text:
\section{Introduction}

Lifelogging describes an egocentric vision of the experiences of a person. Nowadays, the use of small wearable cameras, which capture images in certain intervals, is increasing considerably. Such images provide an overview of the daily activities of a person that can be interpreted as a visual log of the day. This information can be used to examine a person's pattern of behaviour; daily habits, such as eating habits, social interactions, indoor or outdoor activities, are recorded in such images. Although our mood is influenced by the environment and social context that surrounds us, egocentric data do not always catch our attention or induce the same emotion when retrieved. We consider that the creation of a diary of positive moments in an electronic way, by combining several cues, will help to improve the inner perception of the user's own life. Therefore, in this work we seek for \textit{positive moments} that can raise the user's positiveness.

Several experiments have been conducted in that direction, in \cite{Santos2013TheReview.} the authors presented a survey of positive psychology strategies that demonstrated to be potentially effective as tools for the treatment of depression. As an example, in \cite{Beck1986StimulatingConnotation.} the authors suggested to the participants activities such as \textit{walking in a park}, \textit{visiting a friend}, or \textit{going to the Student Union and saying hello to someone}, that resulted in participants' suffering to decline or disappear. Their study gives ideas about the type of moments that can retrieve positive feelings; nature, landscapes, friends, or smiling people staring at us, among others.

Sentiment analysis from images is a novel research field. Given the challenge of image sentiment recognition and the ambiguity of the problem, we analyse images sentiment assigning a discrete ternary sentiment value (positive (1), neutral (0) or negative (-1) value), similar to \cite{You2015RobustNetworks}. In the literature, sentiment recognition from images has been approached based on different types of data features. Attributes such as facial expressions are used for sentiment prediction in \cite{Levi2015EmotionPatterns,Yuan2013SentributeDescriptors}. The combination of visual and textual information from the images \cite{Wang2014MicroblogModel,You2016Cross-modalityMultimedia} appeared due to the wide use of online social media and microblogs. In such websites, images are posted with short descriptive comments by the user. Audio features were also included in the models presented by \cite{Nojavanasghar2016EmoReact:Children,Poria2014FusingContent}.

\begin{figure}[ht!]
\centering
    \includegraphics[width=1\textwidth,height=7cm]{Images/eurocastFig2Events.png}
    \caption{Example of events extracted from the photostream by applying the temporal segmentation method introduced in \cite{Dimiccoli2015SR-Clustering:Segmentation}. Each row of the figure represents an event of the day. Image events labelled as Positive (green), Neutral (yellow) and Negative (red).}
\label{fig:imagesexamples}

\end{figure}

Despite several works having approached the understanding of how people can be affected seeing images, the field of sentiment analysis from images has not been yet settled. The labelling of the images is not yet established, leading to different questions when addressing sentiment recognition from images. As examples, we can find available datasets labelled as: amusement, anger, awe, disgust, excitement, fear, sad \cite{Machajdik2010AffecitveTheory,You2016BuildingBenchmark}, Positive/Negative \cite{Borth2013Large-scalePairs}-Twitter, Positive/Negative/Neutral\cite{Dan-Glauser2011TheSignificance}, with values of Pleasure, Arousal and Dominance \cite{Lang1997InternationalRatings}, with sentiment values from -2 to 2\cite{Borth2013Large-scalePairs}, where the extremes correspond to Negative and Positive sentiments, respectively. Despite the above mentioned works, to the best of our knowledge, none of them has dealt with the sentiment recognition from egocentric photostreams.

Recently, with the outstanding performance of the Convolutional Neural Networks (CNN), several approaches on sentiment analysis have relied on supervised learning through deep learning techniques, such as \cite{Campos2015DivingPrediction,Levi2015EmotionPatterns,Ma2016GoingRecognition,You2016BuildingBenchmark}. One of the more remarkable approaches, in \cite{Borth2013Large-scalePairs}, introduced a Visual Sentiment Ontology (VSO), based on the Plutchik's wheel of emotions \cite{Plutchik.1980Emotion:Synthesis}, and a visual concept detector called SentiBank. The VSO is built by 3022 semantic concepts called Adjective Noun Pairs (ANP) represented by images from the social net Flickr with them as tags. The ANPs are composed by a pair of a noun and an adjective, with a sentiment value associated between [-2 : 2]. They defend that an object, according to its appearance has a different sentiment value associated to it, like 'lonely boat' (-1.43) and 'traditional boat' (1,37), or 'noisy bird' (-1) and 'cute bird' (1,37). They proposed the semantic concepts baseline classification based on visual features (RGB, SIFT, LBP, etc) extracted from the images. In \cite{Chen2014DeepSentiBank:Networks}, a Deep Neural Network named DeepSentiBank was trained on Caffe for the VSO semantic concepts classification. The authors relied on the concepts with higher number of images and with a classification accuracy associated. From the original 3022 concepts in \cite{Borth2013Large-scalePairs} they select 2089 in \cite{Chen2014DeepSentiBank:Networks}.

To the best of our knowledge, previous to our work \cite{Talavera2017SentimentPhotostreams} there was no approaches addressing sentiment recognition from egocentric photostreams. We propose to analyse the output of the DeepSentiBank per image as semantic representation. We defined a classification model where the one-vs-all SVM classifiers were trained and evaluated with the features describing semantic and global information from the images.

In this work, we approach the same problem from a different perspective. We analyse the relation to each other semantic concepts extracted from images that belong to the same scene. A scene is described as a group of sequential images related between them and describing the same event. Our contribution is an analytic tool for positive emotion retrieval seeking for events that best represent a positive moment to be retrieved within the whole set of a day photostream. We focus on the event's sentiment description where we are observers without inner information about the event, i.e. from an objective point of view of the moment under analysis.

The rest of the paper is organized as follows. In Sect. 2, we describe the sentiment analysis method and the features selection procedure. In Sect. 3, we describe the proposed dataset, while in Sect. 4, we describe the experimental setup, the evaluation, and discuss our findings. Finally, Sect. 5 draws conclusions and outlines future lines of work.

\section{Method}

\begin{figure}[ht!]
\centering
    \includegraphics[width=1\textwidth,height=6cm]{Images/eurocastFig1.pdf}
    \caption{Sketch of the proposed method. First, a temporal segmentation is applied over the egocentric photostream \textbf{(a)}. Later, semantic concepts are extracted from the images using the DeepSentiBank \cite{Chen2014DeepSentiBank:Networks} \textbf{(b)}. The semantic concepts with higher occurrence are selected as event descriptors \textbf{(c)}. Finally, the ternary output is obtained by merging the sentiment values associated to the event's semantic concepts \textbf{(d)}.}
\label{fig:SketchModel}
\end{figure}

Given an egocentric photostream, we propose scene emotion analysis seeking for events that represent and can retrieve a positive feeling from the user. We apply  event-based analysis since single egocentric images cannot capture the whole essence of the situation. By combining information from several images that represent the same scene, we get closer to a better understanding of the event.

\subsection{Temporal segmentation:}
\label{Temporalsegmentation}

We apply temporal segmentation on the egocentric photostreams using the proposed method in \cite{Dimiccoli2015SR-Clustering:Segmentation}. The clustering procedure is performed on an image representation that combines visual features extracted by a CNN with semantic features in terms of visual concepts extracted by Imagga's auto-tagging technology\footnote{http://www.imagga.com/solutions/auto-tagging.html}. In Fig. \ref{fig:imagesexamples} we present some examples of events extracted from the dataset, we introduce below.

\subsection{Event's sentiment recognition:}
\label{Semanticrecognition}

The model relies on semantic concepts extracted from the images to infer the event sentiment associated. However, it relies not only on the semantic concepts extracted by the net with their sentiment associated, but also on how those semantic concepts can be interpreted by the user. We apply the DeepSentiBank Convolutional Neural Network\cite{Chen2014DeepSentiBank:Networks} to extract the images semantic information since it is the only introduced model that extract semantic concepts (ANPs) with sentiment values associated. Given an image, the output of the network is a 2089-D feature vector, where the values correspond to the ANPs likelihood in the image.

