dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion VisualTE is a graphical interface that reads, extracts, analyzes, and displays TE information from annotation and repeat data. The interface is composed of three distinct areas: 'Data Selection' , 'Graphical Option' , and 'Graphical Panel' (Figure  1 ). The last area, which constitutes the main part of the VisualTE tool, dynamically interacts with the 'Option' area through several buttons and functions. The AtREP1, AtREP3, AtREP5 families (194, 550, and 275 TE copies in A. thaliana genome, respectively) used in this work belong to the Helitron superfamily  [18, 19].
Data selection area This area is composed of a 'Help' button, a clickable genome tree, a textfield for entering a TE family name, a 'List of Transposable Elements' button, and the 'RUN VisualTE' button. Clicking on the 'Help' button opens a new interface window that explains all functions and buttons of the interface. To use the VisualTE main interface the user starts by selecting one or several transposable element families (manually within the 'Selected TEs' area or from the 'List of Transposable Elements' button) with one or several genomic items within the 'Data Selection' area (Area 1 in Figure  1 ). The 'Selected TEs' textfield allows the user to enter the name of the desired TE family up to a maximum of 20 TE names (e. g AtREP1, AtREP3, AtREP5 in Figure  1 ). However, we recommend that the user limits this number to three TE names for better visualization.
The 'List of Transposable Elements' button also opens a new interface window with the complete list of TE families generated from the input file (classified by organisms), and, therefore, allows for the selection of TE families of interest. The genome tree allows for the selection of particular chromosomes, as shown in Figure  1  for ' All' chromosomes of A. thaliana. Adding (or removing) new chromosome(s) to (or from) memory is accomplished by clicking on the ' Add sequence(s)' (or 'Remove sequence(s)') button in the interface. Finally, with at least one selected chromosome and at least one valid TE family name, the user runs the VisualTE program through the 'RUN VisualTE' button.
In this paper, we introduce a violent scenes and violence-related concept detection dataset named VSD2014. It contains annotations as well as auditory and visual features of typical Hollywood movies and user-generated footage shared on the web. The dataset is the result of a joint annotation endeavor of different research institutions and responds to the real-world use case of parental guidance in selecting appropriate content for children. The dataset has been validated during the Violent Scenes Detection (VSD) task at the MediaEval benchmarking initiative for multimedia evaluation.
In the presented dataset, both kinds of material are considered. To support the emerging research on the task of violence detection, this paper introduces an annotated dataset, named VSD2014, which is intended for benchmarking violence detection in typical Hollywood movies and short user-generated videos. The remainder of the paper is organized as follows. Section II overviews the related work and positions our contribution accordingly. Subsequently, we introduce the dataset, report statistical details, outline the methodology used to create the dataset, and explain its format in Section III. Section IV discusses the validation of the dataset during the 2014 MediaEval  2  Violence Scenes Detection (VSD) task  3   [1]  and proposes several baselines for benchmarking. We round off the paper by a summary and possibilities for future extension of the dataset in Section V.
II. RELATED WORK Due to the complexity of the research problem, which first necessitates defining the concept of violence, before elaborating on methods to infer semantic concepts out of low-level information, violence detection in videos has been marginally studied in the literature until recently  [2] ,  [3] ,  [4]. In particular, related work reveals a wide variety in the definition of violence and the used datasets. The high variability of violent events in videos results in a high diversity of interpretations for violence detection. For instance, the authors of  [5]  target "a series of human actions accompanied with bleeding"; in  [6] ,  [7] , authors search for "scenes containing fights, regardless of context and number of people involved"; in  [8] , the focus is on "behavior by persons against persons that intentionally threatens, attempts, or actually inflicts physical harm"; in  [9] , authors are interested in "fast paced scenes which contain explosions, gunshots and person-on-person fighting".
