Miracle-dz/UniFAR-FTUs huggingface dataset:
For whom the siren sounds: Public perceptions of outdoor warning sirens in northeast Alabama

In tornado-prone Alabama, outdoor emergency sirens are used as a major component of the Emergency Alert and Notification System. However there have been no studies to date of the effectiveness of these sirens in Calhoun County. In April 2011 a major tornado swept through this area of northeast Alabama, leaving in its wake over 300 fatalities and massive destruction. This preliminary study examines public perceptions and reactions to the sirens for notification of tornadoes. Faculty and students of a mid-sized university in Calhoun County, Alabama were surveyed before and after the devastating 2011 tornado. Although the respondents find the sirens helpful, they have difficulty understanding the tones and spoken messages emitted by the sirens. Concerns about tornadoes did increase slightly after the 2011 tornado. Those who did experience an increase in concern were likely to change their behavior in preparations and response to tornadoes.
Credal Sentential Decision Diagrams

Probabilistic sentential decision diagrams are logical circuits annotated by probability mass functions on the disjunctive gates. This allows for a compact representation of joint mass functions consistent with logical constraints. We propose a credal generalisation of the probabilistic quantification of these models, that allows to replace the local probabilities with (credal) sets of mass functions specified by linear constraints. This induces a joint credal set, that sharply assigns probability zero to states inconsistent with the constraints. These models can support cautious estimates of the local parameters when only small amounts of training data are available. Algorithmic strategies to compute lower and upper bounds of marginal and conditional queries are provided. The task can be achieved in linear time with respect to the diagram size for marginal queries. The same can be done for conditional queries if the topology of the circuit is singly connected.
Block-Based Image Compression With

This correspondence presents an image compression ap- proach that integrates our proposed parameter-assistant inpainting (PAI) to exploit visual redundancy in color images. In this scheme, we study different distributions of image regions and represent them with a model class. Based on that, an input image at the encoder side is divided into featured and non-featured regions at block level. The featured blocks fitting the predefined model class are coded by a few parameters, whereas the non-featured blocks are coded traditionally. At the decoder side, the featured regions are restored through PAI relying on both delivered parameters and surrounding information. Experimental results show that our method outperforms JPEG in featured regions by an average bit-rate saving of 76% at similar perceptual quality levels.
Chemical Basis of Biological Homochirality during the Abiotic Evolution Stages on Earth

Spontaneous mirror symmetry breaking (SMSB), a phenomenon leading to non-equilibrium stationary states (NESS) that exhibits biases away from the racemic composition is discussed here in the framework of dissipative reaction networks. Such networks may lead to a metastable racemic non-equilibrium stationary state that transforms into one of two degenerate but stable enantiomeric NESSs. In such a bifurcation scenario, the type of the reaction network, as well the boundary conditions, are similar to those characterizing the currently accepted stages of emergence of replicators and autocatalytic systems. Simple asymmetric inductions by physical chiral forces during previous stages of chemical evolution, for example in astrophysical scenarios, must involve unavoidable racemization processes during the time scales associated with the different stages of chemical evolution. However, residual enantiomeric excesses of such asymmetric inductions suffice to drive the SMSB stochastic distribution of chiral signs into a deterministic distribution. According to these features, we propose that a basic model of the chiral machinery of proto-life would emerge during the formation of proto-cell systems by the convergence of the former enantioselective scenarios.
Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Due to the proliferation of global monitoring sensors, the Internet of Things (IoT) is widely used to build smart cities and smart homes. 5G HetNets play an important role in the IoT video stream. This paper proposes an improved Call Session Control Function (CSCF) scheme. The improved CSCF server contains additional modules to facilitate IoT traffic prediction and resource reservation. We highlight traffic prediction in this work and develop a compressed sensing based linear predictor to catch the traffic patterns. Experimental results justify that our proposed scheme can forecast the traffic load with high accuracy but low sampling overhead.
Modelling Interactions between Nodes in a Credibilist Social Network. (Modélisation des Interactions entre les Nœuds dans un Réseau Social Crédibiliste)

