What's with the robot voice?
Is your brand seen but not heard?
We banish the blah, bull and bunkum from business writing.
We give organisations the hands-on help they need to ditch corporate speak and write with real personality.
Business writing can be complex.
Say 'writing training' and most people think of a back to school grammar lesson.
Our clients think of our writers as part of their team.
And that's exactly how you'll feel.
Is your writing animated or automated?
Take our quiz to find out (it only takes a minute).
1. What does the first line in your emails usually say?
2. How much of your writing is 'standard text'?
None. I prefer to write from scratch. Half and half. I like to mix it up. A lot. More standard text = more work done.
Most of the time. Only if I'm giving good news. Rarely. The third person is preferred.
5. When do you use company jargon?
When I'm speaking to the people in my team. I've been guilty of it in the pub. I test myself daily on the company's acronyms.
Good news – your writing shows you're a living, breathing human being with a personality and everything. Want to see how your team's writing matches up? Send a sample to hello@thefirstword.co.uk and we'll give you our take on the language.
Uh oh. A mechanical tone is creeping into your writing. Email us at hello@thefirstword.co.uk and we'll stage an intervention before you start sounding like the Terminator.
Oh dear. Looks like you've been brainwashed by corporate speak. Email hello@thefirstword.co.uk and we'll help you put the human touch back into your writing. Unless you're one of those creepy robots that can play chess and have a conversation and do surveys.


While we're not staying in Phoenix, we are much closer thanks to the park we're staying in. The Black Canyon Ranch RV Resort is located in….. Black Canyon City, AZ. It's a pretty nice park, and I thought I'd share a little video on this one.
And if you're asking yourself, "Is this park a client of yours?" Well, the answer is no. Their website is pretty well set. Still, I thought it would be nice to pop up a quick video of the park as it's a pretty nice place to stay if you need to be close to Phoenix, but still outside of the city. And you never know….maybe they'll want a real park tour video for their own site.
Video tours – that's a great business!
It's always so cool to see what businesses different RV-ers are doing to live the life they want!


Nine compositions were chosen winners from the recently concluded essay writing contest held this month here in Monol. Students from both the General and Post ESL courses have put their best on writing their essays to recount and share their most memorable experiences during their summer vacation, Chuseok celebration, and stay in Baguio.
Visit the following links to read the works of the students who bagged first and second place.


text:
Adderall – drug for ADHD – Addiction on US Campuses rising
Adderall – drug for ADHD – Addiction on US Campuses rising February 11, 2013 Desh Kapoor
Kids and youth need to focus in this age of lots of information. So what are they doing? Abusing a medicine that is used for a particular psychological ailment. This could have long term impact and situations for the youth who are caught in this web!
The United States has four percent of the world's population, and produces 88 percent of the world's legal stimulant drugs. Including Adderall, the amphetamine-based drug widely prescribed for ADHD, Attention Deficit Hyperactivity Disorder. Impressive numbers.
But visit an American college campus, and really pay attention, and they add up. Lots of young Americans, many with no ADHD, taking Adderall just to focus.
To read. To write. To perform. Now there's Adderall addiction and suicide in the news, but it's bigger than that.
One kids death has woken people up to the night mare that this addiction creates:
Every morning on her way to work, Kathy Fee holds her breath as she drives past the squat brick building that houses Dominion Psychiatric Associates.
It was there that her son, Richard, visited a doctor and received prescriptions for Adderall, an amphetamine-based medication for attention deficit hyperactivity disorder. It was in the parking lot that she insisted to Richard that he did not have A.D.H.D., not as a child and not now as a 24-year-old college graduate, and that he was getting dangerously addicted to the medication. It was inside the building that her husband, Rick, implored Richard's doctor to stop prescribing him Adderall, warning, "You're going to kill him."
It was where, after becoming violently delusional and spending a week in a psychiatric hospital in 2011, Richard met with his doctor and received prescriptions for 90 more days of Adderall. He hanged himself in his bedroom closet two weeks after they expired.
Here are the guests:
Donald Misch, assistant vice chancellor for health and wellness, and director of the student health center at the University of Colorado Boulder. He's a psychiatrist and internist.
Alan DeSantis, professor of communications at the University of Kentucky. He's spent the last decade researching the illegal use of ADHD drugs on college campuses, gathering interview data from 300 students and survey data from more than 10,000 students.
Emma Diehl, student studying journalism at Lehigh University. Reporter for the student newspaper there "The Brown and White," where her piece "Students Resort to Study Drug Adderall" ran last month.
Image Credit – (Flickr/Alex Dodd)
ADHD In Teenagers
Stimulant
Is International Adoption by American parents over?
February 11, 2013 Pope Resigns: Did he fear the Perfect Storm over uncovering of Vatican Bank's Scandal and Money Laundering
POPULAR AT PATHEOS Hindu
Maha Mrityunjaya Mantra Mp3 Download -...
Hindu 2.0
Parvati: Everything You Need to Know
The White Hindu
On this International Day of Yoga,...
Samudra
Miracles in the Eastern Traditions: Buddhist,...
Seeking Shanti
Why do Hindus do Idol Worship? A Scientific...
Netanyahu's Horrible Mistake on Iran and History of...
How Republicans (America's "Poor man's Pakistan Army") can...

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}

    With the advance of multimedia technology and network infrastructures, video is ubiquitous, occurring in numerous everyday activities such as education, entertainment, surveillance, etc. There is a massive amount of needs for people to edit videos and share with others. However, video editing is challenging for non-professionals since it is not only laborious but also needs a lot of cinematography and design knowledge. Some editing tools like \textit{Adobe Premier} and \textit{Apple Final Cut Pro} are developed to assist video editing, however their main target users are professionals while novices may find it difficult to learn. Moreover, they still lack the ability of automatic video editing, i.e., users have to manipulate videos on their own. Recently, popular video editing tools like \textit{InShot Video Editor} and \textit{CapCut} provide the function of creating videos in one-click. Nevertheless, since they only utilize simple strategies or fixed video templates and ignore the content of input vision/audio, the quality of generated video is unsatisfactory.

    The video transition effects play an important role in video editing to join shots for creating smooth and cohesive videos. In this paper, we introduce a new task of automatic video transition recommendation (VTR) and provide a systematic solution. Specifically, VTR is defined as: given a sequence of raw video shots and the companion audio (which can either be original sound or overwritten music), recommend a sequence of video transitions for each neighboring shots. Different from conventional classification problems which choose only one most probable category as the output, VTR aims to provide a ranking of candidate transition categories so that users can choose freely in practical usage.

    When working on this task, we encounter following challenges.
    First, there is no publicly available video transition dataset for training and evaluation. It takes enormous efforts to manually collect and annotate a large-scale video dataset. Meanwhile, due to the large complexity and diversity of video editing, the evaluation of video quality is subjective and vary from person to person. Thus during creating the dataset, it is crucial to design proper criteria for selecting video samples that are appealing to most.
    Besides dataset, solving VTR is also challenging. A good video transitions recommender should ensure top transitions match well with both the dynamics and contents of videos and the rhythm and theme of audio (or music). Moreover, transitions recommended at multiple video connections should be harmonious so that the final video is visually smooth and unified. Being the first effort in addressing VTR, we need to take all of above factors into consideration for delivering the optimal solution.

    We start with building a video transition dataset from those video editing templates that are publicly available on video editing tools.
    We design comprehensive rules via trials to select high-quality video templates followed by preprocessing for refinement.
    Afterwards, we extract video shots and corresponding transitions (used as ground-truth in training), creating the first large-scale video transition dataset. More details are introduced in Sec. \ref{sec:data-collection}.

    To figure out the best way for modeling VTR, we conduct extensive experiments to compare the classification-based and retrieval-based solutions and finally demonstrate the latter performs better. In the classification-based solution, the model takes neighboring video shots as inputs and output the prediction of transition categories. The cross-entropy loss between predictions and ground-truth transitions is used.
    In the retrieval-based solution, we first pre-train a transition classification network to learn the embedding of transitions. Then we propose a multi-modal transformer to learn the fused vision/audio features in sequential video shots. A triplet margin loss is devised to minimize the distance between fused input features and pre-trained transition embedding. Similarly, in testing, according to the distance calculated between input features and transition embedding, the transition categories with smaller distances are in higher ranking position.

    \smallskip

    To conclude, our contributions are threefold:
    \begin{enumerate}[leftmargin=*] {
        \item We introduce a new task of automatically recommending video transition effects given video and audio inputs. We collect the first large-scale video transition dataset to facilitate future research on this task.
        \item We formulate the transition recommendation task as a multi-modal retrieval problem and propose a framework for learning the correspondence between input vision/audio and video transitions in feature space. The proposed framework is capable of fully utilizing the multi-modal input information for generating sequential transition outputs.
        \item Through both quantitative and qualitative evaluation, we demonstrate that the proposed method can successfully learn the matching from vision/audio to transitions and generate reasonable recommendation results. Moreover, a user study conducted to evaluate the quality of generated videos further demonstrates the effectiveness of our method.
        }
    \end{enumerate}

\section{Related work}

    \noindent\textbf{Video editing.}
    Automatic video editing is challenging due to following reasons.
    First, the model has to fully understand the spatial-temporal context and multi-modal information in videos to obtain semantically coherent results.
    Second, video editing requires lots of professional knowledge to endow videos with creativity and particular aesthetic taste. Third, the evaluation of the quality of the generated videos may be subjective.
    Recently, there are some progresses towards performing automatic video editing, each focusing on different aspects. Frey et al. \cite{frey2021automatic} proposes an automatic approach to transfer the editing styles of an edited video to the new raw shots. Wang et al. \cite{wang2019write} builds a tool for creating video montage based on the text description. Koorathota et al. \cite{koorathota2021editing} proposes a method to perform video editing according to a short text query by utilizing contextual and multi-modal information. Liao et al. \cite{liao2015audeosynth} introduces a method for music-driven video montage.
    Several methods focus on solving video ordering and shot selection \cite{frey2021automatic,wang2019write,pardo2021learning}. Distinguished from all tasks above, VTR is still unexplored although its equal importance in creating high-quality videos in video editing. In this work, we take the first step to close the research gap.

