active duty or as a result of a service-connected disability may also apply.
3. Portfolio Funds/Loans - Banker/Lenders originate the loan, retain it in a "Portfolio Fund"/do not sell the loan in the Secondary Market, and service it.
Borrowers must remember: All loans are created and formed in their own unique way. All Borrowers are different.
In all my years as an Originator, I've never seen clients with identical financial and lending scenarios. I don't believe I ever will.
Choosing and receiving the "best" mortgage for your needs is all about having as many options as possible to choose from.
1. Seek the advice of a Mortgage Originator as soon as you decide you want to buy (or refinance) a home. The earlier in the process, the better.
2. Work with an Originator that will conduct a thorough analysis of your finances and credit ... and then follow through on the results found.
3. Align yourself with an Originator that develops and maintains a "team" concept leading up to Pre-Qualification and throughout your mortgage processing. They will be your advocate, keeping your best credit and financial interests in mind past Closing and in the future.
The team concept (Borrower, Originator/Lender, Realtor, Underwriter) is key to finding the right loan, completing a successful transaction, and enjoying your home. And that's a "win", right?
* Hoping to Buy or Refinance a home in New Lenox or elsewhere in the Chicagoland area? Contact Me! I'll put my 40 years of Mortgage experience and expertise hard to work on your behalf.


Captain Brad was born and grew up on a river and was always told by his friends that he should be a captain. He has now been fishing the Florida waters professionally for 17 years around areas such as the world-famous Mosquito Lagoon, the coast line running from Daytona to New Smyrna beach, and offshore deep fishing up to 40 miles. For the last 11 years he has devoted himself full-time to offering guided charters, doing what he loves. He uses all the knowledge he has learnt over the years to make sure you're in with the best possible chance of finding fish.
Capt. Brad loves artificial lure fishing the most ,but he loves live bait fishing as well especially when the bite is down. He finds having the right bait and presentation is the most important aspect of fishing.
Shark and Tarpon right now is a blast!
Offshore the Bite is great!
Redfish Bite is on !
It was a beautiful day but maybe too nice. Recent weather had kept the waters warmer than usual and the fish were spread out. Capt Brad took up into a creek where many do not go. We caught and saw redfish, caught a sea trout and small snappers. Saw several fish working. Capt Brad is very knowledgeable of the area and works hard to put fish on the line.
I took my 13 year old grandson fishing for his first time. Captain Brad was very patient and showed him how to hold the rod and cast the line. By the end of our short trip he was able to cast with confidence. He even reeled in 3 fish and had a good experience. We will go back out with Captian Brad in the near future.


Elegance Roller Blinds are custom made quality blockout blinds designed to complement popular Dulux paint colours. Blinds can be front rolled (to suit recess fit) or back rolled (to suit face-fit).
Stated blind widths are INLCUDING brackets. Fabric width is 176cms.


Another sample to full-size purchase: Snowise EX Brightening Serum Price: $200/1.7oz Product Claims: "Brightening serum that rejuvenates skin exhausted from the heat and UV light for a translucent complexion" "white ginseng saponin suppresses formation of melanin…prevent skin from forming dark spot…carefully maintaining healthy skin" Ingredients: Multiple plant extracts "Whitening properties" - licorice root, Albiflora Root, Honey, Hydrolyzed Ginseng saponin, Lilium Candidum "Anti-oxidant and antiaging properties" – Ginseng, Ginko Biloba, Honey, Paeonia albiflora root Review The product was a clear and light serum, with a herbal scent, not overbearing. It feels cool on the skin and spread quickly with no residue. I use it after my actives (Vit C and AHA), it provides enough hydration to lock in previous actives and prepares your skin to boost further products. It has not caused any irritation or redness on my sensitive skin. I have noticed my skin becoming slightly fairer, and radiant and even tone. A whitening serum is a must in my routine for treatment of existing pigmentation and to prevent future brown spots. Pros/Cons The only downside is the price. This is certainly a big investment, however I never would use 'radiance' to describe my skin prior to this product. Certainly pigmentations are improving without any obvious additional ones forming. Prices are comparative to my other favourite, Tatcha Radiant Brightening Serum ($185/1.7oz), and Amore Pacific Luminous Effect Brightening Serum $240/1oz (have not tried this) What are your favorite whitening serums?
I am in love with the @sulwhasoo.us Brightening serum BB. This is formulated with white ginseng, which brightens skin and white cloud grass, which protects skin from light and heat. I tried 'natural beige'. I wasn't really sure to apply it as a serum or as a bb cream. In the end, I used this as the last step of my morning skin routine. It has a silky texture, glides on smooth, and set almost immediately like a serum. With a pea size amount, my skin is dewy but not greasy, and it brightened my skin and gives just enough coverage for redness, discoloration and uneven skin tone. It helped to minimize pores and gives a translucent appearance to skin and SPF 50 coverage. The stay power is pretty impressive after a ten hr day of work. $60 US for 1.01 oz, fairly comparable price for a luxury spf, and it has all the goodness of a brightening product! Have you tried this before?
The essence soakes into the skin and deals with uneven skintone and dull skin, the texture is pretty thick but it's absorbing and smells really good. Improves dark, dull and redness so it makes my skin white and clear.


