Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-modal and deep learning for robust speech recognition

Automatic speech recognition (ASR) decodes speech signals into text. While ASR can produce accurate word recognition in clean environments, system performance can degrade dramatically when noise and reverberation are present. In this thesis, speech denoising and model adaptation for robust speech recognition were studied, and four novel methods were introduced to improve ASR robustness. First, we developed an ASR system using multi-channel information from microphone arrays via accurate speaker tracking with Kalman filtering and subsequent beamforming. The system was evaluated on the publicly available Reverb Challenge corpus, and placed second (out of 49 submitted systems) in the recognition task on real data. Second, we explored a speech feature denoising and dereverberation method via deep denoising autoencoders (DDA). The method was evaluated on the CHiME2-WSJ0 corpus and achieved a 16% to 25% absolute improvement in word error rate (WER) compared to the baseline. Third, we developed a method to incorporate heterogeneous multi-modal data with a deep neural network (DNN) based acoustic model. Our experiments on a noisy vehiclebased speech corpus demonstrated that WERs can be reduced by 6.3% relative to the baseline system. Finally, we explored the use of a low-dimensional environmentally-aware feature derived from the total acoustic variability space. Two extraction methods are presented: one via linear discriminant analysis (LDA) projection, and the other via a bottleneck deep neural network (BN-DNN). Our evaluations showed that by adapting ASR systems with the proposed feature, ASR performance was significantly improved. We also demonstrated that the proposed feature yielded promising results on environment identification tasks. Thesis Supervisor: James R. Glass Title: Senior Research Scientist
Gibberellin A1 Metabolism Contributes to the Control of Photoperiod-Mediated Tuberization in Potato

Some potato species require a short-day (SD) photoperiod for tuberization, a process that is negatively affected by gibberellins (GAs). Here we report the isolation of StGA3ox2, a gene encoding a GA 3-oxidase, whose expression is increased in the aerial parts and is repressed in the stolons after transfer of photoperiod-dependent potato plants to SD conditions. Over-expression of StGA3ox2 under control of constitutive or leaf-specific promoters results in taller plants which, in contrast to StGA20ox1 over-expressers previously reported, tuberize earlier under SD conditions than the controls. By contrast, StGA3ox2 tuber-specific over-expression results in non-elongated plants with slightly delayed tuber induction. Together, our experiments support that StGA3ox2 expression and gibberellin metabolism significantly contribute to the tuberization time in strictly photoperiod-dependent potato plants.
Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users

Based on a large data set of emoji using behavior collected from smartphone users over the world, this paper investigates gender-specific usage of emojis. We present various interesting findings that evidence a considerable difference in emoji usage by female and male users. Such a difference is significant not just in a statistical sense; it is sufficient for a machine learning algorithm to accurately infer the gender of a user purely based on the emojis used in their messages. In real world scenarios where gender inference is a necessity, models based on emojis have unique advantages over existing models that are based on textual or contextual information. Emojis not only provide language-independent indicators, but also alleviate the risk of leaking private user information through the analysis of text and metadata.
An adaptive multi-agent system for the distribution of intelligence in electrical distribution networks : state estimation. (Un système multi-agent auto-adaptatif pour la distribution de l'intelligence dans les réseaux électriques de distribution : estimation d'état)

L'electricite joue un role de plus en plus important dans notre societe. En effet, nous nous dirigeons vers l'ere du "tout electrique". Les besoins evoluant, il est indispensable de repenser la maniere dont l'electricite est produite et distribuee. Cela introduit le concept de Smart Grid. Le Smart Grid est un concept de reseau electrique capable de supporter de maniere autonome et intelligente les changements et pannes qui pourraient survenir dans un reseau. Cela repond directement au fait que de part la nature fortement distribuee et l'impredictibilite de l'environnement (meteo, .), ces evenements sont imprevisibles. Pour cela, cette these propose un cadre applicatif (framework) innovant base sur les multi-agents ainsi que la conception et l'implementation de comportements cooperatifs pour resoudre deux problemes courants dans les reseaux electriques: l'analyse des ﬂux de puissance et l'estimation d'etat. Ces problemes ont ete abordes avec l'approche des Systemes Multi-Agent Adaptatifs. Ces systemes sont efﬁcaces pour resoudre des problemes complexes et ont la capacite d'adapter leur fonctionnement aux evolutions de leur environnement. Les resultats obtenus indiquent la pertinence d'utiliser de tels systemes adaptatifs pour resoudre les problemes inherents au concept de Smart Grid.
Tip identification in atomic force microscopy using nonlinear dynamics