In  [8] , 50 video segments ripped from 10 different movies (totaling 150 minutes) are used. The lack of a common definition and the resulting absence of a substantial reference dataset make it very difficult to compare methods, which are frequently developed for a very specific type of violence. This is precisely the issue that we attempt to correct with this release. We propose a wellformulated violence detection use case and a corresponding annotated dataset, VSD2014, for benchmarking violent scenes detection in movies and videos. We provide annotations of violent scenes and of violence-related concepts for a significant collection of (i) Hollywood movies and (ii) user-generated videos shared on the web. In addition to the annotations, pre-computed audio and visual features and various metadata are provided to facilitate the contribution of different research communities, such as signal processing or machine learning, and to encourage multimodal approaches.
This dataset is also intended to support related areas such as event detection, multimedia affect detection, and multimedia content analysis. Parts of the VSD2014 dataset are used in the MediaEval Violent Scenes Detection task  [1] , which has been run annually since 2011. The main novelties of the dataset at hand compared to previous years' versions can be summarized as follows: (i) it considers a violence definition closer to the targeted realworld scenario, (ii) it addresses a substantial Hollywood movie dataset containing 31 movies, (iii) it provides a dataset of 86 web video clips and their metadata retrieved from YouTube to serve for testing the generalization capabilities of approaches to different types of footage, and (iv) it includes state-of-the-art audio-visual content descriptors.
III. DATASET DESCRIPTION The VSD2014 dataset is split into three different subsets, called Hollywood: Development, Hollywood: Test, and YouTube: Generalization. 4 Table  I  gives an overview of the three subsets and provides basic statistics, including duration, fraction of violent scenes (as percentage on a per-framebasis), and average length of a violent scene. Note that space limitations prevent us from providing detailed statistics on the YouTube: Generalization subset. The two Hollywood sets include annotations for 24 and 7 movies, respectively. In addition to annotations, the YouTube set provides the actual videos of 86 short YouTube clips, which are licensed under the Creative Commons Attribution 3. 0 Unported license. These videos are between 6 seconds and 6 minutes in length. Along with them, we further provide a variety of metadata from YouTube, including identifier, title, upload date, duration, and view count.
In addition, we include in the dataset standard audio and visual features for all movies and videos. For all subsets, the VSD2014 dataset contains binary annotations of all violent scenes, where a scene is identified by its start and end frames. For 17 of the Hollywood movies (printed in italics in Table  I ), we additionally provide violence-related concept annotations, where the concept is indicated either visually or aurally. These concepts include, for instance, blood, fire, and explosion (cf. Section III-C). The entire VSD2014 dataset is publicly available for download as one single compressed file 5  of 11. 5 GB or as 10 smaller files. 6 he data in the VSD2014 set can be categorized into three types: movies/videos (and metadata), features, and annotations. In the following, we describe in detail the data acquisition process and the data format.
A. Movies/Videos For the Hollywood part of the dataset, we selected various popular movies, ranging from very violent ones (e. g. , Saving Private Ryan with 34% violent frames) to movies with (almost) no violence (e. g. , Dead Poets Society with ¡1% of violent frames and Legally Blond with no violence at all). The chosen movies range in their release year between the 1930s and the 2000s, with a strong focus on the 1990s and 2000s. A total of 31 movies were eventually included. For the web videos, we considered video clips shared on YouTube under a Creative Commons license in order to be able to redistribute the actual video material as part of the VSD2014 dataset. To retrieve relevant videos, we identified queries targeted at violent material, such as "brutal accident" or "killing video games".
The results to each query underwent a preliminary informal investigation based on which we selected an approximately uniform number of violent and non-violent videos. This process yielded a total of 86 clips, which are included as MP4 files in the dataset. For these clips, we additionally retrieved metadata offered through the YouTube API: 7 video identifier, publishing date, updating date, category, title, author, uploader identifier, aspect ratio, duration, user rating (minimum, maximum, average), number of raters, and number of likes and dislikes. This metadata is provided as XML files.
