Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model for the Generation and Combination of Emotional Expressions

In this paper we introduce a model, called Expression Generator, which generates facial expressions related to pseudo emotions and behavior of chatterbots, which are characters used as front ends of dialogue systems employed in Web sites. The emotions are articulated as sequences of MPEG-4 compliant facial displays, and visualized in a browser-embedded player. We define a set of operators to combine facial displays and propose an algebraic model to control the stream of expressions.
Evaluation of acoustic respiration rate monitoring after extubation in intensive care unit patients.

1 Bellomo R, Bion J, Finfer S, Myburgh J, Perner A, Reinhart K. Open letter to the Executive Director of the European Medicine Agency concerning the licensing of hydroxyethyl starch solutions for fluid resuscitation. Br J Anaesth 2014; 112: 595–600 2 Brunkhorst FM, Engel C, Bloos F, et al. Intensive insulin therapy and pentastarch resuscitation in severe sepsis. N Engl J Med 2008; 358: 125–39 3 Perner A, Haase N, Guttormsen AB, et al. Hydroxyethyl starch 130/ 0.42 versus Ringer’s acetate in severe sepsis. N Engl J Med 2012; 367: 124–34 4 Myburgh JA, Finfer S, Bellomo R, et al. Hydroxyethyl starch or saline for fluid resuscitation in intensive care. N Engl J Med 2012; 367: 1901–11 and Supplement to: Myburgh JA, Finfer S, Bellomo R, et al. Hydroxyethyl starch or saline for fluid resuscitation in intensive care. N Engl J Med 2012. doi:10.1056/ NEJMoa1209759 5 Van Der Linden P, James M, Mythen M, et al. Safety of modern starches used during surgery. Anesth Analg 2013; 116: 35–48 6 Martin C, Jacob M, Vicaut E, et al. Effect of waxy maize-derived hydroxyethyl starch 130/0.4 on renal function in surgical patients. Anesthesiology 2013; 118: 387–94 7 Annane D, Siami S, Jaber S, et al. Effects of fluid resuscitation with colloids vs crystalloids on mortality in critically ill patients presenting with hypovolemic shock—the CRISTAL randomized trial. J Am Med Assoc 2013; 310: 1809–17 8 James MF, Michell WL, Joubert IA, et al. Resuscitation with hydroxyethyl starch improves renal function and lactate clearance in penetrating trauma in a randomized controlled study: the FIRST trial (Fluids in Resuscitation of Severe Trauma). Br J Anaesth 2011; 107: 693–702 9 Cittanova ML, Leblanc I, Legendre C, et al. Effect of hydroxyethylstarch in brain-dead kidney donors on renal function in kidneytransplant recipients. Lancet 1996; 348: 1620–22 10 Rational fluid therapy in Germany (RaFTinG). Available on ClinicalTrials.gov (NCT01122277) last updated on 7 July 2011. http://clinical trials.gov/ct2/show/NCT01122277? term1⁄4NCT01122277&rank1⁄41 11 Meybohm P, Van Aken H, De Gasperi A, et al. Re-evaluating currently available data and suggestions for planning randomised controlled studies regarding the use of hydroxyethyl-starch in critically ill patients—a multidisciplinary statement. Crit Care 2013; 17: R166 (Epub ahead of print)
A Role-dependent Data-driven Approach for High Density Crowd Behavior Modeling

In this paper, we propose a role-dependent data-driven modeling approach to simulate pedestrians' motion in high density scenes. It is commonly observed that pedestrians behave quite differently when walking in dense crowd. Some people explore routes towards their destinations. Meanwhile, some people deliberately follow others, leading to lane formation. Based on these observations, two roles are included in the proposed model: leader and follower. The motion behaviors of leader and follower are modeled separately. Leaders' behaviors are learned from real crowd motion data using state-action pairs while followers' behaviors are calculated based on specific targets that are obtained dynamically during the simulation. The proposed role-dependent data-driven model is trained on crowd video data in one dataset and is then applied to two other different datasets to test its generality and effectiveness. The simulation results demonstrate that the proposed role-dependent data-driven model is capable of simulating crowd behaviors in crowded scenes realistically and reproducing collective crowd behaviors such as lane formation.
Airway inflammation in COPD: physiological outcome measures and induced sputum

