Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined direct and remote sensing measurements of air-sea interaction parameters during the temperature front passage

The paper presents the results of the experiment CAPMOS'05 performed on an offshore oceanographic platform in the Black Sea in June 2005. Measurements on the platform were carried during June 2–20 round a clock. A set of meteorological and oceanographic parameters was measured: water and air temperature and water salinity at several levels, sea current vectors at various depths, turbulence in sea and atmospheric boundary layers, water vapor and carbon dioxide concentration in the atmosphere. Two upwelling events were observed when sea surface temperature dropped by 10 degrees. Parameters of sea and atmospheric turbulence were measured while the temperature front passed the platform. It is demonstrated that the front intensified the turbulence in the air and in the water boundary layers and significantly increased energy flux across the water-air interface.
Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach

Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance
Behavioral training promotes multiple adaptive processes following acute hearing loss

The brain possesses a remarkable capacity to compensate for changes in inputs resulting from a range of sensory impairments. Developmental studies of sound localization have shown that adaptation to asymmetric hearing loss can be achieved either by reinterpreting altered spatial cues or by relying more on those cues that remain intact. Adaptation to monaural deprivation in adulthood is also possible, but appears to lack such flexibility. Here we show, however, that appropriate behavioral training enables monaurally-deprived adult humans to exploit both of these adaptive processes. Moreover, cortical recordings in ferrets reared with asymmetric hearing loss suggest that these forms of plasticity have distinct neural substrates. An ability to adapt to asymmetric hearing loss using multiple adaptive processes is therefore shared by different species and may persist throughout the lifespan. This highlights the fundamental flexibility of neural systems, and may also point toward novel therapeutic strategies for treating sensory disorders. DOI: http://dx.doi. org/10.7554/eLife.12264. 001
Analysis and Prediction of Application Categories on Online Application Stores

We explore quality control problem related to online application stores: automating reviews of submitted applications and organizing online store using categories. We approach both problems by predicting application categories using application descriptions and describe potential positive and negative outcomes of our approach. We discuss suitable multi-class and binary classification methods and describe three experiments we conduct, including details about data preprocessing, validation, testing and the final results. For evaluating our approach, we use an F-measure and Confusion Matrix. As the result, we were able to identify confusing categories that should be taken into consideration.
Designing Information Systems Security Policy Methods: A Meta-Theoretical Approach

Information systems security policy (ISP) is the critical foundation of information systems security. Despite the criticality of the ISP, information systems security scholars have expressed concerns about the lack of theory and limited methodological support for the development of ISP. Existing literature on ISP Development (ISPD) is scattered and lack meta-theoretical approach toward designing ISPD Methods (ISPDM). This paper aims to fill the gap by consolidating extant ISPD approaches and put forth a systematic way by adopting a meta-theoretic approach in defining essential principles for designing ISPD method. After presenting the principles we demonstrate that none of the existing methods are based on all the essential principles.
Flux measurements of biogenic VOCs during ECHO 2003

Within the framework of the AFO 2000 project ECHO, two PTR-MS instruments were operated in combination with sonic anemometers to determine biogenic VOC fluxes from a mixed deciduous forest site in North-Western Germany using the eddy covariance (EC) technique. The measurement site was characterised by a forest of inhomogeneous composition, complex canopy structure, limited extension in certain wind directions and frequent calm wind conditions during night time. As a consequence, a considerable fraction of the measurements did not qualify for flux calculations by EC and had to be discarded. The validated results show light and temperature dependent emissions of isoprene and monoterpenes from this forest, with average emissions (normalised to 30°C and 1000 µmoles m ? 2 s ? 1 PAR) of 1.5 and 0.39 µg m ? 2 s ? 1 , respectively. Emissions of methanol reached on average 0.087 µg m ? 2 s ? 1 during daytime, but fluxes were too small to be detected during night time. Upward fluxes of the isoprene oxidation products methyl vinyl ketone (MVK) and methacrolein (MACR) were also found, being two orders of magnitude lower than those of isoprene. The observed fluxes are consistent with upscalings from leaf-level emission measurements of representative tree species in this forest and, in the case of MVK and MACR, can plausibly be explained by chemical production through oxidation of isoprene within the canopy. Calculations with an analytical footprint model indicate that the observed isoprene fluxes correlate with the fraction of oaks within the footprints of the flux measurement.
INTEGRAL observations of the Small Magellanic Cloud

