Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential for Modeling Human-Robot Interaction with GOMS

Human-robot interaction (HRI) has been maturing in tandem with robots’ commercial success. In the last few years HRI researchers have been adopting—and sometimes adapting—human-computer interaction (HCI) evaluation techniques to assess the efficiency and intuitiveness of HRI designs. For example, Adams (2005) used Goal Directed Task Analysis to determine the interaction needs of officers from the Nashville Metro Police Bomb Squad. Scholtz et al. (2004) used Endsley’s (1988) Situation Awareness Global Assessment Technique to determine robotic vehicle supervisors’ awareness of when vehicles were in trouble and thus required closer monitoring or intervention. Yanco and Drury (2004) employed usability testing to determine (among other things) how well a search-andrescue interface supported use by first responders. One set of HCI tools that has so far seen little exploration in the HRI domain, however, is the class of modeling and evaluation techniques known as formal methods.
Major disease threats to European amphibians

Disease threats to amphibians in Europe are generally poorly understood. The effects that disease can have on amphibian populations can range from minimal to local extirpation. Currently, two infectious agents are emerging as disease threats to European amphibian populations: Batracbochytrium dendrobatidis (Bd), which is the causative agent of amphibian chytridiomycosis, and ranavirus(es). Both pathogens are listed by the World Organization for Anima) Health (OIE). The incidence of other infectious diseases, such as amphibiocystidium, might also be increasing. In this review, we discuss known and potential disease threats to European amphibians, including their current and potential impact on amphibian populations, and factors driving their emergence and spread. We provide recommendations on how to proceed with investigations into cases where disease is thought to be involved in mortality or decline. We also stress that a multidisciplinary approach to these investigations is required.
Biases in Metallicity Measurements from Global Galaxy Spectra: The Effects of Flux Weighting and Diffuse Ionized Gas Contamination

Galaxy metallicity scaling relations provide a powerful tool for understanding galaxy evolution, but obtaining unbiased global galaxy gas-phase oxygen abundances requires proper treatment of the various line-emitting sources within spectroscopic apertures. We present a model framework that treats galaxies as ensembles of H ii and diffuse ionized gas (DIG) regions of varying metallicities. These models are based upon empirical relations between line ratios and electron temperature for H ii regions, and DIG strong-line ratio relations from SDSS-IV MaNGA IFU data. Flux-weighting effects and DIG contamination can significantly affect properties inferred from global galaxy spectra, biasing metallicity estimates by more than 0.3 dex in some cases. We use observationally motivated inputs to construct a model matched to typical local star-forming galaxies, and quantify the biases in strong-line ratios, electron temperatures, and direct-method metallicities as inferred from global galaxy spectra relative to the median values of the H ii region distributions in each galaxy. We also provide a generalized set of models that can be applied to individual galaxies or galaxy samples in atypical regions of parameter space. We use these models to correct for the effects of flux-weighting and DIG contamination in the local direct-method mass–metallicity and fundamental metallicity relations, and in the mass–metallicity relation based on strong-line metallicities. Future photoionization models of galaxy line emission need to include DIG emission and represent galaxies as ensembles of emitting regions with varying metallicity, instead of as single H ii regions with effective properties, in order to obtain unbiased estimates of key underlying physical properties.
Supply chain integration:challenges and solutions

Since its introduction by management consultants in the early 1980s, supply chain management (SCM) has been primarily concerned with the integration of processes and activities both within and between organisations. The concept of supply chain integration (SCI) is based on documented evidence that suggests that much of the waste throughout businesses is a consequence of fragmented supply chain configurations. However, there is also evidence to suggest that the achievement of higher levels of intra- and inter-firm integration presents an array of managerial challenges. The need for innovation in all aspects of SCM is widely recognised. Given the pivotal role of the integration paradigm within SCM, any meaningful innovation in this area must focus heavily on this issue. This chapter outlines some of the challenges by exploring the evolving SCM business context. It goes on to relate SCM theory to the widely cited Porter value chain concept. The core of the chapter provides a detailed description of SCI based on a wide variety of literature. It does so with particular reference to the challenges inherent in implementing an integrated business paradigm with a view to identifying a range of possible innovative solutions. The adoption of more integrated supply chain structures raises questions regarding the nature of both internal and external customer/supplier relationships. The effective management of such relationships is, therefore, given particular focus.
MODELLING AND SIMULATION OF THE MECHANICAL BEHAVIOUR OF WEFT-KNITTED FABRICS FOR TECHNICAL APPLICATIONS