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Learning Bartitsu
The Core Tactics of Bartitsu
Canonical Bartitsu
Extending Bartitsu
How to Start a Bartitsu Club
Pugilism
by Barton-Wright | Nov 14, 2021 | glossarium
Pugilism is a name for boxing which typically refers to the un-gloved bare knuckle art from Britain. Known as other names including classical pugilism, fisticuffs and the noble science of defence, it has a long a varied history going back hundred of years. The early...
by Barton-Wright | Oct 27, 2021 | glossarium
Wing Chun, also known as Ving Tsun or Wing Chun Kuen is a Southern Chinese Kung Fu style. This self defence system is a concept-based art which requires quick arm movements and strong legs to defeat it's opponents. Practitioners typically hold a high, narrow stance...
Defendu – MMA to WW2
Defendu is a combatives system developed by William Fairbairn and Eric Sykes during the 20th century, also referred to as the Close Quarters Combat System. Combing boxing, wrestling, Savate, Jujutsu, Judo and street fighting, Fairbairn developed his system initially...
What was Sherlock Holmes fighting style?
Baritsu was the name given to the martial arts used by Sherlock Holmes to defeat Moriarty as described by Conan Doyle, the author of Sherlock Holmes. A misprint or perhaps a deliberate misspelling, Baritsu is correctly known as Bartitsu, a scientific mixed martial art...
Glima – Nordic Wrestling
Glima is a folk wrestling style, or styles which have Nordic origins. It is practised both as a sport and combat system and has a wealth of rich history. It's history can be found back as far as the Norwegian settlement of Iceland. Known also as Viking Glima...
The Bartitsu Club
Welcome to the home of Bartitsu. The Bartitsu club webpage seeks to preserve, extend and promote the art of Bartitsu.
Here you will find pages about learning Bartitsu, it's history and a selection of resources around the art.
We also promote the community with our directory of Bartitsu clubs and related events.
Bartitsucon 3
Bartitsu News
The Bartitsu Club © 2020 - Managed by Fitted Commerce

redpajama_set_name: RedPajamaCommonCrawl


An addiction can be anything from biting nails to snorting up heroin but the difference is that, some may cause a lot more harm than the other. Biting nails is of course a bad habit and it can result in appendicitis but snorting heroin can mess up with everything from your nervous system to your skin so some addictions need to be addressed more carefully.
There are many side effects involving substance abuse addictions and the same goes for addictions such as smoking too but there are many ways that are being used currently. The methods that have been implemented to help people overcome their addictions by using various methods is a great way to help yourself beat an addiction. For an example, In order to stop smoking, hypnosis is being used and many other new methods too have shown promising results. Listed below are a few ways that you could use to help beat whatever addiction that you are facing.
Before the readers start freaking out and stating their dislike towards group therapy and support groups, I would like to inform that what we are talking about is a support system that could be anyone of your family members or your significant other. A proper support system is absolutely essential when you are trying to kick a bad addiction. Keep yourself surrounded by people who will motivate you when you feel like relapsing back into your old habit.
When you are going through the process of trying to kick this habit, the best method is to keep yourself distracted from whatever it is that you are trying to avoid. If one wants to stop smoking hypnosis Sydney and other methods might work but there will be times when you will feel the need to go for another cigarette so in moments like that, distract yourself by switching on your television or reading a good book.
It is said that most people who drink, drink to forget about certain memories but even if you drink yourself silly on a night out and you will forget all the bad memories, they will still come back to you in the morning when you wake up with a big hangover. You could looking for stop drinking hypnosis Sydney or a psychologist at your local hospital and you will get all the help you need in order to get rid of your addiction.
The tips provided above are good for you if you are planning on beating any sort of addiction.