Chronic obstructive pulmonary disease (COPD) is a result of airway inflammation, and the best predictor of COPD is the early detection of airflow limitation by spirometry. The Global Initiative for Obstructive Lung Disease Workshop Report defines airflow limitation using simple spirometric indices. Available guidelines categorise the severity of COPD using forced expiratory volume in one second (FEV1) and forced vital capacity (FVC), with symptoms playing a minor role in the assessment. Current standards define COPD by progressive loss of FEV1, and thus longitudinal decline in FEV1 will be the primary outcome variable for intervention studies aimed at preventing or reducing the loss of pulmonary function. There is evidence, however, that the variable FEV1/FVC and FEV1 are often not measured properly in all settings. This article will discuss the roles of physiological measurements in diagnosing COPD and physiological outcome measures for COPD. It does not formally compare physiological measures with other outcome measures, such as symptoms or quality of life. Additionally, improved treatment of established disease requires a better understanding of the inflammatory process and its clinical effects and treatment. The inflammatory process, and how drugs affect it, can be studied noninvasively or relatively noninvasively by using refined methods of examining spontaneous or induced sputum. Enhanced understanding of the use of induced sputum will assist in predicting patients' responses to short- and long-term inhaled corticosteroid treatment, and the methods of sputum examination need to be simplified so that they can be applied more easily to clinical practice.
A Case Study on the Construction of Application Ontologies

Appropriate techniques for the development of application ontologies are needed and GAODT ("Goal-Oriented Application Ontology Development Technique") technique described in this article contributes to this purpose. GAODT translates the goals and facts in natural language expressing the requirements of a knowledge-based system into rules and facts in first-order logic. Next, this knowledge base is mapped to an application ontology. GAODT was evaluated through the development of a case study on the construction of the application ontology of a knowledge-based system for the domain of Intestate Succession. A software tool to support the application of GAODT was also developed.
An exploration of the meanings of innovation held by students, teachers and SMEs in Spain

ABSTRACT Innovation has been at the forefront of the reforms and changes in the vocational education and training (VET) system in Spain and is defined as a necessary competence to be developed in students to improve their adaptation to the work context. However, there is still only limited knowledge about the role VET plays in the innovation process. Given that meanings are considered the base of all human expression, such as attitudes to innovation or innovative behaviours, this study qualitatively explores the meanings 42 VET teachers, 20 Small and Medium sized Enterprises (SMEs) managers and 24 VET students in Catalonia (Spain) give to innovation, with the purpose of clarifying the ‘black box’ of the innovative process. A diversity of meanings for innovation was found among the social actors in the study. In particular, SMEs shared a homogeneous and neo-Schumpeterian view of innovation. VET teachers held diverse views with the majority ascribing to the neo-Schumpeterian view and a minority sharing a broader view that included the social dimension of innovation. Finally, VET students were characterised by having an idealistic and vague idea of innovation. The theoretical and practical implications of the different meanings in fostering innovation are discussed.
Predicting Thread Linking Structure by Lexical Chaining

Web user forums are valuable means for users to resolve specific information needs, both interactively for participants and statically for users who search/browse over historical thread data. However, the complex structure of forum threads can make it difficult for users to extract relevant information. Thread linking structure has the potential to help tasks such as information retrieval (IR) and threading visualisation of forums, thereby improving information access. Unfortunately, thread linking structure is not always available in forums. This paper proposes an unsupervised approach to predict forum thread linking structure using lexical chaining, a technique which identifies lists of related word tokens within a given discourse. Three lexical chaining algorithms, including one that only uses statistical associations between words, are experimented with. Preliminary experiments lead to results which surpass an informed baseline.
Combining rough sets and rule based classifiers for handling imbalanced data

The paper presents two rough sets based filtering approaches combined with rule based classifiers suited for handling imbalanced data sets, i.e., data sets where the minority class of primary importance is under-represented in comparison to the majority classes. We introduced two techniques to detect and process inconsistent majority cases in the boundary between the minority and majority classes. The experiments showed that the best results were obtained for the relabel filtering, where inconsistent majority examples were reassigned to the minority class, combined with MODLEM rule induction algorithm.
Optimizing the Learning of a Second-Language Vocabulary.

The problem is to optimize the learning of a large German-English vocabulary. Four optimization strategies are proposed and evaluated experimentally. The first strategy involves presenting items in a random order and serves as a benchmark against which the others can be evaluated. The second strategy permits 5 to determine on each trial of the experiment which item is to be presented, thus placing instruction under "learner control. " The third and fourth strategies are based on a mathematical model of the learning process; these strategies are computer controlled and take account of S's response history in making decisions about which items to present next. Performance on a delayed test administered 1 wk. after the instructional session indicated that the learner-controlled strategy yielded a gain of 53% when compared to the random procedure, whereas the best of the two computer-controlled strategies yielded a gain of 108%. Implications of the work for a theory of instruction are considered.
Generation of time-varying target lines for an automatic parking system using image-based processing

This study develops a novel method of detecting a parking space and generating time-varying target lines of an automatic parking system according to image processing technique. The aim of this research is to detect the parking frame drawn on the floor by a robot equipped with a single camera and produce the image information, the slope and intercept of the target line for the control system. The time-varying target lines to be followed by the robot are generated by using Hough transform from a captured image, together with thresholding and Canny edge detector. The proposed method is evaluated by some experimental results.
Classification of ECG waveform using feature selection algorithm