    \smallskip
    \noindent\textbf{Video transitions.} Video transition is a widely used post-production technique for achieving smooth transitions between neighboring shots via special image/video transforms. There are various kinds of transitions including straight cuts, fades, and 3D animations among many others.
    To professionals, each type of transition is with a dedicated meaning to convey specific emotions, feelings or scene information to viewers, thus should be used meticulously. Moreover, when multiple video transitions are used, they should work in a harmonious way to ensure the visual unification of the final video. Due to above reasons, it is difficult for non-professionals to apply video transitions in their edit. Our work can substantially assist these people by automatically recommending reasonable video transitions on the fly.

    \smallskip
    \noindent\textbf{Visual-semantic embedding.}
    Many recent works on video retrieval are based on the alignment of visual-semantic embedding \cite{hendricks2017localizing,akbari2021vatt}. Embedding techniques are employed to measure the similarity of different modalities in cross-modal video retrieval tasks, where features from different modals are mapped into a shared embedding space for better alignment \cite{karpathy2017deep,miech2019howto100m}. Miech et al. \cite{miech2019howto100m} utilizes millions of video text pairs to learn the text-video embedding. Escorcia et al. \cite{escorcia2019temporal} aligns the embedding of text and moments in the videos. They share the same idea of jointly aligning representations from two different modals.
    Triplet loss is initially proposed for learning the distance metric \cite{schultz2003learning}. It is used for learning multi-modal embedding through deep neural networks in recent works \cite{faghri2017vse++,schroff2015facenet}. By optimizing the distance directly with a soft margin, triplet loss is suitable for ranking tasks~\cite{hoffer2015deep}.
    Our method also employs triplet loss to learn the distance between representations. A multi-modal transformer is used therein to learn the features of vision/audio inputs which aim to match with the pre-trained video transition embedding. Through extensive experiments, we demonstrate the effectiveness of our methods.

    \smallskip
    \noindent\textbf{Multi-modal transformer and sequence modeling.}
    Our task is closely related to recent progress in modeling the spatial-temporal and multi-modal information in vision, speech and text. Transformer \cite{vaswani2017attention} is widely used in these tasks to encode cross-modal and spatial-temporal information \cite{arnab2021vivit,gabeur2020multi,lin2021survey,hendricks2021decoupling}.
    It employs an attention mechanism to represent multi-modal information in a common latent space. In other sequential problems, Lin et al. \cite{lin2021vx2text} uses a modality-specific classifier and a differentiable tokenization scheme to fuse multi-modal information via transformer. Gabeur et al. \cite{gabeur2020multi} introduces a video encoder architecture with multi-modal transformer for video retrieval. We also use modal-specific networks to extract embeddings from vision and audio inputs. Specifically, we use SlowFast \cite{feichtenhofer2019slowfast} and Harmonic CNN \cite{won2020data} to extract video and audio features respectively.
    Different with the vanilla transformer architecture which adopts an encoder-decoder architecture~\cite{vaswani2017attention}, Lei et al.~\cite{lei2020mart} uses unified encoder-decoder transformer model for sequential modeling.
    In our method, we exploit a multi-modal transformer for learning the multi-modal representations as well as capturing the context information in sequential transitions.

    \begin{figure}[htbp]
        \centering
        \includegraphics[width=0.90\textwidth]{figures/task_definition_h.pdf}
        \caption{The definition of the task. We take a sequence of raw video shots, for example $\{v_1, v_2, v_3\}$, and companion audio as the inputs, the task is to recommend categories of the transitions $\{t_{1,2}, t_{2,3}\}$ used in the edited video.}
        \label{fig:task_defination}
    \end{figure}

    \section{Task Definition}

        We aim to solve the task of video transitions effect recommendation (VTR), the goal of which is to recommend appropriate transitions between neighboring video shots. As shown in Fig. \ref{fig:task_defination}a, we take a sequence of raw video shots $\{v_1, v_2, \dots , v_n\}$ as inputs. To simplify the task, we assume that the order of the videos is already determined, all the videos are already cut and scaled to the target range, and the background audio (either original sound and/or overwritten music) is already specified. Then for a pair of video shots $v_{k}$ and $v_{k+1}$, a transition effect is added to join them. We denote the video clip added with transition effect as $t_{k,k+1}$.
        Since video transitions generally mix neighboring video shots, we separate the final video after adding transitions into two parts, one is the clips added by transitions (i.e. $t_{k,k+1}$), the other is uncontaminated video shots (i.e. $v_k'$ and $v_{k+1}'$).
        Then the output video after adding transitions can be denoted as $v'=\{v_1', t_{1,2}, v_2', \dots, t_{n-1,n}, v_n'\}$. The dataset we collect contains both the start and end timestamps of each transition so that we can obtain the exact positions of video shots and the categories of transitions. Note that in the collected dataset, we can only get access to the output video $v'$ since the original videos $v$ are not publicly available.

        In our method, we use the video clip $t_{k,k+1}$ and the label of corresponding transition effect $c_{k,k+1}$ to train the transition classification network, for learning the embedding of transitions based on their visual representation. When training the transition recommendation model, we remove $t_{k,k+1}$ from the input to be consistent with the inference setting where input videos are without any transition. The uncontaminated video shots $v_{k}'$ and $v_{k+1}'$ are used to represent the original video shots, i.e. $v_{k}$ and $v_{k+1}$ respectively. This strategy is reasonable since the duration of transition effect is short, $v_{k}'$ and $v_{k+1}'$ can serve as good approximations to their original counterparts.

    \section{Video Transition Dataset} \label{sec:data-collection}

        \subsection{Raw Data Collection}
        With the development of video editing tools and platforms, a large amount of well-designed video templates are publicly available.
        Produced by professionals, video templates define fixed combinations of essential editing elements, including the number of video shots, the length of video, music, transitions, animations, and camera movement, etc. By simply replacing materials in templates with self videos, even novice users can create edited videos easily.
        Each video template also comes with an example video made by the designer using this template and his/her original videos. In our experiments, we collect video templates from these online video platforms and
        get the annotations related to transition effects, including each transition's
        category and corresponding start/end time. Though video templates may contain other special visual effects like animations and 3D movements, we only consider video transition effects in this paper.

        \subsection{Data Filtering}

        \begin{figure}[tbp]
            \centering
            \begin{subfigure}{0.46\textwidth}
              \includegraphics[width=\linewidth]{figures/label_distribution_not_filtered.pdf}
              \caption{The label distribution on the small dataset at the first stage of data collection.}
              \label{fig:label_distribution_first}
            \end{subfigure}
            \hfill
            \begin{subfigure}{0.46\textwidth}
              \includegraphics[width=\linewidth]{figures/label_distribution_filtered.pdf}
              \caption{The label distribution on the final dataset after filtering.}
              \label{fig:label_distribution_second}
              \begin{minipage}{.1cm}
              \vfill
              \end{minipage}
            \end{subfigure}
            \caption{Label distribution of top-30 categories in the dataset.}
            \label{fig:label_distribution}
        \end{figure}

        We perform data filtering to improve the quality and diversity of the dataset.
        At first, we limit the maximum duration of the videos to 60 seconds, and the templates are filtered depending on the number of user likes and usages. We ignore those videos without any transition. We gather a small dataset through manual crawling and examine the overall distribution of collected samples. To guarantee that there are enough training samples for each category, we only select top-30 categories for training and testing according to the amount of samples. By statistical results, we observe that there are many duplicated transitions in a single video, and different types of transitions are distributed in a severe long-tail manner. The label distribution of the small dataset is shown in Fig. \ref{fig:label_distribution_first}.

        \setlength{\tabcolsep}{4pt}
        \begin{table}[tbp]
        \begin{center}
        \caption{The statistical information of the transition dataset.}
        \label{tab:dataset_statistic}
        \resizebox{0.5\textwidth}{!}{
        \begin{tabular}{c|cc}
            \hline
            Dataset & train set & test set \\
            \hline
            Number of videos & 29,998 & 5,000 \\
            Number of transitions & 118,984  & 19,869 \\
            Transitions per video & 3.966 & 3.973 \\
            Average video length & 15.83 secs  & 15.79 secs \\
            \hline
        \end{tabular}
        }
        \end{center}
        \end{table}
        \setlength{\tabcolsep}{1.4pt}

        We believe that the duplication and long-tailed distribution are harmful to the diversity of recommendation. To solve this issue, we use two additional rules to select samples: each video should contain more than two different types of transitions, and the usage times of the same transition should be no more than six. Following these rules, we acquire the final dataset which contains 34998 videos (train-29998 and test-5000) in total and 138.8K valid transitions between neighboring video shots. Table \ref{tab:dataset_statistic} shows more statistical results of the dataset. The label distribution is shown in Fig. \ref{fig:label_distribution_second}.