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Kindle is a hand-held portable device intended to make it easy to read books and watch movies supplied by Amazon. The Kindle Fire is a new and improved Kindle with a color display and more capability.
The Kindle Fire is a tablet computer version of Amazon.com's Kindle e-book reader. Announced on 28 September 2011, the Kindle Fire has a color 7" multi-touch display with IPS technology and runs a forked version of Google's Android operating system. It includes access to the Amazon Appstore, streaming movies and TV shows, and Kindle's e-books. It was released in the USA on November 14, 2011.
The device sells for US$199. It has been suggested that Amazon's business strategy is to make money on selling content through the device, as well as the device acting as a storefront for physical goods sold through Amazon.
The Kindle Fire is equipped with a 1 GHz Texas Instruments OMAP 4430 dual-core processor. The display is a 7 inches (180 mm) multi-touch color screen with a 600×1024 pixel resolution. Connectivity is through 802.11n Wi-Fi and USB 2.0 (Micro-B connector). The device includes 8 GB of internal storage — said to be enough for 80 applications, plus either 10 movies or 800 songs or 6,000 books. According to Amazon's list of technical details, the Kindle Fire's 4400 mAh battery sustains up to 8 hours of consecutive reading and up to 7.5 hours of video playback with wireless off.
The Kindle Fire is running a customized Android 2.3 Gingerbread OS. Besides access to Amazon Appstore the Kindle Fire includes a cloud-accelerated "split browser" called Amazon Silk using Amazon EC2 for off-device cloud computation; including webpage layout and rendering, and Google's SPDY protocol for faster webpage content transmission. The user's Amazon digital content is given free storage in the Amazon Cloud's web-storage platform, and a built-in email application allows webmail ( Gmail, Yahoo!, Hotmail, AOL Mail, etc.) to be merged into one inbox. The subscription-based Amazon Prime, which includes unlimited streaming of movies and TV shows, is available with a free trial period. The current version of the Kindle Fire OS as of November 29, 2011 is 6.2_User_3003020.
The new Kindle will give you access to almost 20 million movies, television series, songs and books. But you will also be able to download lots of applications from Amazon's very own App Store. You can expect to see all the popular applications and games in there, such as Fruit Ninja and Angry Birds.
The Fire tablet comes with Amazon's own Silk browser, which will allow you to browse the web quickly. Amazon's servers are doing all the heavy lifting when it comes to browsing the web. This means that the hardware in the Kindle Fire will be spared from having to perform heavy calculations. Amazon's servers build the pages for you and the Kindle Fire receives one data stream which displays the page.
With your Kindle Fire, you also receive free cloud storage for everything that you buy from Amazon with your tablet. What you buy will always be yours to store and retrieve on the cloud hosting that Amazon offers you with your Kindle.
The Fire tablet has a 7" touch screen display with a very impressive, high resolution (1024 x 600 pixels) display quality (169 pixels per inch).
Thanks to the IPS (in plane switching) technology, the display has a very wide viewing angle.
The Kindle's display has undergone a chemical process to strengthen it. The result of this is the display is 30 times harder and 20 times stiffer than regular plastic displays. The Kindle Fire's Gorilla Glass display is of very high quality and it will be able to take more than a few bumps without breaking or getting scratched.
The Kindle Fire also has a very powerful 1GHz dual core processor. Dual core processors have shown to greatly improve performance in mobile devices. The Kindle's powerful hardware is spared because of the fact that Amazon's servers handle the heavy lifting. This helps increase performance even more!
The Kindle Fire's dimensions are 7.5" x 4.7" x 0.45" and it weighs a mere 14.6 ounces. This makes the Fire pretty portable. It's certainly small enough to stuff in your bag so you can take it wherever you like. It's wireless connection to the Internet makes it as mobile as can be.
The new Amazon Kindle has 8GB of internal storage. That's plenty of storage for dozens of applications, movies, music titles and books. Let's see if you can read / view / watch / hear them all in the 8 hours of battery life that the Fire offers you!
The Fire has 3.5 mm stereo audio jack. You don't necessarily need to use a headset, because the Fire also has integrated speakers at the top.
The Kindle Fire also has very good support for Wi-Fi connectivity. You can surf the web wherever you like, whether you are at home and using a private Wi-Fi network or whether you are out of the house and making use of a public Wi-Fi network. Supported networks are: 802.11b, 802.11g, 802.11n and 802.1X. There is also support for WEP, WPA and WPA2 security.
The Kindle Fire tablet has a USB 2.0 micro-B connector, so that you can hook up your USB devices to it.
The Kindle Fire tablet allows you to stream music and movies straight to your tablet. You can store your favorite music on Amazon's Cloud Drive, which is accessible at all times. You can choose from no less than 17 million music titles from the Amazon MP3 Store. Movies and TV shows are available for renting, purchasing, streaming and downloading.
You can also read magazines on your tablet in magazine style: you can flip through the pages as if you are reading an actual paper magazine. Magazines are display in full screen, so nothing distracts you from you and your magazine. But it doesn't stop here: magazines on the Kindle Fire will have integrated video and audio. This is the future we're living in, after all!