The ECG classification problems have been solved by means of a methodology, which has the capability to enhance the ECG classification performance. This method reduces the computational complexity which mainly occurs during the feature selection. The computational requirements of exhaustive search method (those which test all possible subsets) increase exponentially with the number of features in the original set. The proposed system use particle swarm optimization for the selection of feature subset. PSO is attractive for feature selection, in that particle swarms will discover best feature combination as they fly within the best subset space. Some classifiers such as MLP, which start at random chosen point and then adjust weights to move in the direction. Although the training phase takes long time. Thus SVM is used for classification, which is based on local approximation strategy. It reduces the number of operations in learning mode and it is well suited for larger datasets.
Predicting information diffusion on social networks with partial knowledge

Models of information diffusion and propagation over large social media usually rely on a Close World Assumption: information can only propagate onto the network relational structure, it cannot come from external sources, the network structure is supposed fully known by the model. These assumptions are nonrealistic for many propagation processes extracted from Social Websites. We address the problem of predicting information propagation when the network diffusion structure is unknown and without making any closed world assumption. Instead of modeling a diffusion process, we propose to directly predict the final propagation state of the information over a whole user set. We describe a general model, able to learn predicting which users are the most likely to be contaminated by the information knowing an initial state of the network. Different instances are proposed and evaluated on artificial datasets.
Barriers and Supports to Parent Involvement and Collaboration During Transition to Adulthood

Parent involvement and parent–professional collaboration influence positive transitions from school into adult life among young adults with disabilities. However, parents frequently report being uninformed and uninvolved in transition planning and there is a paucity of information on how to develop these relationships in high school and postsecondary settings. The purpose of this study was to investigate the perspectives of parents of young adults who graduated from a postsecondary education program. We conducted semistructured interviews with 26 parents of young adults with intellectual and developmental disabilities who graduated from a postsecondary education program in the United States. Participants identified five primary barriers to parent–professional collaboration and six strategies to support parent involvement and collaboration. Implications for practice and research are discussed.
Influence of swim training on cardiac activity, tissue capillarization, and mitochondrial density in muscle tissue of zebrafish larvae.

Larval zebrafish (Danio rerio) of two different age classes ("swim-up" larvae, 9 days old; "free-swimming" larvae, 21 days old) were exposed to either an endurance/continuous training or interval training. Control animals were kept in stagnant water. A comparison of cardiac activity of trained (either endurance or interval) and untrained animals at the end of the training regime revealed no differences in heart rate, end-diastolic and end-systolic ventricular volume, and cardiac output. Training also had no influence on the concentration of erythrocytes in the blood. Thus, at the level of total oxygen transport in the blood, training did not provoke any improvement during the first 32 days of development. Significant changes, however, were observed at the tissue level. In free-swimming larvae [i.e., between 21 and 32 days postfertilization (dpf)] endurance training increased the capillarization of both axial muscle caudal to the anus and the tail fin. In addition, mitochondrial density of red and intermediate muscle fibers increased significantly. In contrast to capillarization, even swim-up larvae, trained between 9 and 15 dpf, were affected. The observed increase in mitochondrial content indicates a high demand for oxygen and energy-rich metabolites for oxidative phosphorylation. In older larvae, this is met by the increase in capillarization that improves the blood supply and with it the required oxygen and metabolite supply of muscle tissue. Both of these adaptational changes result in a reduction of diffusion distances (between capillary and muscle fiber as well as mitochondria) and may contribute to a higher resistance toward oxygen deficiency. Furthermore, this study indicates that plasticity of muscle tissue is already established in early stages of development at both the tissue and cellular levels.
Feedforward neural network for controlling qbmove maker pro variable stiffness actuator

This paper presents an application of neural networks in control of cutting-edge robotic actuators. Namely, the latest version of robot actuators is inevitable multivariable and distinctively non-linear system in order to enable accurate but at the same time safe robot behavior. Therefore, non-linear elastic elements in transmission are desired to enable actuator stiffness variation. Inconsistency in serial production of such non-linear elastic elements as well as challenges in control of such complex mechanism, are solved using neural networks. Thus, efficient computation necessary for real time control is achieved. Superiority of feedforward control using neural networks over model-based feedforward are validated by experiments on the laboratory setup - qbmove maker pro actuator.
Human factors and everyday routine in the maritime work domain