\section{Video Transition Recommendation}

    \subsection{Pre-training Transition Embedding} \label{section:pre-training_transition}

        We formulate the video transition effects recommendation as a multi-modal retrieval problem. Since retrieving from vision/audio to video transitions requires the model to learn correspondence between vision/audio inputs and video transitions, the first problem we need to solve is thus how to learn a strong representation for each transition. We notice that some video transitions have similar visual effects like ``pull in'' and ``pull out''. It is natural that we expect the learned embedding can also reflect these connections.
        To achieve this goal, we employ a video classification network to learn transition embedding based on their visual appearance. As shown in Fig. \ref{fig:pre-training_transition_embedding}, we take the transition clips $t$ as input and use the video backbone to extract visual representations. After passing through linear transform and normalization, we obtain a unit vector for each transition. Then we apply another linear transform and use the cross-entropy loss to optimize the classification objective. As expected, the embedding of the transitions are separably distributed in latent space and similar transitions stay close to each other. The visualization result of learned embedding through t-SNE is illustrated in Fig. \ref{fig:tsne}.

        \begin{figure}[tbp]
        \centering
        \includegraphics[width=\textwidth]{figures/pretraining_transition_embedding.pdf}
        \caption{Extracting transition embedding. A transition classification network is built to learn the transition embedding.}
        \label{fig:pre-training_transition_embedding}
        \end{figure}

    \subsection{Multi-modal Transformer} \label{section:multi-modal trans}
         As shown in Fig. \ref{fig:framework_matching}, we propose a multi-modal transformer to extract representations from the raw video shots and audio. For recommending a transition $t_{k,k+1}$ between video shots $v_{k}$ and $v_{k+1}$, we take both the video frames and audio waves from the end period of video shot $v_{k}$ and the start period of video shot $v_{k+1}$ as input. Specifically, $n$ video frames are sampled uniformly from each video shot.

         After obtaining video frames and corresponding audio waves, we extract their features by feeding into visual and audio backbone respectively. Note that these backbones can be conveniently replaced by other common video or audio models. In our experiments, we use the SlowFast \cite{feichtenhofer2019slowfast} and the Harmonic CNN \cite{won2020data} as video and audio backbone respectively.

         In order to make full use of multi-modal information of vision/audio, we combine the visual and audio features as the inputs for the multi-modal transformer. By doing so, the model not only learns the matching relationship from inputs to transitions but also captures the context cues among sequential transitions.

         Before being fed into the transformer, visual tokens and audio tokens are projected to the same dimension by independent linear transformations. Then learnable positional embedding and modal embedding are element-wisely added to these tokens. We share the positional embedding for vision/audio tokens at the same time point. Modal embedding is applied to inform the model which modality the token belongs to. After above processing, tokens from all video shots are input into transformer as a whole. In this way, the transformer is encouraged to learn the contextual relationship in sequential transitions. As demonstrated by experimental results, such a context-aware training method contributes to generate harmonious sequential outputs.
         The self-attention mechanism in transformer encoder can model complex mutual relationship among input tokens. From all output tokens, we concatenate those four tokens which corresponds to each transition point (for each modality of vision and audio, there are two tokens before and after the transition point) along the feature dimension and get the final representation by a projection to the same dimension of transition embedding.

        \begin{figure}[tbp]
            \centering
            \includegraphics[width=\textwidth]{figures/transition_recommendation_framework.pdf}
            \caption{Transition recommendation model for retrieving matching transitions based on vision/audio inputs. First, we use modality-specific networks to extract visual and audio features. After that, a multi-modal transformer encoder fuses the tokens from different modalities. Finally, a triplet loss is used to optimize the network end-to-end. }
            \label{fig:framework_matching}
        \end{figure}

    \subsection{Transition Recommendation}

        As introduced in Sec.~\ref{section:multi-modal trans}, the multi-modal video embedding is extracted by the multi-modal transformer, which is used as the query to retrieve the transitions by matching the pre-trained transition embedding mentioned in Section \ref{section:pre-training_transition}. During retrieval, we apply learnable linear transformations to both the pre-trained transition embedding and the video embedding for achieving better alignment.

        We denote $E^{trans}= \{e^{trans}_1, \dots, e^{trans}_N\}$ as the set of pre-trained transition embedding where $N$ is the number of categories and $N=30$ in our experiments. $E^{video}= \{ e_1^{video}, \dots, e_{V}^{video} \} $ is denoted as the multi-modal input embedding in a batch. $V$ is the number of all transition points in a batch. For each sample $e_{v}^{video}$, we calculate its matching score with every transition embedding through a similarity metric $\mathrm{\Phi}(e_{v}^{video}, e^{trans}_k)$. In our implementation, $\mathrm{\Phi}$ adopts the form of dot-production due to its simplicity, i.e.
        \begin{equation}
        \label{eq:phi}
        \mathrm{\Phi}(e^{video}_v, e^{trans}_k) = \left<e_v^{video},e^{trans}_k \right>.
        \end{equation}
        Eq.~(\ref{eq:phi}) is used to calculate the ranking loss in the following training steps.

        \smallskip
        \noindent\textbf{Training.}
        We expect that the model can learn to rank transitions by their distances with inputs in embedding space. To achieve so, we utilize the triplet margin loss to optimize the similarity between the transition embedding and multi-modal video embedding. For the embedding of each training sample $e^{video}$ with ground truth label $c$, we define our training objective with triplet loss as
        \begin{equation*}
        \mathcal{L}(e^{video}) = \frac{1}{N-1}\sum_{k\ne c,k\in \{1,\dots,N\}}{\mathcal{T}(e^{video},e^{trans}_c,e^{trans}_k)}
        \end{equation*}
        where $\mathcal{T}$ calculates the triplet margin loss for each triplet $(e^{video}, e_c^{trans}, e_k^{trans})$:
        $$ \mathcal{T}(a,p,n) = max(\mathrm{\Phi}(a, p) - \mathrm{\Phi}(a, n) + M, 0). $$ $M$ is the soft margin, $a$, $p$ and, $n$ are anchor, positive sample, and negative sample, respectively. $\mathrm{\Phi}$ follows the definition of Eq.~(\ref{eq:phi}). In our settings, we take video embedding $e^{video}$ as the anchor, the transition embedding with category $c$ as the positive sample, others transitions as negative samples. The final objective is the average over all samples, i.e.
        \begin{equation}
        \label{eq:final-loss}
            \mathcal{L}(E^{video}) = \frac{1}{V}\sum_{v\in\{1,\dots,V\}}{\mathcal{L}(e^{video}_v)}.
        \end{equation}
        By optimizing Eq.~(\ref{eq:final-loss}), the model encourages the similarity between the multi-modal video embedding and its ground-truth transition embedding higher than the similarity between non-matching pairs with a margin of $M$.

        \smallskip

        \noindent\textbf{Evaluation.}
        In evaluation, we follow Eq.~(\ref{eq:phi}) to measure the matching degree between multi-modal video embedding and candidate transition embedding. For two neighboring video shots, we sort the similarities of candidate transitions in descending order and select the top one as final result.

\section{Experiments}

    \subsection{Implementation Details}

        \noindent\textbf{Model details.}
        We employ SlowFast 8$\times$8 \cite{feichtenhofer2019slowfast} as the backbone to extract visual features. The same backbone is also used as the transition embedding network. By default, we freeze SlowFast from stage 1 to stage 3 during our experiments to save memory. We train all models on one machine with 8 NVIDIA V100 GPUs, except the experiment without freezing the SlowFast backbone, in which we use two machines and 16 GPUs in total. For audio modal, we use Harmonic CNN \cite{won2020data} to extract the local audio features around the transition.
        Then we linearly project the feature of two modals to the same dimension of $d_{model}$ and take them as the input tokens of the multi-modal transformer. The multi-modal transformer consists of two transformer layers with $d_{model} = 2048$ and $n_{head}=8$.
        Before matching, we apply linear projections to both the video embedding and pre-trained transition embedding in order to map them into a joint space. Such a projection is experimentally proved to be beneficial as shown in Table \ref{tab:pre-training_transition_embedding}.

        \smallskip

        \noindent\textbf{Data preprocessing and training.}
        When training the transition embedding network, we uniformly sample 16 frames with the image size of 224$\times$224 from the transition duration as the input. The batch size is set to 256. The training process is 30 epochs in total, and start with a warm up for 5 epoch to raise the learning rate from 1e-6 to the initial learning rate 1e-3, then decay by a factor of 0.1 every 10 epochs. We use the model parameters of the last epoch to generate the transition embedding.

        When training the transition recommendation model, we uniformly sample 16 frames with the image size of 224$\times$224 as the visual inputs. The local audio features are extracted using the pre-trained Harmonic CNN before training. Given a time point, the Harmonic CNN generate a feature vector with a dimension of 100 based on the audio in around one second. For the sequential inputs, we set the maximum sequence length to 8. Redundant transitions beyond the maximum length are dropped while zero tensors are padded if the length is less than 8. We use Adam optimizer in all the experiments and set the soft margin $M=0.3$ in triplet margin loss. The initial learning rate is set to 1e-5, then decay by a factor of 0.1 every 10 epochs. The total training epoch is 30 epochs.