Since its invention, the Atomic Force Microscope has emerged into one of the most useful tools in nanotechnology due to its acclaimed abilities in exploring surface topography, micro- and nanoscale manipulation and characterization. The nonlinear interaction between the cantilever tip and the sample surface has been studied in great detail and a thorough understanding of the cantilever dynamics can improve measurements and lead to new methods for identification, manipulation and characterization. One of the biggest challenges in the field of AFM is related to the identification of the cantilever tip size. The accuracy of measurements in AFM is directly related to the size and geometry of this tip. It is desired that the tip condition can be continuously monitored in between or even throughout measurements in order to provide high quality measurements. In this thesis project, a methodology for tip assessment is introduced based on the nonlinear dynamic response of the cantilever. The method consists of the acquisition of frequency response curves in the attractive regime where the influence of the tip radius is predominant. Experimental frequency response curves are fitted to the tip-sample interaction model that includes the van der Waals forces. It is found that exploiting the nonlinear dynamic response of the cantilever is a safe and accurate method for tip assessment.
Identification of avian vasotocin receptor subtype-specific antagonists involved in the stress response of the chicken, Gallus gallus

Vasotocin 1a and 1b receptors (V1aR and V1bR) have been shown to play important roles in the neuroendocrine regulation of stress responses via the anterior pituitary (AP) of birds. To identify effective subtype-specific antagonists for the chicken V1aR (cV1aR) and cV1bR, potential antagonists to the mammalian V1R were screened against the cV1aR and cV1bR 3D structural models by molecular docking analysis with determination of binding pocket/amino acid residues involved in the interaction. The antagonistic effects of the selected ligands were examined by measuring pro-opiomelanocortin (POMC) heteronuclear RNA (hnPOMC) levels following the in vitro stress administration to primary chicken AP cells. Results of in silico analysis showed that the Manning compound and several other antagonists were bound to cV1bR with higher affinity than the natural agonist, arginine vasotocin (AVT). Similarities and differences in the antagonist-receptor binding interface with receptors were characterized for each ligand. Non-peptide mammalian V1bR antagonists, SSR-149415 and L-368899, were shown to be effective and had an additive effect in blocking POMC hnRNA expression in pituitary cell culture studies. SR-49059 antagonized the effect(s) of AVT/CRH on the downregulation of the cV1aR and the upregulation of the cCRH-R2 expression but not the cV1bR and cCRH-R1. The Manning compound antagonized the downregulation of cV1aR, cV1bR and cCRH-R1 and the upregulation of cCRH-R2 expression. The specificity of antagonists apparently resulted from unique differences in the interacting residues and their binding affinities. Collectively, these results provide valuable leads for future development of novel compounds capable of blocking or attenuating the AP stress response of avian species and perhaps other non-mammalian vertebrates as well.
Strategies for Geographical Scoping and Improving a Gazetteer