The detection of communities in social networks has become a very important task. Indeed, as its role consists in partitioning the nodes of a network into subgroups having properties in common, this makes it possible to analyse the behaviour of the entities of the network and to predict the evolution of the latter in time. In social networks, information about nodes, links, and messages may be imperfect. From there, the analysis of such a type of network necessitates the use of a theory of uncertainty. In this thesis, we propose three contributions applied in the framework of the theory of belief functions: First, we were interested in showing the advantage of using evidential attributes in social networks. Indeed, we compared the results of the classification of nodes with uncertain attributes (numerical, probabilistic, evidential) generated according to the structure of the network. To do this, we considered two scenarios: attributes generated randomly and others sorted. We also performed the tests in the case of data that was noisy. In order to measure the quality of clustering results, we used normalised mutual information (NMI). The second contribution consists on the correction of noisy information in social networks. To do this, we proposed a model based on the comparison of the calculated distances between the triplets of the network and the coherent triplets defined initially. A triplet is composed of two nodes connected to each other by a link. In order to test the proposed approach, we first tested three cases: only the nodes are noisy, only the links are noisy and finally the nodes and the links are noisy simultaneously. Then we tested the method by varying several network parameters. In order to measure the quality of the obtained results, we calculated the accuracy. The third contribution is to detect which links are spammed in a social network. A link is considered spammed if its initial class changes according to the types of messages transiting on it. To do this, we used the theory of belief functions to combine the information of links and messages. In order to test our approach, we considered two cases: only the messages are noisy and the messages as well as the links are noisy simultaneously. The quality of the classification results was measured using accuracy, precision and recall measurements.
Robustness in Sum-Product Networks with Continuous and Categorical Data

Sum-product networks are a popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. After learning sum-product networks from scarce data, small variations of parameters could lead to different conclusions. We adapt the robustness measure created for categorical credal sum-product networks to domains with both continuous and categorical variables. We apply this approach to a real-world dataset of online purchases where the goal is to identify fraudulent cases. We empirical show that such credal models can better discriminate between easy and hard instances than simply using the probability of the most probable class.
A new technique for noise filtering of SAR interferogram phase images

This paper addresses the noise filtering problem for SAR interferogram phase images. The phase noise is characterized by an additive noise model, and a filtering algorithm based on this noise model was developed by filtering noise along fringes. In addition, this filter adaptively adjusts the amount of filtering according to the coherence. The effectiveness of this filter is demonstrated using SIR-C/X-SAR multi-pass generated interferograms.
High-quality segmentation of low quality cardiac MR images using k-space artefact correction

Deep learning methods have shown great success in segmenting the anatomical and pathological structures in medical images. This success is closely bounded with the quality of the images in the dataset that are being segmented. A commonly overlooked issue in the medical image analysis community is the vast amount of clinical images that have severe image artefacts. In this paper, we discuss the implications of image artefacts on cardiac MR segmentation and compare a variety of approaches for motion artefact correction with our proposed method Automap-GAN. Our method is based on the recently developed Automap reconstruction method, which directly reconstructs high quality MR images from k-space using deep learning. We propose to use a loss function that combines mean square error with structural similarity index to robustly segment poor-quality images. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted CMR k-space data and uncorrected reconstructed images. In the experiments, we apply the proposed method to correct for motion artefacts on a large dataset of 1,400 subjects to improve image quality. The improvement of image quality is quantitatively assessed using segmentation accuracy as a metric. The segmentation is improved from 0.63 to 0.72 dice overlap after artefact correction. We quantitatively compare our method with a variety of techniques for recovering image quality to showcase the influence on segmentation. In addition, we qualitatively evaluate the proposed technique using k-space data containing real motion artefacts.
Evidential Strength of Intonational Cues and Rational Adaptation to (Un-)Reliable Intonation

Intonation plays an integral role in comprehending spoken language. Listeners can rapidly integrate intonational information to predictively map a given pitch accent onto the speaker's likely referential intentions. We use mouse tracking to investigate two questions: (a) how listeners draw predictive inferences based on information from intonation? and (b) how listeners adapt their online interpretation of intonational cues when these are reliable or unreliable? We formulate a novel Bayesian model of rational predictive cue integration and explore predictions derived under a concrete linking hypothesis relating a quantitative notion of evidential strength of a cue to the moment in time, relative to the unfolding speech signal, at which mouse trajectories turn towards the eventually selected option. In order to capture rational belief updates after concrete observations of a speaker's behavior, we formulate and explore an extension of this model that includes the listener's hierarchical beliefs about the speaker's likely production behavior. Our results are compatible with the assumption that listeners rapidly and rationally integrate all available intonational information, that they expect reliable intonational information initially, and that they adapt these initial expectations gradually during exposition to unreliable input. All materials, data, and scripts can be retrieved here: https://osf.io/dnbuk/.
An Experimental Study of Prior Dependence in Bayesian Network Structure Learning