        \smallskip

        \noindent\textbf{Metrics.} For testing the fine-grained performance of our model, we evaluate the individual recommendation results in testing.
        The commonly used Recall@K metric ($k\in \{1,5\}$) and Mean Rank are employed as evaluation metrics.
        Since in our dataset, there is only one ground-truth for each transition, Recall@K indicates the likelihood of hitting the target in top K retrieval results.
        Mean rank is the averaged rank of all ground-truth transitions, whose math formula is
        $$
        \mathrm{MR} = \frac{1}{|\mathcal{S}|} \sum_{i \in {S}} \mathrm{Rank}(i)
        $$
        where $\mathcal{S}$ represents the set of all transitions in test.

    \subsection{Extracting Transition Embedding}

        \begin{figure}[tbp]
        \centering
        \includegraphics[width=0.40\textwidth]{figures/t-SNE-drop_outliers.pdf}
        \includegraphics[width=0.41\textwidth]{figures/categories.pdf}
        \caption{t-SNE visualization of the transition embedding on the train set after pre-training. We use cosine similarity as the distance metrics when running t-SNE. We drop the outliers and randomly pick 10K samples from the t-SNE output for visualization. We can see that transitions with similar visual effects, such as ``left'' (8), ``right'' (11), and ``down'' (19) are close to each other in the embedding space.}
        \label{fig:tsne}
        \end{figure}

        A transition classification network is trained using annotated transition category in the dataset as the transition embedding network. After training, we use the pre-trained network to extract the embeddings of
        all transitions in the training set. Normalization is then applied for the embedding as shown in Fig. \ref{fig:pre-training_transition_embedding} to convert the embedding into a unit vector. We drop the outliers utilizing the three-sigma rule by assuming the embeddings follow a normal distribution. The embeddings after dropping are visualized in Fig. \ref{fig:tsne}. The remaining embeddings for each category are averaged to generate the final transition embedding.
        We then demonstrate the effectiveness of the transition embedding by the experiments shown in Table \ref{tab:pre-training_transition_embedding}. Notably, the transition embedding network is advantageous in extending the model to support new transition categories since retraining the recommendation model is circumvented.

    \subsection{Ablation Studies and Comparisons}

        We start by showing the advantages of leveraging contextual and multi-modal information in inputs. Then we verify the effectiveness of the pre-trained transition embedding by comparing it with random initialization. We also compare with the classification method to demonstrate the superiority of retrieval methods on this task.
        Due to space limit, more details of results are referred to the supplementary material.

        \smallskip
        \noindent\textbf{Context and multi-modal.}
        In this experiment, we study the impact of contextual and multi-modal inputs on the recommendation performance, and the result is shown in Table \ref{tab:context_and_modal}. From the first and third rows of Table \ref{tab:context_and_modal}, we can see that sequential inputs introduce the context information to the model, which is helpful for modeling the temporal relations between the transitions. From the second and third rows of Table \ref{tab:context_and_modal}, visual modal inputs perform much better than audio as input, which indicates that visual content is more related to the transition effects than audio.
        The results in the last three rows demonstrate that the multi-modal inputs can improve the accuracy of recommendations than the single modal inputs.

        \setlength{\tabcolsep}{4pt}
        \begin{table}[tbp]
        \begin{center}
            \caption{The impact of contextual and multi-modal information to the recommendation results.}
            \label{tab:context_and_modal}
            \begin{tabular}{c|cc|ccc}
                \hline

                \multirow{2}{*}{Sequential (Context)} & \multicolumn{2}{|c|}{Modal} & \multirow{2}{*}{Recall@1} & \multirow{2}{*}{Recall@5}  & \multirow{2}{*}{Mean Rank} \\
                \cline{2-3}

                & Visual & Audio & & & \\
                \hline

                                        & \checkmark&           & 24.12\% & 66.25\% & 5.758 \\
                \checkmark              &           & \checkmark& 19.39\% & 56.61\% & 7.012 \\
                \checkmark              & \checkmark&           & 25.40\% & 66.33\% & 5.665 \\
                \checkmark              & \checkmark& \checkmark& \textbf{28.06\%} & \textbf{66.85\%} & \textbf{5.480} \\
                \hline
            \end{tabular}
        \end{center}
        \end{table}
        \setlength{\tabcolsep}{1.4pt}

        \smallskip
        \noindent\textbf{The effectiveness of pre-trained transition embedding.}
        In this experiment, we study the effectiveness of our proposed pre-trained transition embedding, and the results are shown in Table \ref{tab:pre-training_transition_embedding}. All the embedding is frozen during training. In the random initialization setting, we use a normalized random embedding as the replacement of the pre-trained transition embeddings. It is observed that the performance of using random embedding is worse compared to our pre-trained embedding.
        From the results in second and third rows in Table \ref{tab:pre-training_transition_embedding}, the importance of the linear projection can be demonstrated. We conjecture the linear projection helps learning better mapping between the pre-trained transition embedding and multi-modal input embedding in a shared space.

        \setlength{\tabcolsep}{4pt}
        \begin{table}[tbp]
            \caption{Advantages of pre-training transition embedding from the visual effects. We demonstrate its effectiveness by comparing it with a random initialized embedding.}
            \label{tab:pre-training_transition_embedding}
            \centering
            \resizebox{\textwidth}{!}{
            \begin{tabular}{l|c|ccc}
                \hline
                Transition Embedding            & Projection    & Recall@1 & Recall@5 & Mean Rank \\
                \hline
                Random initialization               &               & 25.67\% & 66.3\% & 5.646 \\
                Pre-trained transition embedding &               & 26.24\% & 66.03\%  & 5.623 \\
                Pre-trained transition embedding & \checkmark    & \textbf{28.06\%} & \textbf{66.85\%} & \textbf{5.480}  \\
                \hline
            \end{tabular}
            }
        \end{table}
        \setlength{\tabcolsep}{1.4pt}

        \setlength{\tabcolsep}{4pt}
        \begin{table}[tp]
            \caption{Comparing with the classification method.}
            \label{tab:classification}
            \centering
            \resizebox{\textwidth}{!}{
            \begin{tabular}{l|ccc}
                \hline

                Methods       & Recall@1 & Recall@5 & Mean Rank \\
                \hline

                Classification                                  & 22.27\% & 61.82\% & 6.099 \\
                Matching with pre-trained transition embedding   & \textbf{28.06\%} & \textbf{66.85\%} & \textbf{5.480}  \\
                \hline
            \end{tabular}
            }
        \end{table}
        \setlength{\tabcolsep}{1.4pt}

        \smallskip
        \noindent\textbf{Comparing with the classification method.}
        In this ablation study, we remove the transition embedding, replace triplet margin loss with cross-entropy, and train the recommendation model utilizing the transition category label.
        The comparison result is shown in Table \ref{tab:classification}.
        The classification model performs worse compared with the retrieval model. The reason is that the learned transition embeddings in retrieval model contain richer visual properties of the transitions compared with semantically meaningless one-hot vectors used in the classification model. In addition, the cross-entropy loss may impose excessive punishment for negative categories due to using one-hot ground-truth, thus
        neglecting the fact that there are similar transitions as the ground-truth transition and they can also be used as favorable alternatives.

\subsection{User Study}
\label{section:user_study}

Since the transition recommendation is subjective, the viewer's feeling is essential to the evaluation. Therefore, we conduct a user study to further verify its effectiveness. Specifically, we collect raw video shots from online copyright free video sources, e.g. videvo.net\footnote{https://www.videvo.net/}, covering various topics such as travel, life, entertainment, sports, nature, and animals. For each set of video shots, we fix their orders and assign an appropriate background music, leaving only the transitions between neighboring video shots to be added. After selecting
video transitions, we use the tool of \textit{CapCut} to connect the raw video shots by transitions, producing the final videos.
We compare among following three methods of selecting video transitions.

\begin{enumerate}
    \item \textbf{Weighted random pick.} At each transition point, select the transition category by a random sampling from a multinomial distribution. The probability of each category is its frequency in our collected video transition dataset.
    \item \textbf{Professional video editor.} We ask a professional editor who has 6 years of video editing to select transitions. He is free to take as long as he wants to select the best transitions depending on his understanding of the given video/audio.
    \item \textbf{Our method.} The top-1 video transition predicted by our method at each transition point is used as the best selection.
\end{enumerate}

We collect 20 groups of video results in total for user study. Each group contains three videos edited using above three methods respectively. We invite overall 15 non-expert volunteers to participate in the evaluation.
They are asked to choose a favorite video from each group (Q1) and rate each video on a scale of 1 to 5 (1 = poor, 5 = excellent, Q2), taking into account the general visual quality of videos and the matching degree between transitions and video/audio.
Table \ref{tab:user_study_results} and Fig. \ref{fig:vote} show the statistics of the results. As shown in Table \ref{tab:user_study_results}, the videos generated using random pick receive the lowest score. Our method is pretty close to the professional editing in terms of average score, but being much more efficient by drastically reducing the average processing time of each video from 7.5 minutes to only 1.5 seconds (a \textbf{300\scalebox{1.25}{$\times$}} speedup). Interestingly, in Fig. \ref{fig:vote} which shows the voting results of Q1, one can see that videos from our methods are slightly more appealing than that from the professional editor. Above experimental results clearly demonstrate the advantages of our method in producing high-quality video transitions recommendations.

