dataesr/scientific-paragraphs-categorization huggingface dataset:
There are some ongoing initiatives to use PathVisioRPC in larger analysis approaches. We are aware of pathway visualisation modules planned for a project regarding gene-based clinical assays to guide therapy in leukaemia patients and one for the Open PHACTS [66] project that will use the PathVi-sioRPC interface as well. Finally, the ArrayAnalysis. org pipeline is part of the larger dbNP project [67] that would contain other data analysis pipelines that are meant to make use of the pathway module provided at ArrayAnalysis. org as well. PathVisioRPC thus offers versatile solutions to integrate pathway analysis and visualisation approaches in many workflows.
Architecture Description Languages (ADL) provide descriptions of a software system in terms of its structure. Such descriptions give a high-level overview and come from the need to cope with arbitrarily complex dependencies arising from software components. In this paper we present Painless, a novel ADL with a declarative trait supporting parametrized specifications and architectural reconfigurations. Moreover, we exhibit its reliable facet on its integration with ProActive -a middleware for distributed programming. This is achieved by building on top of Mefresa, a Coq framework for the reasoning on software architectures. We inherit its strong guarantees by extracting certified code, and subsequently integrating it in our toolchain.
Introduction Typically, one uses an Architecture Description Language (ADL) as a means to specify the software architecture. This promotes separation of concerns and compels the software architect to accurately define structural requisites. Nevertheless, this task is seldom trivial as arbitrarily complex architectures may need to be defined. It is thus important to provide the means for expressive and intuitive, yet reliable, specifications. In this paper we present Painless, a novel ADL for describing parametrized software architectures, and its related formal verification support. We discuss its integration with ProActive  [1] , a middleware for distributed programming, and the reference implementation for the Grid Component Model (GCM)  [2]. The GCM ADL lacks support for architectural reconfigurations and parametrization.
We also want to rely on formal methods to guarantee a safe deployment and reconfiguration of the considered systems. In  [11] , we presented Mefresa -a Coq  [16]  framework providing the means for the formal reasoning on software architectures. Here, we extend Mefresa with the ability to interpret Painless specifications, and provably correct functions computing their compliance with the GCM technical specification  [10]. We take advantage of Coq's strong guarantees by extracting certified code, and subsequently integrate it with the ProActive middleware. In our previous work we focused on the mechanization of the GCM, and facilities for developing arbitrarily complex proofs regarding its intricacies. In this paper, we focus on the pragmatical aspects of deployment and reconfiguration by providing an ADL, and all the toolchain that allows us to deploy and reconfigure GCM applications in ProActive while reusing the guarantees provided by our proven back-end.
We see our contribution as two-fold. Firstly, we propose Painless, a novel ADL supporting parametrized specifications and architectural reconfigurations. Its declarative nature promotes concise and modular specifications. Secondly, we describe the integration of its related tool support with ProActive. This provides a case study on the use of certified code, fostering the application of formal methods in a software engineering context. The remainder of this paper is organised as follows. Section 2 briefly discusses GCM and Mefresa. Section 3 overviews our approach for extending the Pro-Active middleware to cope with Painless specifications. Section 4 introduces the semantics of Painless. Section 5 shows the specification of the use case depicted by Figure  1  in Painless. Related work is discussed in Section 6.
Background Mefresa provides a mechanized specification of the GCM, a simple operation language for manipulating architectural specifications, and the means to prove arbitrary complex properties about instantiated or parametrized architectures. It is developed with the Coq proof assistant  [16]. 1 he GCM is constituted by three core elements: interfaces, components, and bindings. An interface is defined by an id denoting its name, a signature corresponding to its classpath, and a path identifying its location in the component's hierarchy (i. e. the component it belongs to). It is of internal or external visibility, has a client or server role, is of functional or non-functional functionality, has an optional or mandatory contingency, and its cardinality is singleton, multicast or gathercast.
e. , they do have a functionality of their own. Client interfaces however, are used by service methods that require other service methods to perform their task. It therefore follows that a client and mandatory interface must be bound to another mandatory interface of server role. As expected, interfaces of optional contingency are not guaranteed to be available. Mefresa captures these requirements by defining a well-typed predicate. Basically, it requires that both the contingency and cardinality concerns are met throughout the component hierarchy. Architectures not meeting these requirements are said to be ill-typed. An operation language for manipulating GCM architectures Another important element of Mefresa is an operation language that allows the manipulation of GCM architectures. It possesses seven constructors: Mk component, Rm component, Mk interface, Mk binding, Rm binding, Seq, and Done.
