Miracle-dz/UniFAR-FTUs huggingface dataset:
STRUCTURAL ESTIMATION OF OUTPUT GAP : THE CASE OF BRAZIL

The present paper estimates the output gap for Brazil by applying the major methodologies referenced in the literature and by introducing an alternative method that has not yet been tested for the Brazilian case. This approach estimates the output gap using a dynamic stochastic general equilibrium (DSGE) model. The output gap estimation by the DSGE model managed to identify the recession periods dated by FGV. However, in addition to the episodes reported by FGV, another two critical periods were identified. This type of approach has some advantages such as the possibility to decompose the estimated output gap into the shocks observed in the model. From the decomposition of shocks, it was observed that demand shocks were the ones that most contributed to output gap fluctuations. Still with regard to the decomposition of shocks, it was possible to identify that commodity price shocks could be understood as demand shocks instead of supply shocks. The output gap estimated by a DSGE model is better at forecasting long-term free-price inflation compared to other methods. Thus, output gap estimation by the DSGE model could be an additional tool for monetary policy conduct.
SPATIAL OISE REDUCTIO I BI AURAL HEARI G AIDS

Binaural hearing aids are configured to have a wireless transmission link between the left and the right hearing aid. State-of-the-art hearing aids use two microphones in each instrument for compactness and power consumption constraints. In environments with multiple interferers, directional signal processing in hearing aids use techniques such as differential microphone arrays to improve speech intelligibility. However, these hearing aids have maximum sensitivity to target sources located directly in front or directly behind the user. In this paper, a novel binaural system is presented for steering the look direction of the hearing aid to other angles than 0/180 degrees. The new system was tested in a real-time environment to confirm the results.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Deep learning techniques for an upper body strength exercise classification system

Most research behind the use of Machine Learning models in the field of Human Activity Recognition focuses mainly on the classification of daily human activities and aerobic exercises. In this study, we focus on the use of 1 accelerometer and 2 gyroscope sensors to build a Deep Learning classifier to recognise 5 different strength exercises, as well as a null class. The strength exercises tested in this research are as followed: Bench press, bent row, deadlift, lateral rises and overhead press. The null class contains recordings of daily activities, such as sitting or walking around the house. The model used in this paper consists on the creation of consecutive overlapping fixed length sliding windows for each exercise, which are processed separately and act as the input for a Deep Convolutional Neural Network. In this study we compare different sliding windows lengths and overlap percentages (step sizes) to obtain the optimal window length and overlap percentage combination. Furthermore, we explore the accuracy results between 1D and 2D Convolutional Neural Networks. Cross validation is also used to check the overall accuracy of the classifiers, where the database used in this paper contains 5 exercises performed by 3 different users and a null class. Overall the models were found to perform accurately for window’s with length of 0.5 seconds or greater and provided a solid foundation to move forward in the creation of a more robust fully integrated model that can recognize a wider variety of exercises.
Twitter-based reporting system for public infrastructure in Saudi Arabia

In recent years, many governments have adopted the open government concept to serve several purposes such as increasing transparency and facilitating communication with their citizens. One of the aims of open government is to help society by engaging the citizens in the country’s development processes. Citizens' participation is one of the most important factors in improving countries today, and this motivates governments to focus on encouraging their citizens to play a role in the improvement process. In this regard, the citizens can participate in several areas, and problem reporting is one of the most common ways of citizen participation. There are many applications that can perform these tasks in different countries; however the Saudi Arabian government does not currently employ these applications. This paper aims to propose a model and application that allow citizens' participation in reporting problems with public infrastructure. The application can inform the Saudi Arabian government about the problems facing public infrastructure that are encountered everyday by the citizens, such as potholes, graffiti, damaged lights and any broken or dirty streets that need repairing or cleaning. Because of the shortage of classic methods of reporting, this project depends on Twitter as an intermediate platform to collect the citizens' reports and deliver them to the relevant government agencies, due to the large number of Twitter users and problems with communication between government and citizens. The result of the study shows that the proposed application yields significant results with regard to improving services in Saudi Arabia. It helps to improve the quality of public infrastructure, facilitate the communication between government and citizens, and increase transparency by publishing reports and enabling citizens to view them and express their opinions.
Independent degree project – second cycle