Besides taking into account the sentiment associated to the ANPs, the influence of the common concepts within an event are also analysed. We categorize the noun into Positive, Neutral or Negative. There is a wide range of semantic concepts within the ontology, but many of them seem to repeat concepts that even from the user perspective would be difficult to differentiate when looking at an image; such as "girl" from "woman" or "lady".

When facing our egocentric images challenge, the VSO presents several drawbacks. On one hand, this tool is trained to recognise up to 2089 concepts, which can not describe all possible scenarios. On the other hand, despite including that big amount of concepts, many of them categorize objects into categories difficult to visually interpret or differ by the human eye. Examples can be the distinction between 'child', 'children', 'boy', or 'kid' from an image. In order to overcome this problem, we generate a parallel ontology with what we consider an egocentric view of the concepts, i.e., we cluster the concepts a person would merge based on their semantic.

\textit{Egocentric analysis of the VSO:} We cluster the semantic concepts based on the similarities between the noun components of the ANPs, which are computed using the wordNet tool\footnote{ http://wordnet.princeton.edu}. Following what would be considered as similar from an egocentric point of view, we manually refine the resulted clusters into 44 categories. We label the clusters as Positive, Neutral or Negative. In Table \ref{table:egoview_vso} we present some of the egosemantic clusters.

\begin{table}[ht!]
\centering
\caption{Examples of clustered concepts based on their semantic similarity, initially grouped following the distance computed by the WordNet tool.}
\label{table:egoview_vso}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{ccc|ccc|ccc}
\multicolumn{3}{c|}{Positive} & \multicolumn{3}{c|}{Neutral} & \multicolumn{3}{c}{Negative} \\ \hline
petals & christmas & award & car & study & bible & tumb & bug & nightmare \\
rose & winter & present & cars & science & book & tumbstone & bugs & accident \\
flora & snow & honor & machine & history & card & monument & insect & shadows \\
park & santa & gift & vehicle & economy & stiletto & grave & worm & noise \\ \cline{2-2}
yard & sketch & heroes & rally & market & sins & memorial & cockroach & scream \\ \cline{3-3} \cline{8-8}
plant & cartoon & dolls & train & industry & record & stone & decay & night \\ \cline{5-5}
garden & drawing & dolls & competition & statue & paper & graveyard & garbage & darkness \\
 & comics & toy & race & sculpture & poem & cementery & trash & shadow \\
 & illustration & toys & control & museum & interview & grief & shit &  \\
 & humor & lego & metal &  &  & pain &  &
\end{tabular}
}
\end{table}

\subsection{Sentiment Model:}
\label{sentimentmodel}

Given an event, the event's sentiment analysis model (see Fig. \ref{fig:SketchModel}) performs as follows;
\begin{enumerate}

\item Given the ego-photostream we apply the temporal segmentation, analyse events with a minimum of 6 images, i.e. that last for at least 3 minutes.

\item Extract the ANPs of each event frame and rank them by their probability ($Prob_{ANP_j}$) of describing an image.

\item Select the top-5 ANPs per image, since we consider that those are the concepts with higher relevance, thus better capturing the image's information. After this step the model ends up with a total of \textit{M} semantic concepts per event, where $\{M =$  Number of images $\times \; 5\}$.

\item Cluster the $M$ semantic concepts based on their Wordnet-based nouns semantic distances. As a result, we have clusters of concepts with semantic similarity. For the event sentiment computation ($S_{event}$), focus on the largest cluster.

\item Finally, fuse the sentiment associated to the ANPs and noun's cluster following the eq. (\ref{eq:sentim}):
\begin{equation}
\label{eq:sentim}
S_{event} =
\sum_j (\alpha *  S_{ANP_j}+\beta*S_{Noun_j}), j=1:N_{ANP}, \\
\vspace{-0.5em}
\end{equation}

where $S_{ANP_j} = (S_{ANP_j}^{VSO} * Prob_{ANP_j}), $ $S_{ANP_j}^{VSO}$ is the ANP's sentiment given by the VSO and $S_{Noun}$ is the label of the noun, $\alpha$ and $\beta$ are the contributions (\%) of the ANPs and the nouns. Take into account the probability associated to the ANPs aiming to penalize the ANPs with low relation to the image content.
\end{enumerate}

\section{Experiments Setup}
\label{sec:experimentsetup}

\subsection{Dataset:}
We collected a dataset of 4495 egocentric pictures, which we call UBRUG-Senti. The user was asked to wear the Narrative Clip Camera\footnote{http://getnarrative.com/} fixed to his/her chest during several hours every day and was asked to continue with his/her normal life. Since the camera is attached to the chest, the frames vary following the user's movement and describe the user's view of his/her daily indoor/outdoor activities. It involves challenging backgrounds due to the scene variation, handled objects appearing and disappearing during images sequences, and the movement of the user. The camera takes a picture every 30 seconds, hence each day around 1500 images are collected for processing. The images have a resolution of 5MP and JPG format.

After the temporal clustering \cite{Dimiccoli2015SR-Clustering:Segmentation}, we obtained a dataset composed of 4495 images grouped in a total of 98 events. The events were manually labelled based on how the user felt while reviewing them. The labels assigned were \emph{Positive} (36), \emph{Negative} (43) and \emph{Neutral} (19). Some examples are given in Fig \ref{fig:imagesexamples}.

\subsection{Experiments:}
During the experimental phase, we evaluated the contributions of ANPs and nouns by defining different combinations of $\alpha$ and $\beta$. We performed a balanced 5-fold cross validation. For each of the folds, we used 80\% of the total of events per label of our dataset and compute the best pair of $\alpha$ and $\beta$ values. This is a parameters selection process that is later re-evaluated in a test phase with a different set of events.

\textbf{Validation:} To evaluate the effectiveness of the scene detection approach, we use the \textit{Accuracy}, as the rate of correct results, and the \textit{F-Score} (F1). The F1 is defined as : $F1= 2(RP)/(R+P)$, where $P$ is the precision $(P=TP/(TP+FP)$, $R$ is the recall $(R=TP/(TP+FN)$ and $TP$, $FP$ and $FN$ respectively are the number of true positives, false positives and false negatives of the event's sentiment label correctly identified.

\textbf{Results:} Tables \ref{trainingresults} and \ref{Testresults} present the results achieved by the proposed method at image and event level, respectively. The model achieves an average training accuracy of 73$\pm$3.8\% and F-score of 59$\pm$5.4\% and test accuracy of 75$\pm$8.2\% and F-score of 61$\pm$13.2\%, when $\alpha = 0.8$ and $\beta = 0.2$, i.e. when the ANP information is considered; although the major contribution comes from the noun sentiment associated. As expected, neutral events are the most challenging ones to classify.

\begin{table}[ht!]
\centering
\caption{Parameter-selection results}
\label{trainingresults}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{cc|c|c|c|c|c}
\cline{2-7}
 & \multicolumn{3}{c|}{Accuracy} & \multicolumn{3}{c}{F-Score} \\ \cline{2-7}
 & \begin{tabular}[c]{@{}c@{}}beta = 0.2\\ alpha = 0.8\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.5\\ alpha = 0.5\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.8\\ alpha = 0.2\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.2\\ alpha = 0.8\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.5\\ alpha = 0.5\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.8\\ alpha = 0.2\end{tabular} \\ \hline
\multicolumn{1}{c|}{\textbf{Ours}} & 0.60 & 0.63 & \textbf{0.73} & 0.35 & 0.43 & \textbf{0.59} \\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ 3 Clusters\end{tabular}} & 0.68 & 0.66 & 0.68 & 0.48 & 0.45 & 0.48 \\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ with weights\end{tabular}} & 0.65 & 0.65 & 0.66 & 0.41 & 0.43 & 0.47 \\ \hline
\end{tabular}
}
\end{table}

\begin{wraptable}{r}{5.5cm}
\centering
\caption{Test set results}
\label{Testresults}
\begin{tabular}{ccc}
\cline{2-3}
 & \multicolumn{1}{c|}{Accuracy} & F-Score \\ \cline{2-3}
 & \multicolumn{2}{c}{\begin{tabular}[c]{@{}c@{}}beta = 0.8\\ alpha = 0.2\end{tabular}} \\ \hline
\multicolumn{1}{c|}{Ours} & \multicolumn{1}{c|}{\textbf{0.75$\pm$0.08}} & \textbf{0.60$\pm$0.13} \\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ 3 Clusters\end{tabular}} & \multicolumn{1}{c|}{0.69$\pm$0.1} & 0.50$\pm$0.15\\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ with weights\end{tabular}} & \multicolumn{1}{c|}{0.74$\pm$0.1} & 0.58$\pm$0.15
\end{tabular}
\end{wraptable}