The first INTEGRAL observations of the Small Magellanic Cloud (SMC) (carried out in 2003) are reported in which two sources are clearly detected. The first source, SMC X-1, shows a hard X-ray eclipse and measurements of its pulse period indicate a continuation of the long-term spin-up now covering approximately 30 years. The second source is likely to be a high-mass X-ray binary, and shows a potential periodicity of 6.8 s in the Imager on Board INTEGRAL Satellite lightcurve. An exact X-ray or optical counterpart cannot be designated, but a number of proposed counterparts are discussed. One of these possible counterparts shows a strong coherent optical modulation at ∼2.7 d, which, together with the measured hard X-ray pulse period, would lead to this INTEGRAL source being classified as the fourth known high-mass Roche lobe overflow system.
3D Gaze Estimation using Eye Vergence

We propose a fast and robust method to estimate the 3D gaze position based on the eye vergence information extracted from eye-tracking data. This method is specially designed for Point-of-Regard (PoR) estimation in non-virtual environments with the aim to make it applicable to the study of human visual attention deployment in natural scenarios. Our approach starts with a calibration step at different depth distances in order to achieve the best depth approximation. In addition, we investigate the distance range, for which state-of-the-art eyetracking technology allows 3D gaze estimation based on eye vergence. Our method provides a mean accuracy of 1. 2â¦ at a working distance between 200 mm and 400 mm from the user without requiring calibrated lights or cameras.
Revised Pulsar Spin-down

We address the issue of electromagnetic pulsar spin-down by combining our experience from the two limiting idealized cases that have been studied thoroughly in the past: that of an aligned rotator, in which ideal MHD conditions apply, and that of a misaligned rotator in vacuum. We construct a spin-down formula that takes into account the misalignment of the magnetic and rotation axes, and the magnetospheric particle acceleration gaps. We show that near the death line, aligned rotators spin down more slowly than orthogonal ones. In order to test this approach, we use a simple Monte Carlo method to simulate the evolution of pulsars and find a good fit to the observed pulsar distribution in the P-Ṗ diagram without invoking magnetic field decay. Our model may also account for individual pulsars spinning down with braking index n < 3, by allowing the corotating part of the magnetosphere to end inside the light cylinder. We discuss the role of magnetic reconnection in determining the pulsar braking index. We show, however, that n ~ 3 remains a good approximation for the pulsar population as a whole. Moreover, we predict that pulsars near the death line have braking index values n > 3, and that the older pulsar population has preferentially smaller magnetic inclination angles. We discuss possible signatures of such alignment in the existing pulsar data.
Computer aided venture projection and evaluation using process control paradigm

The paper is dealing with a new approach and technique to project and evaluate any venture by computer aided technology concept. The groundwork consist in the use of process control paradigm that enables a consistent and rigorous view of the problem. An application to assessment of a business venture is concisely described. The term “venture projection” represents ability to display given venture from entrepreneur’s point of view (in constructive, object and process-oriented way) into financial reports and indicators over time. The required reports and indicators are mainly income statement, balance sheet, cash flow statement, financial ratios, funding structure of investors, and investment indicators such as PBP, NPV, IRR, ROR, EPS, B/C, PI and more can be defined. Eventually it enables the user to carry out financial, investment, sensitivity, risk, scenario and what-if analyses according to requirements. It opens possibilities to carry out different changes to optimize given venture in terms of economic results.
A Self-Impact Analysis by Artificial Market Simulation

We constructed an evaluation system of the self-impact in a financial market using an artificial market and text-mining technology. Economic trends were first extracted from text data circulating in the real world. Then, the trends were inputted into the market simulation. Our simulation revealed that an operation by intervention could reduce over 70% of rate fluctuation in 1995. By the simulation results, the system was able to help for its user to find the exchange policy which can stabilize the yen-dollar rate
Learning programming using program visualization techniques

This paper describes the programming knowledge and skills that learners need to develop, and concludes that this is an area of computer science education where those involved in the teaching of programming need to further explore the nature, structure and function of domain specific knowledge. It has been argued that conceptual models can serve to enhance learners' conceptual understanding of programming. The methods used to enhance the development of accurate mental models include: designing the interface so that users can interact actively with it; using metaphors and analogies to explain concepts; and using spatial relationships so that users can develop capabilities for mental simulations. With the insights afforded by looking more closely at conceptual understanding, we describe how visualization techniques may effectively be used in the learning and reaching of programming.
Placing exurban land‐use change in a human modification framework