IV. THE AFFECT TASK AT MEDIAEVAL In the following, we illustrate an application of the proposed dataset to the real-world use case of guiding parents in deciding which movies or videos are suited for their children with respect to violence. Parents may choose to select or reject movies after previewing the most violent parts of the movies. This use case was defined based on real commercial demands by the company Technicolor. 12 Targeting this use case, we have been running the violent scenes detection task as part of the MediaEval evaluation campaign since 2011. The amount of movies and annotations has increased from year to year and in 2014 it reached the figures reported for the VSD2014 dataset in this paper.
Rec. MAP@100 MAP2014 FUDAN  [22]  41. 1% 72. 1% 72. 7% 63. 0% NII-UIT  [23]  17. 1% 100. 0% 77. 3% 55. 9% FAR  [24]  28. 0% 71. 3% 57. 0% 45. 1% MIC-TJU  [25]  17. 0% 98. 4% 63. 6% 44. 6% RECOD  [26]  33. 0% 69. 7% 49. 3% 37. 6% VIVOLAB  [27]  38. 1% 58. 4% 38. 2% 17. 8% TUB-IRML  [28]  31. 7% 17. 3% 40. 9% 17. 2% MTMDCC  [29]  15. 8% 24. 6% 16. 5% 2. 6% in order to investigate how well the proposed algorithms generalize to video material other than typical Hollywood movies. The data used in the generalization task also addresses the emerging interest in user-generated videos shared on the web, which are characterized by short duration and inferior quality. For this task, we distributed the actual video clips and the corresponding metadata given by YouTube.
C. Evaluation and Results In the following, we present the results of the 2014 edition of the Violent Scenes Detection task in MediaEval, which Team Prec. Rec. MAP@100 MAP2014 FAR  [24]  49. 7% 85. 8% 86. 0% 66. 4% RECOD  [26]  48. 1% 88. 4% 86. 8% 61. 8% FUDAN  [22]  59. 0% 43. 4% 71. 9% 60. 4% MIC-TJU  [25]  44. 4% 97. 3% 55. 5% 56. 6% TUB-IRML  [28]  63. 3% 25. 2% 58. 2% 51. 7% VIVOLAB  [27]  51. 3% 33. 6% 56. 5% 43. 0% may serve as a baseline for prospective users of the VSD2014 dataset. As evaluation metric, we used an adapted version of mean average precision (MAP), which we call MAP2014. It avoids the problem that participating teams could artificially increase their algorithms' MAP scores by predicting more, but shorter segments within the boundaries of a violent scene. Hence, MAP inherently penalizes algorithms that identify larger segments, while such algorithms should rather be rewarded, because larger segments more likely correspond to a whole scene and ease interpretation.
Algorithms are sorted with respect to MAP2014. The best result in the main task was achieved by using a novel variant of deep neural networks for both classification and fusion  [30]. The other aspect that stood out in the main task was the power of auditory features. In fact, some teams achieved their best result by using only audio. Comparing the numbers between Tables III and IV it would appear that algorithms performed better in the generalization task. However, when interpreting these results, it is important to consider the much higher percentage of violence in the generalization task subset. Likewise, the performance of a random run, which is 29. 4% MAP2014 on the YouTube dataset, but only 5. 5% on the Hollywood dataset, has to be taken into account. The random runs were generated by alternating nonviolent and violent scenes with normally distributed lengths.
V. CONCLUSIONS AND FUTURE WORK In this paper, we presented a dataset, VSD2014, intended for benchmarking violence detection in Hollywood movies and YouTube videos. The dataset includes manual annotations of violent and non-violent segments as well as of violencerelated concepts. It was validated during the 2014 edition of the Violent Scenes Detection task as part of the MediaEval campaign. The high number of submitted runs and the variety of approaches make VSD2014 a substantial validation dataset for violence detection. Future extensions of this dataset will mainly focus on: (i) addressing fine-grained violence-related concepts instead of binary violence/non-violent annotations, e. g. , pushing in a soccer game vs. brutal torturing; (ii) extending the definition of violence to other scenarios, e.
