Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Psychological Status of Children with Asthma: A Randomized Controlled Trial

Children with asthma are at special risk for problems in psychological functioning, as are children with other chronic illnesses. We conducted a controlled trial of a combined education and stress management program among children ages 6 to 14 years with asthma. Eighty-one children were randomly assigned to an intervention or a control group; 56 children completed data collection, 29 intervention and 27 control. Psychological status was assessed by the Child Behavior Checklist (CBCL) before and after the intervention, as were children's knowledge of asthma, stress (as measured by children's life events), and functional status (as indicated by such activities as school attendance, time playing with friends, and daily chore performance). Children in the intervention group had a significant improvement in the total Behavior Problems score (p < .04) and Internalizing scale (p < .01) on the CBCL and a significant increase in daily chores (p < .04) compared with the control group. Before intervention, the two groups had statistically significant positive relationships between negative life events and behavior problem scores. After intervention, children in the control group still demonstrated a significant relationship between negative life events and total and Internalizing Behavior Problem scores, although participation in the intervention group negated that relationship. Children in the intervention group whose knowledge of asthma increased were more likely to report an increase in daily chores (p < .02). We conclude that the intervention had a beneficial effect on psychological status and on children's daily activities. The effect may have occurred in part by decreasing the likelihood that perceived stress from negative life events led to poorer adjustment.
Workshop on Developing a Generalized Intelligent Framework for Tutoring ( GIFT ) : Informing Design through a Community of Practice

Adaptive training and education (ATE) systems are the convergence of intelligent tutoring system (ITS) technologies and external training and education capabilities (e.g., serious games, virtual humans and simulations). Like ITSs, ATEs provide instructional experiences that are tailored to the learner and may be more effective than the training or educational systems alone. ATEs also leverage existing environments, content and domain knowledge to reduce the authoring workload. The Generalized Intelligent Framework for Tutoring (GIFT) is an open-source ATE architecture with the primary goal to support easy authoring, automated instructional management during ATE experiences, and a testbed to evaluate the effect of ATE tools and methods. While this paper addresses challenges and goals in bringing ATE solutions from state-of-art to state-of-practice within GIFT, it also highlights generalized challenges in making ITS technologies ubiquitous and practical on a large scale across a broader variety of domains.
Fast approximate DCT: basic-idea, error analysis, applications

The discrete cosine transform (DCT) has a variety of applications in image and speech processing. The idea of the subband-DFT (SB-DFT) is applied by Jung, Mitra and Mukherjee (see IEEE Trans. on Circuits and Systems for Video Technology, vol.6, no.3, 1996) to the DCT. In this paper the basic idea of the SB-DCT is discussed which is based on subband decomposition of the input sequence. Approximation is done by discarding the computations of bands of little energy. The complexity of this fast approximate method is examined in comparing it with a fast cosine-transform method in terms of program running-time. New accurate analysis of the errors due to the approximation is presented for any number of decomposition stages. New applications of the SB-DCT in the speech cepstrum analysis and in echo detection are also included by using the SB-DCT instead of the full-band FFT in calculating the real and complex cepstra.
Whole body vibration: unsupervised training or combined with a supervised multi-purpose exercise for fitness?

Abstract The aim of the study was to compare the effect of an unsupervised whole body vibration (WBV) training and two different supervised multi-purpose exercise programmes, with and without WBV, on body composition, functional fitness and self-reported well-being in middle-aged adults. Fifty-four healthy participants (age 48.6 ± 6.7 years) were randomly assigned to a vibration group (VG), a multi-purpose exercise group (MG) and a multi-purpose exercise with vibration group (VMG) and trained 3 days a week for 4 months. VG performed a standardised unsupervised WBV protocol, MG a supervised multi-purpose exercise and VMG a multi-purpose exercise including vibration. After training, drop out was significantly higher in VG group (P = 0.016) when compared to VMG group. In both MG and VMG, body composition, sit-up, push-up, sit and reach, agility test, hopping test and self-reported general health significantly improved (P < 0.05). No additive effects were generated by the vibration stimulus. Percentage of body fat and agility test in VG had a significant opposite trend compared to VMG group (P < 0.05). In summary, an unsupervised WBV training should not be chosen for training protocol. However, positive effects on physical fitness and the best results in adherence could be achieved integrating WBV practice into a multi-purpose exercise training.
Carbon Nanotube Network Embroidered Graphene Films for Monolithic All‐Carbon Electronics

