Miracle-dz/UniFAR-FTUs huggingface dataset:
Explanatory and Predictive Factors of Tourists' Behavior: A case from a Private Airport Transfer Company

Tourism is one of the biggest industries in the World and its growth in the past years has been considerable. The application of IT (Information Technology) in this industry is critical and includes countless opportunities due to some of its natural characteristics and to the amount of information that has been continuously generated through the past years. This project, whose main goals are the understanding of the trends and factors that shape the company’s demand and the proposal and comparison of forecast models for the annual, monthly and weekly data, is based on data from a private airport transfer company. The methodology of this study is based on the Knowledge Discovery in Databases (KDD) process. After the understanding of the provided data, its relations and main attributes, it is necessary to undertake preprocessing and exploratory steps. There is evidence that the seasonality issues are the ones that most impact the demand, so time series of the total number of services for different frequencies are studied. Flights and airport’s information are also likely to affect the demand, as they are highly correlated with the number of services, but the acquired data is not enough to perform a reliable analysis, so these are not considered in the prediction. For the annual data, a linear regression detects the slope of the company’s growth. Exponential Smoothing, ARIMA (Auto-Regressive Integrated Moving-Average) and Artificial Neural Networks are applied to both monthly and weekly number of services. Regarding the monthly data, a Holt-Winters method, both for the raw data and for a logarithmic transformation, accurately fits the series. This achieves better results than an ARIMA and an Artificial Neural Network model, which are known for capturing complex patterns. These results might be a consequence of a limited amount of data or of the high importance of the trend and seasonal components in these series. Withal, weekly series present some distinct results as, considering the error measures, the ARIMA model outperforms the Holt-Winters and ANN. However, the plot of the observed and estimated values leads to some distinct conclusions as the high error in Holt-Winters results from a lag in time whereas in ARIMA the problem relies in the trend. Finally, it is relevant to stress the error increase with the time frequency decrease and the augmented prediction intervals when forecasting further into the future.
Proceedings of Meetings on Acoustics

The human impact of sonic booms varies with listening environment. Given the incident sonic boom waveform, the specular field around a realistic geometry has been predicted via a C++ implementation of image source method (ISM) tailored to this outdoor application. This work explores the necessity of including the diffracted field when predicting time series and perceived loudness (PLdB), both in and out of the shadow zone. The impulsive nature of the excitation, and the sensitivity of the PLdB to temporal details, constrains appropriate diffraction modeling techniques to those capable of time domain accuracy. Uniform Theory of Diffraction (UTD) and Biot Tolstoy Medwin (BTM) models are considered, exploring the benefits and challenges of each approach, particularly with regards to scalability and bandwidth. The importance of accurately predicting diffraction for this application is evaluated through comparison with booms recorded around a building corresponding to the simulated geometry.
Psychiatric and psychological observations in a geriatric clinic.

1. A report is given on 47 patients seen during the first 10 months of operation of a multidisciplinary geriatric clinic.2. Psychological tests were performed on 42 of these patients. They revealed a low average I.Q. and E.Q. of the group as a whole probably due, in part, to the life long low socio-economic status of the patients, and more so to the fact that about two-thirds of those tested showed “organic signs” on psychological tests.3. In spite of this, only 19% of the group showed clinical signs of organic brain disease. This is taken as an indication of a remarkable capacity to compensate for organic deficits even at advanced age. It is suggested that “personal factors” may be responsible for this capacity.4. Therapeutic measures to be taken with the different diagnostic groups are suggested.
LW-CCA: A New Energy Efficient Method for Low-Power Radio Operation in Wireless Sensor Networks for IoT Applications

The radio activity in wireless sensor networks (WSN) in the Internet of Things (IoT) applications are the most common source for power consumption. However, recognizing and controlling the factors affecting radio activity can be valuable for managing the node power consumption. ContikiMAC is a low-power Radio Duty-Cycle protocol in Contiki OS used in WakeUp mode, which is a clear channel assessment (CCA) to check radio status periodically. The time spent to check the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in Radio Duty-Cycles and ContikiMAC. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Then, we propose lightweight CCA (LW-CCA) as an extension to ContikiMAC to reduce the percentage of Radio Duty-Cycles in false WakeUps and idle listenings by using dynamic received signal strength indicators (RSSI) status check time. The simulation results in the Cooja simulator show that LW-CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).
Multi-Horizon Dependence between Crude Oil and East Asian Stock Markets and Implications in Risk Management