Naturally, the Kindle Fire supports all the Android games that you've played and loved on other Android mobile devices.
Next to games, you'll also get access to all the applications you're used to. Weather prediction apps, navigational apps, social media apps, and more. Like with the Android Market, there is a small fee for applications and games.
Thanks to the integrated email app, you will be able to retrieve all of your webmail right to your Kindle. Whether you are using AOL, Yahoo!, Hotmail or Gmail… the Fire's email app can handle it and gives you a central place to retrieve, read and reply to all of your email.
The Fire's user interface has been specifically designed to be very intuitive and easy to use. You might think that when device has a touch screen, it is automatically easy to use. But not every mobile device implements touch screen input functionality as well as the rest. The Kindle Fire, however, is a great example of a touch screen user interface done the right way.
The Kindle Fire's powerful dual core processor no doubt helps a great deal in making the touch screen interface very smooth. All you have to do is tap & drag. Everybody can effortlessly navigate his or her way through the Fire's user interface. The whole world is at your fingertips. All your recently played music titles, all your recently watched movies, all your recently accessed games & apps… everything is always within reach.
Not all of Amazon's Kindle tablets support multi touch. Rest assured that the Fire does, in fact, support multi touch.
There are literally millions and millions of books available to you when you are the proud owner of an Amazon Kindle Fire tablet.
The great thing is that by far the most books available for the Kindle, will cost you $9.99 or less. And then there are the more than 2 million books that have no more copyright and which are available for free!
The Kindle Fire is available November 15th, 2011 through Amazon Digital Services in the USA only. It will be sold at the very competitive price of $199.
Read bestsellers, children's books, comic books, and cookbooks in vibrant color. The Kindle Store offers over 1 million books, including 800,000 titles at $9.99 or less. In addition, over 2 million free, out-of-copyright, pre-1923 books are also available.
The Amazon Kindle Fire is the first small tablet that average users can pick up and immediately use, with a simple, clear interface. Then there's the price: Android along with amazing specs for just $199.
Both the memory and battery are sealed in, and the only interruptions in its smooth, black form are the headphone jack, Power button, MicroUSB jack, and dual stereo speakers. There's no camera. It uses 802.11b/g/n Wi-Fi networks to get online; there's no cellular radio or Bluetooth connectivity.
Turn the Kindle Fire on and the 7-inch 1024-by-600 IPS LCD screen lights up. This display is very sharp and clear, but it's also rather reflective. You may have trouble reading in bright light because of the screen's sometimes mirror-like gloss.
The Kindle Fire packs a dual-core, 1GHz TI OMAP4 processor and runs a very highly customized version of Android 2.3.4 (Gingerbread).
Home page access includes Newsstand, Books, Music, Video, Docs, Apps, and Web. Most of the rest of the home screen is devoted to a Cover Flow-like carousel of your most recently used content, with four user-assignable favorites at the bottom.
Each of the seven sections gives you a virtual "bookshelf" of items stored on your Fire along with a link to Amazon's relevant store. Yes, this tablet is designed to make you buy stuff from Amazon. You don't have to—you can load your own files—but it's very, very easy to buy and arrange items from Amazon's many digital shops.
Most people are familiar with Kindle books, which read and sync well on the Fire. There are some new kinds of content in the bookstore for Amazon too, like color childrens books, for example. But they show up in landscape format.
The Music option lets you stream songs from your Amazon Cloud Drive or play files stored on the device. To get them onto your tablet, you can buy them from the Amazon MP3 store, or drag and drop or sync them from your PC. The Kindle Fire comes with free, unlimited cloud storage for anything you buy from Amazon. You can store non-Amazon files, too, but you only get 5GB; upgrading to 20GB costs $20 per year.
That cloud storage is very important. With only 6.5GB of free, onboard storage, you can only store three or four movies and some choice playlists on the tablet at a time. Everything else resides in the cloud locker, and you swap items in and out when you need more room.
Video lets you rent, buy or download movies or TV shows, or play Amazon Prime's subscription streaming video service. Docs lets you view documents you've sent to the Kindle's dedicated email address. Apps shows the apps you've downloaded from Amazon's app store or sideloaded onto the tablet, and Web loads the Silk browser.
The Kindle Fire handles MP3, AAC, and OGG music, including album art. For video, it plays H.264 and MPEG4 only, at resolutions up to 1080p. There's no Bluetooth stereo support, HDMI out, or way to connect the Kindle to a TV; Amazon would rather you play its cloud content through an Amazon-enabled set-top box like a TiVo.
Documentation Quick Start Guide (included in box); Kindle Fire User's Guide (pre-installed on device). Additional information available online.
Warranty and Service 1-year limited warranty and service included. Optional 2-year Extended Warranty available for U.S. customers sold separately. Use of Kindle is subject to the terms found here.
The switch to digital broadcast TV.