Let -→* be the reflexive transitive closure of the step predicate. Then, the theorem depicted by Listing 1. 1 should be intuitive. Informally, it expresses that if s is a well-formed state, and if we are able to reduce op to Done, then we know that the resulting state s' is well-formed. Proving this theorem is achieved by induction on the operation language constructors. 3 Overview of our approach Figure  2  gives an overview of our approach. In short, we obtain an extension to ProActive that is able to cope with Painless architectures. We extend Mefresa with functions -build state and well typed boolresponsible for ensuring the compliance of a deployment/reconfiguration specification with the GCM requirements. We prove these functions correct w. r. t the GCM mechanized specification, and use Coq's extraction mechanism to obtain certified OCaml code.
Computing states from operations Painless specifications are translated to Mefresa's operation language. The details of this process are discussed in Section 4. As discussed above, one can check the feasibility of reducing an operation by interactively applying its reduction rules and attempting to prove the required premises. This ability is of great value when attempting to prove arbitrary complex properties about parametrized architectures. Yet, if we intend to build a state representing the result of an operation reduction, then we would be better with a function performing such task. This is the purpose of the function depicted by Listing 1. 3. Apart from the Seq and Done constructors, the remaining operation constructors are handled analogously. Seq is composed by two operations (line 11), the leftmost operation is fully evaluated, and the resulting state is used for evaluating the rightmost operation (lines  [12] [13] [14] [15].
Painless semantics Table  2  gives an excerpt of the rules for translating expressions to Mefresa's operation language. We use Γ e ⇓ v for denoting the evaluation of e under the environment Γ being reduced to v, and t stands for type inference. Rule nf sem dictates that a normal form yields immediately a semantic value. The rule skip sem simply depicts that skip is translated to Mefresa's done operation. Rules mk c sem and mk i sem illustrate the polymorphic constructor mk at work. It can be used to build components, interfaces and bindings -making bindings is omitted for the sake of space. These proceed by fully reducing the expression e into a component/interface/binding that can be used into Mefresa's operations. Rule c sem shows the reduction of a Component: all its elements (identifier, subcomponents,.
In our work, the user solely defines the architecture of its application, structural constraints are implicit: they are within the mechanized GCM specification. Further, our tool support is tightly coupled with ProActive. Also from the realm of process algebras, Archery  [15]  is a modelling language for software architectural patterns. It is composed by a core language and two extensions: Archery-Core, Archery-Script and Archery-Structural-Constraint. These permit the specification of structural and behavioural dimensions of architectures, the definition of scripts for reconfiguration, and the formulation of structural constraints, respectively. Moreover, a bigraphical semantics is defined for Archery specifications. This grants the reduction of the constraint satisfaction verification to a type-checking problem.
Final Remarks In this paper we presented Painless and its related novel approach for the specification of software architectures. Its declarative trait allows for intuitive and concise specifications, liberating the software architect from highly verbose specifications such as the ones obtained via machine languages like XML. Moreover, its support for parametrized architectures eases deployment -it becomes a matter of instantiation -, and thus boosts productivity. Further, in ProActive, mapping components to physical resources is achieved through application/deployment descriptors. While this information is not an aspect of the architecture per se, extending Painless with such feature could be envisaged. Another key ingredient is the treatment of structural reconfigurations as firstclass citizens.
Indeed, by supporting the specification of the topological changes that may occur at runtime, it yields a better understanding of the application. Moreover, it is worth noticing that the specified reconfigurations become easily accessible from a programming perspective: through a simple method call with the name of the desired reconfiguration. Furthermore, reconfiguration specifications are evaluated at runtime. The clear benefit is that one can be highly confident that the reconfiguration will not leave the application in a ill formed or ill typed state as the evaluation process is carried out by provably correct code extracted from Mefresa. Additionally, a further inherent advantage is that it all happens without stopping the application. Indeed, actually performing the reconfiguration requires it to be stopped at the composite level.
INTRODUCTION A FFECTIVE video content analysis aims at auto- matic recognition of emotions elicited by videos. It has a large number of applications, including moodbased personalized content delivery  [1] , video indexing, video indexing, and summarization (e. g. [2] ,  [3] ). While major progress has been achieved in computer vision for visual object detection, scene understanding and high level concept recognition, a natural further step is modeling and recognition of affective concepts. This has received increasing interest from research communities, e. g. , computer vision, machine learning, with an overall goal of endowing computers with human-like perception capabilities. However, while human affective perception is highly subjective, machinebased affective modeling and recognition require large amounts of reliable ground truth data for training and testing.
