Miracle-dz/UniFAR-FTUs huggingface dataset:
A sentiment analysis system for the hindi language by integrating gated recurrent unit with genetic algorithm

The growing availability and popularity of opinion rich resources such as blogs, shopping websites, review portals, and social media platforms have attracted several researchers to perform the sentiment analysis task. Unlike English, Chinese, Spanish, etc. the availability of Indian languages such as Hindi, Telugu, Tamil, etc., over the web have also been increased at a rapid rate. This research work understands the growing popularity of Hindi language in the web domain and considered it for the task of sentiment analysis. The research work analyses the hidden sentiments from the movie reviews collected from the review section of Hindi language e-newspapers. The reviews are multilingual, which makes sentiment analysis a challenging task. To overcome the challenges, this research work proposes a deep learning based approach where a Gated Recurrent Unit network is combined with the Hindi word embedding model. The strategy enables the network to efficiently capture the semantic and syntactic relation between Hindi words and accurately classify them into the sentiment classes. Gated Recurrent Unit network's performance is profoundly dependent upon the selection of its hyper-parameters; therefore, this research work also utilizes a Genetic Algorithm to automatically build a gated recurrent network architecture enabling it to select the best optimal hyper-parameters. It has been observed that the proposed Genetic Algorithm-Gated Recurrent Unit (GA-GRU) model is effective and achieves breakthrough performance results on the Hindi movie review dataset as compared to other traditional resource-based and machine learning approaches
USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments

The Unified Suite for Experiments (USE) is a complete hardware and software suite for the design and control of behavioral neuroscience experiments. The software, developed using the Unity 3D video game engine, supports both dynamic activities in immersive 3D environments, and static 2D tasks commonly used in more traditional visual experiments. The hardware, based around an Arduino Mega2560 board, enables the sub-millisecond integration and synchronization of multiple data streams. A hierarchical, modular architecture makes USE a flexible framework to realize experiments, with (i) variable nested task structures used in complex cognitive and reinforcement learning tasks, (ii) flexible use of 3D versus 2D scenes and objects, (iii) precise online control of touchscreen-, button-, joystick- and eye movement behavior, and (iv) a complete offline reconstruction of experiments for post-processing and sub-millisecond temporal alignment of data streams. In summary, USE provides an integrated, open source framework for a wide variety of behavioral neuroscience experiments in human and nonhuman subjects.
Specification of synchronous network flooding in temporal logic

In distributed network algorithms, network flooding is considered one of the simplest and most fundamental algorithms. This research specifies the basic synchronous memory-less network flooding algorithm where nodes on the network don’t have memory, for any fixed size of network, in Linear Temporal Logic. The specification can be customized to any single network topology or class of topologies. A specification of the termination problem is formulated and used to compare different topologies for earlier termination. This paper gives a worked example of one topology resulting in earlier termination than another, for which we perform a formal verification using the model checker NuSMV.
A deep learning approach for the romanized tunisian dialect identification

Language identification is an important task in natural language processing that consists in determining the language of a given text. It has increasingly picked the interest of researchers for the past few years, especially for code-switching informal textual content. In this paper, we focus on the identification of the Romanized user-generated Tunisian dialect on the social web. We segment and annotate a corpus extracted from social media and propose a deep learning approach for the identification task. We use a Bidirectional Long Short-Term Memory neural network with Conditional Random Fields decoding (BLSTM-CRF). For word embeddings, we combine word-character BLSTM vector representation and Fast Text embeddings that takes into consideration character n-gram features. The overall accuracy obtained is 98.65%.
Modeling and Validating Role-Based Authorization Policies for a Port Communication System with UML and OCL

Modern sea or inland ports rely on digital communication and systems to boost rapid turnover of trade. Stakeholders like shippers, shipping lines, container terminals and port authorities collaborate and compete using their own legacy applications. Many sea ports operate Port Community Systems (PCS) to orchestrate processes between the players. These software systems are potential targets of security threats that may lead to payment fraud, espionage of competitors, smuggling, theft, export control violations, up to disasters involving dangerous goods possibly effecting public mains. In our approach we apply modeling to the ﬁeld of information security. We combine and focus on Role-Based Access Control (RBAC) with constraints and Attribute-Based Access Control (ABAC) for ﬁner grained authorization constraints. In a concrete case study we model authorization policies within port communities that partly utilize dedicated PCS. The purpose is to increase the integrity of exchanged data and thus reduce the risks of attacks or failures. We employ the UML-based Speciﬁcation Environment (USE) and its OCL support to validate speciﬁed security properties for a typical container shipping scenario.
Home-range size and movement patterns of Hooded Vultures Necrosyrtes monachus in southern Africa