ACKNOWLEDGMENTS This work is supported by the following projects:  Austrian Science Fund (FWF)   P25655 ,  UEFISCDI SCOUTER  grant  28DPST/30-08-2013 ,  ESF POSDRU/159/1. 5/S/132395InnoRESEARCH  program,  National Natural Science Foundation of China  grants  61201387  and  61228205 ,  Vietnam National University Ho Chi Minh City  grant  B2013-26-01 , and  EU FP7-ICT-2011-9  project  601166  ("PHENICX").
The other strategy is to use an intermediate representation prior to classification that models the scene using a set of higher level features that are usually captured by a vocabulary or dictionary of "acoustic atoms". These atoms usually represent acoustic events or streams within the scene which are not necessarily known a priori and therefore are learned in an unsupervised manner from the data. Sparsity or other constraints can be adopted to lead to more discriminative representations that subsequently ease the classification process. An example is the use of non-negative matrix factorization (NMF) to extract bases that are subsequently converted into MFCCs for compactness and used to classify a dataset of train station scenes  [15]. Building upon this approach, the authors in  [16]  used shift-invariant probabilistic latent component analysis (SIPLCA) with temporal constrains via hidden Markov models (HMMs) that led to improvement in performance.
Several tasks on audio-only, video-only or multimodal tracking and event detection were proposed, among them an evaluation on "Acoustic Event Detection and Classification". The datasets were recorded during several interactive seminars and contain events related to seminars (speech, applause, chair moving, etc). From the datasets created for the evaluations, the "FBK-Irst database of isolated meeting-room acoustic events" has widely been used in the event detection literature; however, the aforementioned dataset contains only non-overlapping events. The CLEAR evaluations, although promising and innovative at the time, were discontinued with the end of the CHIL project. One further related challenge in audiovisual research is TRECVID Multimedia Event Detection, where the focus is on audiovisual, multi-modal event detection in video recordings  [35].
It was this discussion that led to the decision to report three different evaluation measures for event detection (see Section III-C3). Other issues discussed included annotation data formats, the nature of synthetic sequences, and the use of other existing datasets. Our motivation was to design challenge tasks to reflect useful general-purpose inferences that could be made in an everyday audio environment, pertinent to a broad range of machine listening applications. Our focus was on everyday sounds beyond speech and music, since the latter are already well-studied. We also wished to design tasks for which performance could be improved without necessarily being overly reliant on other processing components such as high-quality source separation or ASR. We decided to design challenge tasks separately for scene classification and for event detection and classification, using data relating to urban and office environments.
Many applications of machine listening relate to processing embodied in a fixed hardware setup, such as a mobile phone or a robot. This differs from applications such as audio archive analysis, for which a system must be robust to signal modifications induced by variation of microphones and preprocessing across the dataset  [36]. For embodied machine listening, aspects such as the microphone frequency response will be constant factors rather than random factors. We chose to design our tasks each with a fixed configuration of recording equipment. One pertinent question was whether existing data could be used for our evaluation, or whether it would be important to create new datasets. Previous studies have used relatively small datasets; further, some of these are not publicly available. Alternatively, online archives such as Freesound hold a large amount of soundscape data.
Such systems might be designed with a simplifying monophonic assumption; with source separation used to feed multiple monophonic analyses; or with full polyphonic inference. There is little data available to suggest how these different strategies perform as the event density varies. In order to have experimental control over the event density, we chose two parallel approaches to creating event detection audio data. In one, we made live recordings of scripted monophonic event sequences in controlled environments. In the other, we made live recordings of individual events, and synthetically combined these (along with ambient background recordings) into synthetic mixtures with parametrically controlled polyphony. We describe these approaches further in Section III-C. In December 2012 we conducted a survey of potential participants to characterise their preferred software platforms.