A unique cage growth of graphene is developed by using carbon nanotube (CNT) spider webs as porous templates, resulting in CNT/graphene hybrids with high conductivity and mechanical flexibility. Furthermore, monolithic all-carbon transistors with graphene as active elements and CNT/graphene hybrids as contacts and interconnects are directly formed by chemical synthesis, and flexible all-carbon bioelectronics are subsequently demonstrated for in vivo mapping of cardiac signals.
Real-time foreground segmentation via range and color imaging

This paper describes a real-time method for foreground/background segmentation of a color video sequence based primarily on range data of a time-of-flight sensor. This method uses depth information of a TOF-sensor paired with a high resolution color video camera to efficiently segment foreground from background in a two-step process. First a trimap is produced using only range data: areas are located in each frame that have a high probability of being background or foreground, respectively. Pixels which cannot be definitively classified as foreground or background, typically about 1-2% of the frame, are assigned alpha-matte values using a cross bilateral filtering, applied directly to an estimate of the alpha-matte.
Speeding up Document Ranking with Rank-based Features

Learning to Rank (LtR) is an effective machine learning methodology for inducing high-quality document ranking functions. Given a query and a candidate set of documents, where query-document pairs are represented by feature vectors, a machine-learned function is used to reorder this set. In this paper we propose a new family of rank-based features, which extend the original feature vector associated with each query-document pair. Indeed, since they are derived as a function of the query-document pair and the full set of candidate documents to score, rank-based features provide additional information to better rank documents and return the most relevant ones. We report a comprehensive evaluation showing that rank-based features allow us to achieve the desired effectiveness with ranking models being up to 3.5 times smaller than models not using them, with a scoring time reduction up to 70%.
A music and health perspective on music's perceived “goodness”

In this paper consideration is given to aspects of social and professional music use premised from a “music as health” perspective. This critical exploration is intended to reveal values about music participation and music listening, with consideration of the way music is constructed as a contributor to social gains within music as health application. The frequently encountered expectation that music participation and music listening are innately good and “helpful” is examined. A range of projects are described and examined with reference to the theme of music's “commodified goodness” or what has elsewhere been termed, the ubiquitous “goodness of music” (Edwards, 2008b).
Improving the usability of integrated applications by using interactive visualizations of linked data

Today, data is contained in many different, heterogeneous information systems. For turning that data into valuable information, those systems need to be integrated. System integration on the user interface level, e.g., in portals or mashups, allows users to access multiple applications in parallel and explore the information contained therein while reusing familiar and powerful user interfaces. Linked data can significantly add value by allowing uniform access to the data contained in the systems, and by letting reasoners discover hidden, non-explicit knowledge from that data. In this paper, we present an approach combining both paradigms. A framework for integrating user interfaces is enhanced by the Semantic Data Explorer, which is interlinked with the integrated applications in a hybrid view. With a quantitative user study conducted in the emergency management area, we show that hybrid visualizations of annotated data help gathering information from integrated systems, even for users who are not trained with ontologies and semantic networks.
The Impact of Coercion on Services From the Perspective of Mental Health Care Consumers With Co-occurring Disorders

Objective: Disengagement from services by people with serious mental illnesses continues to be a major challenge for the mental health system. Assertive community treatment combined with Housing First services is an intervention targeted toward consumers whom the system has failed to engage. The processes involved in engaging and maintaining consumers in mental health services play an important role but remain an understudied aspect of the intervention. This study examined the social interaction between consumers and case managers from the perspective of consumers. Methods: Seventy service contacts between unique consumer–case manager dyads were sampled. Consumers with co-occurring serious mental illness and substance use disorders completed interviews after each service contact. They provided information on sociodemographic characteristics, service contact characteristics, consumer-provider relationship, utilization of coercive strategies, perceived coercion, and service contact evaluation. Multivariate regression analyses examined the association of consumer-provider relationship and perceived coercion with service contact evaluation. Results: Consumer-provider relationship was negatively associated with perceived coercion (effect size=.08). Perceived coercion was negatively associated with service contact evaluation (effect size=.34). Perceived coercion was positively associated with time in the program (effect size=.17) and negatively associated with length of the service contact (effect size=.14). Effect sizes ranging from . 08 to . 34 are typically considered small to medium. Conclusions: Findings demonstrate that for consumers, a positive response to service contacts indicated that they did not feel coerced. With consumers whose connection to services is tenuous, an immediate positive response to service contacts may be vital to maintain engagement. Research is needed to identify supportive case manager strategies that facilitate relationship building. (Psychiatric Services 60:183–188, 2009)
Identification of learning styles online by observing learners' browsing behaviour through a neural network