\setlength{\tabcolsep}{4pt}
\begin{table}[tbp]
    \caption{The statistical results of user study. The inference time is reported as the time cost of our method.}
    \label{tab:user_study_results}
    \centering
    \begin{tabular}{l|cc}
        \hline
        Method & Avg. score & Avg. time (per video) \\
        \hline
        Weighted random pick & 2.96 & - \\
        Professional video editor & 3.80 & 7.5 min \\
        Our method & 3.76 & 1.5 secs \\
        \hline
    \end{tabular}
\end{table}
\setlength{\tabcolsep}{1.4pt}

\begin{figure}[tbp]
    \centering
    \includegraphics[width=0.8\textwidth]{figures/vote.pdf}
    \caption{The voting results for three methods in Q1.}
    \label{fig:vote}
\end{figure}

\section{Conclusions and Future Works}

    The recent development of online video tools and platforms creates a high demand for a user-friendly video editing experience, which asks for a computational method or artificial intelligent model to lower the barrier, improve efficiency and ensure quality for doing video editing. Therefore, we propose a new task of video transition recommendation (VTR) to automatically recommend transitions based on any visual and audio inputs. We start with building a large-scale transition dataset. Then we formulate VTR as a multi-modal retrieval problem and propose a flexible framework for addressing the task. Through extensive qualitative and quantitative evaluations, we clearly demonstrate the effectiveness of our method. We hope this work can inspire more researchers to work on VTR and bring creativity and convenience to both professionals and non-professional users. Future works include but are not limited to extending the framework to support more video editing effects
   like video animation, 3D movements, etc., developing more efficient models for mobile deployment and integrating with other video editing techniques to create more comprehensive video editing systems.

\section{Acknowledgement}

Yaojie Shen did this work when interning at ByteDance Inc.

\clearpage

\bibliographystyle{splncs}

redpajama_set_name: RedPajamaArXiv


Vietnam Pangasius Association is a social – professional organization. It was established by Vietnamese individuals and organizations who are working in the field of feeding, processing, exporting or conducting the research and catering for pangasius supporting services in Vietnam.
The e-commerce website for Vietnam Pangasius - Fisheries sector (the website) is established by Vietnam Pangasius Association. Vietnam Pangasius Association is a social – professional organization. It was established by Vietnamese individuals and organizations who are working in the field of feeding, processing, exporting or conducting the research and catering for pangasius supporting services in Vietnam.
The website aims to provide information and an open business environment for Vietnamese pangasius exporters. The website also plays the role of a reliable shopping channel for foreign partners.
The website is managed and operated by Vietnam Pangasius Association (VNPA). The website's members are businessmen, organizations, and individuals who are doing the legal business activities (according to Vietnamese and international laws) domestically and internationally. The mentioned individuals and organizatons must be allowed by VNPA to use the website's services.
This rule is applied for members who register users, stands for introducing or selling products on the website.
The businessmen, organizations, and individuals who do the transactions on this website will be able to negotiate. The negotiation must ensure the rights and legal interests of all parties involved in the transaction. Transactions are done based on contracts that comply with Vietnamese laws and international commercial law.
The products and services on the website must comply with all related laws. They must not belong to people or businesses who are banned from either doing business or doing advertisement.
All of the contents in this regulation must conform to the current law system of Vietnam and the international commercial law. All members themselves have to understand their own legal responsibilities with the current laws of Vietnam and commit to implement all the website's contents.
Sellers: are businessmen, organizations, and individuals that need the services including: setting up stands, posting products on-line, or providing company information.
Buyers: are businessmen, organizations, and individuals that need information about the products and services that are posted on the website. Buyers may register user accounts.
Members: includes sellers and buyers.
- Members who join the mentioned transactions are businessmen, organizations, and individuals that need to sell or buy the products and/or sevices that are listed on the website.
- Members declare all related information. Once they are officially confirmed by the website's board of management, the members can use the website's sevices.
+ A member can create an individual account for personal usage.
+ A member can buy the products and/or services with the correct prices and qualities that were provided by sellers on the website.
- The contents of this regulation comply with current law of Vietnam. When members join the website, they themselves have to understand their legal responsibilities and commit to comply with the website's regulation. If some principles are not included in this regulation, the members can make reference to the current E-commercial law of Vietnam.
- Search and refer to information about the products, services and sellers that buyers may be interested in.
- Buyers can contact sellers for further information about the products or services using the contactinformation provided on the website. In some cases, the two parties can contact in person.
- Buyers decide to place an order and choose the means of transaction according to the specific policies of each seller..
- The buyers and sellers do the transaction through foreign trade contract or other means of contracting via the website.
- The buyers and the sellers take responsibility for the delivery, liquidation and handling of complaints (if there are complaints).
- Vietnam Pangasius Association does not participate in or be responsible for signing contracts between buyers and sellers.
The information used for uploading on the website must be devided into two parts: test and pictures.
The test should be typed (using MS Word, Open Ofice) with the following format: font: Arial; size: 12.
The format for pictures are: jpg, bmp or gif.
- Log in by using the account registered on the website.
- Go to "Stand Management" section to upload the products.
The delivery may be done using carrier services, logistics services or other means of transportation.
The sellers are responsible for ensuring the products' quality and quantity as mentioned in the commitment about the products.
Buyers can raise the complaints in case the products they receive do not meet the commitments of sellers.
If the buyers want to return the products, they can directly contact the sellers. In case the buyers and sellers do not come to an agreement, the buyers can advise the website management board. The website management board will do all it can within its ability to resolve the matter, or refer to for arbitration..
The sellers are responsible for receiving the buyers' complaints as well as helping the buyers solve the problems related to transactions on the website.
Address: 6th Floor, VCCI Building, No. 12, Hoa Binh Street, An Cu Ward, Ninh Kieu District, Can Tho City.
The parties respect the legal responsibilities in protecting the buyers' rights and comply with these responsibilities seriously. Due to this reason, the sellers on this website have to provide the correct and full detailed information about the products or services. All commercial cheats/frauds are not accepted and must be punished by law.
As prescribed by law, those who commit commercial fraud will be punished. The sellers are responsible for providing documents and information that relate to the other parties if the other parties request the documents.
After the buyers and sellers have finished their dispute, they must report to the website's management board. If it is the fault of sellers, Vietnam Pangasius Association will warn the sellers, remove all information about the sellers' products and services on the website and request the sellers to compensate the buyers for breaking the contract. Vietnam Pangasius Association can pass the case to legal authorities if the fault is serious.
In case the buyers and sellers cannot come to an agreement for the dispute, one of the two parties can sue at court or at the international commercial arbitration center (according to the terms of contract).
Vietnam Pangasius Association interconnect with the International Arbitration Center in Vietnam (VIAC). The parties in the commercial contracts are encouraged to include in their contracts, the ability to refer disputes to the VIAC for solving.
The fee for using services of the website is publicized. Vietnam Pangasius Association does not accept any prices that are not presented in the website.
Account holder: Vietnam Pangasius Association.
Unless the Vietnamese legal authorities request (and in conformity with the international commercial law), all transaction information is secured.
In case the buyers contact the sellers in person, the buyers have to make payment before the delivery.
The website will not be responsible for any damage occurring during the transaction if the buyers and sellers contact via other methods (like personal email) instead of using the website's services.
The buyers do not provide the payment's details to any other party via email or other methods of contact. The buyers are responsible for making payment by using the ensured international payment services. In case the buyers contact the sellers in person, the buyers have to make payment before the delivery.
The sellers have to provide enough information as requested in the form of the website.
The website's management board uses different ways to protect the sellers' product information in order to ensure the transaction.
Other payment methods can be used according to the agreement of the buyers and sellers.
The collected information includes: email address, phone number, login name, login password, andaddress of members. All the information is required when the parties register for a member account. The collected information is used to protect the members' right.
The members themselves are responsible for all actions under their names and passwords. The members are also responsible for reporting the violations of using accounts or violations of the security procedures.
- Give notices about the actions, and exchange information between the website and its members.
- Prevent the action of destroying the members' account or the action of disguising the website's members.
- Contact and help the members solve problems in specific situations.
- Do not use the members's information for other purposes, except for confirmation and transactions on the website.
- If requested from the legal authorities, the website will provide its members' information. The legal authorities are: procuracy; court, or police officers that are investigating the illegal actions of customers. Apart from these situations, no one can violate the members' personal information.
The personal information of a member will be maintained until that member wants to delete his/her information.
- Head office: 6th Floor, VCCI Building, No. 12, Hoa Binh Street, An Cu Ward, Ninh Kieu District, Can Tho City.
- By logging into their user accounts, the website's members can check, update, edit or cancel their personal information.
- Members can raise complaints about losing personal information. When receiving the complaints, the website's management board will check the information, then reply to members' complaints and assist them to restore the personal information.
6) Commitment for members' personal information security.
- The personal information of members will be absolutely secured according to the policies of the website. If any party want to take or use a member's information, he/she has to get permission from that member (unless there are other stipulations of the laws).
- Do not use, transfer or provide a member's personal information to any other party unless that member allows the website to do that.
- In case there is information lost due to hackers, the website will directly report the situation to legal authorities and advise the members.
- All members are required to provide information about the representatives of "legal person status" in their transaction. Information includes: full name, address, email address, identity card number, phone number, and bank account number. Members take responsibility for the legality of the information provided. The website will not be responsible for the members' interest if members provide incorrect information.
All representatives of "legal person status" are requird to provide related information (full name, address, email address, identity card number, phone number, and bank account number) when they register for user accounts. Representatives of "legal person status" take responsibility for the legality of provided information. The website will not be responsible for the buyers' rights/interests if buyers provide incorrect information.
Buyers have the right to directly give complaints to sellers if the products delivered by sellers do not meet the qualities announced (by the sellers).
In case the dispute occurs between buyers and sellers affects the buyers' legal rights, the website will play the role of arbitration to protect the buyers' legal rights.
Members take responsibility for keeping and securing all of their transaction information under their accounts. Members are responsible for reporting the violations of security procedures, keeping accounts of the third party or reporting any unauthorized use of accounts so that the website can make appropriate responses.
Members are not allowed to use the website's srvices for bad purposes (such as illegal actions, illogical actions, cheat, destructive actions, creating or spreading viruses on the system, speculating or engrossing the market, creating fake purchase orders or offers). If the members violate any of these mentioned prohibitions, they themselves will face the legal responsibility.
Members are not allowed to make a copy of the website's services and transfer to the third party without permission from the website's management board.
Members do not take the bad actions to discredit (harm the good reputation of) the website.
2) All products in the transactions on this website must be the fisheries ones.
- Toxic chemical, chemical use in the fisheries industry that may be hazardous to consumers, the environment, or species of fish.
- Wild animals or trees.
- Fish that are not allowed to be caught; seafood that contains residues exceeding the limitation; toxic seafood; fish that are not allowed to be caught or sold in breach of to the international law for sustainable development of the fisheries industry.
- Medicine for human being; kinds of vaccines; medical bio products; chemical and insecticide chemical; anti-bacteria chemiscal that is not allowed in Vietnam.
- Merchandise or products that are not allowed to be traded (according to the laws).
- Advertisements that are vulgar; or advertisements that are not suitable to the Vietnamese fine custom.
- Advertisements that insult the honor or prestige of individuals or organizations.
- Advertisements that use the photos, citations, or handwriting of individuals or organizations without permission from the individuals or organizations.
- Advertisements that directly compare the price, quality, or effects of a product to the products of other individuals and/or organizations.
- Advertisements with unfair competition (according to the law of competiton).
- Advertisements that violate intellectual property rights.
- Kinds of advertisement that are not allowed by law.
Mechanism for reviewing and controlling the products and/or services on this website.
The sellers provide details including information of the companies and/or individuals that need to open stands on the website. Only when the website provides user accounts, the companies or individuals are able to upload their products on the stands.
The website's management board have the total right to remove members' products from their stands if there is evidence to believe that those products violate this website's regulation. The inappropriate products will be removed from the stands or will be moved to the appropriate section.
If the products harm the good reputation of Vietnamese fishery industry or violate either Vietnamese or international commercial laws, the website's management board have the right to remove those products from the stands with or without giving warning to the members.
The website's management board commit to ensure the safety and stability of the entire system. However, the website's management board will immediately take action to protect the buyers' interest if any technical error (of the website) occurs.
When doing transactions on the website, all members have to comply with the guided process.
The website's management board commit to provide the best quality services to members (of this site). Where technical errors or objective errors occur, and members cannot do the transactions, they should immediately inform the website's management board. The management board will solve the problem as soon as possible to ensure the members' convenience in transactions.
However, it is not the responsibility of the website's management board if members fail to inform the board about the errors due to transmission problem or other problems that are not caused by the management board.
After finishing all required procedures for registering, members are conferred the accounts and/or services by the website's management board.
In cases where there is evidence that a member provided incorrect and inadequate information or the information provided may harm the fine custom of Vietnam, the website's management board have the right to stop, pause, orcancel that menber's right to use the website's services.
The website's management board will terminate a member's rights or cancel his/her right to use the website's services if that member violates the website's regulation or takes actions to harm the business of this website. The notice will be send to the member within 01 month.
The website's management board will terminate the members' accounts if those members do not have any transaction for 03 months. If those members want to be website's members again, they have to follow procedures for registering a new user account.
The website's management boad can terminate the members' accounts if the members are bankrupt; are sentenced, the members' business causes dispute about legality, commit commercial fraud or violate Vietnamese laws.If the members' accounts are terminated, all of their rights on the website are terminated too.
The copyright of this website and its content belong to the website's management board (according to the law of intellectual property rights). All of the symbols and contents in different languages belong to the website. All actions of copying, using or spreading the copyright of this website (without permission of the website's management board) are illegal.
The website's management board have the right to change the prices of services or methods of payment during the time membes use this website's services. The changing is made based on the website's need and possibility. Members will be notified about the change within 01 month.
The website's management board is responsible for establishing and performing the "Mechanism to ensure the sellers' information on this website is correct and adequate" (in order to comply with clause 04, article 36 of Decree No. 52/2013/NĐ-CP).
Research, design and buy either the hardware or software for this website.
Set up the internet connection.
Build up the service policies that fit the website's ability.
Build up the system of services.
Build up the tools used for serving the website's users.
The website's mamagement board will become the middle man to reconcile the dispute of buyers and sellers. The website will receive the complaints from buyers and do the audit. If the complaints are considered to be true, the website will seek an explanation from the sellers. Depending on the violations of the sellers, the website may request the sellers to compensate the buyers or the website may even terminate the sellers' accounts.
Professional qualification for foreign trade.
System of legislation for domestic and foreign trade.
Information about the foreign markets.
News relating to the website.
The website's management board will promote and advertise the website in foreign markets to develop the market and help the members to find more customers.
Technical errors of the hardware.
The command from legal authorities or from a third party.
However, if the problems are beyond the website's ability or control, then, the website does not take responsibility.
If the members have finished registering their accounts, they can buy and sell products on the website.
Each member will be provided with an account (user name and pass word) to log in and control their transaction on the website.
The website's staff will guide members to use tools for buying, posting advertisements and other services on the website.
Members can give comments on the working process of this website. Comments are directly sent to the website (in form of letter, fax or email).
Members must report the violation of any third party about the illegal use of their accounts and password so that the website can help them to resolve the problem.
Members have to ensure that all information they provide and upload on the website is correct.
Members themselves have to be responsible for: the content, pictures and information about their companies as well as all of their transactions.
The sellers and buyers must provide information about the transaction and products to help the website's management board handle the dispute (between the sellers and buyers).
The sellers have to compensate the buyers if faults are proved to belong to the sellers.
Make fake orders and/or offerings.
If the members take the illegal action as mentioned, they have to be accountable for their actions under the laws.
Members are not allowed to make any changing, copy or provide the website's services to any third party without permission of the website's management board.
Use a second account to cause disunity among the website's members.
Spread information that may harm the good reputation of the website.
When the decision for the website's promulgation takes effect, the website's regulation takes effect. The website has the right to make adjustment of this regulation (by notifying on this website). The new regulation takes effect as soon as its decision for adjustment takes effect. If the members continue to use this website's services after the new regulation takes effect, it is considered that the members accept the new regulation.
The regulation will be updated continuously without warning. All members have to comply with the current regulation (at the time members use services) to do the transactions.
All members and their partners are considered to accept this regulation if they use the website's services.