Hooded Vulture Necrosyrtes monachus populations have declined dramatically in recent years, but we know little about their ecology. We radio-tagged four vultures in northern Botswana to gather data on animal movement and home-range patterns. Hooded Vultures were primarily sedentary at night. Hooded Vultures moved similar distances and speeds during the wet and dry season, and travelled over similar home ranges as measured using minimum convex polygons (MCP), but used much smaller core areas during the dry (breeding) season. We found significant differences in mean distances and speeds moved among different birds, and when comparing day to night, but not between the wet (non-breeding) and dry (breeding) season or by year. All of the variables we tested, including individual vulture differences, season, year and number of fixes, significantly influenced 95% MCP and kernel density estimate (KDE) home-range sizes. Hooded Vultures used significantly smaller KDE home-range sizes during the dry (breeding season) than in the wet (non-breeding) season. Hooded Vultures travelled smaller daily distances over smaller home ranges than most other vulture species for which data exist.
Fixed charge network design problem with user-optimal flows. (Conception de réseaux avec flots optimaux pour les usagers)

This thesis addresses a class of bi-level network design problems. We are interested in investigating applications from different domains and in developing exact algorithms to solve the corresponding bi-level network problem. In particular, we study a bi-level network design problem where the leader selects a part of the network to be activated, then, in the second level, the solution must be optimal for a network flow problem in the selected sub-network. In this thesis, three applications of this problem are studied: hazmats transportation, telecommunication, and social networks analysis. The second level problem in the first and the last applications is a shortest path problem while a minimum cost flow is required in the second application.The first studied problem is the fixed charge network design problem with shortest path constraints, which is modeled as a bi-level program and can be applied in hazardous transportation. For this problem, we propose two new binary integer programming (BILP) formulations inspired by path and cycle inequalities. We incorporate these formulations in a branch-and-cut algorithm and another cutting-plane based method. Numerical experiments are performed on real instances, and random data sets generated with different criteria to examine the difficulty of the instances. The results show that the proposed cutting plane algorithms can solve up to 19% more instances than the compact formulations.The second studied problem is the energy-aware traffic engineering while using multi-path routing to minimize link capacity utilization in ISP backbone networks. We propose a bi-level optimization model where the upper level represents the energy management function, and the lower one refers to the deployed multi-path routing protocol. Then, we reformulate it as a one-level MILP replacing the second level problem by different sets of flow optimality conditions. We further use these formulations to solve the problem with classical cutting plane and branch-and-cut algorithms. The computational experiments are performed on real instances to compare the proposed algorithms and to evaluate the efficiency of our model against existing single-path and multi-objective models.Finally, we study the problem of maximization influence in signed social networks. To the best of our knowledge, it is the first time that this problem is modeled as a bi-level programming problem. We reformulate the problem as one-level MILP models using three different optimality conditions of the shortest path problem appearing in the second level. These formulations are strengthened by adding a set of valid inequalities. Computational experiments are performed using random instances to compare the different proposed formulations. Finally, explicit solutions and bounds are proposed for particular cases of instances.
Need for recovery amongst emergency physicians in the UK and Ireland: a cross-sectional survey

Objectives To determine the need for recovery (NFR) among emergency physicians and to identify demographic and occupational characteristics associated with higher NFR scores. Design Cross-sectional electronic survey. Setting Emergency departments (EDs) (n=112) in the UK and Ireland. Participants Emergency physicians, defined as any registered physician working principally within the ED, responding between June and July 2019. Main outcome measure NFR Scale, an 11-item self-administered questionnaire that assesses how work demands affect intershift recovery. Results The median NFR Score for all 4247 eligible, consented participants with a valid NFR Score was 70.0 (95% CI: 65.5 to 74.5), with an IQR of 45.5–90.0. A linear regression model indicated statistically significant associations between gender, health conditions, type of ED, clinical grade, access to annual and study leave, and time spent working out-of-hours. Groups including male physicians, consultants, general practitioners (GPs) within the ED, those working in paediatric EDs and those with no long-term health condition or disability had a lower NFR Score. After adjusting for these characteristics, the NFR Score increased by 3.7 (95% CI: 0.3 to 7.1) and 6.43 (95% CI: 2.0 to 10.8) for those with difficulty accessing annual and study leave, respectively. Increased percentage of out-of-hours work increased NFR Score almost linearly: 26%–50% out-of-hours work=5.7 (95% CI: 3.1 to 8.4); 51%–75% out-of-hours work=10.3 (95% CI: 7.6 to 13.0); 76%–100% out-of-hours work=14.5 (95% CI: 11.0 to 17.9). Conclusion Higher NFR scores were observed among emergency physicians than reported in any other profession or population to date. While out-of-hours working is unavoidable, the linear relationship observed suggests that any reduction may result in NFR improvement. Evidence-based strategies to improve well-being such as proportional out-of-hours working and improved access to annual and study leave should be carefully considered and implemented where feasible.
Traitement automatique des langues et génération automatique d'exercices de grammaire. (Automatic language processing and automatic generation of grammar exercises)

