Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Language Model for Tunisian Dialect

In this paper, we describe the process of creating a statistical Language Model (LM) for the Tunisian Dialect. Indeed, this work is part of the realization of Automatic Speech Recognition (ASR) system for the Tunisian Railway Transport Network. Since our eld of work has been limited, there are several words with similar behaviors (semantic for example) but they do not have the same appearance probability; their class groupings will therefore be possible. For these reasons, we propose to build an n-class LM that is based mainly on the integration of purely semantic data. Indeed, each class represents an abstraction of similar labels. In order to improve the sequence labeling task, we proposed to use a discriminative algorithm based on the Conditional Random Field (CRF) model. To better judge our choice of creating an n-class word model, we compared the created model with the 3-gram type model on the same test corpus of evaluation. Additionally, to assess the impact of using the CRF model to perform the semantic labelling task in order to construct semantic classes, we compared the n-class created model with using the CRF in the semantic labelling task and the n- class model without using the CRF in the semantic labelling task. The drawn comparison of the predictive power of the n-class model obtained by applying the CRF model in the semantic labelling is that it is better than the other two models presenting the highest value of its perplexity.
The effect of heterogeneity on unsteady-state displacements

In this paper, we discuss the effect of heterogeneity on unsteady-state core-level displacements. We show that if the results are interpreted as if the core were homogeneous, significant errors in calculation of both the relative permeability (Kr) and capillary pressure (Pc) can occur. We describe a series of high-resolution numerical simulations performed on heterogeneous permeability fields of various correlation lengths. Pressures and fractional flows were calculated at the ends of the core, as well as saturations along the length of the core, mimicking in-situ measurements. For each grid block in the high-resolution simulation, simple Corey exponent Kr curves were used and Pc was set to zero. First, the results are interpreted as if the sample were homogeneous using JBN analysis. With this approach, it is impossible to fit the saturation profiles within the core. This is due to the effects of heterogeneity and the inability for a single Kr curve to capture the smearing of the invading front at all length scales. The saturation profiles can be fitted by history matching only when a pseudo-Pc is included, even though at the local scale Pc=0. Surprisingly, this numerical pseudo-Pc has the same shape as a real laboratory capillary pressure curve. Finally, we show how tracer tests on core samples can be used to quantify the effect of heterogeneity and decouple the effects of heterogeneity from the effects of petrophysical properties.
emphasizes the importance of quality education and lifelong learning for all children, knowledge and understanding as to how to ensure quality in early childhood education

This paper presents findings from a case study intended to develop understanding of the practices within education for sustainable development at a preschool in Sweden and highlights its work with two themes: The Health of People and the Planet and Human and Animal Societies. This case study was part of a large school development project conducted by a university in collaboration with a municipality between 2017 and 2019. The preschool had two units with a total of 36 children aged 1–6 years, and 8 preschool teachers. Empirical materials were collected from observations of educational activities at two events, as well as group discussions with teachers and the preschool head teacher. Findings show that the interconnectedness of, and interdependencies between, the environmental, social, and, to some extent, economic aspects of sustainable development were present in educational practices of the preschool. They also indicate that young children, with support and encouragement from their teachers, can take responsibility for activities that are meaningful to them. In this preschool, children’s opinions were respected, and they were given the opportunity to participate in decision-making activities of relevance to their lives.
The Effects of Health Promotion Program by Application of Health Belief Model and Social Support on Behavioral Modification for Weight Control Among Overweight Students at Level 5 of Primary School, Muang District, Phitsanulok Province

Obesity is the risk factor of non communicable disease especially cardiovascular disease which it is cause of high death rate. In addition, the obese children risk to difficulty breathing, fracture of bone, hypertension, and insulin resistance . This quasi – experimental study aimed to investigate the effects of health promotion program by application of health belief model and social support on behavioral modification for weight control among overweight students at level 5 of primary school, Muang District, Phitsanulok Province. The subjects were 60 level 5 of primary school students that divided to be an experimental group and a comparison group, each group were 30 students. The duration time was intervened 10 weeks. The experimental group was received health promotion program and composed of different activities such as lecture with about obesity, demonstration and practice, modeling, group discussion, role play, stimulation and emotional support. Data were collected by questionnaires. The descriptive data were analyzed by descriptive statistics such as frequency distribution, proportions, mean, standard deviation, and min-max values. Paired t-test and independent t-test were used for variables comparision. Statistical significance was set at p<0.05. The results showed that after implemention, the experimental group had mean scores significantly higher than before as followed :knowledge about obesity, perception about severity of obesity, perception about benefit of weight control, appropriate eating, practice, perception about risk to obesity and exercise regularly (p < 0.001,0.01). ค�าส�าคัญ : การควบคุมน�้าหนัก ความเชื่อด้านสุขภาพ แรงสนับสนุนทางสังคม
On the Evaluation of Intelligence Process Automation