Emerging research on human-modified ecosystems can better inform land-use planning by integrating information from ecological and social science. In particular, low-density land use beyond the urban fringe (exurban) has been poorly defined and under-studied. Here I develop the human modification framework that characterizes landscapes by the degree to which natural processes are free or controlled and landscape patterns are natural or artificial. Ecologists are encouraged to explicitly place their researchwithin such a framework using quantitative metrics. As society's concern over ecological degradation and loss of biodiversity grows, ecologists must recognize that research on human-modified landscapes, particularly beyond the urban fringe, is not just an unexploited opportunity, but is critical to protecting biodiversity on private land.
Derma: Monitoring the Evolution of Skin Lesions with a 3D System

We present a new integrated tool, DERMA, which allows to measure and assess the time evolution of chronic wounds. A laser triangulation 3D scanner is used to acquire the wound geometry with high precision and to capture an RGB image aligned to the geometry. DERMA provides a single and uniform interface to: manage patient data, 3D scanning of the lesion region, and to perform different kinds of measurements and comparisons: geometric (on the 3D model) and colorimetric (on the image). All acquired data (3D geometries and images) as well as the measures calculated are stored in a database for monitoring the evolution of the skin lesion over time. The tool, developed in collaboration with dermatologists, is now under field evaluation in two dermatological clinics monitoring the evolution of wounds due to different types of skin diseases.
Doubly Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization with Factorized Data

We proposed a doubly stochastic primal-dual coordinate optimization algorithm for regularized empirical risk minimization that can be formulated as a saddlepoint problem. Different from existing coordinate methods, the proposed method randomly samples both primal and dual coordinates to update solutions, which is a desirable property when applied to data with both a high dimension and a large size. The convergence of our method is established not only in terms of the solution’s distance to optimality but also in terms of the primal-dual objective gap. When applied to the data matrix already factorized as a product of two smaller matrices, we show that the proposed method has a lower overall complexity than other coordinate methods, especially, when data size is large.
A two-layer hierarchical framework for activity sequence recognition by wearable sensors

As the aging population grows, the elderly care service has become an important part of the service industry in the aging population era. Activity monitoring is one of the most important services in the field of the elderly care service. In this paper, we proposed a wearable solution to provide an activity monitoring service on elders for caregivers. This service monitors restroom activities, such as washing hands, urinating and defecation. In the proposed solution, wireless motion sensors are wore on elder's wrist and waist to measure their body movement. The measured motion data are processed to statistical features and aggregated to cloud servers through gateways. A two-layer hierarchical framework is used for the activity recognition. In the first layer, a preliminary recognition is performed by a supervised Reduced Error Pruning (REP) Tree classifier to detect the transition of the activity. In the second layer, a Variable Order Hidden Markov Model (VOHMM) is proposed to determine the sequence of the activities. The experiment results show that the recognition accuracy is 70 percent. We developed a prototype service App to provide a life log for the recording of the activity sequence. The caregivers can make use of this information to take necessary actions accordingly.
Neural correlate of the construction of sentence meaning

Significance How do circuits of neurons in your brain extract and hold the meaning of a sentence? To start to address this unanswered question, we measured neural activity from the surface of the human brain in patients being mapped out before neurosurgery, as they read sentences. In many electrodes, neural activity increased steadily over the course of the sentence, but the same was not found when participants read lists of words or pronounceable nonwords, or grammatical nonword strings (“Jabberwocky”). This build-up of neural activity appears to reflect neither word meaning nor syntax alone, but the representation of complex meanings. The neural processes that underlie your ability to read and understand this sentence are unknown. Sentence comprehension occurs very rapidly, and can only be understood at a mechanistic level by discovering the precise sequence of underlying computational and neural events. However, we have no continuous and online neural measure of sentence processing with high spatial and temporal resolution. Here we report just such a measure: intracranial recordings from the surface of the human brain show that neural activity, indexed by γ-power, increases monotonically over the course of a sentence as people read it. This steady increase in activity is absent when people read and remember nonword-lists, despite the higher cognitive demand entailed, ruling out accounts in terms of generic attention, working memory, and cognitive load. Response increases are lower for sentence structure without meaning (“Jabberwocky” sentences) and word meaning without sentence structure (word-lists), showing that this effect is not explained by responses to syntax or word meaning alone. Instead, the full effect is found only for sentences, implicating compositional processes of sentence understanding, a striking and unique feature of human language not shared with animal communication systems. This work opens up new avenues for investigating the sequence of neural events that underlie the construction of linguistic meaning.
Radiative properties of magnetic neutron stars with metallic surfaces and thin atmospheres