Many applications that use geographical databases (a.k.a. gazetteers) rely on the accuracy of the information in the database. However, poor data quality is an issue when data is integrated from multiple sources with different quality constraints and sometimes with little information about the sources. One major consequence of this is that the geographical scope of a location and/or its position may not be known or may not be accurate. In this paper, we study the problem of detecting the scope of locations in a geographical database and its applications in identifying inconsistencies and improving the quality of a gazetteer. We develop novel strategies, including probabilistic and geometric approaches, to accurately derive the geographical scope of places based on the spatial hierarchy of a gazetteer as well as other public information (such as area) that may be available. We show how the boundary information derived here can be useful in identifying inconsistencies, enhancing the location hierarchy and improving the applications that rely on gazetteers. Our experimental evaluation on two public-domain gazetteers reveals that the proposed approaches significantly outperform, in terms of the accuracy of the geographical bounding boxes, a baseline that is based on the parent-child relationship of a gazetteer. Among applications, we show that the boundary information derived here can move more than 20% of locations in a public gazetteer to better positions in the hierarchy and that the accuracy of those moves is over 90%.
Novel preparation of controlled porosity particle/fibre loaded scaffolds using a hybrid micro-fluidic and electrohydrodynamic technique

The purpose of this research was to produce multi-dimensional scaffolds containing biocompatible particles and fibres. To achieve this, two techniques were combined and used: T-Junction microfluidics and electrohydrodynamic (EHD) processing. The former was used to form layers of monodispersed bovine serum albumin (BSA) bubbles, which upon drying formed porous scaffolds. By altering the T-Junction processing parameters, bubbles with different diameters were produced and hence the scaffold porosity could be controlled. EHD processing was used to spray or spin poly(lactic-co-glycolic) (PLGA), polymethysilsesquioxane (PMSQ) and collagen particles/fibres onto the scaffolds during their production and after drying. As a result, multifunctional BSA scaffolds with controlled porosity containing PLGA, PMSQ and collagen particles/fibres were obtained. Product morphology was studied by optical and scanning electron microscopy. These products have potential applications in many advanced biomedical, pharmaceutical and cosmetic fields e.g. bone regeneration, drug delivery, cosmetic cream lathers, facial scrubbing creams etc.
Application of the Ranking Relative Principal Component Attributes Network Model (REL-PCANet) for the Inclusive Development Index Estimation

In 2018, at the World Economic Forum in Davos it was presented a new countries' economic performance metric named the Inclusive Development Index (IDI) composed of 12 indicators. The new metric implies that countries might need to realize structural reforms for improving both economic expansion and social inclusion performance. That is why, it is vital for the IDI calculation method to have strong statistical and mathematical basis, so that results are accurate and transparent for public purposes. In the current work, we propose a novel approach for the IDI estimation - the Ranking Relative Principal Component Attributes Network Model (REL-PCANet). The model is based on RELARM and RankNet principles and combines elements of PCA, techniques applied in image recognition and learning to rank mechanisms. Also, we define a new approach for estimation of target probabilities matrix to reflect dynamic changes in countries' inclusive development. Empirical study proved that REL-PCANet ensures reliable and robust scores and rankings, thus is recommended for practical implementation.
1 Situated compassion : affect , discretion and policy

Care has become a focus of debate in feminist technoscience studies, with a recent call for researchers to be care-full about the politics of research and theorising and also to challenge ‘care’ as a taken-for-granted good (Martin et al., 2015). Care is a current focus of concern in Britain, where a crisis has been declared in national health and social care, and where previous policy has focused on quantity at the expense of quality (Keogh, 2013). The influential, government-commissioned Francis Report documents ‘appalling and unnecessary patient suffering’ and calls for action to put ‘compassion at the heart of healthcare’ (DOH, 2013a, p. 3–4). The report’s conclusions are widely supported, and there are new policy interventions to promote compassionate care (DOH, 2013b, 2013c, 2013d, 2013g; DOH, 2014). However, policy/care tensions exist; for example, the legislative responses are critiqued as meaningless to the realities of practice and as attempting to control rather than to support practitioners. In order to explore how compassion is being done differently in diverse instantiations and enactments, we juxtapose feminist technoscience studies on the politics of care with an analysis of health policy and with vignettes of located practices. The vignettes articulate affects, materials and relations of compassion in locations of practice and explore not only the good of what is considered compassion, but also possible harms, contestation and vulnerabilities. Thereby, the analytical approach aligns with Martin et al.’s (2015, p. 627) call for ‘critical care’ research that understands care as an ‘affectively charged and selective mode of attention’ and that exposes the dark side of care. The analysis also reflects on possibilities for formulating policy as a form of care that attends to situatedness and to affective and discretionary aspects of compassion as ethical relating.
Invasion of gas into mica nanopores: a molecular dynamics study

