dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank  Jade Whitlam  for collating the crop yield data used in Figure 2 and  Alessio Palmisano  for creating the map used in Figure 3. Neil Edwards ,  Phillip Holden , and  Oluwole Oyebamiji  were supported by the  EU   Seventh Framework Programme , grant agreement no. 265170 'ERMITAGE. 'This work was supported by a  John Templeton Foundation  grant to the  Evolution Institute , entitled "Axial-Age Religions and the  Z-Curve of Human Egalitarianism ," a  Tricoastal Foundation  grant to the Evolution Institute, entitled " The Deep Roots of the Modern World: The Cultural Evolution of Economic Growth and Political Stability ," an  ESRC   Large Grant  to the  University of Oxford , entitled "Ritual,  Community , and  Conflict"  ( REF RES-060-25-0085 ), and a grant from the  European Union   Horizon 2020 research and innovation programme  (grant agreement No  644055  [ALIGNED, www.
Performances Indicators. In Part 5-1-2  [18] , each activity is associated with a set of constitutive tasks that shall be considered as Base Practices. 6 Deployment Packages are provided with the Basic Profile. DP 1  [21]  covers the whole Project Management Process; DPs 2 to 6  [20, 22, 23, 24, 25 ] cover all of the activities of Software Implementation Process. We carefully examined DPs 1 to 6, which cover the majority of the Basic Profile activities. Almost none excepted DP Software Design is using the standardized tasks decomposition and almost all -DP Software Design excepted -are providing alternative activity decomposition with input and output WPs. The analysis is available at http://29110. univ-brest. fr/en. nexus/index. php/AAB Each corresponding DP provides a task decomposition -although not standardized -and for each task, a step-by-step guideline to perform the task.
Establish a detailed design, including a database schema • To detail SW components and interfaces • To detail and update the SW design document • To normalize a database schema and to understand the normalization impact on queries performances • To perform human-centred design activities B. 1. 5. Develop batch modules interacting with the database • To understand the difference between the SQL set model and a procedural language model • To grasp procedural constructs and their execution conditions • To master the development of procedural constructs and the exception handling mechanism • To develop and perform unit testing, including a regression test strategy • To manage the backup, storage, archiving, handling and delivery of configured items.
3. 3 Establish a detailed design, including a database schema • To detail SW components and interfaces • To detail and update the SW design document • To normalize a database schema and to understand the normalization impact on queries performances • To perform human-centred design activities B. 1. 5. Develop batch modules interacting with the database • To understand the difference between the SQL set model and a procedural language model • To grasp procedural constructs and their execution conditions • To master the development of procedural constructs and the exception handling mechanism • To develop and perform unit testing, including a regression test strategy • To manage the backup, storage, archiving, handling and delivery of configured items.
Some recent contributions  [54, 55]  have introduced a method called the Cornell Scintillation Model (CSM) to synthesize realistic scintillation, embedded in the so-called Cornell Scintillation Simulation Matlab toolkit  2. Notice that for the simulation of a scintillation data set only two parameters must be specified, {S 4 , τ 0 }, which determine the intensity and correlation of the scintillation complex gain components, respectively. Generally speaking, higher S 4 and lower τ 0 lead to more severe scintillation, where the ranges of possible values are 0 < S 4 ≤ 1 and 0. 1 ≤ τ 0 < 2 seconds, respectively  [54]. Three main scintillation regions are considered: weak (S 4 ≤ 0. 3), moderate (0. 3 < S 4 ≤ 0. 6) and strong/severe (0. 6 < S 4 ).
We wanted here to determine the size and the content of the core genome of 17 strains of P. aeruginosa which gapless genomes were available in January 2014 on the NCBI database. The issue of annotation inconsistencies was circumvented by the re-annotation of all the genomes with the best performing annotation tool. We estimated the extent to which the addition of new genomes will further reduce the size of the core genome or expand that of the pangenome. We discussed the conservation in the species of the genes implicated in the resistance to antibiotics, in the metabolism and respiration, and in the virulence.
Bacterial isolates and their genomic data We downloaded from NCBI the nucleotidic sequences of the 17 gapless chromosomes of P. aeruginosa available in January 2014 (http://www. ncbi. nlm. nih. gov/nuccore/). The newest curated version of each isolate has been downloaded. The characteristics of the isolates are detailed in Table  1. Fourteen of the isolates were of clinical origin, of which 4 were isolated from cystic fibrosis patients (DK2, LESB58, PACS2, and RP73). Of note, LESB58 and LES431 are two UK isolates of the Liverpool epidemic strain and 19BR and 213BR are two Brazilian isolates that are nearly clonal  [11]. Three isolates were isolated from the environment (M18, YL84, and MTB-1). When not available, we predicted the sequence type (ST) of the isolates from their whole genome sequence data with the MLST 1.