The traditional approach to the study of human factors in the maritime work domain is the analysis of accidents. These analyses provide valuable information, but they are not sufficient to get insight in the causal relationship between factors that shape performance and actual human performance in everyday routine work. A more suitable approach to the study of human factors of everyday routine work in the maritime domain is the quasi-experimental field study where variations in performance can be observed as a function of natural variations in performance shaping factors (for example workload demand). In this paper it is demonstrated how these natural variations of workload demand very easily can be observed as a function of the different stages or phases of a voyage on one out of four selected Roll-on Roll-off (Ro-Ro) ferries in regular service. A very simple method for measurement of crew attention through the measurement of communication at the bridge of the ship is suggested. This method was used in a pilot study of the relationship between workload and attention and some interesting mechanisms were found. These findings could lead to development of hypotheses about the causal relationship between workload and attention, and the hypotheses could be tested by means of quasi-experimental field studies as suggested in this paper.
NP chunking using ILP

This is to report the results of approaching the problem of NP chunking using Inductive Logic Programming techniques. The problem, as de-ned in (Ramshaw and Marcus, 1995), is the machine learning of rules that recognise non-recursive, base NPs in text annotated with part-of-speech tags, by tagging each word as beingìnside' oròutside' an NP. (Consecutive NPs are appropriately treated.) The same input data as in the original experiment is used here, but the machine learning technique is Inductive Logic Programming, and more speciically the Progol algorithm. The problem is formulated as the machine learning of a Prolog predicate that will accept a part-of-speech tagged word and its context as input and associate it with the appropriate syntactic tag.
Sketches vs skeletons: video annotation can capture what motion capture cannot

Good posture is vital to successful musical performance and music teachers spend a considerable amount of effort on improving their students' posture. This paper presents a user study to evaluate a skeletal motion capture system (based on the Microsoft Kinect™) for supporting teachers as they give feedback to learners about their posture and movement whilst playing an instrument. The study identified a number of problems with skeletal motion capture that are likely to make it unsuitable for this type of feedback: glitches in the capture reduce trust in the system, particularly as the motion data is removed from other contextual cues that could help judge whether it is correct or not; automated feedback can fail to account for the diversity of playing styles required by learners of different physical proportions, and most importantly, the skeleton representation leaves out many cues that are required to detect posture problems in all but the most elementary beginners. The study also included a participatory design stage which resulted in a radically redesigned prototype, which replaced skeletal motion capture with an interface that allows teachers and learners to sketch on video with the support of computer vision tracking.
Severely Heat Injured Survivors of E. coli O157:H7 ATCC 43888 Display Variable and Heterogeneous Stress Resistance Behavior

Although minimal food processing strategies aim to eliminate foodborne pathogens and spoilage microorganisms through a combination of mild preservation techniques, little is actually known on the resistance behavior of the small fraction of microorganisms surviving an inimical treatment. In this study, the conduct of severely heat stressed survivors of E. coli O157:H7 ATCC 43888, as an indicator for the low infectious dose foodborne enterohemorrhagic strains, was examined throughout their resuscitation and outgrowth. Despite the fact that these survivors were initially sublethally injured, they were only marginally more sensitive to a subsequent heat treatment and actually much more resistant to a subsequent high hydrostatic pressure (HHP) shock in comparison with unstressed control cells. Throughout further resuscitation, however, their initial HHP resistance rapidly faded out, while their heat resistance increased and surpassed the initial heat resistance of unstressed control cells. Results also indicated that the population eventually emerging from the severely heat stressed survivors heterogeneously consisted of both growing and non-growing cells. Together, these observations provide deeper insights into the particular behavior and heterogeneity of stressed foodborne pathogens in the context of food preservation.
Logical database design with ontologically clear entity relationship models

It is argued that ontologically clear entity relationship models can model the real world domains more accurately than ontologically unclear models. However, transformation of such models into the relational model at the logical level has not yet been studied sufficiently with a view to formulate new transformation rules. This paper presents a set of new transformation rules to convert ontologically clear conceptual models to relational models. Finally we did a comparison of two relational models that were developed from the ontologically clear and unclear models using a quality criterion synthesized from the extant literature. The preliminary results of this ongoing research study shows that the quality of relational model developed from ontologically clear conceptual model is superior to its ontologically unclear counterpart.
A Method for Trajectory Optimization of Robots having Contacts or Motion Constraints

The problem of motion trajectory optimization with constraints is considered for robotic system with actuated joints. Constraints concern contacts and path-following in the Cartesian space. These constraints arise in walking patterns. In this study, we are dealing with cases where the same constraints hold during a given motion. The trajectory optimization is performed by defining some state variables as polynomials, and the best parameters of the polynomial functions are obtained from suitable optimization programs. By taking into account motion constraints, it appears that the number of the problem's optimization parameters can be reduced. Subsequently, the structure of the optimization problem is different. A procedure to solve this optimization problem is proposed. A simple case is used to compare the proposed methods with the one that is used for solving the similar problem without parameters reduction