Abstract This paper is in four parts. The first is related to general considerations and experimental analyses, and each of the others is related to different approaches to the theoretical analyses of the mechanical behaviour of weft-knitted fabrics and weft-knitted reinforced composites made of glass fibre. The objective is to find ways of improving the mechanical properties and simulating the mechanical behaviour of knitted fabrics and knitted reinforced composites so that the engineering design of such materials and structures may be improved. In Part II the first model is presented, a 3D model based on the classic elastica theory which is used to calculate the load-extension curves of a plain weft-knitted fabric in the coursewise and walewise directions. Good agreement is obtained between theoretical and experimental results.
Steering vector errors and diagonal loading

Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. The authors consider its application to the case of steering vector errors, i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. More precisely, the problem addressed is that of optimally selecting the loading level with a view to maximising the signal-to-interference-plus-noise ratio in the presence of steering vector errors. First, an expression is derived for the optimal loading for a given steering vector error and it is shown that this loading is negative. Next, random steering errors are considered and the optimal loading is averaged with respect to the probability density function of the steering vector errors, yielding a very simple expression for the average optimal loading. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the derived optimal loading factor provides a performance close to optimum.
Abstract—A new language resource for sentiment analysis (SA) and an application of SA to a new domain—discussions on an online Polish news forum—are developed. A scheme for human annotation of textual samples is proposed using online

A new language resource for sentiment analysis (SA) and an application of SA to a new domain—discussions on an online Polish news forum—are developed. A scheme for human annotation of textual samples is proposed using online questionnaires. A method for classifying the samples based on the annotations is introduced and put into practice. A method, applying existing advanced Information Retrieval (IR) techniques, for SA within a Bayesian learning framework is explored. Preliminary experimental results show the IR techniques, in conjunction with the Naïve Bayes classifier, can be expected to produce good sentiment classification performance both for Polish texts and for the news discussion domain.
HOW ARE PLANT SPECIES IN CENTRAL EUROPEAN BEECH (FAGUS SYLVATICA L.) FORESTS AFFECTED BY TEMPERATURE CHANGES? SHIFT OF POTENTIAL SUITABLE HABITATS UNDER GLOBAL WARMING

This study reveals which temperature range is favoured or avoided by 156 forest plant species and how the distribution of potential suitable habitats of species in beech forests may change in the future. We performed 140 phytosociological releves along a temperature gradient (4.1 to 9.8 °C) in Bavaria, southern Germany, on south exposed slopes. One half of the plots were located on acidic substrate, the other half on base-rich substrate. Generalized linear models (GLM) were used to analyse species occurrence along the temperature gradient and to model habitats for species in beech forests under a present (1971-2000) and a future climate (2071-2100) scenario assuming a temperature increase of 1.8 °C. Herb species of beech forests are more adapted to lower temperatures and tree species more to higher temperatures. Current habitats will clearly change under increasing temperatures. We found large habitat losses for Luzula sylvatica (Huds.) Gaudin, Maianthemum bifolium (L.) F. W. Schmidt, Picea abies (L.) H. Karst. , Prenanthes purpurea L. and large habitat gains for Carpinus betulus L. , Impatiens parviflora DC. , Prunus avium (L.) L. and Quercus petraea (Matt.) Liebl. on both substrates. Forestry will be affected positively as well as negatively with a change in tree cultivation. Losses in biodiversity might be strong for mountainous forests and must also be considered in future conservation plans.
User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.
Energetic Sustainability Using Renewable Energies in the Mediterranean Sea

The paper is focused on the analysis of the electrical energy sector in the Maltese islands, focusing on the employment of Renewable Energies in order to increase its energy independence. The main renewable source here proposed is wave energy: thanks to its strategic position, Malta will be able to generate electrical energy through the use of an innovative type of Wave Energy Converter (WEC) based on the prototype of linear generator designed and developed by the University of Palermo. This new technology will be able to cut down the electrical energy production from traditional power plants and, consequently, the greenhouse gas emissions (GHG). Wave energy source and off-shore photovoltaic (PV) technology are proposed here. Particularly, the installation of 18 wave farms, for a total installed capacity of 130 MW, will generate about 5.7% of Malta’s energy requests in 2025, while the installation of 60 MW of off-shore PV will generate about 4.4%.
The Complex Structure of Receptive Fields in the Middle Temporal Area