Le theme support de cette these la generation de paraphrases sur support neuronal. Nos perspectives sont educatives : creer des exercices de grammaire pour le francais. La paraphrase est une operation de reformulation. Nos travaux tendent a attester que les modeles sequence vers sequence ne sont pas de simples repetiteurs mais peuvent apprendre la syntaxe. Nous avons montre, en combinant divers modeles, que la representation de l'information sous de multiples formes (en utilisant de la donnee formelle (RDF), couplee a du texte pour l'etendre ou le reduire, ou encore seulement du texte) permet d'exploiter un corpus sous differents angles, augmentant la diversite des sorties, exploitant les leviers syntaxiques mis en place. Nous nous sommes penchee sur un probleme recurrent, celui de la qualite des donnees, et avons obtenu des paraphrases avec une haute adequation syntaxique (jusqu'a 98% de couverture de la demande) et un tres bon niveau linguistique. Nous obtenons jusqu'a 83.97 points de BLEU*, 78.41 de plus que la moyenne de nos lignes de base, sans levier syntaxique. Ce taux indique un meilleur controle des sorties, pourtant variees et de bonne qualite en l'absence de levier. Nous avons ensuite travaille depuis du texte brut en passant, pour la generation de phrases, par la production d'une representation du sens de ce texte qui puisse servir d'entree a la generation de paraphrases. Le passage a du texte en francais etait aussi pour nous un imperatif. Travailler depuis du texte brut, en automatisant les procedures, nous a permis de creer un corpus de plus de 450 000 couples representations/phrases, grâce auquel nous avons appris a generer des textes massivement corrects (92% sur la validation qualitative). Anonymiser ce qui n'est pas fonctionnel a participe notablement a la qualite des resultats (68.31 de BLEU, soit +3.96 par rapport a la ligne de base, qui etait la generation depuis des donnees non anonymisees). La representation formelle de l'information dans un cadre linguistique particulier a une langue est une tâche ardue. Cette these offre des pistes de methodes pour automatiser cette operation. Par ailleurs, nous n'avons pu traiter que des phrases relativement courtes. L'utilisation de modeles neuronaux plus recents permettrait sans doute d'ameliorer les resultats. Enfin, l'usage de traits adequats en sortie permettrait des verifications poussees. *BLEU (Papineni et al., 2002) : qualite d'un texte sur une echelle de 0 (pire) a 100 (meilleur)
F0 modeling for isarn speech synthesis using deep neural networks and syllable-level feature representation

The generation of the fundamental frequency (F0) plays an important role in speech synthesis, which directly influences the naturalness of synthetic speech. In conventional parametric speech synthesis, F0 is predicted frame-by-frame. This method is insufficient to represent F0 contours in larger units, especially tone contours of syllables in tonal languages that deviate as a result of long-term context dependency. This work proposes a syllable-level F0 model that represents F0 contours within syllables, using syllable-level F0 parameters that comprise the sampling F0 points and dynamic features. A Deep Neural Network (DNN) was used to represent the relationships between syllable-level contextual features and syllable-level F0 parameters. The proposed model was examined using an Isarn speech synthesis system with both large and small training sets. For all training sets, the results of objective and subjective tests indicate that the proposed approach outperforms the baseline systems based on hidden Markov models and DNNS that predict F0 values at the frame level
Cross-Domain Learning for Classifying Propaganda in Online Contents

As news and social media exhibit an increasing amount of manipulative polarized content, detecting such propaganda has received attention as a new task for content analysis. Prior work has focused on supervised learning with training data from the same domain. However, as propaganda can be subtle and keeps evolving, manual identification and proper labeling are very demanding. As a consequence, training data is a major bottleneck. In this paper, we tackle this bottleneck and present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. We devise informative features and build various classifiers for propaganda labeling, using cross-domain learning. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step. We further analyze the influence of various features, and characterize salient indicators of propaganda.
Java Bytecode Verification with OCL Why, How and Whenc