The Bayesian Dirichlet equivalent uniform (BDeu) function is a popular score to evaluate the goodness of a Bayesian network structure given complete categorical data. Despite its interesting properties, such as likelihood equivalence, it does require a prior expressed via a user-deﬁned parameter known as Equi-valent Sample Size (ESS), which signiﬁcantly affects the ﬁnal structure. We study conditions to obtain prior independence in BDeu-based structure learning. We show in experiments that the amount of data needed to render the learning robust to different ESS values is prohibitively large, even in big data times.
Training Deep Networks on Domain Randomized Synthetic X-ray Data for Cardiac Interventions

One of the most significant challenges of using machine learning to create practical clinical applications in medical imaging is the limited availability of training data and accurate annotations. This problem is acute in novel multi-modal image registration applications where complete datasets may not be collected in standard clinical practice, data may be collected at different times and deformation makes perfect annotations impossible. Training machine learning systems on fully synthetic data is becoming increasingly common in the research community. However, transferring to real world applications without compromising performance is highly challenging. Transfer learning methods adapt the training data, learned features, or the trained models to provide higher performance on the target domain. These methods are designed with the available samples, but if the samples used are not representative of the target domain, the method will overfit to the samples and will not generalize. This problem is exacerbated in medical imaging, where data of the target domain is extremely scarse. This paper proposes to use Domain Randomization (DR) to bridge the reality gap between the training and target domains, requiring no samples of the target domain. DR adds unrealistic perturbations to the training data, such that the target domain becomes just another variation. The effects of DR are demonstrated on a challenging task: 3D/2D cardiac model-toX-ray registration, trained fully on synthetic data generated from 1711 clinical CT volumes. A thorough qualitative and quantitative evaluation of transfer to clinical data is performed. Results show that without DR training parameters have little influence on performance on the training domain of digitally reconstructed radiographs, but can cause substantial variation on the target domain (X-rays). DR results in a significantly more consistent transfer to the target domain.
An interactive tool for C.V. tutorials

This work proposes TuLIP: a tool for the creation and fruition of tutorials on image processing and analysis. TuLIP is built around the idea that code in a very high level language can work both as as the way of performing a task and as the reference of the task it implements. TuLIP supports users and authors with tailored working modalities and supplies an interactive environment leading to practical experimentation thanks to the built-in interpreter and a set of object visualization libraries.
GIS supporting data gathering and fast decision making in emergencies situations

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A Novel Converter Topology for Photo-Voltaic Application

This paper proposed a simple dc-dc converter for stand-alone photo-voltaic power conversion in order to reduce the complexity in existing system. The proposed converter topology has the simple structure and has the ability to eliminate the higher order harmonics. Also due to the presence of transformer, the efficiency will be more than that of other converters. The control signal for this converter switch is generated by means of maximum power point tracking (P&O) algorithm. The whole system is modeled by using MATLAB/ Simulink model.
Using a waiting protocol to separate concerns in the mutual exclusion problem

How to implement process synchronization in a general-purpose software library while incurring a minimum of policy decisions on the system as a whole? We propose that in dealing with the problem of mutual exclusion in concurrent systems, a separation of concerns between the mechanism of detecting contention and the policy decision of what to do when such contention is detected is appropriate. We show with code how a waiting protocol can guide and aid the design and implementation of both lower-level primitives and the higherlevel, policy-making parts of a system. We also show how the technique can be extended so as to help preventing some deadlock situations.
Systematic review: Factors related to injuries in small- and medium-sized enterprises

The purpose of this systematic review was to identify the antecedent factors of workplace injuries in small- and medium-sized enterprises (SMEs). A customized systematic review protocol included the research question, literature search, quality appraisal, data management and extraction, and evidence synthesis. The evidence was evaluated using the Critical Appraisal Skills Programme checklists and the Cochrane Collaboration “Risk of Bias” assessment tools. A total of 1355 articles were identified before duplicate removal. Ten articles were relevant to the study objective. Of these, two articles examined antecedents related to physical injuries, three examined those related to psychological injuries, and four focused on a combination. Antecedent factors included older workers, unsafe acts, unsafe working conditions, accident type and type of work performed, trips and falls, loss in productivity, social isolation, financial stress, and lack of employer support during the return to the workplace. The findings of this systematic review support the need for increased research to identify antecedent factors associated with injury in SMEs. Research should focus on interventions to mitigate injury rates that associate employees with employers, thus promoting collaboration in augmenting health and safety in SMEs.
Acoustic correlates of perceived emotions among hindustani singers