text:
Unit Studies & All-In-One Programs >
Unit Studies >
Comprehensive Unit Studies >
The Weaver Curriculum
Rebecca Avery
The Interlock
The Interlock program, designed for preschool and kindergarten, studies Genesis 1-10. The program includes manipulatives, visual aids, a music tape, math, and a writing program. This is a low-key approach, covering social studies, science, language arts, arithmetic, observation projects, art, physical education, health, memory work, music, and penmanship. It is designed to meet the developmental needs of the young child, so lessons are provided for only three days per week.
Volumes One - Five
Each volume of The Weaver Curriculum comes in a large softcover book. Any of the five volumes can be used for any number of students in first through sixth grades, and each volume should take about one year to complete. (The Weaver can be expanded to cover grades 7-12 with Supplement volumes.)
Each chapter is divided into subject areas, and each subject area is divided into grade levels. Assignments for each grade level are printed on a specific color of paper so you can quickly identify those you need. This makes it easy to organize your own lesson plans according to the learning level of each child.
Bible, health and safety, and art are offered as group lessons rather than on individual grade levels. Many language arts activities are included. Wisdom Words (another companion resource for The Weaver that is reviewed under Language Arts) provides a foundation for a complete language program when used in conjunction with The Weaver. You must still supplement with basic math and phonics instruction, except at kindergarten level.
Each volume of The Weaver gives recommendations for specific books that can be found at the library to be used as supplements. They also offer supplemental resource books that we can purchase. These resource books are from the Usborne line, and also include titles from Milliken Publishing Company that are easier to use as teaching tools. The Milliken books also contain higher-grade-level material that makes them usable with other Weaver volumes and for older children.
The Weaver provides much information within the program itself for parents to present to the child orally. It uses less hands-on, activity-based learning than KONOS, although there should be plenty of hands-on ideas for most families. The Weaver should appeal most to Sociable Sues and others who like creative approaches to learning but who do not need as much activity as Wiggly Willys.
Volume One uses Genesis 1:11-50, following the Scriptural chronology, as the underlying organizational theme with topics including cities, archaeology, and transportation for unit organization. The Scriptural emphasis is stronger than in KONOS. Daily lesson plan guides for all five Weaver volumes, called Day by Day ($50-55 each), help make using The Weaver very easy. Each volume lists the needed materials and sequence for the objectives for each level, harmonizes the objectives with the Bible lessons, lists pages of the Resource Section that go with the lessons, and lists selections from Wisdom Words to be used for language arts. This should cut preparation time to a minimum.
Volume One and Wisdom Words come with a cassette tape to walk us through the program as we begin.
Volume Two is based upon Exodus and the Books of the Law. Although it is recommended as a sequel to Volume One, it can be our starting point. Examples of topics are royalty and respect, families and responsibility, and deserts and humility.
Volume Three covers Joshua, Judges, Ruth, and I Samuel through verse 10. Topics covered are exploration and navigation, espionage and communication, recording of history, fortifications, music, thinking skills, United States history from 1789-1860, time, social structures and the family, and service.
Volume Four covers the period of the Kings and Prophets. Topics are spiritual giants; civil war; the wisdom of Solomon (includes an introduction to biology); the temple; ancient civilizations of Assyria, Babylon, Persia and Greece; and introduction to the Roman Empire.
Volume Five studies the Life of Christ through the gospels and in relation to the Old Testament. Topics covered are World and American History from 1865 to the present, the concept of covenant, government, ancient Rome, families, reproduction, the solar system, stars, the human body, miracles, biographies of men and women of faith, agriculture and stewardship, geology, map making, the temple.
Alpha Omega also sells a complete phonics program, 1-2-3 Read!, that was written as a companion to The Weaver
Get the lowest price anywhere & FREE shipping & Bonus SmartPoints at the Homeschool Buyers Co-op by clicking here
The Weaver Curriculum: Interlock for Preschool and
Weaver Curriculum, Volume 1
$139.50 at Christianbook.com
$195.00 at Amazon.com
Weaver Curriculum Supplement Volume 1, Grades 7-12
Need For Parent/Teacher Instruction: moderate to high
Grade Level: grades K-12
Suitable For: group or one-on-one
Educational Approaches: unit study
Educational Methods: activities, interactive, real books
804 N. 2nd Ave. E.
Rock Rapids, IA 51246
https://www.aop.com/