Public transport organizations, such as Din Tur, require affordable modern solu­ tions to improve their public image and passenger satisfaction. An easy way to create positive associations in people is to provide either useful services or, preferably, entertainment. This report covers the design, development and eval­ uation of an entertainment system – consisting of a smartphone game, a cloudhosted backend, and a supporting on-bus hardware system – with the objective of making Din Tur's bus service seem more modern and appealing. The smart­ phone game, “Håll Platsen”, is developed in Unity game engine, focuses on providing brief entertainment during bus commutes, and incorporates gamifica­ tion design elements. The Python-based back-end resides in Google's App En­ gine and Datastore platforms, and provides a unified virtual game environment enabling player cooperation and competition. The prototype on-bus hardware system uses the Raspberry Pi as a Light-Emitting Diode control system to sup­ ply real-world feedback of the game's virtual environment. The systems incor­ porate real-world busstop positioning, player location, online mapping services, team location-control mechanics, reflex-based minigames, player progression mechanics, and mobile-focused design. The resulting system can be useful in estimating public response to non-standard “smart” promotion methods, the use of games to improve everyday routines (i.e. commuting), and serve as a basis for further research in human & smart-technology interaction.
A Study of Regular and Irregular Neutrosophic Graphs with Real Life Applications

Fuzzy graph theory is a useful and well-known tool to model and solve many real-life optimization problems. Since real-life problems are often uncertain due to inconsistent and indeterminate information, it is very hard for an expert to model those problems using a fuzzy graph. A neutrosophic graph can deal with the uncertainty associated with the inconsistent and indeterminate information of any real-world problem, where fuzzy graphs may fail to reveal satisfactory results. The concepts of the regularity and degree of a node play a significant role in both the theory and application of graph theory in the neutrosophic environment. In this work, we describe the utility of the regular neutrosophic graph and bipartite neutrosophic graph to model an assignment problem, a road transport network, and a social network. For this purpose, we introduce the definitions of the regular neutrosophic graph, star neutrosophic graph, regular complete neutrosophic graph, complete bipartite neutrosophic graph, and regular strong neutrosophic graph. We define the d m - and t d m -degrees of a node in a regular neutrosophic graph. Depending on the degree of the node, this paper classifies the regularity of a neutrosophic graph into three types, namely d m -regular, t d m -regular, and m-highly irregular neutrosophic graphs. We present some theorems and properties of those regular neutrosophic graphs. The concept of an m-highly irregular neutrosophic graph on cycle and path graphs is also investigated in this paper. The definition of busy and free nodes in a regular neutrosophic graph is presented here. We introduce the idea of the μ -complement and h-morphism of a regular neutrosophic graph. Some properties of complement and isomorphic regular neutrosophic graphs are presented here.
PROTEIN-PROTEIN INTERACTION DATABASES: AN OVERALL VIEW ON INTERACTOME ORGANIZATION

Protein-protein interaction (PPI) maps or "interactome" represent a global view of cellular functions and biological processes. There are more than 130 public databases which have been already established to store the sizeable amount of PPI data. The valuable data is available for a wide group of researchers via adopting common data standards and coordinated curation practices between the databases. Three major types of databases have deposited PPI data; primary databases capture experimentally proven protein- interactions from literatures according to different curation polices; metadatabases integrate the work of other databases; prediction databases combine both predicted and experimental PPIs data from different approaches. Several lines of evidence indicate that primary databases report PPI data according to their different organizing strategies; as a result, this issue may affect the structure of derived PPI networks. Since PPI network's structure is useful to get biologically meaningful information, quality of PPI data needs more attention. In this sense,
Parameters for Rhetorical Structure Theory Ontology1

Rhetorical Structure Theory (RST), a framework for analysing discourse structure above the clausal level, has been used extensively by both text analysts and researchers in text generation for the past decade. The proliferation of relations posited by various researchers using RST has highlighted an ontological problem with the theory: there have been no well-defined criteria determining which types of relations should belong within the framework, and what degree of granularity should be used to distinguish between these relations. Sanders, Spooren & Noordman (1992) have proposed the relational criterion as a criterion to perform this task. I examine the implications of applying their criterion to temporal relations, as a case study, and the implications the criterion may have for RST as a whole.
ffects the development of Carotenoid availability a a colour-based mate preference and the sensory bias to which it is genetically linked