This paper examines the dynamic dependence structure of crude oil and East Asian stock markets at multiple frequencies using wavelet and copulas. We also investigate risk management implications and diversification benefits of oil-stock portfolios by calculating and comparing risk and tail risk hedging performance. Our results provide strong evidence of time-varying dependence and asymmetric tail dependence between crude oil and East Asian stock markets at different frequencies. The level and fluctuation of their dependencies increase as time scale increases. Furthermore, we find the time-varying hedging benefits differ at investment horizons and reduced over the long run. Our results suggest that crude oil could be used as a hedge and safe haven against East Asian stock markets, especially in the short- and mid-term.
Retinal vessel segmentation using modified symmetrical local threshold

: Retinal vessel segmentation is important for the identification of many diseases including glaucoma, hypertensive retinopathy, diabetes, and hypertension. Moreover, retinal vessel diameter is associated with cardiovascular mortality. Accurate detection of blood vessels improves the detection of exudates in color fundus images, as well as detection of the retinal nerve, optic disc, or fovea. A retinal vessel is a darker stripe on a lighter background. Thus, the objective is very similar to the lane detection task for intelligent vehicles. A lane on a road is a light stripe on a darker background (i.e. asphalt). For lane detection, the symmetrical local threshold (SLT) is found to be the most robust feature extractor among the tested algorithms in the road marking (ROMA) dataset. Unfortunately, the SLT cannot be applied directly for retinal vessel segmentation. The SLT is a 1D filter and is designed for detecting vertical or close to vertical light stripes with predictable width. In this paper, the SLT is modified to detect dark stripes and four kernels, instead of one, are designed to detect both vertical and horizontal features of a retinal vessel with variable thickness. The proposed algorithm is tested using the High Resolution Fundus (HRF) image database and the accuracy is estimated to be 95 . 53% . Furthermore, when tested with the Digital Retinal Images for Vessel Segmentation (DRIVE) database, the accuracy is estimated to be 93 . 69% .
Photochemical Aging Processes in Iron Containing Aerosols

Aerosol aging refers to the multitude of physical and chemical transformations that atmospheric particles undergo, which plays an important role in the impact on climate, air quality and public health. Out of many different aerosol types in the atmosphere, aqueous particles are of particular interest as photochemistry and radical reactions in the liquid phase are the main drivers of aerosol chemical conversions. Compared to the uptake of oxidants (e.g., •OH, O3) from the gas phase, which are often reacting at or near the surface, photochemically active radiation penetrates the entire volume of aerosol particles, generating radicals throughout their bulk. In the lower troposphere, where UV light intensity with sufficiently low wavelength to directly photolyze aerosol components is low, indirect photochemistry (catalyzing redox processes of non-absorbing molecules) is especially relevant. Important indirect photochemical processes are initiated by either transition metal complexes or photosensitizers, both acting as photocatalysts for aerosol oxidation. We choose iron(III)-citrate (FeIII(Cit)) induced citric acid (CA) degradation as a model system due to the abundance of carboxylate complexes and reactivity of FeIII(Cit) in the atmosphere, and because CA is a good proxy for carboxylic acids in secondary organic aerosol (SOA) and the thermodynamic properties of CA have been studied in detail. FeIII(Cit) absorption extends far into the blue spectral range, inducing the reduction of iron(III) to iron(II) and the oxidation of carboxylate ligands. In the presence of O2, ensuing production of •OH and HO2 will lead to more decarboxylation, production of oxygenated volatile organic compounds, as well as re-oxidation of iron(II) back to iron(III), closing this photocatalytic cycle, which is an important sink of organic acids in the troposphere. It has recently been recognized that aqueous aerosol particles may attain highly viscous, semi-solid, or even glassy states under a wide range of atmospheric conditions. While the impact of reduced molecular mobility in highly viscous particles on dark chemistry has received substantial attention over the last decade, systematic studies on the effect of high viscosity on photochemical processes are scarce. In order to fill the gap, we developed a multilayered photochemical reaction and diffusivity (PRAD) model targeted at the FeIII(Cit)/CA photochemistry system, to assess how viscosity and diffusivity influence photochemical processes. While FeIII(Cit) photochemistry is reasonably well established, there are still a number of ill-constrained parameters in the model, such as the diffusivity of CO2 and O2 in the citric acid solution, the re-oxidation pathways and their reaction constants of iron(II) to iron(III). Therefore, we conducted experiments with single aqueous FeIII(Cit)/CA particles levitated in electrodynamic balance to determine some of these
Proceedings of Meetings on Acoustics