text:
Mikhail Lipyanskiy Joins The Cork Report as Photographer
By Lenn Thompson on September 6, 2017 0 Comments
I'm a writer — not a photographer — and I think that's shown over the years. I can occasionally get an iPhone photo to turn out the way that I want. If I'm not lazy and bring out my digital SLR, I know just enough to get a decent shot here and there. But again, I'm a writer.
Now I have a photographer on board to help with special projects — Mikhail Lipyanskiy. Mikhail and I have known one another solely via Facebook for several years now. It's amazing how wine lovers will find their tribes most anywhere that you look. Over the years, I've come to trust Mikhail's palate and always enjoy when he visits a winery in an area that I cover here on the site. He offers detailed notes and always takes great pictures — including the one that you see above from a recent visit to Hermann J. Wiemer or below from a recent cabernet franc tasting panel I held that he photographed.
Like many of us in the blogging world, Mikhail has a day job that has little to do with photography. But, it's a great passion of his and I'm thrilled that he's agreed to lend his skills to a few things I'm always working on.
You'll probably see him do some writing as well. He brings a unique perspective that I always find interesting.
Mikhail was born in Ukraine, during the Soviet Union and moved to the United States with his family as a teenager. He grew up in Queens, where he still resides with his wife and son. He's an adjunct professor of comparative literature at Queens College and John Jay College of Criminal Justice. He became interested in wine in the early 2000s and quickly joined the online community as well as the busy New York City crowd.
His personal wine journey began with the love for German rieslings, but it has evolved into an appreciation for various styles of wine – from the cooler climates like the northeast to the sherries of Southern Spain. Growing up in Queens, his family often traveled east – to Long Island. Once he became interested in wine, the vineyards of Long Island were the first he visited. Since then, he has traveled to Spain, Italy and the Willamette Valley of Oregon. While he enjoys writing and tasting wines, his true passion is photography. He is never without his trusty Nikon, trying to capture the emotion of the moment. Some of the photography can be seen on his website – www.LipyanskiyPhoto.com.
He is passionate about the local culture and especially the farms, wineries and breweries of the northeast and hopes to bring that photographic passion to The Cork Report.
Mikhail Lipyanskiy
Lenn Thompson
Lenn Thompson, a proud Pittsburgh, PA native, moved to Long Island more than a decade ago and quickly fell in love with the region's dynamic and emerging wine community. A digital and content marketing and community professional by day, he founded NewYorkCorkReport.com in early 2004 to share his passion for the wines, beers and spirits of New York State. After running that site -- which became the premier source for independent New York wine commentary, reviews and news -- for 12 years, he launched TheCorkReport.us in late 2016 to add the wines of Virginia, Maryland, Michigan, Pennsylvania, Vermont and beyond to his beat. Lenn currently serves as the wine columnist for The Suffolk Times weekly newspaper and is the former editor of the Long Island Wine Gazette. He contributes or has contributed to publications like Wine Enthusiast Magazine, Beverage Media, Edible Brooklyn, Edible East End and Edible Hudson Valley. Lenn served on the board of directors for Drink Local Wine, and is the creator and founder of TasteCamp, an annual regional wine immersion conference for writers and trade. An admitted riesling and cabernet franc fanatic, he's intensely passionate about eating local and the many local wine regions of America. Lenn lives in Miller Place, NY with his wife Nena, son Jackson, daughter Anna and their dog, Casey Lemieux Thompson.
Valerie Kathawala Joins The Cork Report as a Correspondent-at-Large
Gina Shay Joins The Cork Report as a Regional Correspondent Covering Michigan
Carrie Dykes Joins The Cork Report as a Contributor


Gamania Digital Entertainment announced that Bright Shadow is now open to anyone using a U.S. Beanfun! account. Since a successful European re-launch back in July, Bright Shadow was only available to E.U. Beanfun! players. Now, just in time for the Curse of Hera Expansion, U.S. players can join in on the fun and receive a special bonus-package invitation.
Bright Shadow is an anime-inspired card collecting MMORPG where players can choose from over 20 classes to explore a lush world that's filled with exciting dungeons and more than 500 collectible monsters. Providing countless hours of free-to-play entertainment, players can use monster cards in battle, learn special skills and transformations, or just collect and trade with friends.
Players both new and old will be greeted with a slew of fresh content, as the Curse of Hera Expansion just launched on January 5th. The update brings 30 new areas and dungeons, eight new classes, over 40 new bosses, and over 400 new enemies and monster cards to collect.
Card collecting and MMORPG fans alike can get their fix by signing up at the Bright Shadow homepage at http://www.brightshadowonline.com/.
Care to expand on that as in WHY is it bad?


text:
Lucius Valerius Flaccus est un homme politique de la République romaine.

Carrière politique

Il est le fils de Publius Valerius Flaccus, consul en 227 ). Son frère, Caius Valerius Flaccus, terminera sa carrière comme préteur.

En 201 , il est édile curule. En 199 , il est préteur en Sicile. En 195 , il est consul. Il défait les Boïens et les Insubriens pendant son consulat.

En 191  il est légat au Thermopyles.

Comme triumvir en 190 , il aide à la défense de Plaisance et de Crémone et fonde Bononia (Bologne).

En 184 av. J.-C., il est censeur avec Caton l'Ancien et devient princeps senatus à la mort de Scipion l'Africain. Il meurt en 180

Politiquement, Flaccus était un conservateur.

Références

Bibliographie
 .
 .

Consul de la République romaine
Histoire de Bologne
Consul romain du IIe siècle av. J.-C.
Censeur romain du IIe siècle av. J.-C.