Context. Simple models fail to describe the observed spectra of X-ray-dim isolated neutron stars (XDINSs). Interpretating these spectra requires detailed studies of radiative properties in the outermost layers of neutron stars with strong magnetic fields. Previous studies have shown that the strongly magnetized plasma in the outer envelopes of a neutron star may exhibit a phase transition to a condensed form. In this case thermal radiation can emerge directly from the metallic surface without going through a gaseous atmosphere, or alternatively, it may pass through a “thin” atmosphere above the surface. The multitude of theoretical possibilities complicates modeling the spectra and makes it desirable to have analytic formulae for constructing samples of models without going through computationally expensive, detailed calculations. Aims. The goal of this work is to develop a simple analytic description of the emission properties (spectrum and polarization) of the condensed, strongly magnetized surface of neutron stars. Methods. We have improved our earlier work for calculating the spectral properties of condensed magnetized surfaces. Using the improved method, we calculated the reflectivity of an iron surface at magnetic field strengths B ∼ 10 12 G–10 14 G, with various inclinations of the magnetic field lines and radiation beam with respect to the surface and each other. We constructed analytic expressions for the emissivity of this surface as functions of the photon energy, magnetic field strength, and the three angles that determine the geometry of the local problem. Using these expressions, we calculated X-ray spectra for neutron stars with condensed iron surfaces covered by thin partially ionized hydrogen atmospheres. Results. We develop simple analytic descriptions of the intensity and polarization of radiation emitted or reflected by condensed iron surfaces of neutron stars with the strong magnetic fields typical of isolated neutron stars. This description provides boundary conditions at the bottom of a thin atmosphere, which are more accurate than previously used approximations. The spectra calculated with this improvement show different absorption features from those in simplified models. Conclusions. The approach developed in this paper yields results that can facilitate modeling and interpretation of the X-ray spectra of isolated, strongly magnetized, thermally emitting neutron stars.
A multifrequency Electrical Impedance Tomography (EIT) system for biomedical imaging

Multifrequency Electrical Impedance Tomography (EIT) is preferred for imaging of biomedical objects to study their wide range of tissue conductivity profiles among different type of subjects under test. In this direction, a multifrequency EIT system is developed for biomedical imaging and the resistivity imaging of a practical phantom is studied at different frequencies. Practical biological phantoms are developed with NaCl solution as the bathing medium and vegetable tissue cylinders as the inhomogeneity. A sinusoidal constant current is injected to the boundary of the practical phantoms at different frequency levels and the surface potentials are measured. Resistivity images are reconstructed from the boundary potential data using EIDORS and images are analyzed with image contrast parameters. Results show that the instrumentation part of the developed EIT system injects constant current at different frequency levels and measures the boundary potential data. Boundary data are successfully generated for all the frequencies and found suitable for image reconstruction. CNR, PCR and COC of the resistivity images show that the resistivity images are efficiently reconstructed from the boundary data acquired from the multifrequency EIT system.
Scheduling in Grid Systems using Ant Colony Algorithm

Task scheduling is an important factor that directly influences the performance and efficiency of the system. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. In this study, a proposed ACO algorithm for scheduling in Grid systems will be presented. Simulation results indicate our ACO algorithm optimizes total response time and also it increase utilization.
Attitude Estimation using Low-Cost Sensors: a comparative analysis

In this paper several algorithms to estimate the attitude of a UAS using low-cost sensors are reviewed and a new one including the angular velocity is proposed. The fusion of measurement coming from gyroscopes and accelerometers is based on the Kalman filter. A quadrotor experimental platform is used to compare the measurement systems. The estimation results are evaluated under different conditions and compared against the results obtained with an industrial measurement device.