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Q: Spring Security SAML using multiple subdomains to access the same application (same IP) I have one application which is accessed via multiple subdomains, because of branding and support information which is dynamically shown.  We're moving to an SSO solution, which is now making this a little interesting.  Currently I added Spring Security SAML (1.0.1) into the Spring Security mix, and am using WSO2 Identity Server at the IdP.  It runs great.
Where were are having issue is preserving the subdomain of the Service Provider side when there are multiple DNS entries for the same address.  For example, let's say I have the following subdomains going to the exact same place, guy1.mistersite.com and guy2.mistersite.com.  When accessing the application for absolute first time when the app server is first started via guy1.mistersite.com, the AuthnRequest coming from the SP will have indicate in the AssertionConsumerServiceURL and Issuer that it is coming from guy1.mistersite.com.  Good.  Now the next access tried from guy2.mistersite.com, the AssertionConsumerServiceURL and Issuer will actually still have guy1.mistersite.com in the AuthnRequest.  Bummer.
Looking through the code, you can see it's effectively cached in
org.springframework.security.saml.metadata.MetadataGeneratorFilter.  When the processMetadataInitialization() method is hit, is checks whether the hostedSPName, entityAlias, and defaultBaseURL were set.  If they were, well, move onward.  That makes sense.
Is subclassing MetadataGeneratorFilter and have an override of processMetadataInitialization() really an effective and "safe" solution?
I already made a little proof of concept and commented out all the null checks so each value is reestablished each time this method is hit, so at a simple case this works fine.  I am wondering since this bean is essentially a singleton, what's going to happen when a whole of a different subdomain requests are coming at once.  Is there a chance stuff's going to get jumbled up?  Is the synchronized block in that method enough to protect against that consideration?
So yes, if this is a safe and sane way to approach this, like if I'm on the right track or not, let me know!  Anything else to think about.  What I am right about, what am I not? :)
We are considering altering the model of approach as far as the infrastructure and subdomains go, so I'd please like to stick on the topic of the viability of extending Spring Security SAML with a new class or not.
Gratitude in advance! ;)
Below is my quick fix on the method (just commented some stuff out basically):
    protected void processMetadataInitialization(HttpServletRequest request) throws ServletException {