The present work investigates the physics of constrained layer damping treatments for plates and beams by a semi-analytical finite element method and presents applications of the method to the optimization of damping treatments. The method uses finite element discretizations in the thickness coordinate and propagating wave solutions in the remaining coordinates, and therefore provides more generality and accuracy than existing analytical approximations. The resulting dispersion equation is solved for complex-valued wave numbers at each frequency of interest. By choosing sufficiently fine discretizations in the thickness coordinate, the method gives accurate estimates of wave numbers. The numerical implementation of the method is an efficient yet general tool for optimizing damping treatments with respect to material properties and dimensions. It explicitly allows for the possibility of analyzing structures with several layers where the material of each layer may be isotropic or orthotropic. Examples illustrate the numerical efficiency of the implementation and use this efficiency to provide optimizations of constrained layer damping treatments.
Manufacturing Investments in Finland 2005-2015: an Analysis of Published News in the Finnish Media Archives

KIRSI LINDFORS: Manufacturing Investments in Finland 2005-2015: an Analysis of Published News in the Finnish Media Archives Tampere University of Technology Master of Science Thesis, 60 pages November 2015 Master’s Degree Programme in Industrial Engineering and Management Major: Industrial Management Examiner: Professor Jussi Heikkilä
Magnetic Particles-Based Analytical Platforms for Food Safety Monitoring

Magnetic nanoparticles (MNPs) have attracted growing interest as versatile materials for the development of analytical detection and separation platforms for food safety monitoring. This review discusses recent advances in the synthesis, functionalization and applications of MNPs in bioanalysis. A special emphasis is given to the use of MNPs as an immobilization support for biomolecules and as a target capture and pre-concentration to increase selectivity and sensitivity of analytical platforms for the monitoring of food contaminants. General principles and examples of MNP-based platforms for separation, amplification and detection of analytes of interest in food, including organic and inorganic constituents are discussed.
Instant Liquids: Fast Screen-Space Surface Generation for Particle-Based Liquids

We present Instant Liquids, a fast GPU-based method for rendering boundary surfaces from 3D particle-based ﬂuid simulations. Instead of extracting and tracking polygonal meshes of boundary surfaces in 3D world space, we devise a new meshless 2D screen-space approach. First, we generate a smooth view-dependent depth map from the 3D particle cloud. Then, surface normals are derived directly from the depth map and lighting calculations are carried out in screen-space. We extend this basic algorithm with eﬃcient reﬂection and refraction approximations. The advantage of our method over existing rendering approaches for particle-based ﬂuids models is the fact that we do not have to extract 3D or 2D meshes at any time. Thus, we improve visualization time for particle-based liquids to a level were it falls signiﬁcantly below simulation time. Furthermore, eﬀects such as breaking waves and splashes are supported automatically.
Antenna-Based Popup Vapor Sensor Guided by Controlled Compressive Buckling

A novel highly stretchable gas sensor is reported that is based on popup antenna reconfiguration due to the strain induced by the swelling of a polydimethylsiloxane (PDMS) substrate when exposed to diethyl ether. When the swollen substrate is removed from the volatile solvent environment, the PDMS volume increase is reversed leading to compressive stress in an attached antenna transforming a 2D structure to a 3D structure through mechanically induced shaping. This provides a low cost and simple route to tune the antenna resonant frequency and gain in direct response to a chemical stimulus. Our proposed solvent sensor is able to measure 0 to 60% PDMS swelling corresponding to diethyl ether concentrations up to 1620 ppm via a resonant frequency shift from 4 to 2.4 GHz. A fatigue life study indicated 103.5 life cycles which demonstrates the durability of these sensors to accommodate large strain and repeatability of the sensing process. Multiphysics Finite Element Method (FEM) modelling of the mechanical and RF simulations along with analytical results based on an equivalent circuit model were in good agreement with experimental data and demonstrate the potential of these structures as sensors.
The impact of information asymmetry on public-private partnership contracts: Theoretical approaches

Institutional arrangements for making the benefits of expert information systems technology available to state enterprises in South Africa have been impacted by information asymmetry. The proliferation of infrastructure improvements has become palpable, hence the necessity to address information asymmetry in the public sector. The reliance on the private sector for expertise and knowledge has exacerbated the information asymmetry gap. A systematic literature review was done on 102 articles using words such as public value, return on government investments, public-private partnerships governance and public accountability, risk management, risk-sharing, and information asymmetry. Content analysis with the use of ATLAS/TI an automated tool was used to analyse the 102 articles that were published in management journals, conference proceedings, and books. The struggle faced by the public sector to overcome information asymmetry was discussed and described. The findings indicated that there is a specific gap in the literature relating to information asymmetry, regarding information and communication technology service delivery using PPP contracts. The contribution of this paper provides literature on information asymmetry and the management of PPP contracts. The employment of skilled information technology professionals in the public sector could eliminate the dependency on private sector resources, thereby minimising the risk of information asymmetry. Key words: Governance, risk sharing, public value, accountability.
DCTD: DEEP CONDITIONAL TARGET DENSITIES