Intelligent Process Automation (IPA) is emerging as a sub-field of AI to support the automation of long-tail processes which requires the coordination of tasks across different systems. So far, the field of IPA has been largely driven by systems and use cases, lacking a more formal definition of the task and its assessment. This paper aims to address this gap by providing a formalisation of IPA and by proposing specific metrics to support the empirical evaluation of IPA systems. This work also compares and contrasts IPA against related tasks such as end-user programming and program synthesis.
Lipid Profile as a Marker of Pre-stage Cancer and Oral Cancer in Tobacco Users

Aim: To investigate the serum lipid profile in conventional smokers, reverse smokers and tobacco chewers and smokers and chewers associated with precancer and oral cancer. Study Design: A cross sectional longitudinal study. Place and Duration of Study: Department of Oral medicine and Radiology, Vishnu dental college, Bhimavaram, between October 2011 and November 2013. Methodology: A total of 100 subjects with age range of 20-80 years were included and divided into five groups with 20 subjects in each group. The subjects in Group 1 were Conventional smokers, Group 2 were Reverse smokers, Group 3 were Tobacco chewers, Group 4 were Smokers and chewers and Group 5 with no habit of tobacco usage. Serum lipid profile levels were estimated and analyzed for all the five groups. Results: Total cholesterol (TC) and HDL were significantly reduced in all forms of tobacco abusers of oral precancer group. There was significant decrease of TC, HDL in conventional smokers (Group 1) and Reverse smokers (Group 2), significant decrease of total cholesterol, triglycerides, HDL, LDL, VLDL in tobacco chewers (Group 3) and smokers and chewers (Group 4) of oral cancer Original Research Article Thabusum et al.; IBRR, 3(1): 26-35, 2015; Article no.IBRR. 2015.003 27 group compared with the controls. Total cholesterol, triglycerides, HDL, LDL, VLDL were much lower in the oral cancer group compared with the control. Although these parameters were low in the precancer group compared with the controls and triglycerides showed non –significant p value. Conclusion: An inverse relationship was obtained between serum lipid levels and oral precancer and oral cancer subjects. Thus the serum lipid profile can be used as an indicator to reflect the initial changes occurring in neoplastic cells, thus substantiating their use as a diagnostic adjuvant in monitoring the oral precancer and oral cancer condition.
Effect of collisions on plasma sheath in the presence of a gradient magnetic field

A low-pressure magnetized plasma is studied to find out the dependency of sheath properties on ion-neutral collisions in the presence of a gradient magnetic field. A two-fluid hydrodynamic model is considered and the system of equations is solved numerically. The study reveals that the width of the plasma sheath expands and pre-sheath length decreases with collisions. The ion-neutral collisions and gradient magnetic field restrict the ions to move towards the surface. The movement of the ions towards the surface can be controlled by choosing a suitable configuration of the magnetic field and ion-neutral collision frequency. In addition, an extensive comparison has been made between the effect of gradient magnetic field and oblique magnetic field on sheath physics. The outcome of the study is supposed to help in understanding the complex dynamics of ions in plasma confinement and plasma processing of materials. Furthermore, the present work seeks to create a framework for two fluid modeling of magnetized plasmas with any arbitrary magnetic field profiles. The analysis provided here is supposed to act as a basis for any future work in the respective field.
On the eccentricity evolution of massive black hole binaries in stellar backgrounds