        // In case the hosted SP metadata weren't initialized, let's do it now
//Forcing this to reload the host SP name every time to deal with various subdomains where a user could come to for the same app (think branding for partner companies)
//        if (manager.getHostedSPName() == null) {

            synchronized (MetadataManager.class) {

//                if (manager.getHostedSPName() == null) {

                    try {

                        log.info("No default metadata configured, generating with default values, please pre-configure metadata for production use");

                        // Defaults
                        String alias = generator.getEntityAlias();
                        String baseURL = getDefaultBaseURL(request);

                        // Use default baseURL if not set
//                        if (generator.getEntityBaseURL() == null) {
                            log.warn("Generated default entity base URL {} based on values in the first server request. Please set property entityBaseURL on MetadataGenerator bean to fixate the value.", baseURL);
                            generator.setEntityBaseURL(baseURL);
//                        } else {
                            baseURL = generator.getEntityBaseURL();
//                        }

                        // Use default entityID if not set
//                        if (generator.getEntityId() == null) {
                            generator.setEntityId(getDefaultEntityID(baseURL, alias));
//                        }

                        EntityDescriptor descriptor = generator.generateMetadata();
                        ExtendedMetadata extendedMetadata = generator.generateExtendedMetadata();

                        log.info("Created default metadata for system with entityID: " + descriptor.getEntityID());
                        MetadataMemoryProvider memoryProvider = new MetadataMemoryProvider(descriptor);
                        memoryProvider.initialize();
                        MetadataProvider metadataProvider = new ExtendedMetadataDelegate(memoryProvider, extendedMetadata);

                        manager.addMetadataProvider(metadataProvider);
                        manager.setHostedSPName(descriptor.getEntityID());
                        manager.refreshMetadata();

                    } catch (MetadataProviderException e) {
                        log.error("Error generating system metadata", e);
                        throw new ServletException("Error generating system metadata", e);
                    }

//                }

            }

//        }

    }

And portion of the security config:
    <bean id="metadataGeneratorFilter" class="org.springframework.security.saml.metadata.MetadataGeneratorFilter">
        <constructor-arg>
            <bean class="org.springframework.security.saml.metadata.MetadataGenerator">
                <property name="extendedMetadata">
                    <bean class="org.springframework.security.saml.metadata.ExtendedMetadata">
                        <property name="idpDiscoveryEnabled" value="false"/>
                        <property name="signMetadata" value="false"/>
                    </bean>
                </property>
                <!-- We leave these out and values are defaulted to entityId = url that user came in on
                and entityId = <entityBaseUrl>/saml/metadata.  We have to configure a SP in WSO2 for every
                subdomain we have -->
                    <property name="wantAssertionSigned" value="false" />
            </bean>
        </constructor-arg>
    </bean>

A: I have been googling around for this and it have seen two possible approaches. According to the spec you can sign the authentication requests as the SP in order to specify an AssertionConsumerServiceURL without the requirement that it was published and configured earlier as part of the SP metadata exchange, but I don't fully understand how to do that with Spring Security SAML extendtion. Here is the source of that information.
Another that I have been debating doing myself would be to override the buildSPSSODescriptor in MetadataGenerator and then look up the possible subdomains for that environment (we have that available in our database) and then I would loop over that list and create an entry for each one.
Like your way of doing it this last way seems like a hack as well. Hopefully this helps.
What did you end up doing?