tive an female respon Regardless of their origins, mate preferences should, in theory, be shaped by their benefits in a mating context. Here we show that the female preference for carotenoid colouration in guppies (Poecilia reticulata) exhibits a phenotypically plastic response to carotenoid availability, confirming a key prediction of sexual selection theory. Earlier work indicated that this mate preference is genetically linked to, and may be derived from, a sensory bias that occurs in both sexes: attraction to orange objects. The original function of this sensory bias is unknown, but it may help guppies find orange-coloured fruits in the rainforest streams of Trinidad. We show that the sensory bias also exhibits a phenotypically plastic response to carotenoid availability, but only in females. The sex-specificity of this reaction norm argues against the hypothesis that it evolved in a foraging context. We infer instead that the sensory bias has been modified as a correlated effect of selection on the mate preference. These results provide a new type of support for the hypothesis that mate preferences for sexual characters evolve in response to the benefits of mate choice—the alternatives being that such preferences evolve entirely in a non-mating context or in response to the costs of mating.
A FLEXIBLE NONPARAMETRIC TEST FOR CONDITIONAL

This paper proposes a nonparametric test for conditional independence that is easy to implement, yet powerful in the sense that it is consistent and achieves n−1/2 local power. The test statistic is based on an estimator of the topological “distance” between restricted and unrestricted probability measures corresponding to conditional independence or its absence. The distance is evaluated using a family of Generically Comprehensively Revealing (GCR) functions, such as the exponential or logistic functions, which are indexed by nuisance parameters. The use of GCR functions makes the test able to detect any deviation from the null. We use a kernel smoothing method when estimating the distance. An integrated conditional moment (ICM) test statistic based on these estimates is obtained by integrating out the nuisance parameters. We simulate the critical values using a conditional simulation approach. Monte Carlo experiments show that the test performs well in finite samples. As an application, we test an implication of the key assumption of unconfoundedness in the context of estimating the returns to schooling.
Time-Domain Pre-Distortion Technique Using Raised Cosine Shaping for High-Speed Serial Signaling

In this paper the innovative time-domain pre-distor tion method is proposed. This method (PWM-RC) uses raised-cosine (RC) pulse shaping and pulse-width mo dulation (PWM) scheme to achieve better output chan nel data response. The conventional pre-distortion meth ods based on finite impulse response (FIR) filterin g are not compatible with modern low-power CMOS design. Exist ing perspective time-domain pre-distortion methods show many high frequency harmonic components in the pre-emphasized signal. It can cause more system crosstalk susceptibility, where the additional preemphasis boosted the undesirable high frequency com ponents. The proposed method reduces the content of higher h armonic components at the pre-emphasized output sig nal and ISI (inter-symbol interference) effects. Advant ageous properties of the PWM scheme such as high ch annel loss compensation are preserved. Finally, the simul ation results realized in MathCAD and ADS Agilent Development Studio are introduced.
Aprecise standardized volume conductormodel for EEG source localization and tES targeting

Article history: Accepted 12 December 2015 Available online 17 December 2015 In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semiautomated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus headmodeling is often based on the anatomy of an ‘arbitrary’ individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck—two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5 mm 3 resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the ‘ground truth’) is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an ‘individualized’ BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms FEMs of mismatched individual anatomies as well as the BEM of the ICBM anatomy according to both criteria. We therefore propose the New York Head as a new standard head model to be used in future EEG and tES studies whenever an individual MRI is not available. We release all model data online at neuralengr.com/nyhead/ to facilitate broad adoption. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
3D IMAGERY OPTIMIZATION FOR MOBILE AUTOSTEREOSCOPIC DISPLAYS

Stereoscopic displays (also known as 3D displays) attempt to create visual replica of a 3D scene. Due to technical limitations such displays introduce visible distortions to their output. Such distortions are interpreted by the human vision as visual artifacts. In this paper we discuss the principles of operation of the main types of mobile autostereoscopic displays, and present an overview of the potential visual artifacts that affect each type. For each case we present different approaches for artifact mitigation and optimization of the visual quality.
IOT based Parking System using Android and Google Maps