In order to contextualize our results, we  fine-tune the well-known \textit{GoogleNet} deep convolutional neural network \cite{Ma2016GoingRecognition} to classify into Positive, Neutral and Negative. We use 80\%, 10\% and 10\% of the dataset for training, validation and testing respectively. The network achieves an accuracy of \textbf{55\%}.

From the results we can conclude that the application of the DeepSentiBank presents drawbacks when applied to egocentric photostreams. To begin with and as commented before, the 2089 ANPs not necessarily have the power to represent what the image captured about the scene, taking into account the difficulty to detect them automatically (Mean average accuracy of the net $\sim$25\%). Moreover, the ANPs present the limitation that they are classified strictly into Negative or Positive concepts. Thus, moments from our daily routine, which are often considered as neutral, are difficult to recognize.

\section{Conclusions}
\label{sec:conclusions}

We present a new model for positive moments recognition from our digital memory, composed by images recorded by the Narrative wearable camera.
It analyses  semantic concepts called ANPs extracted from the images. These semantic concepts have a sentiment value associated and describe the appearance of concepts in the images. The sentiment prediction tool is based on new semantic distance of ANPs and fusion of ANPs and nouns sentiments extracted from egocentric photostreams.
The proposed approach obtained a classification accuracy of 75\% on the test set, with deviation of 8\%.
Future experiments will address the generalization of the model over datasets collected by other wearable cameras, as well as  recorded by different users.
Analysing the results obtained, we conclude that the polarity of the ANPs makes it difficult to classify '\textit{Neutral}' events. However, most of our daily life is composed by neutral events, which can be considered as routine. Thus, in future lines we will address the routine recognition and retrieval.

\textbf{Acknowledgements:}
This work was partially founded by Ministerio de
Ciencia e Innovaci\'on of the Gobierno de Espa\~na, through the research project TIN2015-66951-C2. SGR 1219, CERCA, \textit{ICREA Academia 2014} and Grant 20141510 (Marat\'{o} TV3). The funders had no role in the study design, data collection, analysis, and preparation of the manuscript.

\bibliographystyle{abbrv}

\section{Introduction}

Lifelogging describes an egocentric vision of the experiences of a person. Nowadays, the use of small wearable cameras, which capture images in certain intervals, is increasing considerably. Such images provide an overview of the daily activities of a person that can be interpreted as a visual log of the day. This information can be used to examine a person's pattern of behaviour; daily habits, such as eating habits, social interactions, indoor or outdoor activities, are recorded in such images. Although our mood is influenced by the environment and social context that surrounds us, egocentric data do not always catch our attention or induce the same emotion when retrieved. We consider that the creation of a diary of positive moments in an electronic way, by combining several cues, will help to improve the inner perception of the user's own life. Therefore, in this work we seek for \textit{positive moments} that can raise the user's positiveness.

Several experiments have been conducted in that direction, in \cite{Santos2013TheReview.} the authors presented a survey of positive psychology strategies that demonstrated to be potentially effective as tools for the treatment of depression. As an example, in \cite{Beck1986StimulatingConnotation.} the authors suggested to the participants activities such as \textit{walking in a park}, \textit{visiting a friend}, or \textit{going to the Student Union and saying hello to someone}, that resulted in participants' suffering to decline or disappear. Their study gives ideas about the type of moments that can retrieve positive feelings; nature, landscapes, friends, or smiling people staring at us, among others.

Sentiment analysis from images is a novel research field. Given the challenge of image sentiment recognition and the ambiguity of the problem, we analyse images sentiment assigning a discrete ternary sentiment value (positive (1), neutral (0) or negative (-1) value), similar to \cite{You2015RobustNetworks}. In the literature, sentiment recognition from images has been approached based on different types of data features. Attributes such as facial expressions are used for sentiment prediction in \cite{Levi2015EmotionPatterns,Yuan2013SentributeDescriptors}. The combination of visual and textual information from the images \cite{Wang2014MicroblogModel,You2016Cross-modalityMultimedia} appeared due to the wide use of online social media and microblogs. In such websites, images are posted with short descriptive comments by the user. Audio features were also included in the models presented by \cite{Nojavanasghar2016EmoReact:Children,Poria2014FusingContent}.

\begin{figure}[ht!]
\centering
    \includegraphics[width=1\textwidth,height=7cm]{Images/eurocastFig2Events.png}
    \caption{Example of events extracted from the photostream by applying the temporal segmentation method introduced in \cite{Dimiccoli2015SR-Clustering:Segmentation}. Each row of the figure represents an event of the day. Image events labelled as Positive (green), Neutral (yellow) and Negative (red).}
\label{fig:imagesexamples}

\end{figure}

Despite several works having approached the understanding of how people can be affected seeing images, the field of sentiment analysis from images has not been yet settled. The labelling of the images is not yet established, leading to different questions when addressing sentiment recognition from images. As examples, we can find available datasets labelled as: amusement, anger, awe, disgust, excitement, fear, sad \cite{Machajdik2010AffecitveTheory,You2016BuildingBenchmark}, Positive/Negative \cite{Borth2013Large-scalePairs}-Twitter, Positive/Negative/Neutral\cite{Dan-Glauser2011TheSignificance}, with values of Pleasure, Arousal and Dominance \cite{Lang1997InternationalRatings}, with sentiment values from -2 to 2\cite{Borth2013Large-scalePairs}, where the extremes correspond to Negative and Positive sentiments, respectively. Despite the above mentioned works, to the best of our knowledge, none of them has dealt with the sentiment recognition from egocentric photostreams.

Recently, with the outstanding performance of the Convolutional Neural Networks (CNN), several approaches on sentiment analysis have relied on supervised learning through deep learning techniques, such as \cite{Campos2015DivingPrediction,Levi2015EmotionPatterns,Ma2016GoingRecognition,You2016BuildingBenchmark}. One of the more remarkable approaches, in \cite{Borth2013Large-scalePairs}, introduced a Visual Sentiment Ontology (VSO), based on the Plutchik's wheel of emotions \cite{Plutchik.1980Emotion:Synthesis}, and a visual concept detector called SentiBank. The VSO is built by 3022 semantic concepts called Adjective Noun Pairs (ANP) represented by images from the social net Flickr with them as tags. The ANPs are composed by a pair of a noun and an adjective, with a sentiment value associated between [-2 : 2]. They defend that an object, according to its appearance has a different sentiment value associated to it, like 'lonely boat' (-1.43) and 'traditional boat' (1,37), or 'noisy bird' (-1) and 'cute bird' (1,37). They proposed the semantic concepts baseline classification based on visual features (RGB, SIFT, LBP, etc) extracted from the images. In \cite{Chen2014DeepSentiBank:Networks}, a Deep Neural Network named DeepSentiBank was trained on Caffe for the VSO semantic concepts classification. The authors relied on the concepts with higher number of images and with a classification accuracy associated. From the original 3022 concepts in \cite{Borth2013Large-scalePairs} they select 2089 in \cite{Chen2014DeepSentiBank:Networks}.