Program transformations are frequently developed, e.g., to realize programming language extensions or dynamic program analyses such as proﬁling. They are typically implemented by manipulating bytecode as the availability of source code is not guaranteed. There are standard libraries such as ASM that are typically used for implementing Java bytecode manipulations. To check their correctness, they are usually tested by applying them to different programs, running the manipulated programs and observing their behaviors. As part of the second step, the Java virtual machine veriﬁes the bytecode, which can uncover errors in the bytecode introduced by the manipulation. That approach uses different technologies that are not well linked making the process of developing and testing bytecode manipulations difﬁcult. In this paper, we intend to perform bytecode manipulation by using concepts and techniques of model-driven engineering. We are convinced that the declarative nature of model transformation rules allows the debugging and analyzing of bytecode manipulations in more details than classically done. Following this path, a meta-model for bytecode is needed including OCL constraints for bytecode veriﬁcation. We analyze the semantic rules of the bytecode veriﬁer according to their complexity factor, present a meta-model for Java bytecode, show how the semantic rules can be expressed as OCL constraints on top of this meta-model, and show that basing bytecode manipulation on model transformation can provide more immediate guidance and feedback to the developer.
On Developing and Validating Dynamic Systems: Simulation Engineering

Engineering dynamic systems – systems where the behaviour is the dominant characteristic – has some fundamental differences and challenges that are often neglected in model-based software engineering. In engineering simulations, observable system behaviour is built up from the behaviour of low-level components; such simulations are used as research tools in biological and social systems research. A necessary part of simulation engineering is to show that the simulation is ﬁt for its intended purpose, which is easier at the model level than the code level, because the models can be presented in a readable form that domain experts can access. We explore issues in the use of model transformation for simulation development, using a published Java Mason simulator, created using the CoSMoS approach from UML-style state diagrams. We succeed in recreating part of the class structure of the code by manual transformation, but also expose many issues to be overcome before an automated transformation can be developed, in terms of what needs to be modelled to enable transformation, and how and when design decisions are taken. We identify that a transformation of a behavioural design into an OO simulation also needs a means to capture the low-level simulation and visualisation mechanisms, and a means to capture the design of the behavioural and data aspects of simulation experimentation.
Turbulence in the Hippocampus: An Ansatz for the Energy Cascade Hypothesis

Mesoscopic neural activity may play an important role in the cross-scale integration of brain activity and in the emergence of cognitive behavior. Mesoscale activity in the cortex can be defined as the organization of activity of large populations of neurons into collective actions, such as traveling waves in the hippocampus. A comprehensive description of collective activity is still lacking, in part because it cannot be built directly with methods and models developed for the microscale (individual neurons): the laws governing mesoscale dynamics are different from those governing a few neurons. To identify the characteristic features of mesoscopic dynamics, and to lay the foundations for a theoretical description of mesoscopic activity in the hippocampus, we conduct a comprehensive examination of observational data of hippocampal local field potential (LFP) recordings. We use the strong correlation between rat running-speed and the LFP power to parameterize the energy input into the hippocampus, and show that both the power, and the nonlinearity of mesoscopic scales of collective action (e.g., theta and gamma rhythms) increase as with energy input. Our results point to a few fundamental characteristics: collective-action dynamics are stochastic (the precise state of a single neuron is irrelevant), weakly nonlinear, and weakly dissipative. These are the principles of the theory of weak turbulence. Therefore, we propose weak turbulence as an ansatz for the development of a theoretical description of mesoscopic activity. The perspective of weak turbulence provides simple and meaningful explanations for the major features observed in the evolution of LFP spectra and bispectra with energy input, such as spectral slopes and their evolution, the increased nonlinear coupling observed between theta and gamma, as well as specific phase lags associated with their interaction. The weak turbulence ansatz is consistent with the theory of self organized criticality, which provides a simple explanation for the existence of the power-law background spectrum, and could provide a unifying approach to modeling the dynamics of mesoscopic activity.
A new genus of gall-forming aphids of tribe Fordini Baker, 1920 (Hemiptera: Aphididae: Eriosomatinae) from Near East

Summary Inbaria n. gen. is proposed based on type species Geoica swirskii Remaudière & Inbar, 2004, resulting in a new combination, Inbaria swirskii (Remaudière & Inbar, 2004) n. comb. Inbaria easily differs from all Pistacia gall-producing aphids of the tribe Fordini by the size of the primary and secondary rhinaria in fall migrants. Inbaria n. gen. is differentiated morphologically from the similar genera Geoica Hart, 1894 and Slavum Mordvilko, 1927. Genetically (COI), the two genera Geoica and Inbaria show a large divergence of more than 9% of the sequence. Notes on some morphological characters in apterous fundatrigeniae and descriptions of the embryo of the apterous exule, within alate viviparous females of I. swirskii, are provided.