Identification of individual learning style is important when developing adaptive educational hypermedia systems. Current systems ask learners to complete questionnaires to identify their learning styles, which might not be appropriate in some contexts. The goal of this research is to identify the learner's learning style by simply observing his/her browsing behaviour without asking the learner to answer any questions or filling out any form. It is implemented through a multi-layer feed forward neural network (MLFF). Browsing behaviour, in this research, includes three factors, the use of embedded support devices (ESDs), the selection of link types, and the navigation between visited/unvisited nodes. The experiment results showed the proposed model performed well in identifying learning styles. Link type is the dominant factor and Time shift may not be a major factor in the identification of learning styles. Because of the fast execution property of neural networks and identification of learning styles online, it is possible to incorporate learning styles into online adaptive educational web-based systems.
Screening of Multiple Myeloma by Polyclonal Rabbit Anti-Human Plasmacytoma Cell Immunoglobulin

Antibody-based immunotherapy has been effectively used for tumor treatment. However, to date, only a few tumor-associated antigens (TAAs) or therapeutic targets have been identified. Identification of more immunogenic antigens is essential for improvements in multiple myeloma (MM) diagnosis and therapy. In this study, we synthesized a polyclonal antibody (PAb) by immunizing rabbits with whole human plasmacytoma ARH-77 cells and identified MM-associated antigens, including enlonase, adipophilin, and HSP90s, among others, via proteomic technologies. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay showed that 200 µg/mL PAb inhibits the proliferation of ARH-77 cells by over 50% within 48 h. Flow cytometric assay indicated that PAb treatment significantly increases the number of apoptotic cells compared with other treatments (52.1% vs. NS, 7.3% or control rabbit IgG, 9.9%). In vivo, PAb delayed tumor growth and prolonged the lifespan of mice. Terminal deoxynucleotidyl transferase dUTP nick end labeling assay showed that PAb also induces statistically significant changes in apoptosis compared with other treatments (P<0.05). We therefore conclude that PAb could be used for the effective screening and identification of TAA. PAb may have certain anti-tumor functions in vitro and in vivo. As such, its combination with proteomic technologies could be a promising approach for sieving TAA for the diagnosis and therapy of MM.
Goal and Preference Identification through natural language

Goal models allow efficient representation of stakeholder goals and alternative ways by which these can be satisfied. Preferences over goals in the goal model are then used to specify criteria for selecting alternatives that fit specific contexts, situations and strategies. Given such preferences, automated reasoning tools allow for efficient exploration of such alternatives. Nevertheless, to be amenable to such automated processing, goals and preferences need to be specified in a formal language, making automated processing inaccessible to the very bearers of goals and preferences, i.e., the stakeholders. We combine natural language processing techniques to allow specification of preferences through natural language statements. The natural language statement is first matched through regular expressions to distinguish between the preference component and the goal component. The former is then mapped to a preferential strength measure, while the latter is used to identify the relevant goal in the goal model through statistical semantic similarity techniques. The result constitutes a formal representation that can be used for alternatives analysis. In this way, stakeholders can access advanced goal reasoning techniques through simple natural language preference expressions, facilitating their decision making in various requirements analysis contexts. An experimental evaluation with human participants shows that the proposed system is of substantial precision and that a mapping from natural preferential verbalizations to predefined preferential strength labels is possible through sampling from crowds.
Effect of Reading with Reversed Speech on Frequency of Stuttering in Adults

This study explored the effect of reading with reversed speech on the frequency of stuttering. Eight adults who stutter served as participants and read four 300-syllable passages while listening to three types of speech stimuli: normal speech (choral reading), reversed speech at normal speed, reversed speech at half speed, and a control condition of no auditory feedback. A repeated-measures analysis of variance showed a significant decrease in stuttering frequency in the choral reading condition but not in reversed speech at normal and half speed. However, the reversed speech at half-speed condition showed a large effect size (ω2 = 0.32). Data suggest that a forward moving speech feedback is not essential to decrease the frequency of stuttering in adults who stutter.
On BiasWrappers: New Regularization Techniques for Machine Learning Regression