To the best of our knowledge, previous to our work \cite{Talavera2017SentimentPhotostreams} there was no approaches addressing sentiment recognition from egocentric photostreams. We propose to analyse the output of the DeepSentiBank per image as semantic representation. We defined a classification model where the one-vs-all SVM classifiers were trained and evaluated with the features describing semantic and global information from the images.

In this work, we approach the same problem from a different perspective. We analyse the relation to each other semantic concepts extracted from images that belong to the same scene. A scene is described as a group of sequential images related between them and describing the same event. Our contribution is an analytic tool for positive emotion retrieval seeking for events that best represent a positive moment to be retrieved within the whole set of a day photostream. We focus on the event's sentiment description where we are observers without inner information about the event, i.e. from an objective point of view of the moment under analysis.

The rest of the paper is organized as follows. In Sect. 2, we describe the sentiment analysis method and the features selection procedure. In Sect. 3, we describe the proposed dataset, while in Sect. 4, we describe the experimental setup, the evaluation, and discuss our findings. Finally, Sect. 5 draws conclusions and outlines future lines of work.

\section{Method}

\begin{figure}[ht!]
\centering
    \includegraphics[width=1\textwidth,height=6cm]{Images/eurocastFig1.pdf}
    \caption{Sketch of the proposed method. First, a temporal segmentation is applied over the egocentric photostream \textbf{(a)}. Later, semantic concepts are extracted from the images using the DeepSentiBank \cite{Chen2014DeepSentiBank:Networks} \textbf{(b)}. The semantic concepts with higher occurrence are selected as event descriptors \textbf{(c)}. Finally, the ternary output is obtained by merging the sentiment values associated to the event's semantic concepts \textbf{(d)}.}
\label{fig:SketchModel}
\end{figure}

Given an egocentric photostream, we propose scene emotion analysis seeking for events that represent and can retrieve a positive feeling from the user. We apply  event-based analysis since single egocentric images cannot capture the whole essence of the situation. By combining information from several images that represent the same scene, we get closer to a better understanding of the event.

\subsection{Temporal segmentation:}
\label{Temporalsegmentation}

We apply temporal segmentation on the egocentric photostreams using the proposed method in \cite{Dimiccoli2015SR-Clustering:Segmentation}. The clustering procedure is performed on an image representation that combines visual features extracted by a CNN with semantic features in terms of visual concepts extracted by Imagga's auto-tagging technology\footnote{http://www.imagga.com/solutions/auto-tagging.html}. In Fig. \ref{fig:imagesexamples} we present some examples of events extracted from the dataset, we introduce below.

\subsection{Event's sentiment recognition:}
\label{Semanticrecognition}

The model relies on semantic concepts extracted from the images to infer the event sentiment associated. However, it relies not only on the semantic concepts extracted by the net with their sentiment associated, but also on how those semantic concepts can be interpreted by the user. We apply the DeepSentiBank Convolutional Neural Network\cite{Chen2014DeepSentiBank:Networks} to extract the images semantic information since it is the only introduced model that extract semantic concepts (ANPs) with sentiment values associated. Given an image, the output of the network is a 2089-D feature vector, where the values correspond to the ANPs likelihood in the image.

Besides taking into account the sentiment associated to the ANPs, the influence of the common concepts within an event are also analysed. We categorize the noun into Positive, Neutral or Negative. There is a wide range of semantic concepts within the ontology, but many of them seem to repeat concepts that even from the user perspective would be difficult to differentiate when looking at an image; such as "girl" from "woman" or "lady".

When facing our egocentric images challenge, the VSO presents several drawbacks. On one hand, this tool is trained to recognise up to 2089 concepts, which can not describe all possible scenarios. On the other hand, despite including that big amount of concepts, many of them categorize objects into categories difficult to visually interpret or differ by the human eye. Examples can be the distinction between 'child', 'children', 'boy', or 'kid' from an image. In order to overcome this problem, we generate a parallel ontology with what we consider an egocentric view of the concepts, i.e., we cluster the concepts a person would merge based on their semantic.

\textit{Egocentric analysis of the VSO:} We cluster the semantic concepts based on the similarities between the noun components of the ANPs, which are computed using the wordNet tool\footnote{ http://wordnet.princeton.edu}. Following what would be considered as similar from an egocentric point of view, we manually refine the resulted clusters into 44 categories. We label the clusters as Positive, Neutral or Negative. In Table \ref{table:egoview_vso} we present some of the egosemantic clusters.

\begin{table}[ht!]
\centering
\caption{Examples of clustered concepts based on their semantic similarity, initially grouped following the distance computed by the WordNet tool.}
\label{table:egoview_vso}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{ccc|ccc|ccc}
\multicolumn{3}{c|}{Positive} & \multicolumn{3}{c|}{Neutral} & \multicolumn{3}{c}{Negative} \\ \hline
petals & christmas & award & car & study & bible & tumb & bug & nightmare \\
rose & winter & present & cars & science & book & tumbstone & bugs & accident \\
flora & snow & honor & machine & history & card & monument & insect & shadows \\
park & santa & gift & vehicle & economy & stiletto & grave & worm & noise \\ \cline{2-2}
yard & sketch & heroes & rally & market & sins & memorial & cockroach & scream \\ \cline{3-3} \cline{8-8}
plant & cartoon & dolls & train & industry & record & stone & decay & night \\ \cline{5-5}
garden & drawing & dolls & competition & statue & paper & graveyard & garbage & darkness \\
 & comics & toy & race & sculpture & poem & cementery & trash & shadow \\
 & illustration & toys & control & museum & interview & grief & shit &  \\
 & humor & lego & metal &  &  & pain &  &
\end{tabular}
}
\end{table}

\subsection{Sentiment Model:}
\label{sentimentmodel}

Given an event, the event's sentiment analysis model (see Fig. \ref{fig:SketchModel}) performs as follows;
\begin{enumerate}

\item Given the ego-photostream we apply the temporal segmentation, analyse events with a minimum of 6 images, i.e. that last for at least 3 minutes.

\item Extract the ANPs of each event frame and rank them by their probability ($Prob_{ANP_j}$) of describing an image.

\item Select the top-5 ANPs per image, since we consider that those are the concepts with higher relevance, thus better capturing the image's information. After this step the model ends up with a total of \textit{M} semantic concepts per event, where $\{M =$  Number of images $\times \; 5\}$.

\item Cluster the $M$ semantic concepts based on their Wordnet-based nouns semantic distances. As a result, we have clusters of concepts with semantic similarity. For the event sentiment computation ($S_{event}$), focus on the largest cluster.

\item Finally, fuse the sentiment associated to the ANPs and noun's cluster following the eq. (\ref{eq:sentim}):
\begin{equation}
\label{eq:sentim}
S_{event} =
\sum_j (\alpha *  S_{ANP_j}+\beta*S_{Noun_j}), j=1:N_{ANP}, \\
\vspace{-0.5em}
\end{equation}

where $S_{ANP_j} = (S_{ANP_j}^{VSO} * Prob_{ANP_j}), $ $S_{ANP_j}^{VSO}$ is the ANP's sentiment given by the VSO and $S_{Noun}$ is the label of the noun, $\alpha$ and $\beta$ are the contributions (\%) of the ANPs and the nouns. Take into account the probability associated to the ANPs aiming to penalize the ANPs with low relation to the image content.
\end{enumerate}

\section{Experiments Setup}
\label{sec:experimentsetup}

\subsection{Dataset:}
We collected a dataset of 4495 egocentric pictures, which we call UBRUG-Senti. The user was asked to wear the Narrative Clip Camera\footnote{http://getnarrative.com/} fixed to his/her chest during several hours every day and was asked to continue with his/her normal life. Since the camera is attached to the chest, the frames vary following the user's movement and describe the user's view of his/her daily indoor/outdoor activities. It involves challenging backgrounds due to the scene variation, handled objects appearing and disappearing during images sequences, and the movement of the user. The camera takes a picture every 30 seconds, hence each day around 1500 images are collected for processing. The images have a resolution of 5MP and JPG format.