Gene prediction Usual bioinformatics software tools (Prodigual, GeneMarkS, and Glimmer3) have been tested with the raw genome sequence of the reference strain PAO1 and confronted to the manually annotated sequences of the same strain  [24] [25] [26]. Prodigal and Glimmer3 run in unsupervised mode, GeneMarkS can run either unsupervised with a heuristic approach or guided with a species-dependent configuration file.
Ortholog annotation The most abundant nucleotidic sequence of each cluster (called "ortholog") was functionally annotated with different databases. Clusters of orthologous groups (COGs) were determined with the Batch CD-Search tool  [28]. Antibiotic resistance genes were identified by clustering (>98%) with the Antibiotic Resistance Database  [29]  and potential virulence factors investigated with the Virulence Factors Database  [30].
Validation of the method We compared the gene detection by three usual bioinformatics tools (Prodigual, GeneMarkS, and Glimmer3) with the 5,542 manually annotated genes of the reference strain PAO1  [24] [25] [26]. Prodigual provided the best result compared to GeneMarkS and to Glimmer3 (Table  2 ). Hence, Prodigual predicted 90. 9% of the annotated genes and found 238 false positive genes. In comparison, the performances for GeneMarkS (78. 2% of accuracy and 373 false positive genes) and Glimmer3 (84. 7% of accuracy and 475 false positive genes) were much lower. We therefore submitted all genomes to an ab initio gene prediction using Prodigal software. Gene and protein clustering can be handle with a rapid algorithm using a greedy incremental approach  [31]  or by graph clustering on similarity matrix  [27, 32].
Here, we performed a hybrid approach. Genes were first rapidly clustered using the Sumaclust program that uses the same clustering algorithm as CD-HIT  [31]. Then the seed of each cluster served as a proxy for pairwise comparison and the whole matrix went through a classification process with the MCL clustering program. In order to validate our clustering model which is a crucial step for the determination of core and pan-genomes, we assessed the influence of the similarity cutoff on their size. The size of the core and the pan genomes was stable with gene similarity cutoffs between 65 and 75% with a minimal influence of the inflation parameter (S1 Fig). Cutoff was set at 70% because it maximizes the number of orthologs within the core genome with only one gene per genome, therefore reducing the risk of overclustering.
In this environment we also may find real-time 3D video representations of users that we call replicants and virtual humanoids known as agents. Agents are autonomous; they do not represent a human user in the real world but act like one. In this paper we describe how modules of the REVERIE system running on web-connected client systems control autonomous agents, analyze captured data from the real human and model the interaction between virtual and real, including facial expressions, gaze and gestures as they are essential aspects of non-verbal communication. Related work is presented in Section 2. Section 3 is an overview of the modules that can be configured to run on individual user clients. Section 4 describes the behaviour of agents that can be obtained by the system and Section 5 describes how the system can support avatars.
REVERIE's Configurable Clients The REVERIE system is a distributed architecture of webconnected clients. Each of the clients can be configured to run the components needed for a particular application. In Figure  1we  see two such clients, one configured as a server just for reasoning and navigation and one configured as a user client that has a complete set of modules for user analysis. User clients in particular need a configuration of components customized for the application at hand. Communication between modules of the REVERIE system is done by means of both shared global parameters and publish/subscribe messages. A REVERIE constellation should have only one navigation module and one reasoning module. These components can run on a separate "server" client, but they may even run on one of the user clients as desired.
Kinect Navigation A Kinect Navigation module provides the user with the ability to interact with REVERIE's Graphic User Interface (GUI) by interpreting different user gestures. In this way the user has the option to interact with the system without need for keyboard and mouse interfaces, intended to provide a natural and immersive experience. The workflow is shown in Figure  2. Once a user walks into the scene, the OpenNI skeleton tracking system automatically "picks up" (i. e. tracks) the user within 1 to 2 seconds. After that, the user can control settings and navigate through the world using a set of gestures. As the OpenNI component can track two skeletons at the same time, the first user that walks into the scene will have control of this module.
Kinect Body Capturing A user can participate in the virtual environment as a body puppeted avatar. For this, the user's system should be configured to activate the Kinect Body Capturing component of a Puppeting module (see Section 5. 1). This module is responsible for creating an ALM channel and publishing data to it. Each skeleton capturing frame is translated to a list of 17 4x4 rotation matrices corresponding to tracked rotations of the 17 OpenNI joints, and a similar list of 3D tracked positions for each joint in the hierarchy. The module proceeds to create a data buffer out of this information, which is published on the capturer's ALM channel.
Speech analysis The role of a module for keyword recognition is to provide the virtual agents with a means to understand the speech of users. For our applications we just needed this module to be capable of recognizing different ways of saying "yes" or "no" and numbers. However its dictionary can be programmed to have more options. The module is based on the CMU Sphinx library  [Lamere, P et al. 2003]. CMU Sphinx provides a powerful open solution for speech recognition and the library does not need a live Internet connection. This component is used in the different scenarios of REVERIE to trigger the virtual agents' reactions.