We study the dynamical evolution of eccentric massive black hole binaries (MBHBs) interacting with unbound stars by means of an extensive set of three-body scattering experiments. Compared to previous studies, we extend the investigation down to a MBHB mass ratio of q = m2/m1 = 10−4, where m1 and m2 are the masses of the primary and secondary hole, respectively. Contrary to a simple extrapolation from higher mass ratios, we find that for q ≲ 10−3 the eccentricity growth rate becomes negative, i.e. the binary circularizes as it shrinks. This behaviour is due to the subset of interacting stars captured in metastable counter-rotating orbits; those stars tend to extract angular momentum from the binary, promoting eccentricity growth for q > 10−3, but tend to inject angular momentum into the binary driving it towards circularization for q < 10−3. The physical origin of this behaviour requires a detailed study of the orbits of this subset of stars and is currently under investigation. Our findings might have important consequences for intermediate massive black holes (IMBHs) inspiralling on to MBHs (e.g. a putative $10^3\,\rm M_{\odot }$ black hole inspiralling on to Sgr A*).
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. Learning such temporal patterns from event data is crucial for understanding disease pathology and predicting prognoses. To this end, we develop deep diffusion processes (DDP) to model "dynamic comorbidity networks", i.e., the temporal relationships between comorbid disease onsets expressed through a dynamic graph. A DDP comprises events modelled as a multi-dimensional point process, with an intensity function parameterized by the edges of a dynamic weighted graph. The graph structure is modulated by a neural network that maps patient history to edge weights, enabling rich temporal representations for disease trajectories. The DDP parameters decouple into clinically meaningful components, which enables serving the dual purpose of accurate risk prediction and intelligible representation of disease pathology. We illustrate these features in experiments using cancer registry data.
On two types of ultrafilter extensions of binary relations.

There exist two distinct types of ultrafilter extensions of binary relations, one discovered in universal algebra and modal logic, and another, in model theory and algebra of ultrafilters. We show that the extension of the latter type is properly included in the extension of the former type, and describe their interaction with the relation algebra operations. Then we provide topological characterizations of both extensions and show that the larger extension continuously maps the space of ultrafilters into the space of filters endowed with the Vietoris topology.
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius

Adversarial training is one of the most popular ways to learn robust models but is usually attack-dependent and time costly. In this paper, we propose the MACER algorithm, which learns robust models without using adversarial training but performs better than all existing provable l2-defenses. Recent work shows that randomized smoothing can be used to provide certified l2 radius to smoothed classifiers, and our algorithm trains provably robust smoothed classifiers via MAximizing the CErtified Radius (MACER). The attack-free characteristic makes MACER faster to train and easier to optimize. In our experiments, we show that our method can be applied to modern deep neural networks on a wide range of datasets, including Cifar-10, ImageNet, MNIST, and SVHN. For all tasks, MACER spends less training time than state-of-the-art adversarial training algorithms, and the learned models achieve larger average certified radius.
Disentangling Representations using Gaussian Processes in Variational Autoencoders for Video Prediction

We introduce MGP-VAE, a variational autoencoder which uses Gaussian processes (GP) to model the latent space distribution. We employ MGP-VAE for the unsupervised learning of video sequences to obtain disentangled representations. Previous work in this area has mainly been confined to separating dynamic information from static content. We improve on previous results by establishing a framework by which multiple features, static or dynamic, can be disentangled. Specifically we use fractional Brownian motions (fBM) and Brownian bridges (BB) to enforce an inter-frame correlation structure in each independent channel. We show that varying this correlation structure enables one to capture different aspects of variation in the data. We demonstrate the quality of our disentangled representations on numerous experiments on three publicly available datasets, and also perform quantitative tests on a video prediction task. In addition, we introduce a novel geodesic loss function which takes into account the curvature of the data manifold to improve learning in the prediction task. Our experiments show quantitatively that the combination of our improved disentangled representations with the novel loss function enable MGP-VAE to outperform the state-of-the-art in video prediction.
RoboFly: An Insect-Sized Robot With Simplified Fabrication That Is Capable of Flight, Ground, and Water Surface Locomotion

Insect-sized ($\sim$100 mg) aerial robots have advantages over larger robots because of their small size, low weight, and low materials cost. Previous iterations have demonstrated controlled flight but were difficult to fabricate because they consisted of many separate parts assembled together and were also unable to perform locomotion modes besides flight. This article presents a new design of a 74-mg flapping-wing robot that dramatically reduces the number of parts and simplifies fabrication. The robot also has a lower center of mass, which allows the robot to additionally land without the need for long legs, even in case of unstable flight. We also show that the new design allows for wing-driven ground and air–water interfacial locomotion, improving the versatility of the robot. During surface ambulation, forward thrust is generated by increasing the speed of the upstroke relative to the downstroke of the flapping wings. Adjusting relative wing stroke amplitudes also allows for steering. The ability to land and subsequently move along the ground first presented here allows the robot to negotiate extremely confined spaces and underneath obstacles. We present results demonstrating these capabilities, as well as hovering flight and controlled landing.
Optimal heuristic trajectories on unknown manifolds by Inverse Integral Sampling