Unfortunately, the subjective nature of "emotions" makes it hard to collect consistent and large volumes of affective annotations suitable for the use as ground truth, while the copyright issues concerning video clips prevent free distribution of existing annotated datasets. Most state of the art work uses a private dataset of a very limited size and content diversity, thus making fair comparisons and results reproducibility impossible, and preventing achievement of major strides in the field. Horvat et al. showed in a survey  [4]  that, for researchers in the affective science field, current emotionally annotated databases lack at least some stimuli inducing a particular emotion. Participants additionally indicated that they would greatly benefit from large emotionally annotated databases composed of video clips.
Soleymani et al. also expressed this major need and defined in  [5]  the specifications to be considered to allow standardized evaluation and to bypass the size and scope of related limitations of existing databases used to train and evaluate computational models in the field of affective content analysis. How can a large and reliable dataset be built that could serve the community as a reliable benchmark? Crowdsourcing is often the recommended solution for creating a large dataset representing a condition. This makes it possible to reach a large number of remunerated annotators, while also guaranteeing reliability of annotators' answers via specific mechanisms. In this paper, to overcome the limitations of the existing affective video datasets and foster research in affective video content analysis, we release a large dataset of quality video excerpts with high content diversities, along with ground truth affective annotations collected from a wide variety of raters through crowdsourcing.
The proposed dataset, namely LIRIS-ACCEDE, contains 9,800 video excerpts shared under Creative Commons licenses, making it possible to release the database without copyright issues. The dataset was first introduced in  [6] , where we described the experimental protocol for ranking video clips along the induced valence axis. In this paper, we highlight the content diversity of the LIRIS-ACCEDE database and detail its composition: the movies from which the excerpts have been extracted are described and the diversity of the database is demonstrated. We also present a novel experimental protocol for ranking video excerpts of the database along the induced arousal axis. Furthermore, all excerpts in LIRIS-ACCEDE are ranked using crowdsourcing in the widely used 2D valence-arousal space. This dimensional space has been preferred to other categorical approaches that classify emotions into a small number of discrete clusters and may not reflect the complexity, diversity and richness of the emotions which could be induced by such a vast number of videos  [7].
To ensure reliability and consistency of raters' affective annotations despite the subjective nature of emotions and the large diversity of their backgrounds, we design a pairwise video excerpt comparison protocol leading to a high inter-rater agreement as revealed by objective measurements. Moreover, to enable fair comparison between future work using the LIRIS-ACCEDE dataset, we also propose four experimental protocols and introduce a baseline using a large set of visual and audio features. The database, containing the 9,800 video clips, the annotations, features and experimental protocols, can be freely downloaded at http://liris-accede. ec-lyon. fr/. We believe that public release of such a database will foster research in the field and benefit various affective computing research communities.
The main contributions of this paper are thus three-fold: • Public release of a large freely accessible database of video excerpts under Creative Commons licenses with a large content diversity; • Consistent affective annotations collected using a pairwise video excerpt comparison protocol, from a large number of raters with a great variety of backgrounds through crowdsourcing; • Design of 4 experimental protocols and introduction of a baseline using a large number of visual and audio features. The paper is organized as follows. Section 2 provides background material on existing affective multimedia databases and computational models of induced emotion. Next, in Section 3, our main LIRIS-ACCEDE contribution is presented, and the process for annotating the database is described in Section 4.
Affective multimedia databases Creation of an affective database is a necessary step in affective computing studies. While there are many databases composed of facial expression videos for emotion recognition, there are not many databases of video clips annotated according to the emotions they induce in viewers (Table  1 ). Philippot  [8] , as well as Gross and Levenson  [9] , were the first to propose small sets of film excerpts assumed to elicit specific emotions in the laboratory. To achieve this goal, they selected specific excerpts most likely to elicit strong emotions, which thus do not represent the full range of emotions potentially elicited by movies. Even if increased efforts have recently been made to standardize film clip databases, there are no multimedia databases annotated along induced emotional axes dealing with the full spectrum of emotions in movies that are large enough to be used in machine learning and that do not suffer from copyright infringement.
The HUMAINE database  [10]  created by Douglas-Cowie et al. consists of a subset of three naturalistic and six induced reaction databases. The purpose of the database is to illustrate key principles of affective computing instead of applying it to machine learning. It is made up of 50 clips: naturalistic and induced data ranging from 5 seconds to 3 minutes. These have been annotated according to a wide range of labels detailed in Table  1. Introduced by Shaefer et al. in  [11] , the FilmStim database consists of 70 film excerpts intended to elicit emotional states in experimental psychology experiments. 10 films are selected per emotional category (i. e. anger, sadness, fear, disgust, amusement, tenderness and neutral state) and cut into clips ranging from 1 to 7 minutes. 364 participants rated each film clip, and ranking scores were computed for 24 classification criteria displayed in Table  1.