User Affect Recognition The User Affect Recognition module gives a continuous predication on the arousal and valence level. Accurate face landmark detection is an important step of user affect recognition. We use the work proposed by  [Xiong et al. 2013]  to track 46 landmarks of the face (Figure  4 ). The normalized coordinates of each landmark are used as the input features. The Support Vector Regression (SVR) technique is used to learn the relationship between the input features and arousal and valence levels. To train the model we used the training and developing dataset from the Audio/Visual Emotion Challenge (AVEC) 2012  [Schuller, Björn, et al 2012].
Reasoning Module Virtual characters that act in REVERIE's virtual environment can be autonomous agents, avatars representing a user and so-called replicants (a real-time 3D video representation of a user). Agents are completely autonomous, fully controlled by the Reasoning Framework. For the non-puppeted avatars, the Reasoning Framework controls gaze, gestures and pose based on user interaction. Agents can be programmed on what to do by means of a use case specific script. A script can describe how the agent should behave, what to say when and where, what to ask whom, where to go to, etc. A script has options for flow control, so it can specify a nonlinear story. User input in particular may influence execution of script instructions. An agent can have a dialog with one user or with a group of users.
The original Kinect sensor on the other hand supports user skeleton tracking algorithms for up to 17 joints, defined in the OpenNI library. The REVERIE avatars created using external tools  [Apostolakis, et. al, 2013b]  provide a high-level abstraction of the joint hierarchy, resulting in mesh assets that are completely deformable via transformations applied directly on the Kinect-based 17 mesh joint counterparts. The Avatar Kinect Body Puppeting module of the REVERIE system prototype is an end-to-end capturing and rendering module that generates animation based on skeleton data received over the network from a Kinect capturing station. The module is deployed using an integrated ALM server-client framework to transmit skeleton data over the network. The complete Kinect Body Puppeting module consists of a single capturer component deployed per user client (see Section 3.
1. 1), and multiple receiver components, which correspond to the total number of capturers identified within the network. Each capturer subscribes to its channel over the network and transmits Kinect skeleton data per frame, which is received by all receivers subscribing to the capturer's channel. The REVERIE system is responsible for distributing the correct parameters for each user to create the required number of receivers on the rendering side component of the module for rendering the amount of remote Kinect bodypuppeted avatar participants. The architecture of the module is shown in Figure  9. The actual process of the puppeting algorithm from Kinect data and implementation thereof in the GPU via skinning shaders is similar to the scheme described in  [Apostolakis, et. al, 2013a]. The Kinect Body Puppeting module makes extensive use of the Kinect sensor.
Avatar Webcam Face Puppeting Module As is the case with the Kinect-based puppeting module, the core module for Webcam-based Face Puppeting of virtual humans is broken down into two components; one intended to perform the user face detection, tracking and feature point extraction from a single, front-facing web camera connected to the system (see Section 3. 1. 2), while deformation and rendering of the character's face mesh geometry happens in a separate component. In order for the aforementioned components to communicate, a publish/subscribe ALM network infrastructure is deployed. In a similar manner to how the Kinect Body Puppeting Module utilizes the OpenNI library to access the Kinect skeleton capturing resources, the Webcam Puppeting capturer invokes a child process to open the user's web camera and gain access to the ASM tracking data by "listening" at the standard output channel.
I. INTRODUCTION Increasingly, mobile devices are infiltrating most of our daily activities, and consumers are using more and more mobile applications (apps for short). They download apps from dedicated app stores (Google Play, Apple Store, Amazon Appstore, etc. ), which are in constant growth. In 2013, Google Play Store reached over 50 billion app downloads  1. Previous studies have shown that these stores deliver a significant portion of buggy apps to mobile devices. For instance, we have identified 10, 658 buggy-suspicious apps in a dataset of 46, 644 apps collected from Google Play Store  [5]. Currently, the repair activity of defective apps is manually performed by app developers. After fixing the identified bugs, developers upload the new version (with the fixes) to the app store. Unfortunately, the time between the release of the defective app and the fixed version can be long.
A. Implementation of the Smart App Store Components Monitoring and Analyzing User Feedbacks. The healing process starts when the store observes user reviews complaining about bugs and crashes. To identify user reviews that treat themes related to bugs, we extract topics discussed in the corpus of reviews using Topic Modelling. Our system classifies as error-related reviews the ones which are mainly composed by topics related to bugs and failures. Then, it flags as buggy-suspicious the apps whose ratio of error-related reviews reaches a predefined threshold (cf. [5]  for implementation details). Our system enables the identification of 10, 658 buggy-suspicious apps in our dataset (composed of 46, 644 apps) collected from Google Play Store. This system enabled the identification of the subject app used in this experiment.