After the temporal clustering \cite{Dimiccoli2015SR-Clustering:Segmentation}, we obtained a dataset composed of 4495 images grouped in a total of 98 events. The events were manually labelled based on how the user felt while reviewing them. The labels assigned were \emph{Positive} (36), \emph{Negative} (43) and \emph{Neutral} (19). Some examples are given in Fig \ref{fig:imagesexamples}.

\subsection{Experiments:}
During the experimental phase, we evaluated the contributions of ANPs and nouns by defining different combinations of $\alpha$ and $\beta$. We performed a balanced 5-fold cross validation. For each of the folds, we used 80\% of the total of events per label of our dataset and compute the best pair of $\alpha$ and $\beta$ values. This is a parameters selection process that is later re-evaluated in a test phase with a different set of events.

\textbf{Validation:} To evaluate the effectiveness of the scene detection approach, we use the \textit{Accuracy}, as the rate of correct results, and the \textit{F-Score} (F1). The F1 is defined as : $F1= 2(RP)/(R+P)$, where $P$ is the precision $(P=TP/(TP+FP)$, $R$ is the recall $(R=TP/(TP+FN)$ and $TP$, $FP$ and $FN$ respectively are the number of true positives, false positives and false negatives of the event's sentiment label correctly identified.

\textbf{Results:} Tables \ref{trainingresults} and \ref{Testresults} present the results achieved by the proposed method at image and event level, respectively. The model achieves an average training accuracy of 73$\pm$3.8\% and F-score of 59$\pm$5.4\% and test accuracy of 75$\pm$8.2\% and F-score of 61$\pm$13.2\%, when $\alpha = 0.8$ and $\beta = 0.2$, i.e. when the ANP information is considered; although the major contribution comes from the noun sentiment associated. As expected, neutral events are the most challenging ones to classify.

\begin{table}[ht!]
\centering
\caption{Parameter-selection results}
\label{trainingresults}
\resizebox{\columnwidth}{!}{%
\begin{tabular}{cc|c|c|c|c|c}
\cline{2-7}
 & \multicolumn{3}{c|}{Accuracy} & \multicolumn{3}{c}{F-Score} \\ \cline{2-7}
 & \begin{tabular}[c]{@{}c@{}}beta = 0.2\\ alpha = 0.8\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.5\\ alpha = 0.5\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.8\\ alpha = 0.2\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.2\\ alpha = 0.8\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.5\\ alpha = 0.5\end{tabular} & \begin{tabular}[c]{@{}c@{}}beta = 0.8\\ alpha = 0.2\end{tabular} \\ \hline
\multicolumn{1}{c|}{\textbf{Ours}} & 0.60 & 0.63 & \textbf{0.73} & 0.35 & 0.43 & \textbf{0.59} \\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ 3 Clusters\end{tabular}} & 0.68 & 0.66 & 0.68 & 0.48 & 0.45 & 0.48 \\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ with weights\end{tabular}} & 0.65 & 0.65 & 0.66 & 0.41 & 0.43 & 0.47 \\ \hline
\end{tabular}
}
\end{table}

\begin{wraptable}{r}{5.5cm}
\centering
\caption{Test set results}
\label{Testresults}
\begin{tabular}{ccc}
\cline{2-3}
 & \multicolumn{1}{c|}{Accuracy} & F-Score \\ \cline{2-3}
 & \multicolumn{2}{c}{\begin{tabular}[c]{@{}c@{}}beta = 0.8\\ alpha = 0.2\end{tabular}} \\ \hline
\multicolumn{1}{c|}{Ours} & \multicolumn{1}{c|}{\textbf{0.75$\pm$0.08}} & \textbf{0.60$\pm$0.13} \\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ 3 Clusters\end{tabular}} & \multicolumn{1}{c|}{0.69$\pm$0.1} & 0.50$\pm$0.15\\ \cline{1-1}
\multicolumn{1}{c|}{\begin{tabular}[c]{@{}c@{}}Evaluating\\ with weights\end{tabular}} & \multicolumn{1}{c|}{0.74$\pm$0.1} & 0.58$\pm$0.15
\end{tabular}
\end{wraptable}

In order to contextualize our results, we  fine-tune the well-known \textit{GoogleNet} deep convolutional neural network \cite{Ma2016GoingRecognition} to classify into Positive, Neutral and Negative. We use 80\%, 10\% and 10\% of the dataset for training, validation and testing respectively. The network achieves an accuracy of \textbf{55\%}.

From the results we can conclude that the application of the DeepSentiBank presents drawbacks when applied to egocentric photostreams. To begin with and as commented before, the 2089 ANPs not necessarily have the power to represent what the image captured about the scene, taking into account the difficulty to detect them automatically (Mean average accuracy of the net $\sim$25\%). Moreover, the ANPs present the limitation that they are classified strictly into Negative or Positive concepts. Thus, moments from our daily routine, which are often considered as neutral, are difficult to recognize.

\section{Conclusions}
\label{sec:conclusions}

We present a new model for positive moments recognition from our digital memory, composed by images recorded by the Narrative wearable camera.
It analyses  semantic concepts called ANPs extracted from the images. These semantic concepts have a sentiment value associated and describe the appearance of concepts in the images. The sentiment prediction tool is based on new semantic distance of ANPs and fusion of ANPs and nouns sentiments extracted from egocentric photostreams.
The proposed approach obtained a classification accuracy of 75\% on the test set, with deviation of 8\%.
Future experiments will address the generalization of the model over datasets collected by other wearable cameras, as well as  recorded by different users.
Analysing the results obtained, we conclude that the polarity of the ANPs makes it difficult to classify '\textit{Neutral}' events. However, most of our daily life is composed by neutral events, which can be considered as routine. Thus, in future lines we will address the routine recognition and retrieval.

\textbf{Acknowledgements:}
This work was partially founded by Ministerio de
Ciencia e Innovaci\'on of the Gobierno de Espa\~na, through the research project TIN2015-66951-C2. SGR 1219, CERCA, \textit{ICREA Academia 2014} and Grant 20141510 (Marat\'{o} TV3). The funders had no role in the study design, data collection, analysis, and preparation of the manuscript.

\bibliographystyle{abbrv}

redpajama_set_name: RedPajamaArXiv


text:
Manono Airport  is an airport serving Manono, a city in Tanganyika Province, Democratic Republic of the Congo. The runway is within the city.

See also

Transport in the Democratic Republic of the Congo
List of airports in the Democratic Republic of the Congo

References

External links
 OpenStreetMap - Manono Airport
 OurAirports - Manono
 FallingRain - Manono Airport

 HERE Maps - Manono

Airports in Tanganyika Province

redpajama_set_name: RedPajamaWikipedia


In last month's Reliability Basics, we took a look at the mathematical function that is the basis for many useful reliability and life data metrics, the probability density function, or pdf. The pdf function is essentially a continuous form of a histogram, which is a bar chart that characterizes the relative frequency of an occurrence. In this month's article, we will look at how the pdf is used to derive other frequently-used life data metrics.
Note that the lower limit is given as zero or negative infinity. The value of the lower limit varies from distribution to distribution. For example, the normal or Gaussian distribution has a lower limit of negative infinity, while the Weibull distribution has a lower limit of zero. Note that the value of the cdf always approaches 1 as time approaches infinity. This is because the area under the curve of the pdf is always equal to 1, and the cdf is essentially measuring the area under the pdf curve from zero to the point of interest. The following graphic shows the relationship between the pdf and the cdf.
Looking at the definition of the cdf, it should be apparent that this function would have a direct application to life data analysis. This function returns the probability of a failure occurring before a certain time. Another useful function is the one that provides the probability of a failure occurring after a certain time. Note that the cdf measures the area under the pdf curve up to a given time, and that the area under the pdf curve is always equal to 1. Given these concepts, subtracting the cdf from 1 would result in the probability of a failure occurring after a given time. This is the widely-used reliability function. Accordingly, the cdf is also known as the unreliability function, and is represented by the function Q(T).
Thus, the failure rate function is simply the pdf function divided by the reliability function, and has the units of failure per unit time among surviving parts, e.g. one failure per month.
Where X is the fraction failing or unreliability expressed as a decimal.
The probability density function is the most important mathematical function in life data analysis. This single function fully characterizes the distribution it describes. The area under the pdf curve between two defined points on the x-axis gives the probability of an event occurring between those two points. This function can be used to derive other functions that are important to life data analysis, including the unreliability function, the reliability function, the failure rate function, the mean life function, and the BX function.