Introduction: Music induces precise corporal process as well as mental processes in listener's mind which is generally perceived as emotion. A raga composition consists of a particular combination of notes which creates a mood or atmosphere which can be specialized to uniqueness of the ragas which are perceived subjectively. This study aimed to understand the correlation between acoustic parameters and emotions among Hindustani singers. Materials and Methods: The experiment was carried out on ten trained Hindustani singers in the age range of 20–40 years. Singing samples of ascending and descending scales were recorded for chosen ragas and each raga was analyzed for various acoustic parameters. Results: The results revealed a significant difference (P < 0.01) for the first three formants in swaras such as S2, S3, and S5. Conclusion: Hence it can be concluded that, among three ragas (R1, R2, and R3), change of formant frequency at the position of S2, S3, and S5 will result in perception of different emotions.
Salivary α2-macroglobulin as a marker for glycemic control in patients with type 2 diabetes mellitus

Introduction Panic from needle and the cost can discourage some diabetic patients from monitoring their blood glucose levels in a regular manner, so there is a critical need to find a new cost-effective, painless, and sensitive glucose-detection method. Many studies have found that α2-macroglobulin (A2MG) concentrations are increased in the blood of both type 1 and type 2 diabetic patients. Aim The aim of the article is to study the salivary A2MG value as a marker for glycemic control in patients with type 2 diabetes mellitus (DM). Patients and methods A total of 60 patients were included and divided into three groups. Group 1 included patients with type 2 DM with glycosylated hemoglobin (HbA1c) levels more than or equal to 7% (inadequate glycemic control). Group 2 included patients with type 2 DM with HbA1c levels less than 7% (adequate glycemic control). Group 3 included healthy persons (control group). All patients were subjected to the following: thorough history taking, full physical examination, and laboratory investigations, including fasting blood glucose, cholesterol, triglycerides, HbA1c, and salivary A2MG. Results There were statistical significant relations between salivary A2MG and both BMI and duration of diabetes (P<0.05) in type 2 uncontrolled diabetes group but not in controlled group (P>0.05). There were statistically significant positive correlations between levels of salivary A2MG and HbA1c, cholesterol, triglycerides, fasting blood sugar, duration of DM, BMI, and age. The best cutoff value of salivary A2MG as a predictor of bad glycemic control, in relation to HbA1c, was more than or equal to 645 ng/ml, with area under the receiver operating characteristic curve of 0.92, sensitivity of 91.7%, specificity of 90%, and P value of less than 0.001. Conclusion and recommendation With the advantages of rapid, accessible, sensitive, cost-effective, and noninvasive method, salivary A2MG is a promising biological marker for glycemic control in patients with type 2 DM.
Decentralized network resource allocation as a repeated noncooperative market game

Market-based methods are an emerging paradigm for controlling large decentralized systems. We introduce in this paper a bidding mechanism for allocation of Internet resources among competing agents, and study it from a game-theoretic perspective. We prove the existence and the uniqueness of Nash equilibrium and present an update algorithm that allows users to converge to the Nash equilibrium in a decentralized manner using feedback of only the common information available to the resource. The necessary conditions for local stability of relaxed versions of the algorithm are derived and verified by simulations.
A STUDY ON THE APPLICATION OF GENETIC ALGORISM TO CALIBRATION OF WATER QUALITY MODEL

Genetic algorithm, GA, is one of the well-known methods to solve global optimization problems and its use is becoming widespread in many branches of engineering. In this study, this optimization method, GA, was made use in order to calibrate the numerical simulation model, and its applicability to this kind of problem was discussed. The results obtained from calibrating the water quality simulation model show that the error defined by calculated and observed results gradually decreases as the proceed of alternation of generation, and GA succeeded to find the parameter set which makes the error much smaller than by other optimization method. Although some trials executed in this study thought the necessities of several implementations for the practical use of GA, it might be concluded that this method has some extent of applicability to the automatic calibration of numerical simulation model.