redpajama_set_name: RedPajamaWikipedia


text:
Progesterone levels
Progesterone and Vitamin D
Progesterone therapy
Estrogen dominance
Pregnancy problems
Signs of infertility
Men and progesterone
Progesterone cream
How to use progesterone cream
Stress control
A personal testimony
Progesterone Stories
Symptoms and Diseases
Prostate cancer treatments
Search over 8,400 pages on this site...
There is no cure for the coronavirus... But there is a defence.
It is well established that the death rate from the coronavirus is highest amongst the already sick, weak and elderly. In other words those with an already compromised immune system. So although there is no known current cure there certainly is a defence... strengthen your own immune system! Click here to learn more.
Although the conventional prostate cancer treatments of chemo or surgery are well known there is a more natural and benign approach to the problem that is gaining some credibility. Hormone imbalances induced by environmental toxins, especially the 'xeno-estrogens', are thought by some authorities to be a major contributor to prostate problems. Progesterone and prostate problems are linked through the action of estrogen. Natural progesterone is a powerful antagonist to these chemical poisons as such can be viewed as one of the more important prostate cancer treatments.
The symptoms of prostate diseases are...
smaller urethras
increased frequency of urination
cellular changes leading to cancer
In many cases these changes can be linked to a rise in estrogen and di-hydrotestosterone and a drop in testosterone. The rise in estrogen also causes fatty tissues to be deposited in mens breasts and a reduction in facial hair. High levels of di-hydrotestosterone have been linked to prostate enlargement and cancer.
Progesterone is as vital for men's health as it is for women's. Approximately 5 to 15mg of this hormone is made on a continuous daily basis in the testes, which convert it into testosterone and other hormones, including estrogen.
As progesterone protects men against excessive estrogen, particularly the xeno-estrogens and di-hydrotestosterone, some researchers believe that the drop in progesterone levels associated with aging combined with the rise in environmental estrogen is causing the alarming increase in these problems.
That the lack of progesterone and prostate problems are interrelated is supported by the fact that the prevention and control of these diseases has been assisted in many cases by the use of supplemental progesterone administered as a skin cream.
There is substantial anecdotal evidence indicating that as little as 20mg/day is sufficient to reduce an enlarged prostate to normal. Some cases of prostate cancer have also responded to progesterone. As it is safe, with no toxic side effects, it is being used increasingly often as an alternative to other prostate cancer treatments.
The following nutritional guides have proven beneficial in assisting the fight against this disease...
Omega 3 (an essential fatty acid) - take between 15 to 75ml/day of flax oilzinc
the herb saw palmetto
all the anti-oxidants - especially vitamins E, C, A and selenium
To find out more about progesterone therapy in general and how it benefits health issues other than as one of the prostate cancer treatments, please click here.
Follow @progesteronetherapy
Do you have a progesterone deficiency?
This free questionnaire
will tell you in 10 minutes.
and click the button...
Do you have a
question or concern?
See what's being discussed, ask for help, give your thoughts or experiences, or just browse...
See the FAQ pages
Bio Identity Creams
Hello Dear, I'm writing to get some answers to my issues...I have taken bio-identity creams for the last 10 yrs, and have been truly satisfied. My doc
question about once or twice a day for transdermal (cream) application
I have been using a total of 20 mg a day divided into two equal doses of 10 mg each. The instructions say twice a day, but other places and formulations
Progesterone lozenge and anxiety
So I have been recommended progesterone lozenges by a hormone specialist. I didn't have blood tests as I was on the contraceptive pill but all my symptoms
When to up my dose
Would anyone please be able to help! I have been on bioidentical progesteone cream for almost 2 months, initially because I gained 25 pounds after having
Progesterone for postpartum and breastfeeding
Hi Wray, I have symptoms of estrogen dominance and am currently pregnant. Can I use progesterone cream right after giving birth? I plan on breastfeeding
Discover the facts
into your language
Is this the future
of medicine?
Watch this important video
Watch these videos
Progesterone News
Its free and will bring you important new information on progesterone therapy and related health matters
Home | Contact | Newsletter | Index of Symptoms | News | Progesterone cream | Privacy policy | Blog | About | FAQ
Although this web site is not intended to be prescriptive, it is intended, and hoped, that it will induce in you a sufficient level of scepticism about some health care practices to impel you to seek out medical advice that is not captive to purely commercial interests, or blinded by academic and institutional hubris. You are encouraged to refer any health problem to a health care practitioner and, in reference to any information contained in this web site, preferably one with specific knowledge of progesterone therapy.
Natpro & logo are ® trade marks of Organic Products LLC


Pink: 'Truth About Love' Album Artwork & Song Snippet!
No man is an island but every man would have a boat in a perfect world. The folks at Oddee agree entirely so they've collected the 10 Most Incredible Luxury Yachts. Enjoy!
Happy Friday! The end of the work week is here. It's time to find some fun. Here are the Top 10 Things To Do On A Friday Night.


text:
General News Tue, 28 Mar 2017
Don't cover every cost under Free SHS policy - Government urged
A member of the Public Interest and Accountability Committee (PIAC), Dr Steve Manteaw, is urging the government to shift from covering every cost under its Free Senior High School (SHS) policy to limited items, in order not to strain the national budget.
He said the programme, earmarked to take off in September 2017, was expected to cover all costs, including feeding, a pledge some experts believe would be rocked by funding challenges considering the current tight fiscal space in the economy.
Instead, Dr Manteaw believes the government should learn from countries such as Uganda whose free education policy was limited to an annual grant of up to US$52 per student, to cover fees and textbooks, for parents to provide uniform, stationery and meals.
In an interview after a forum to discuss sustainable funding options for the policy on March 22, Dr Manteaw said Ghana's Free SHS project was an onerous one considering its dynamics.
"You have to look at the resources available, see what cost it can cover, and cover that in year one for every child. As we proceed to year two, you can bring on additional cost, and then by year three or four, we would have covered all cost.
"To me it is a better strategy than to attempt to cover all cost in year one, and put unnecessary pressure on the budget," he said.
He said unlike Ghana where there was the inclination to make the transition from Primary to Secondary School automatic, and for that matter an omnibus transition, in Uganda, students who obtain set benchmark grades in each of the four primary school-leaving examinations were automatically allowed to proceed to study free in public schools and participating private schools.
ABFA, a better option
Dr Manteaw said devoting nine per cent of the oil revenues for the Heritage Fund, marked a departure from the practice of treating natural resource revenues as income for consumption and rather for investment.
He said though the current rate of return on the heritage fund was low, it does not warrant dissipating it, rather, the country should be looking for better ways of investing it to guarantee better yield.
"The 70 per cent Annual Budget Funding Amount (ABFA) provides a bigger scope for financing free SHS, we could devote a bigger percentage of ABFA to education financing and narrow the other spending areas for greater effect," he said.
Touching on other funding options, he said the mining sector had the capacity to contribute towards funding the Free SHS and there must also be efforts to redirect Corporate Social Responsibility (CSR) spending to prioritise contribution to education.
Dr Manteaw explained that although government's decision to leave the heritage fund alone was welcome, there were two urgent questions that needed to be answered.
"How does the government intend to harmonise the management of the pool of resources accruing to the GETFund, with other revenue streams intended to fund free SHS?"
He further asked "in the medium to long term, is the government ready to consider making a paradigm shift towards treating financial resources accruing to the GETFund as investible funds rather than an account, i.e. as VAT, petroleum, cocoa, mining and tax revenues increase and able to meet immediate requirement?"
The national dialogue was organised by the Public Interest and Accountability Committee (PIAC), in collaboration with the Natural Resource Governance Institute (NRGI) and Starr FM to look at the policy, opportunities and challenges of relying on petroleum revenues as a sustainable funding option for the Free SHS.
The Chairman of PIAC, Mr Joesph Winful, said the committee was interested in knowing decisions taken with respect to the utilisation of oil revenues, rather than churning out reports after the use of the resources.
He said whereas it was important to fund education with oil revenues, it was important for government to be able to account for it to ensure that it was done in a transparent manner.
The Deputy Director of NRGI, Mr Emmanuel Kuyole, said the dialogue was to offer suggestions on funding options for the initiative to ensure that the programme takes off with little hiccups and eventually becomes successful.
Source: Graphic.com.gh


NeNe thinks the other ladies (except Cynthia) have formed an alliance against her.
So Season 3 has come to an end! I'm happy to have it behind me. I've been hard at work, but I've obviously been on these ladies' mind. There are so many issues I could address over the past few weeks with these ladies, but I choose not to because it's easy to attack and judge! Those of you who have formed an opinion, nothing I say will change that! For those of you who love me, you will continue to do so. These ladies have apparently formed an alliance except for Cynthia who happens to have a brain of her own. I don't have the energy nor do I have the time to stoop to their level of ignorance. I understand that everything about me is under attack, from the color of my polish to the style of my shoes, because I'm a threat! I refuse to follow the leader. I am the leader! I have a take charge attitude so that makes me fierce competition. Life is about changing for the better, becoming the very best, learning from your mistakes and doing it better the next time around. So if I attack back that would make me appear low. I'm a star in the sky so I'll just stay up high because through it all, I'm still standing! Thanks for all the comments, the good and the bad. FYI: Check me out on the new season of Celebrity Apprentice March 6th.


text:
Payment Schedules Must be Valid
Melaleuca View Pty Ltd v Sutton Constructions Pty Ltd [2019] QSC 226 The applicant challenged by way of certiorari adjudicator's decision on the grounds, inter alia, that the adjudicator had made a jurisdictional error because the adjudicator erred in finding that the applicant failed to provide a payment schedule.
Although she ultimately rejected the applicant's challenge to the adjudicator's decision, Brown J accepted that the existence of a payment schedule is a jurisdictional fact.
The applicant cited Chase Oyster Bar Pty Ltd v Hamo Industries Pty Ltd (2010) 78 NSWLR 393 as support for its submission that the finding that there was no payment schedule served by it was indeed a finding as to a jurisdictional fact. [40].
Her Honour was inclined to agree. Firstly, she observed that his Honour Muir J's reasoning in Heavy Plant Leasing Pty Ltd v McConnell Dowell Constructors (Aust) Pty Ltd [2013] QCA 386, which she applied, directly supported the notion that the determination by an adjudicator as to whether or not a document is a payment schedule is a jurisdictional fact, (albeit she acknowledged that that matter was decided in relation to the former Act).
In addition, she noted that it is made plain by the wording of s 88 of the Building Industry Fairness (Security of Payment) Act 2017, which prescribes the "only" matters the adjudicator is permitted to consider in making a decision, that the determination of whether or not a document constitutes a "payment schedule" amounts to a jurisdictional fact, and one which must be determined before consideration of the matters provided under s 88(2) and the making of any decision.
The scope of the decision-making process then undertaken is directly influenced by the presence or absence of a payment schedule and where it is found that no schedule has been provided, thereafter a respondent is unable to make any response in opposition to the claimant prior to an adjudication.
Her Honour regarded those matters as lending support to the conclusion that the existence of a payment schedule is a jurisdictional fact. [44]. In the event, her Honour concluded that the adjudicator did not err in determining that there was no payment schedule. [72].