The invasion of gas into liquid-filled nanopores is encountered in many engineering problems but is not yet well understood. We report molecular dynamics simulations of the invasion of methane gas into water-filled mica pores with widths of 2–6 nm. Gas invades into a pore only when the pressure exceeds a breakthrough pressure and a thin residual water film is left on the mica wall as the gas phase moves deeper into the pore. The gas breakthrough pressure of pores as narrow as 2 nm can be modeled reasonably well by the capillary pressure if the finite thickness of residual liquid water film and the liquid–gas interface are taken into account. The movement of the front of the liquid meniscus during gas invasion can be quantitatively described using the classical hydrodynamics when the negative slip length on the strongly hydrophilic mica walls is taken into account. Understanding the molecular mechanisms underlying the gas invasion in the system studied here will form the foundation for quantitative prediction of gas invasion in practical porous media.
A survey of comics research in computer science

Graphical novels such as comics and mangas are well known all over the world. The digital transition started to change the way people are reading comics, more and more on smartphones and tablets and less and less on paper. In the recent years, a wide variety of research about comics has been proposed and might change the way comics are created, distributed and read in future years. Early work focuses on low level document image analysis: indeed comic books are complex, they contains text, drawings, balloon, panels, onomatopoeia, etc. Different fields of computer science covered research about user interaction and content generation such as multimedia, artificial intelligence, human-computer interaction, etc. with different sets of values. We propose in this paper to review the previous research about comics in computer science, to state what have been done and to give some insights about the main outlooks.
CoCo: Compact and Optimized Consolidation of Modularized Service Function Chains in NFV

The modularization of Service Function Chains (SFCs) in Network Function Virtualization (NFV) could introduce significant performance overhead and resource efficiency degradation due to introducing frequent packet transfer and consuming much more hardware resources. In response, we exploit the lightweight and individually scalable features of elements in Modularized SFCs (MSFCs) and propose CoCo, a compact and optimized consolidation framework for MSFC in NFV. CoCo addresses the above problems in two ways. First, CoCo Optimized Placer pays attention to the problem of which elements to consolidate and provides a performance-aware placement algorithm to place MSFCs compactly and optimize the global packet transfer cost. Second, CoCo Individual Scaler innovatively introduces a push-aside scaling up strategy to avoid degrading performance and taking up new CPU cores. To support MSFC consolidation, CoCo also provides an automatic runtime scheduler to ensure fairness when elements are consolidated on CPU core. Our evaluation results show that CoCo achieves significant performance improvement and efficient resource utilization.
Suitability of Coconut Shell Concrete for Precast Cool Wall Panel-A Review

The cost for producing concrete has been increasing steadily. Therefore, the demand for using agricultural waste as a replacement or admixture inside concrete is also increasing gradually. Many researchers are doing research on agricultural waste such as palm oil, corn cobs, rice husks and coconut shells. Agricultural waste is increasingly being utilized in cement, concrete and other construction materials and provides numerous indirect benefits such as reduction in land fill cost, saving energy and protecting the environment from possible pollution. The aim is to produce concrete with improved properties at a lower cost and to maintain environmental sustainability. In this review paper, the suitability of coconut shell for concrete cool wall panels will be the main focus. Coconut shells can be used as aggregates in concrete. The characteristic properties of coconut shell concrete such as workability, bulk density, compressive strength, flexural tensile, water absorption and thermal performance were reviewed in this paper. This paper attempts to answer whether coconut shell is suitable to be used in concrete to produce a concrete cool wall panel in order to reduce heat transfer inside a building.
Looking for Galactic Diffuse Dark Matter in INO-MagICAL Detector