Planning Alternative App Releases. First, the Patch generation component processes the exception trace and extracts the n frames that refer to the suspicious app. In our example, the 2 frames that start with the package name of the app (com. snowbound. pockettool. free). From each suspicious frame, it extracts the name of the suspicious method and the class that implements it. In our example there are 2 suspicious methods (cf. Fig. 2 , lines 5 and 6): getWorldList and onCreate, defined in the class LevelSelector. Next, the component creates n different patches, where n is the number of suspicious methods. In our implementation, the patch wraps the code defined inside the suspicious methods with a try/catch block to capture the runtime exceptions that are not handled by the methods. We create 2 patched versions of the defective app, where each patch wraps a different suspicious method.
To inject the patches, we have implemented a Java program  5  , which instruments the bytecode of Android apps using Dexpler  [2]. Executing the Deployment of Apps. Finally, the different patched app versions are deployed in different user devices. First, the buggy app is uninstalled, and then the new patched version is installed. To communicate with devices, our implementation relies on the Android Debug Bridge (adb)  6. adb is a command line tool (included in the Android SDK), which acts as a middleman between a host and an Android device. We use adb to remotely install/uninstall apps and to read the logcat. To validate the candidate patches, the App monitor component observes if the fixed app throws the same exception as the original app when running on devices. If the exception still arises, then the patch is considered as invalid and the store discards it.
B. Experimental Results We observe that the patch applied in the onCreate method (Patch2) avoids the crash, whereas the patch applied  in the getWorldList method (Patch1) continues throwing the exception. Therefore, the store learns that Patch1 is not effective and automatically discards it. Our proposed implementation takes as input. apk files and works remotely with devices without requiring USB connection. We run the experiments in a rooted device Google Nexus S with Android 4. 1. 2. We generated 2 patched versions of the PocketTool app (original size 395 Kb). Each patch rewrites the bytecode of the subject app to inject a try/catch block that wraps the code defined in the methods getWorldList (Patch1) and onCreate (Patch2), respectively. The size of the two patched apps is 439 Kb, and the total time to rewrite the bytecode and redeploy the apps in the device is 51 seconds, which we consider is an acceptable overhead.
IV. RELATED WORK Recent approaches have investigated different sources of information available on app stores: AR-MINER  [8] , WIS-COM  [4] , Harman et al. [6] , Pagano and Maalej  [9]. The aforementioned approaches provide significant analysis about the user feedbacks available on app stores. Nevertheless, none of them provide mechanisms to exploit the user feedback by the stores themselves. Azim et al. [1]  present a self-healing approach to automatically detect failures and patch Android apps to avoid crashes. As in our approach their patching strategy rewrites the bytecode of apps to insert try/catch blocks to wrap methods that throw unhandled exceptions. Nevertheless, our approach generates different patches for a buggy app and uses user feedbacks to evaluate the feasibility of the patches.
A combination of multiple information retrieval approaches is proposed for the purpose of book recommendation. In this paper, book recommendation is based on complex user's query. We used different theoretical retrieval models: probabilistic as InL2 (Divergence from Randomness model) and language model and tested their interpolated combination. Graph analysis algorithms such as PageRank have been successful in Web environments. We consider the application of this algorithm in a new retrieval approach to related document network comprised of social links. We called Directed Graph of Documents (DGD) a network constructed with documents and social information provided from each one of them. Specifically, this work tackles the problem of book recommendation in the context of INEX (Initiative for the Evaluation of XML retrieval) Social Book Search track.
It is based on the idea that "the more the term occurrences diverge from random throughout the collection, the more informative the term is. " The limit of such models is that the distance between query terms in documents is not considered. In this paper, we present an approach that combines probabilistic and language models to improve the retrieval performances and show that the two models combined act much better in the context of book recommendation. In recent years, an important innovation in information retrieval appeared which consists of algorithms developed The Scientific World Journal to exploit the relationships between documents. One of the important algorithm is Google's PageRank  [7]. It has been successful in Web environments, where the relationships are provided with the existing hyperlinks into documents.
Abolhassani and Fuhr have investigated several possibilities for applying Amati's DFR model  [6]  for content-only search in XML documents  [10]. This work also relates to the category of graph based document retrieval. There has been increasing use of techniques based on graphs constructed by implicit relationships between documents. Kurland and Lee performed structural reranking based on centrality measures in graph of documents which has been generated using relationships between documents based on language models  [11]. In  [12] , Lin demonstrates the possibility to exploit document networks defined by automatically generated content-similarity links for document retrieval in the absence of explicit hyperlinks. He integrates the PageRank scores with standard retrieval score and shows a significant improvement in ranked retrieval performance.