The advent of IOT has alleviated major issues of people living in rural and urban areas. It has expanded its feathers almost in all areas of applications such as Smart Homes, Smart Farming, Smart Supply Chain, Smart Retail, Smart Health Monitoring Systems, Automobile Industry, Smart grid Systems and Smart Cities. The proposed work is mainly focused on providing solution to one of the smart city problem which is vehicle parking Management System. The proposed work is implemented using Arduino Mega, Ultrasonic Sensors, Wi-Fi Module, Android and Google maps. The proposed system is designed to track the vacant parking slots through the IOT technology utilizing Android application and Google maps. The Wi-Fi Module is used to send the information to the server. The proposed work will reduce the time and exertion of looking for empty parking slots. Finally, detailed results were presented in this paper.
Understanding Spatiotemporal Mobility Patterns related to Transport Hubs from Floating Car Data

Transport hubs such as airports and railway stations are places where plenty of passengers are exchanged between vehicles or between transport modes. Analyzing their mobility patterns, for instance traffic flows in/out of the transport hubs, helps better understanding passengers travel behaviors and improving the transportation planning. In this paper, we aim to visually present the spatiotemporal mobility patterns related to transport hubs using floating car data. Following a visual analysis workflow, which consists of computational algorithms, data mining approaches, and visualization techniques, we preprocess the raw data related to the transport hubs, derive relevant pick-up and drop-off events, cluster and aggregate those events, and visually analyze their spatiotemporal distributions using mainly proportional symbol mapping and pie-chart mapping techniques. We use one-week Floating Car Data (FCD) in Shanghai as our test dataset and select Hongqiao international airport as the test transport hub. The preliminary experiment results show that there are obvious temporal mobility patterns as well as significant hotspot places related to Hongqiao airport.
Geothermal resources of former coal mine Nowa Ruda , Poland

The flooded coal mine of Nowa Ruda in south-western Poland is presented as an example. In the Piast mining field the abandoned workings in the Carboniferous Lower Zacler beds contain about 5,000,000 cubic meters of water at a temperature of 16 26°C. The exploitation of the mine water for geothermal heat pumps is planned. Two two-dimensional (2-D) numerical models based on the TOUGH2 program have been developed to introduce heat exchange modeling into this mining field. The model of temperature recovery of abandoned workings shows the time required to return to natural thermal conditions in a rock massif that has cooled down during coal mining. The expected time for temperature recovery is approximately 10 years. The model of a geothermal doublet producing water at a temperature of 23°C from abandoned workings presents the rate of cooling of the reservoir at production rates of 10 l/s (thermal output power ~800 kWt) and 20 l/s (thermal output power ~1600 kWt).
1 Curcumin Attenuates Lead-Induced Cerebellar 2 Toxicity in Rats via Inhibition of Oxidative Stress 3 and Chelating Activity 4

Lead (Pb) is a toxic environmental heavy metal that induces serious clinical defect on all 23 organs with the nervous system being the primary target. Curcumin is the main active constituent of 24 turmeric rhizome (Curcuma longa) with strong antioxidant and anti-inflammatory properties. This 25 study is aimed at evaluating the therapeutic potentials of curcumin on Pb-induced neurotoxicity. 26 Thirty six male Sprague Dawley rats were randomly assigned into five (5) groups with 12 rats in the 27 control (normal saline) and 6 rats for the lead treated group (LTG) (50 mg/kg lead acetate for 4 weeks), 28 recovery group (RC) (50 mg/kg lead acetate for 4 weeks), treatment group 1 (Cur100) (50 mg/kg lead 29 acetate for 4 weeks, followed by 100 mg/kg curcumin for 4 weeks) and treatment group 2 (Cur200) 30 (50 mg/kg lead acetate for 4 weeks, followed by 200 mg/kg curcumin for 4 weeks) groups each. All 31 experimental groups received the oral treatments through orogastric-tube on alternate days. Motor 32 function was assessed using horizontal bar method while Pb concentration in the cerebellum of the 33 rats were evaluated using ICP-MS techniques. Pb-administered rats showed significant decrease in 34 motor scores, SOD activity with increase MDA levels and Pb concentration in their cerebellum with 35 marked alterations in the histological architecture of the cerebellar cortex layers. However, treatment 36 with curcumin improved the motor score, reduced Pb concentration in the cerebellum and 37 ameliorates the markers of oxidative stress as well as restored the histological architecture of the 38 cerebellum. The results this in study suggested that curcumin attenuates Pb-induced neurotoxicity 39 via inhibition of oxidative stress and chelating activity. 40