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text:
Obama visits a worried mom
By ROBERTA RAMPTON
President Barack Obama (L) meets with Lisa Martin (C), her husband Jeff and their son Cooper during a living room discussion at a private residence in Omaha. (REUTERS/Carlos Barria)
President Barack Obama visited a young family in their suburban Nebraska living room on Wednesday, the first stop on what the White House said will be a year-long tour to talk before he leaves office about fixing the country's polarized politics.
His trip to Nebraska, a Republican "red state," is meant to help promote the big-picture ideas he laid out in his final State of the Union speech on Tuesday, where he said he regretted American politics had become more rancorous.
Obama spent about 40 minutes visiting with Lisa Martin, who sent him a letter last year she wrote at 4 a.m. when she was awake feeding her son Cooper. In the letter she described her "overwhelming sense of dread" about climate change.
"Will he be able to experience the small things, such as winter in Nebraska, where he has snow days and sledding?" Martin wrote in the letter, expressing feelings of powerlessness.
"Call it sleep deprivation or desperation, but something made me want to email the president that night, so I did," Martin said on Wednesday.
Afterwards, Obama reprised his address to a packed-to-the-rafters crowd of 11,000 at a hockey rink at the University of Nebraska, a state where he acknowledged he got "whupped" in his 2012 re-election campaign.
Obama said that American economic and foreign policy is stronger than described by "a bunch of folks right across the river" – a jab at Republican presidential candidates campaigning for the party's nomination in the early voting state of Iowa.
"That's just hot air. That's bluster. That's not serious," Obama said. "There's another word for it that starts with a 'b' – it's baloney," he said.
Obama has said he hopes to work with the Republican-led Congress on a few issues where there is common ground in his final year: criminal justice reform, passing a sweeping Asian trade deal, and addressing heroin addiction and poverty.
But on a host of issues Congress does not support him, such as curbing climate change, and the race to win the Nov. 8 election to succeed him overshadows Obama's push to cement his legacy.
Obama was due to fly on Wednesday to Baton Rouge, Louisiana – another red state – where he will take questions from residents at a town-hall meeting on Thursday.
Copyright © 2016 Capitol Hill Blue
Copyright © 2016 Thomson Reuters. All rights reserved
ROBERTA RAMPTON

redpajama_set_name: RedPajamaCommonCrawl


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                    var type = IPython.notebook.get_selected_cell().cell_type;
                    if (mode == "edit" && type == "code") IPython.notebook.edit_mode();
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redpajama_set_name: RedPajamaGithub


text:
John Bush, född 24 augusti 1963 i Los Angeles, USA, är sångare i Armored Saint, ett heavy metalband från Los Angeles. Bandet har släppt fem album, en EP, en Greatest Hits-skiva och två DVD:er. På samtliga är John Bush sångare. Under Metallicas tidiga karriär i början av 1980-talet tillfrågades John Bush om att gå med Metallica då sångaren James Hetfield kände sig osäker på sin sång och hellre ville koncentrera sig på gitarren, men Bush valde att stanna i Armored Saint.

År 1992, efter ett samtal med Scott Ian, gitarristen i Anthrax beslutade sig Bush för att gå med i Anthrax som sångare. Bandet fick ett annat sound, mer seriöst och komplext. Han gjorde fem skivor i Anthrax. Efter tiden i Anthrax gjorde han ett längre avbrott, och 1999 återförenades Armored Saint, med uppsättningen från Symbol. I Armored Saint gjordes albumet Revelation och därefter följdes en turné med Armored Saint.

Diskografi
Album med Armored Saint
March of the Saint (1984)
Delirious Nomad (1985)
Raising Fear (1987)
Symbol of Salvation (1991)
Revelation (2000)
Nod to the Old School (2001)
La Raza (2010)
Win Hands Down (2015)
Carpe Noctum (Live) (2017)
Punching the Sky (2020)

Album med Anthrax
Sound of White Noise (1993)
Stomp 442 (1995)
Volume 8: The Threat Is Real (1998)
We've Come for You All (2003)
The Greater of Two Evils (2004)

Referenser

Externa länkar

Officiell webbplats (Armored Saint)
John Bush på Allmusic
Armored Saint på Allmusic

Amerikanska rocksångare
Män
Födda 1963
Levande personer


Four Eras of Human Communications have influenced our contexts – and Us!
The Scripture Language Report, published by the United Bible Societies, uses data from the Brazil Bible Society to show that at the end of 2011 there were records of 2,538 different languages with published Biblical text: 1,240 New Testaments, 823 Bible Portions, and 475 full Bibles .
According to Hal Erickson (Religious Radio and Television in the United States, 1921-1991: The Programs and Personalities, 1992), a Westinghouse engineer who sang in his church choir helped Pittsburgh's KDKA offer Calvary Episcopal Church's Sunday Evening Prayer service as the first American religious broadcast in 1921.
Between 1923 and 1925, the number of religious organizations holding radio licenses climbed from 12 to 63, and the Federation of Churches (representing mainstream Protestant, Roman Catholic, and Jewish interests as the National Broadcast Company [NBC] Religious Advisory Council) assured that religious groups would receive free or sustaining network time for their productions when NBC debuted as a coast-to-coast network of radio stations in 1927.
By the 1950s, the National Council of Catholic Men, the Jewish Seminary of America and the Federation Council of Churches of Christ (now the NCCC) created the "heyday" of religious television programming when they worked with television networks to produce prime time programming that took advantage of both sight and sound. What had originated as more a sermon-oriented presentation (consider Fulton Sheen's The Catholic Hour) by the more liberal churches, evolved into more radical and experimental forms of drama, music, and dance, and some of the most potent political statements on television.
More conservative religious groups who felt "squeezed out" of radio and television by their more liberal counterparts were willing to pay for airtime. Motivated by both the income paid-time programming produced as well as the entertainment values incorporated in this type of programming, broadcasters began to shift their scheduling priorities to accept 'paid-time' programs. In 1957, only 47% of religious broadcasting was paid time; by 1978, 92% of inspirational programming was bought!
How Should We Connect with Seekers and Saints??
The microsystem: the immediate environment, the settings where people experience their daily lives. It includes relationships with each parent, siblings, the extended family, peers/friends and other adults. The influence of the microsystem comes from direct interactions with the immediate environment. The adolescent is an active agent in the microsystem.
The mesosystem: the network of interconnections between the various microsystems. An example is a teenager who is experiencing abusive treatment from parents may become difficult to handle in relationships with teachers.
The exosystem: the societal institutions that have indirect but potentially important influences on development. These institutions include schools, religious institutions and media.
The macrosystem: the broad system of cultural beliefs and values and the economic and govern-mental systems that are built on those beliefs and values. These beliefs include laws, customs, etc.
The chronosystem: the changes that occur in developmental circumstances over time, both with respect to individual development and historical changes.