The Weakly Interacting Massive Particle (WIMP) is a popular particle physics candidate for the dark matter (DM). It can annihilate and/or decay to neutrino and antineutrino pair. The proposed 50 kt Magnetized Iron CALorimeter (MagICAL) detector at the India-based Neutrino Observatory (INO) can observe these pairs over the conventional atmospheric neutrino and antineutrino fluxes. If we do not see any excess of events in ten years, then INO-Magical can place competitive limits on self-annihilation cross-section ($\langle\sigma v\rangle$) and decay lifetime ($\tau$) of dark matter at 90\% C.L. : $\langle\sigma v\rangle\leq 1.87\,\times\,10^{-24}$ cm$^3$ s$^{-1}$ and $\tau\geq 4.8\,\times\,10^{24}$ s for $m_\chi$ = 10 GeV assuming the NFW as DM density profile.
Bayesian models for anti-sparse coding and Bayesian non parametric principal component analysis. (Modèles bayésiens pour l'identification de représentations antiparcimonieuses et l'analyse en composantes principales bayésienne non paramétrique)

Cette these etudie deux modeles parametriques et non parametriques pour le changement de representation. L'objectif des deux modeles differe. Le premier cherche une representation en plus grande dimension pour gagner en robustesse. Le second projete les donnees dans un sous-espace pertinent a des fins de modelisation. Une introduction au codage antiparcimonieux est presentee. L'objectif est de repartir uniformement l’information d’un signal sur toutes les composantes de sa representation en plus grande dimension dans un but de robustesse. La recherche d'un tel code s'exprime comme un probleme inverse impliquant une regularisation de type norme infinie. A ce jour, seules les methodes deterministes necessitant le reglage de parametres ont ete explorees. Nous proposons une formulation bayesienne du probleme impliquant une nouvelle loi de probabilite baptisee democratique, qui penalise les fortes amplitudes. Un echantillonneur de Gibbs et deux algorithmes MCMC proximaux sont presentes pour approcher des estimateurs bayesiens. La methode non supervisee presentee est appelee BAC-1. Des experiences numeriques illustrent les performances de l’approche pour la reduction de facteur de crete. A des fins de modelisation, il est necessaire d'identifier un sous-espace pertinent de dimension reduite. Mais les methodes probabilistes proposees necessitent generalement de fixer a l'avance la dimension du sous-espace. Ce travail introduit une version bayesienne non parametrique de l'analyse en composantes principales nommee BNP-PCA. La methode couple une loi uniforme sur l'ensemble des bases orthonormales a un a priori non parametrique de type buffet indien pour favoriser une utilisation parcimonieuse des composantes principales. Tous les parametres inconnus sont integres au sein d'un modele hierarchique de sorte qu'aucun reglage n'est necessaire. L'inference est realisee a l'aide des methodes de MCMC. Une etude de l'estimation de la dimension du sous-espace est menee. Le comportement de la methode BNP-PCA est etudie a travers plusieurs experiences numeriques. Nous montrons la flexibilite de la methode BNP-PCA sur deux applications.
How to Impute Missing Ratings?: Claims, Solution, and Its Application to Collaborative Filtering

Data sparsity is one of the biggest problems faced by collaborative filtering used in recommender systems. Data imputation alleviates the data sparsity problem by inferring missing ratings and imputing them to the original rating matrix. In this paper, we identify the limitations of existing data imputation approaches and suggest three new claims that all data imputation approaches should follow to achieve high recommendation accuracy. Furthermore, we propose a deep-learning based approach to compute imputed values that satisfies all three claims. Based on our hypothesis that most pre-use preferences (e.g., impressions) on items lead to their post-use preferences (e.g., ratings), our approach tries to understand via deep learning how pre-use preferences lead to post-use preferences differently depending on the characteristics of users and items. Through extensive experiments on real-world datasets, we verify our three claims and hypothesis, and also demonstrate that our approach significantly outperforms existing state-of-the-art approaches.