Neurons in the middle temporal area (MT) are often viewed as motion detectors that prefer a single direction of motion in a single region of space. This assumption plays an important role in our understanding of visual processing, and models of motion processing in particular. We used extracellular recordings in area MT of awake, behaving monkeys (M. mulatta) to test this assumption with a novel reverse correlation approach. Nearly half of the MT neurons in our sample deviated significantly from the classical view. First, in many cells, direction preference changed with the location of the stimulus within the receptive field. Second, the spatial response profile often had multiple peaks with apparent gaps in between. This shows that visual motion analysis in MT has access to motion detectors that are more complex than commonly thought. This complexity could be a mere byproduct of imperfect development, but can also be understood as the natural consequence of the non-linear, recurrent interactions among laterally connected MT neurons. An important direction for future research is to investigate whether these in homogeneities are advantageous, how they can be incorporated into models of motion detection, and whether they can provide quantitative insight into the underlying effective connectivity.
Leaf Recognition with Deep Learning and Keras using GPU computing

Our work is about using Deep Learning for leaf recognition using Keras and GPU computation. We used 17 CNNs of "Kaggle" [1], a Machine Learning training webpage that using simple challenges with prizes help people to learn how to use Deep Learning. Kaggle made a challenge in August, 30th, 2016 that was about Leaf Recognition. In that challenge more than 1500 users participated in it. They made teams to participate and we grab 17 codes of them to see how their codes was working. We updated the codes, because they were written in 1.0 version of Keras, and we use 2.0. 6 version and then we made a ranking to test their accuracy in leaf recognition using the dataset provided by kaggle. Also we downloaded two more datasets to make more tests with them. On the other hand, we found two papers in "the ImageClef Competition" [2], and we implemented them from the beggining to see how simple is to transform a paper into code. ECOCUAN team, from 2015, used a tunned AlexNet CNN and KDETUT, from 2017, used a ResNet50 modified CNN. Another paper found on the net was one that use ResNet26 CNN, so we think that was a good idea to make a ranking of the three of them to see which is better.
Aerosol organic carbon to black carbon ratios: Analysis of published data and implications for climate forcing

Measurements of organic carbon (OC) and black carbon (BC) concentrations over a variety of locations worldwide, have been analyzed to infer the spatial distributions of the ratios of OC to BC. Since these ratios determine the relative amounts of scattering and absorption, they are often used to estimate the radiative forcing due to aerosols. An artifact in the protocol for filter measurements of OC has led to widespread overestimates of the ratio of OC to BC in atmospheric aerosols. We developed a criterion to correct for this artifact and analyze corrected OC to BC ratios. The OC to BC ratios, ranging from 1.3 to 2.4, appear relatively constant and are generally unaffected by seasonality, sources or technology changes, at the locations considered here. The ratios compare well with emission inventories over Europe and China but are a factor of two lower in other regions. The reduced estimate for OC/BC in aerosols strengthens the argument that reduction of soot emissions maybe a useful approach to slow global warming.
UNITIZATION AND SEMANTIC INFORMATION BY RYAN JAMES HUBBARD THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Arts in Psychology in the Graduate College of the University of Illinois at Urbana-Champaign, 2014

Recognition memory can be supported by two distinct processes: recollection of an item and its related contextual information, and a general sense of familiarity for the item. Performance on recollection-based associative memory tests can be supported by familiarity when stimuli are “unitized”, or encoded as a single representation. Unitization generally produces greater positivity in mid-frontal (FN400) potentials during recognition in ERP studies, also suggesting recruitment of familiarity. However, several experimental manipulations can lead to FN400 modulation, as well as differences in estimates of familiarity; thus, it is unclear what mechanism underlies this increase in familiarity-based retrieval. One proposal is that unitization may modulate the semantic relatedness between the two items; thus, unitization of stimuli with little semantic content would lead to reduced effects. To assess this claim, two ERP experiments were performed with semantically sparse stimuli (abstract images and pseudowords), in which participants either unitized stimuli or encoded them separately, and were later tested for their memory for the items. Results suggest that unitization may still be possible with semantically sparse stimuli, but that the neural correlates of unitization (namely, the FN400) are affected by this manipulation.
Thermal Simulation of Laser Surface Modification of H13 Die Steel