The Seree Grand Resort is located in a tranquil setting, just 5 minutes from the airport and Hat Yai Water Park. The center of town is just a short drive away, and the green surroundings can easily be explored by bicycle.
The resort offers modern, affordable accommodation, equipped with all the necessities such as air conditioning, refrigerator, TV, kettle, free WiFi internet and much more to ensure a relaxing stay. Despite the low rates, guests can enjoy facilities such as an outdoor swimming pool, fitness center, karaoke, restaurant, bicycle rental, function rooms, parking and a range of services.
Seree Grand Resort is a modern style resort and only 1.9 kilometer from Hat Yai International Airport. Seree Grand Resort are comfortable atmosphere in nature to relax and each rooms are offers equipped with full facilities.
[Sawadee.com] [Hadyai info] [Hadyai hotels] [Seree Grand Resort ] © 1997-2017 Web Sawadee Plc.


text:
A developer got Android 12 booting on the legendary Samsung Galaxy S III
In recent days, we've informed you about a variety of official Android 12 updates. They all had one thing in common: Every update was delivered to devices that are currently supported by the respective OEM. But what happens with much older and unsupported smartphones? Do they gather dust? Not a chance at XDA.
When it comes to aftermarket development, some devices never cease to amaze us. And while it may not exactly be the venerable HTC HD2, the Samsung Galaxy S III is certainly carrying on its legacy. Samsung may have long declared the end of upgrade support for this model, but the modding community hasn't forgotten it at all. The 2012's flagship has been dual-booted, made to natively run the Linux-based Sailfish OS, and even received mainline Linux kernel support. The latest in the series of developments for the device is an unofficial LineageOS 19.0 custom ROM based on Android 12!
The Exynos 4412-powered Samsung Galaxy S III 3G (model number GT-I9300) can now boot Android 12, courtesy of XDA Recognized Developer html6405. Among the things that do work out of the box, you can find that the cellular radio, camera, Wi-Fi, Bluetooth, and software/hardware-accelerated video playback are working just fine. However, many LineageOS-specific features are lacking in the ROM, but that is alright as we are still in the early days and these aspects will be fixed on the source side over time.
Samsung Galaxy S III XDA Forums
A few words about bugs, because unfortunately, they are present: The SIM PIN unlock is broken as of now. As a precautionary measure, you must disable your SIM PIN using a different device first, otherwise, you'll end up with a locked SIM. Formatting a microSD card as the adoptable storage results in a bootloop in the current release. Furthermore, the NFC module of the Galaxy S III isn't functional yet under this ROM. We are keeping our fingers crossed for the developer to fix these issues soon.
If you still use a Samsung Galaxy S III or have it somewhere hidden away in your desk, make your way to the custom ROM thread and give this amazing piece of work a try.
Download unofficial LineageOS 19.0 based on Android 12 for the Samsung Galaxy S III
The post A developer got Android 12 booting on the legendary Samsung Galaxy S III appeared first on xda-developers.
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text:
100 7 wmjd online translator
Category: *UNSORTED
by Barr · 28.05.2019
Revolvy Site Map. They promote themselves as having a larger and more varied playlist than most other commercial radio stations. The following is a list of FCC-licensed radio stations in the U. It has been North Dakota's capital city sincewhen the state was created from the Dakota Territory and admitted to the Union. List of radio stations owned by iHeartMedia topic The following is a list of radio stations currently owned by iHeartMedia.
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Listen live USA famous radio WMJD FM live streaming on Live Online Radio and find the FM Rockfords Greatest Hits · hitz · Hitz 90s Alternative · hitz WMJD FM trust this station will get to be referred to all through the world as the Angry Kids Radio by Transposh - translation plugin for wordpress.
Listen live USA famous radio WMJD FM live streaming on Live Online Radio and find WMJD FM website address is Angry Kids Radio. Q FM is still always known as the "Quality Talk, Quality Music" station, and Cleveland translator WBT ( FM), and streams online via iHeartRadio.
Keys Odd-numbered revised release of edition. Revolvy Site Map. It was owned by Percy Crawford from to Tom Hamilton and Jim Rosenhaus currently serve as the network's play-by-play announcers.
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Infraestructura de Estaciones de Radio FM. The station installed a used replacement transmitter, but it experienced problems. Categories : Radio stations in Virginia Country radio stations in the United States Radio stations established in List of Russian-language radio stations topic This is a list of radio stations in Russian language. Established on 3 Mayit airs mainly talk shows and some shows previously from sister station CBC AM, such as obituary announcements.
radio ,7 is Luxembourg's public service radio.
Programming focuses on information, cultural events and music, both classical and contemporary. The best collection of online radios and FM radios from Virginia (USA). More than radio stations from Virginia (USA). This app is loaded with favourites list. Play or Stream your favorite USA or America Virginia Radio, national and local radio stations for FREE.
All you need is an internet connection. USA Virginia.
The base was decommissioned in and later annexed by the city of Victorville. As a hub of retail and health care, Bismarck is the economic center of south-cent.
The following is a list of radio stations currently owned by iHeartMedia. Bob was one of Southwest Virginia's most respected broadcasters, and greeted his listeners each morning with his signature sign-on:. Full broadcasts began in under the Radio Lobo name.
100 7 wmjd online translator Translator at Weldborough WLEV KTHU airs a classic rock music format branded as "Thunder One of CFRM's mottos were "rock'n the rock" and "when the lights go out we stay on the air because we're powered by rock and roll".
The base was decommissioned in and later annexed by the city of Victorville.
Website, WNRV Online. WNRV is a Bluegrass formatted broadcast radio station licensed to Narrows-Pearisburg, Virginia, serving Narrows and Giles County, Virginia.
WNRV is owned and operated by New River Interactive Media, LLC. Contents. 1 History; 2 Station sold; 3 65th birthday; 4 FM Translator; 5 References In JulyWNRV started full-time 24/7 Web-Streaming. Chance the Rapper, a musician and music producer, decides to translate the Early co-funding for the Archive was xml cdata decode online dating under the.
COM · 7 PSYCHEDELIK · 10 PULSRADIO · 10 Radio City · 14 RADIO CLASIC · 6 Radio Romania. You can also choose a display 50 or per page. SUPERROCK KYOI (HQ test translation) Top station - Germanys No.1 Web Hit Station WMJD.
Country, Top 50, Blues, Bluegrass. web,64, 90,
This is a list of radio stations in Russian language.
Video: 100 7 wmjd online translator What's a Good Rate Per Word to Make Money Translating?
Last modified Bismarck is the capital of the U. The network also includes 27 affiliates in the U. Inthe station was sold to a Christian local group called Maranatha Broadcasting.
Since the station was located next to a railroad track, the construction required special measures to prevent outside noises being heard on the air.
Collins-Greeley area.
100 7 wmjd online translator The station broadcasts to part of Western Sydney, mainly centered on the City of Penrith.
The station was branded as Cafe Revolvy Brain revolvybrain.
In JulyWNRV changed its format to Bluegrass and began broadcasting local programming back home out of its old studios between Narrows and Pearisburg. K-Rock Last modified
Tagore :
WRDU
Daizshura :
However, it still kept the Sunny moniker but changed the slogan to "todays hottest music. It was owned by Eduardo Ponce Barbosa and broadcast with watts.
Grokinos :
Its transmitter is located in Pittsburgh's Spring Hill district and its studios are located west of downtown. The transmitter is located off St.
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100 7 wmjd online translator © 2018. All Rights Reserved.


text:
John Busby
By Nikki GalovicSeptember 4th, 2018
John Busby retired as the inaugural General Manager of the Australian Government's Office of Spatial Data Management, a whole-of-government initiative to facilitate use of spatial data within government agencies. John has over 30 years' experience with biodiversity data management in multi-jurisdictional networks. With the Australian Biological Resources Study, he coordinated the Australian Biogeographic Information System and was a member of the team that developed BIOCLIM. He was on the management team of the Environmental Resources Information Network, an Australian Government initiative to build a comprehensive continental-scale biodiversity information infrastructure. While based in the UK, John was inaugural Head of Capacity Building at the UNEP-World Conservation Monitoring Centre and then Program Leader, Biodiversity Conservation Information System, a consortium of 12 international non-government organisations. John has advised on information network governance, protocols and standards under contracts to the Convention on Biological Diversity, European Commission, European Environment Agency, OECD, UNEP, and US Dept of Interior.
Emeritus Professor Michael Frank Hutchinson
BIOCLIM – the first species distribution modelling package
Last updated: July 3rd, 2019