Optimization algorithms appear in the core calculations of numerous Artificial Intelligence and Machine Learning methods, as well as Engineering and Business applications. Following recent works on the theoretical deficiencies of AI, a rigor context for the optimization problem of an unknown objective function is developed. The algorithm stems directly from the theory of probability, instead of a presumed inspiration, thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the $n$-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is by definition satisfied. A theoretical proof is provided, concluding that only falling into the suggested framework, either directly or incidentally, any optimization algorithm converges in the fastest possible time. The numerical experiments, verify the theoretical results on the efficacy of the algorithm apropos finding the optimum, as fast as possible.
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples

AI algorithms are not immune to biases. Traditionally, non-experts have little control in uncovering potential social bias (e.g., gender bias) in the algorithms that may impact their lives. We present a preliminary design for an interactive visualization tool CEB to reveal biases in a commonly used AI method, Neural Networks (NN). CEB combines counterfactual examples and abstraction of an NN decision process to empower non-experts to detect bias. This paper presents the design of CEB and initial findings of an expert panel (n=6) with AI, HCI, and Social science experts.
The stellar and wind parameters of six prototypical HMXBs and their evolutionary status

Context. High-mass X-ray binaries (HMXBs) are exceptional astrophysical laboratories that offer a rare glimpse into the physical processes that govern accretion on compact objects, massive-star winds, and stellar evolution. In a subset of the HMXBs, the compact objects accrete matter solely from winds of massive donor stars. These so-called wind-fed HMXBs are divided in persistent (classical) HMXBs and supergiant fast X-ray transients (SFXTs) according to their X-ray properties. While it has been suggested that this dichotomy depends on the characteristics of stellar winds, they have been poorly studied. Aims. With this investigation, we aim to remedy this situation by systematically analyzing donor stars of wind-fed HMXBs that are observable in the UV, concentrating on those with neutron star (NS) companions. Methods. We obtained Swift X-ray data, HST UV spectra, and additional optical spectra for all our targets. The spectral analysis of our program stars was carried out with the Potsdam Wolf–Rayet model atmosphere code. Results. Our multi-wavelength approach allows us to provide stellar and wind parameters for six donor stars (four wind-fed systems and two OBe X-ray binaries). The wind properties are in line with the predictions of the line-driven wind theory. Based on the abundances, three of the donor stars are in an advanced evolutionary stage, while for some of the stars, the abundance pattern indicates that processed material might have been accreted. When passing by the NS in its tight orbit, the donor star wind has not yet reached its terminal velocity but it is still significantly slower; its speed is comparable with the orbital velocity of the NS companion. There are no systematic differences between the two types of wind-fed HMXBs (persistent versus transients) with respect to the donor stars. For the SFXTs in our sample, the orbital eccentricity is decisive for their transient X-ray nature. The dichotomy of wind-fed HMXBs studied in this work is primarily a result of the orbital configuration, while in general it is likely that it reflects a complex interplay between the donor-star parameters, the orbital configuration, and the NS properties. Based on the orbital parameters and the further evolution of the donor stars, the investigated HMXBs will presumably form Thorne–Żytkow objects in the future.
Automatic melody harmonization with triad chords: A comparative study

The task of automatic melody harmonization aims to build a model that generates a chord sequence as the harmonic accompaniment of a given multiple-bar melody sequence. In this paper, we present a comparative study evaluating the performance of canonical approaches to this task, including template matching, hidden Markov model, genetic algorithm and deep learning. The evaluation is conducted on a dataset of 9226 melody/chord pairs, considering 48 different triad chords. We report the result of an objective evaluation using six different metrics and a subjective study with 202 participants, showing that a deep learning method performs the best.
Benchmark and Survey of Automated Machine Learning Frameworks

Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to automatically build machine learning applications without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 different data sets.