While deep learning-based classification is generally addressed using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidencebased regression, which entails predicting a confidence value for each input-target pair (x, y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences often lack a natural probabilistic meaning. We address these issues by proposing Deep Conditional Target Densities (DCTD), a novel and general regression method with a clear probabilistic interpretation. DCTD models the conditional target density p(y|x) by using a neural network to directly predict the un-normalized density from (x, y). This model of p(y|x) is trained by minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our regression model achieves a 1.9% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-ofthe-art on visual tracking when applied for bounding box regression.
Towards Hierarchical Discrete Variational Autoencoders

Variational Autoencoders (VAEs) have proven to be powerful latent variable models. However, the form of the approximate posterior can limit the expressiveness of the model. Cat-egorical distributions are ﬂexible and useful building blocks for example in neural memory layers. We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hierarchy of variational memory layers. The Concrete/Gumbel-Softmax relaxation allows maximizing a surrogate of the Evidence Lower Bound by stochastic gradient ascent. We show that, when using a limited number of latent variables, HD-VAE outperforms the Gaussian baseline on modelling multiple binary image datasets. Training very deep HD-VAE remains a challenge due to the relaxation bias that is induced by the use of a surrogate objective. We introduce a formal deﬁnition and conduct a preliminary theoretical and empirical study of the bias.
A Low-Cost PIV System for Undergraduate Fluids Laboratories

Particle image velocimetry (PIV) has become a mature technology used in research and industry alike to quantify flow properties non-intrusively. Unfortunately, the cost remains too high for students to apply the method in most undergraduate fluids laboratories. Moreover, high-powered pulsed lasers normally utilized in PIV systems can be a safety risk in the presence of large groups of undergraduates. In this paper, a low-cost PIV acquisition and analysis system that can be readily implemented in undergraduate fluids laboratories is presented. A low-power, constant laser light source is employed to reduce cost and maintain lab safety. An open source analysis code is also used to minimize cost. In working lab exercises with this system, students will understand the PIV data acquisition process, apply MATLAB to analyze the data, and explain the observed flow features. Details of the system are provided so that others may construct a similar system for use in their laboratory. Sample results for flow over a cylinder inside a water flume are included as a demonstration of the system.
Lumped Capacitor Cu wire SMA connector Electrically Small Loop Antenna ( ESLA ) Fixture Fixture with ESLA Groove

Wearable Electrically Small Loop Antennas (ESLAs) are introduced to monitor joint flexion and rotation while overcoming limitations in the state-of-the-art. The reported approach is not restricted to lab environments, does not suffer from integration drift and line-of-sight, and does not impede natural movement. Our previous work introduced wrap-around coils that addressed the challenges above, but were limited to monitoring joint flexion. By contrast, a new class of ESLAs is herewith proposed, placed longitudinally across the joint and operating at 34MHz to monitor both flexion and rotation. An added advantage vs. our previous design is a remarkable improvement in flexion angle resolution: the transmission coefficient dynamic range for 0° to 100° flexion improves by 18.8dB in this work. Two ESLAs are shown to accurately detect flexion/rotation for angular resolutions up to 10°. But if higher resolution is desired, ambiguities arise. To tackle this, a threeESLA system with integrated post-processing is proposed that achieves resolution as high as 2°. Simulations and in vitro experiments are in excellent agreement. Guidelines for system design suited to diverse applications are discussed, and conformance with safety standards is ensured. In future, ESLAs can be seamlessly integrated in garments, enabling transformative benefits to healthcare, sports, and beyond.
Under review as a conference paper at ICLR 2020 based Laplacian to extend GCNs to hypergraphs . Another line of work uses the mathematically appealing tensor methods

Graph-based semi-supervised learning (SSL) assigns labels to initially unlabelled vertices in a graph. Graph neural networks (GNNs), esp. graph convolutional networks (GCNs), inspired the current-state-of-the art models for graph-based SSL problems. GCNs inherently assume that the labels of interest are numerical or categorical variables. However, in many real-world applications such as coauthorship networks, recommendation networks, etc., vertex labels can be naturally represented by probability distributions or histograms. Moreover, real-world network datasets have complex relationships going beyond pairwise associations. These relationships can be modelled naturally and flexibly by hypergraphs. In this paper, we explore GNNs for graph-based SSL of histograms. Motivated by complex relationships (those going beyond pairwise) in real-world networks, we propose a novel method for directed hypergraphs. Our work builds upon existing works on graph-based SSL of histograms derived from the theory of optimal transportation. A key contribution of this paper is to establish generalisation error bounds for a one-layer GNN within the framework of algorithmic stability. We also demonstrate our proposed methods’ effectiveness through detailed experimentation on real-world data. We have made the code available.