Even if it is one of the biggest databases of videos annotated along induced emotional labels, videos are labeled globally. Yet, emotions are a relatively fast phenomenon lasting a few seconds from onset to end  [12]. This is why a unique global label is not sufficient to build ground truth data for induced emotion models. The DEAP database is another publicly available database that has been created recently by Koelstra et al. [13]. It is composed of 120 one-minute long excerpts of music videos. Each one was rated by at least 14 volunteers from an online self-assessment based on induced arousal, valence and dominance. Physiological signals were recorded from participants, while they rated a subset of 40 of the above music videos in terms of arousal, valence, like/dislike, dominance and familiarity levels.
LIRIS-ACCEDE 9,800 excerpts from 8 to 12 seconds long Rankings for arousal and valence dimensions HCI  [14]  which is a multimodal database composed of 20 short emotional excerpts extracted from commercially produced movies and video websites. Participants watching these fragments were asked to annotate their own emotive state on a scale in terms of arousal and valence. Facial videos, EEG, audio, gaze and peripheral physiological recordings were also recorded for all 30 participants. Carvalho et al. built in  [15]  the emotional movie database (EMDB) made up of 52 non-auditory film clips. Film clips are extracted from commercial films and last 40 seconds. They have been selected to cover the entire affective space. 113 participants rated each film clip in terms of induced valence, arousal and dominance on a 9-point scale.
Non-auditory clips were used to enhance the scope for future experimental manipulations. However, this clearly modifies how viewers perceive the video clips. Furthermore, multimodal processing is not possible in this case. Still more recently, the Violent Scene Dataset was made available by Demarty et al. [16]. This is a collection of ground truth annotations based on extraction of violent events in movies, together with high level audio and video concepts. This dataset has been used since 2011 in the MediaEval multimedia benchmarking affect task "Violent Scenes Detection". Violent scene detection and prediction of induced emotions are clearly related since they are both part of the affective content analysis field. Violent scenes are most likely to be highly arousing and elicit negative emotions.
Due to copyright issues, the 25 annotated movies cannot be delivered alongside the annotations. However, the links to the DVDs used for the annotation on the Amazon web site are provided. Last but not least, it is worth mentioning the MIT dataset dedicated to animated GIFs  [17]. Such kinds of short video footage are becoming increasingly popular by means of social networks. They are so widely adopted that the MIT team is currently and seriously working on predicting perceived emotions from such media support. All these databases either have different emotional labels or are not representative of the whole range of emotions in movies. Thus, a huge database of videos annotated using induced emotional labels potentially suitable for research, is a requirement of the affective computing community.
Their training data are made up of 36 full-length popular Hollywood movies divided into 2040 scenes labeled with one or two emotional states. In the work of Sun and Yu  [22] , movie units are first represented in different granularities using an excitement curve based on the arousal curve introduced in  [18]. Then, four HMMs are trained independently using features extracted on these granularities to recognize one of the four emotional states among "joy", "anger", "sadness" and "fear". Their ground truth consists of 10 movies labeled at different levels. Xu et al. added in  [23]  a neutral state to these four emotional states. Fuzzy clustering is used to compute the emotion intensity level, then five HMMs are trained using emotion intensity and low-level features to model valence and individually associate the content with five emotion types.
They evaluated the efficiency of their method for several movie genres, where the highest accuracy was obtained for action movies. Soleymani et al. [24]  compared in the valence-arousal space the values obtained automatically from either physiological responses or from audiovisual features. They showed significant correlations between multimedia features, physiological features and spectators' self-assessments. A dataset composed of 64 movie scenes extracted from 8 Hollywood movies was created. The following year, they introduced a Bayesian framework for video affective representation  [25]  using audiovisual features and textual features extracted from subtitles. The arousal information of each shot is obtained by computing linear weights using a relevance vector machine. Arousal is then used as an arousal indicator feature for scene affective classification.
Irie et al. [26]  proposed an approach based on latent Dirichlet allocation considering the temporal transition characteristics of emotions. The good results obtained may be due to their evaluation protocol. Their data, composed of 206 scenes from 24 movie titles available as DVDs, were randomly selected to form the training and test sets. As a consequence, most films appear both in the training and the test sets, which biases the results. Zhang et al. developed in  [27]  a personalized affective analysis for music videos composed of SVR-based arousal and valence models using both multimedia features and user profiles. Their dataset of 552 music videos is used to train and update the models based on user feedback.