In this study, the effects of laser parameters on the properties of glazed die steel were investigated. A Rofin DC-015 diffusion-cooled CO2 slab laser with 10.6 µm wavelength was used. Die steel sample surfaces were prepared with a 3 µm roughness and chemically treated to improve CO2 laser wavelength absorbance. One set of processing parameters were processed through the thermal simulation program and correlated with physical results determined from actual test samples. Set processing parameters were 1138 W peak power, 2900 Hz PRF, 24% duty cycle and 261 mm/s traverse speed. Scanning Electron Microscopy (SEM) micrographs and micro-hardness properties of the affected surface were measured. An analytical mathematical model of the heat field generated in the laser glazing process was used to predict the nominal temperature distribution in the surface and dimensions of melt pool. A thermal model using point source surface energy inputs was used to predict the thermal profiles in the die steel. This allowed estimation of the depths of microstructurally altered regions. For higher energy absorbance, the depth of the glazed surface increased from 20 μm to 40 μm. At high nominal heating to cooling rate ratios, high micro-hardness values were recorded.
A Study among Individual Sports, Combine Sport and Team Sports on Compitition Sport Anxiety

The purpose of the study was to find out the anxiety level among individual, team and combine sports. 90 subjects were randomly selected, 30 each from individual, combine and team sports. The age of the athletes were from 22±5 years old. The individual sports athletes were chosen from athletics, archery, cycling and horse riding. Combine sports athletes were chess boxing, korfball and team sports athletes were from football, hockey and basketball. All were contestant in the national games from Kerala (31 January 2015 to 14 February 2015). The Sport Competition Anxiety Test by (Merten, 1977) was used for evaluation of anxiety of athletes. One way analysis of variance was used to evaluate among the groups on competition anxiety level. The results explain that in spite of different nature of activities there is also different level of sports anxiety exist in between individual sports and team sports athletes and the differences doesn’t exist between individual sports and combine sports and combine with team sports athletes. And as compared to the mean scores among the sports the anxiety level is slightest in individual sports.
Analysis of between-half work rates in English Premier League soccer referees

OBJECTIVE The analysis of between-half distance coverage can reveal the occurrence of fatigue and/or refereeing strategies; both of which would be classified as determinants of overall physical match performance. However, the literature presents conflicting evidence with regards to this aspect of physical match performance in referees. The aim of the present study was to examine the influence of first half activity on the physical match performances of English Premier League soccer referees during the second half of matches.
Big Textual Data: Lessons and Challenges for Statistics

At issue are a few early stage case studies relating to: research publishing and research impact; literature, narrative and foundational emotional tracking; and social media, here Twitter, with a social science orientation. Central relevance and importance will be associated with the following aspects of analytical methodology: context, leading to availing of semantics; focus, motivating homology between fields of analytical orientation; resolution scale, which can incorporate a concept hierarchy and aggregation in general; and acknowledging all that is implied by this expression: correlation is not causation. Application areas are: research publishing and qualitative assessment, narrative analysis and assessing impact, and baselining and contextualizing, statistically and in related aspects such as visualization.
Global congruence of carbon storage and biodiversity in terrestrial ecosystems

Deforestation is a main driver of climate change and biodiversity loss. An incentive mechanism to reduce emissions from deforestation and forest degradation (REDD) is being negotiated under the United Nations Framework Convention on Climate Change. Here we use the best available global data sets on terrestrial biodiversity and carbon storage to map and investigate potential synergies between carbon and biodiversity‐oriented conservation. A strong association (rS= 0.82) between carbon stocks and species richness suggests that such synergies would be high, but unevenly distributed. Many areas of high value for biodiversity could be protected by carbon‐based conservation, while others could benefit from complementary funding arising from their carbon content. Some high‐biodiversity regions, however, would not benefit from carbon‐focused conservation, and could become under increased pressure if REDD is implemented. Our results suggest that additional gains for biodiversity conservation are possible, without compromising the effectiveness for climate change mitigation, if REDD takes biodiversity distribution into account.
Data Mining Techniques in Prediction of Risk Factors of Diabetes Mellitus

Diabetes mellitus is a chronic disease, lifelong condition that affects the body's ability to use the energy found in food. The level of morbidity and mortality due to diabetes and its potential complications are enormous and pose significant healthcare burdens. It is a complex and time-consuming task in detecting the risk of acquiring diabetes mellitus when a large amount of data is manually processed in the clinical environment. The objective of this paper is to simplify the process of analyzing and detecting the risk of developing diabetes. Patients’ details are gathered and stored in the form of Electronic medical record (EMR). Association rule mining and decision tree induction are applied to the records stored, in order to obtain the set of rules that are to be satisfied. C4.5 or Support vector machine is used to classify the data set accordingly and summarization techniques are used to summarize resultant possibility of acquiring diabetes.