This indicated that most participants wished to use Matlab, Python, R or C/C++ to create their submissions. However, all of these frameworks come in multiple versions across multiple operating systems, and it can be difficult to ensure that code running on one system will run correctly on another. To minimise the risk of such issues, we created and published a Linux virtual machine which participants could use during development, and which would also be the environment used to run the submission evaluations. For this we used VirtualBox software which runs on all common operating systems, together with a disk image based on Xubuntu 12. 10 Linux. 2 The disk image was augmented by adding the public datasets into the home folder, and also by installing Python, R and C/C++, as well as some common audio-processing toolboxes for each environment.
B. Scene Classification Task (SC) Acoustic scene classification can be considered as a singlelabel classification task (see Section II). Alternative designs are possible, such as classification with hierarchical labels  [38] , unsupervised clustering of audio scenes, or multi-label "auto-tagging"  [39]. However, single-label classification is the design most commonly seen in prior literature in acoustic scene recognition  [14] -  [17] ,  [10] , and also lends itself to clear evaluation measures. We therefore designed the SC task as a train/test classification task, of similar design to previous audio classification evaluations  [33]. We created datasets across a pre-selected list of scene types, representing an equal balance of indoor/outdoor scenes in the London area: bus, busystreet, office, openairmarket, park, quietstreet, restaurant, supermarket, tube, tubestation.
We then reviewed the recordings to select 30-second segments that were free of issues such as mobile phone interference or microphone handling noise (totalling around 50% of the recorded duration), and collated these segments into two separate datasets: one for public release, and one private set for evaluating submissions. The duration of 30 seconds is comparable with that of other datasets in this topic, and was judged to be long enough to contain sufficient information in principle to distinguish the classes. The segments are stored as 30-second WAV files (16 bit, stereo, 44. 1 kHz), with scene labels given in the filenames. Each dataset contains 10 examples each from 10 scene types, totalling 50 minutes of audio per dataset. The public dataset is published online under a Creative Commons CC-BY licence.
1) Baseline System for Scene Classification: The "bag-offrames" approach to audio classification (see Section II) is relatively simple, and has been criticized for the assumptions it incurs  [41]. However, it is quite widely applicable in a variety of audio classification tasks. Aucouturier and Pachet  [10]  specifically claim that the approach is sufficient for recognizing urban soundscapes but not for polyphonic music (due to the importance of temporal structure in music). It has been widely used for scene classification among other recognition tasks, and has served as a basis for further modifications  [17]. The model is therefore an ideal baseline for the Scene Classification task. Code for the bag-of-frames model has previously been made available for Matlab. 5 However, for maximum reproducibility we wished to provide simple and readable code in a widely-used programming language.
We created three datasets: a training, a development, and a test dataset. Training recordings consisted of instantiations of individual events for every class. The development (validation) and test datasets consist of roughly 1 min long recordings of scripted every-day audio events. Scripts were created by random ordering of event types; we recruited a variety of participants to perform the scripts. For each script, multiple takes were used, and we selected the best take as the one having the least amount of unscripted background interference. Overall, the OL training dataset includes 24 recordings of individual sounds per class; the development dataset includes 3 recordings of scripted sequences; and the test set consists of 11 scripted recordings (the recording environments in the development and test datasets are non-overlapping).
The OL training dataset  8  and the development dataset,  9  both consisting of B-format recordings, stereo recordings, and annotations, were released under a Creative Commons license. 2) Synthetic Dataset (OS): We also decided that this challenge presented a good opportunity to study the relevance of considering artificial scenes built from a set of isolated events different from those of the training corpus. Though we admit that it is important to evaluate machine listening systems using real audio recordings, the potential gains from using artificial scenes as part of evaluation are numerous: ease of annotation, ability to generate many scenes with similar properties in order to gain better statistical significance, control of the complexity in terms of events overlap, strength of the background, etc.