Regressive models in machine learning require regularization to balance the bias-variance tradeoﬀ and attain realistic predictions in the real world. Two new regularization techniques, referenced as BiasWrappers, will be discussed in this paper: BiasWrapperC1 and BiasWrapperC2. BiasWrapperC1 uses a form of penalization to prevent models from consistently over-shooting or undershooting. BiasWrapperC2 uses a modiﬁed layer of regression stacking to identify correlations of a regression model’s error. The techniques’ logics will be discussed through pseudocode in the context of machine learning regression. The regularization techniques are applied to machine learning models and compared with other regularization techniques through a series of carefully chosen datasets, and these metrics are used to hypothesize about the implications of these new techniques. All implementations are referenced with pseudocode in the paper, with external testing wrappers programmed in Python. An experimental study was conducted with standard regression datasets and showed the regularizations’ value propositions in multi-output data and outlier-based data.
SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails

HypTrails is a bayesian approach for comparing different hypotheses about human trails on the web. While a standard implementation exists, it exposes performance issues when working with large-scale data. In this paper, we propose a distributed implementation of HypTrails based on Apache Spark taking advantage of several structural properties inherent to HypTrails. The performance improves substantially. Our implementation is publicly available.
Purification and Characterization of a Unique Pectin Lyase from Aspergillus giganteus Able to Release Unsaturated Monogalacturonate during Pectin Degradation

A pectin lyase, named PLIII, was purified to homogeneity from the culture filtrate of Aspergillus giganteus grown in submerged culture containing orange peel waste as carbon source. PLIII was able to digest apple pectin and citrus pectins with different degrees of methyl esterification. Interestingly, the PLIII activity was stimulated in the presence of some divalent cations including Pb2+ and was not significantly affected by Hg2+. Like other pectin lyases, PLIII is stimulated by but is not dependent on Ca2+. The main soluble product released during the degradation of pectic substances promoted by the PLIII is compatible with an unsaturated monogalacturonate. PLIII is a unique enzyme able to release unsaturated monogalacturonate as the only soluble product during the degradation of pectic substances; therefore, PLIII was classified as an exo-pectin lyase. To our knowledge, this is the first characterization of an exo-pectin lyase. The PLIII described in this work is potentially useful for ethanol production from pectin-rich biomass, besides other common applications for alkaline pectinases like preparation of textile fibers, coffee and tea fermentation, vegetable oil extraction, and the treatment of pulp in papermaking.
Bibliometric indices for the assessment of the citation curve tail

Scientometrics is an area recently attracting greater research interest. To a great extent this area has been fertilized by the proposal of the h-index (2005), which represents a measure for the quality and quantity of a researcher's impact. The Perfectionism Index has been recently proposed aiming at differentiating between 'influentials' and 'mass producers' (2015); the former category produces articles, which are (almost all) with high impact, whereas the latter category produces a lot of articles with moderate or no impact at all. In this paper, we record a number of metrics that are of similar nature, i.e. they shed light into these publishing patterns (influentials vs. mass producers). We carry out a correlation analysis to reveal which metrics are describing the above phenomenon in a similar way and, thus, retain the most descriptive features. Finally, we report the results of an experiment with a dataset consisting of the academic staff of Greek Computer Science/Engineering departments.
Molecular Autopsy in Unexplained Sudden Death

Sudden death (SD) is defined as an unexpected natural death in apparently healthy individuals that takes place during the first hour after onset of symptoms [1]. There is a large proportion of SD that remains unresolved, without a definitive diagnosis [2]. This fact is due to medical reports are not accessible in all cases, no autopsy is performed or the cause of death given after autopsy is not specific [3,4].
Community Detection Based on Structure and Content: A Content Propagation Perspective

With the recent advances in information networks, the problem of identifying group structure or communities has received a significant amount of attention. Most of the existing principles of community detection or clustering mainly focus on either the topological structure of a network or the node attributes separately, while both of the two aspects provide valuable information to characterize the nature of communities. In this paper we combine the topological structure of a network as well as the content information of nodes in the task of detecting communities in information networks. Specifically, we treat a network as a dynamic system and consider its community structure as a consequence of interactions among nodes. To model the interactions we introduce the principle of content propagation and integrate the aspects of structure and content in a network naturally. We further describe the interactions among nodes in two different ways, including a linear model to approximate influence propagation, and modeling the interactions directly with random walk. Based on interaction modeling, the nature of communities is described by analyzing the stable status of the dynamic system. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.