Have your pet or cat stand over a white countertop, bed sheet or piece of paper.
Part the hair so you can see the skin. Use a comb if your dog or cat is long haired.
Run your fingers through the fur and shake, especially in the neck and tail regions.
Look for evidence of fleas, like the flea dirt (the feces fleas produce and leave on your pet). Small specks that look like pepper could be flea dirt.
Add a drop of water to the specks to confirm. They will turn a dark brownish red color if they are flea dirt. That's because it is actually flea droppings, which is digested blood.
Scratching can be a sign there are fleas on your dog or cat. However, itching and scratching alone is NOT a sign and may indicate other conditions that need your attention.
If your pet check confirms there are fleas, don't wait. There are many types of products available to eliminate fleas in all their stages of development. Be sure to follow directions for the safety of your pet and your household.


text:
You Are Dumb, which is not a blog, wnet dormant in April 2019 after running out of things to say about life under Trump. It carried on, and still carries on, on Twitter @youaredumb, but may start sputtering back to life as a semblance of nuance returns to the world. My blog about being an old nerd, OLDNERD., went dormant around the same time due to generalized ennui, and we'll see what happens there.
YAD On Twitter
Send YAD Money
OLDNERD
Archive - Jan 27, 2014
Bill Clinton's Penis!
Memo to Paul Ryan and other Huckateers: YOU ARE DUMB.
As you may recall, on Friday, we discussed the most recent comments by Mike Huckabee, who got caught in some good old-fashioned slut-shaming. Faced with a large support gap amongst women, Republicans are attempting to do for that group what has so far not succeeded with African-Americans and Latinos. Instead of changing their policies to be friendlier, they're just going to say that Democrats are really hurting these groups, and Republicans are really helping them.
I encourage this strategy, because it's an awful, stupid strategy and it won't work. It'll just sound like what it is - condescending and wrong. But they'll try it anyway, because how else are they going to get women to vote for them? Stop being misogynistic 1950s dinosaurs who think Jesus' primary message was that you whores should shut your damn legs and get in the kitchen where you belong? Please.
So this weekend, it was up to Rand Paul, who loves strong women so much his daddy named him after one, to bring forth the Republican message that Democrats are the real rapists. ACTUAL QUOTE TIME!
"[Clinton] took advantage of a girl that was 20 years old and an intern in his office. There is no excuse for that, and that is predatory behavior."
Now, I will be the first to admit that, under Bill Clinton, if you'll pardon the expression, changes were made to the core principles of the Democratic Party that I disagree with to this day. However, fucking interns was never part of the Democratic platform. It was just a hobby. By that logic, I could say that people shouldn't trust Republicans on drug enforcement because Trey Radel likes a bit of blow every now and then.
Paul went on to declare that women won the War On Women because he knows women that are doing great. So great that he worries about how young men are doing. See, ladies? Vote Republican! That way Democrats won't ask you for the occasional b-jibber in the Oval Office sixteen years ago, and Republicans can devote themselves to the important work of making sure men are back on top again. That's the Rand Paul guarantee!
Also, for the record, taking a thing someone says and following it to its logical conclusion is not "taking it out of context". Just because that logical conclusion is in fact the opposite of the message you're trying to send doesn't mean it's our fault for pointing that out. Maybe you should try learning some self-control before you go blaming other people for the plight you're in. I know I've heard that advice somewhere before.
Main Column
Site concept and editorial content Copyright © 2003-2015 Bryan Lambert, who is solely responsible for its content. Yes, it runs on an ancient-ass version of Drupal and looks like it was last updated before the Obama administration because, well, it was.


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text:
19 December 2018 — City desk
Council Removes Bins in Milltown Because People Were Putting Too Much Dog Poo in Them
by Zuzia Whelan
Dartry Park. Photo by Zuzia Whelan
A couple of weeks back, Gerry Callanan noticed that some rubbish bins along his dog-walking route in Dartry Park, and nearby Orwell Park, had disappeared.
There had been three in Dartry Park, he said, last Friday morning, as he walked his black spaniel Cleo, throwing a tennis ball across the green for her to catch. Now there's just one by the park entrance.
Five years ago, there were way more bins, he says. "I think it's their policy to take them out," he says, of Dublin City Council.
The official line from the council's press office is that the council regularly removes bins that are not "optimal" for an area.
A response from Senior Executive Parks Superintendent Michael Noonan to Green Party Councillor Patrick Costello gives a bit more detail though.
In this case, the bins were removed because one council department empties one kind of bin, and another section of the council removes another kind of bin, and these bins were confusing matters so they were taken out, the email suggests.
Take It Home?
On two concrete stumps where bins used to stand in Dartry Park, there are now large piles of bagged dog waste. "People decided they'd keep putting their rubbish there," Callanan says.
Park-goers often organise clean-ups, he says. "But there's nowhere to put the rubbish if you pick it up."
The bins in Dartry Park and the one in Orwell Park – which were both provided by Parks Services – were removed as they were "predominantly used for dog waste and other litter", Noonan told Costello.
"Parks Staff do not empty dog litter bins, which is a function undertaken by waste management," wrote Noonan, so for hygiene reasons, the litter bins in Dartry and Orwell Parks have been removed, he said.
Costello, of the Green Party, says it's important that there are bins inside parks. "Because that's where fouling happens."
Ripping bins out of parks will have a knock-on impact: littering and illegal dumping, Costello said. "It really seems that we're moving away from the desired outcome because of solving the wrong problem," he said.
Labour Councillor Dermot Lacey said the removal of these bins was "a false policy, designed for the waste management people".
"It doesn't make sense, particularly along routes where people walk," Lacey said.
So where are people supposed to put litter and dog poo? In their pockets, perhaps.
Emptying the bins is "time consuming, with high waste disposal costs", Noonan says in his email to Costello. And "because the bins are contaminated with dog poo and food items, it is very difficult to recycle their contents, and the majority of the waste goes directly to landfill".
So Parks Services officials prefer to "encourage the public to bring their litter home", he said. There, they can dispose of it in a more environmentally-friendly way, rather than "the inefficient manner currently in operation", he said.
A spokesperson for the council said it regularly reviews how to collect and dispose of waste from public parks and is looking at ways to do it better – and ways to ensure that as much can be recycled as possible, rather than chucked into a landfill.
"There is an ongoing programme of replacement of bins and installation of new bin types to encourage recycling," they said.
The council is putting in new bins and upgrading old bins at the moment, and taking some out where they don't seem needed, and trying out hi-tech bins such as solar compactors too, they said. (Some parts of the city are way better served by bins than others.)
At the moment, there are 341 bins in public parks. "In most instances, the litter bins were removed as it was considered that there was an excessive number present, taking account of the size of the park and proximity to residences," the council spokesperson said.
It's mainly dog waste being left where the bins were, said Callanan, last Friday. So regular dog-waste bins could be put in, but they're probably too small. "I think they should put back the bins," he says.
He uses the bin near the end of Orwell Park. Most others, though, just plop their waste on the ground now, right where the old bins once were.
It's winter. But "what happens when the GAA and soccer clubs come back?" Callanan asks.
Zuzia Whelan: Zuzia Whelan is a city reporter for Dublin Inquirer. You can reach her at zwhelan@dublininquirer.com.
Anonymous commenter
at 27 December 2018 at 11:30
There is a real problem here. Bins are necessary for public health reasons as well as ensuring the areas in which we live are attractive and nice places to be. I think we all have to go back and look at he whole wast disposal area once again. The Council re going to have to take a lead here and not allow the executive to continue its policy of downsizing and reducing services. Its bad enough to see Government take powers and actions of Local Authorities but there is no need for them to be assisted in this process by the Council itself.
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