Miracle-dz/UniFAR-FTUs huggingface dataset:
Who is taking MOOCs for teachers’ professional development on the use of ICT? A cross-sectional study from Spain

ABSTRACT Research on the use of Massive Open Online Courses (MOOCs) for teacher professional development (TPD) and the characteristics of their participants is scarce. This article presents a case of a MOOC initiative supported by the Spanish Ministry of Education aiming at teachers’ professional development on the use of ICT for teaching and learning. The analysis is focused on (a) the sociodemographic and school characteristics of the teachers participating in MOOCs; (b) their experience and beliefs about lifelong learning; and (c) the relevance of MOOCs for their work. By contrasting the characteristics of the participants in this initiative with the characteristics of the whole population of teachers in Spain, and a sample of Spanish participants in MOOCs not aimed at TPD, the article sheds light on the specificities of the teachers who use this way of TPD. The results have implications for the design of programmes aimed to develop MOOCs for TPD.
Joint Embeddings of Scene Graphs and Images

Multimodal representations of text and images have become popular in recent years. Text however has inherent ambiguities when describing visual scenes, leading to the recent development of datasets with detailed graphical descriptions in the form of scene graphs. We consider the task of joint representation of semantically precise scene graphs and images. We propose models for representing scene graphs and aligning them with images. We investigate methods based on bag-of-words, subpath representations, as well as neural networks. Our investigation proposes and contrasts several models which can address this task and highlights some unique challenges in both designing models and evaluation.
Treating osteoporotic compression fractures.

Mrs. Smith is a 72-year-old woman who comes in to the clinic complaining of back pain that started “just after I lifted a pile of plates into the upper kitchen cabinet. ” She rates her pain intensity at 8 out of 10 in intensity, and lasts all day and night. She has tried a variety of over-the-counter medications, such as extra strength acetaminophen and nonselective, anti-inflammatory medications such as ibuprofen. No medication helps her pain. She is unable to do her regular activities of housework, church, and grocery shopping. Her husband tells you, “She really needs to get some good sleep. The pain keeps her awake all night, and it is not getting any better. What can we do? Things seem to get worse every day. ”
Influenza A/H1N1 Vaccination Response Is Inadequate in Down Syndrome Children When the Latest Cut-off Values Are Used

We determined the response of 48 Down syndrome children to 2 doses of influenza A/H1N1 vaccination. Ninety-two percent of the children reached the previously defined protective level (hemagglutination-inhibition titer ≥1:40), but only 27% of the children reached the level of ≥1:110 which was recently described to predict the conventional 50% clinical protection rate in children. Further studies, and potentially adaptations of the schedule, are needed.
Bronchoscope insertion route and patient comfort during flexible bronchoscopy.

SETTING Diagnostic flexible bronchoscopy performed in hospitalised and ambulatory patients in a tertiary care academic hospital in Monterrey, Mexico. OBJECTIVE To determine the effect of the route of insertion of the bronchoscope (oral or nasal) on patient comfort, vocal cord visualisation, local anaesthetic and sedation requirements and possible complications. DESIGN Prospective study carried out in patients aged ⩾ 18 years with an indication for flexible bronchoscopy. The route of insertion was randomly assigned. Symptoms related to the procedure were evaluated using a questionnaire. RESULTS Sixty-three patients were included: 32 in the oral insertion group and 31 in the nasal insertion group. There was no statistically significant difference in patient discomfort (1.91 ± 2.95 vs. 2.39 ± 3.56 points on a scale of 1 to 10, P = 0.74) or procedural complications (4 vs. 0 events, P = 0.12) between study groups. Oral insertion was associated with less time to vocal cord visualisation (25.5 ± 156 s vs. 56 ± 61 s, P < 0.01), lower requirement for lidocaine (15 ± 7.50 vs. 16 ± 4 ml, P = 0.01) and fewer insertion failures (0 vs. 6 cases, P < 0.01). CONCLUSIONS With intravenous sedoanalgesia, route of insertion did not affect patient comfort. However, the oral route was associated with faster vocal cord visualisation, less use of lidocaine and no insertion failure.
The influence of dynamics and speech on understanding humanoid facial expressions

Human communication relies mostly on nonverbal signals expressed through body language. Facial expressions, in particular, convey emotional information that allows people involved in social interactions to mutually judge the emotional states and to adjust its behavior appropriately. First studies aimed at investigating the recognition of facial expressions were based on static stimuli. However, facial expressions are rarely static, especially in everyday social interactions. Therefore, it has been hypothesized that the dynamics inherent in a facial expression could be fundamental in understanding its meaning. In addition, it has been demonstrated that nonlinguistic and linguistic information can contribute to reinforce the meaning of a facial expression making it easier to be recognized. Nevertheless, few studies have been performed on realistic humanoid robots. This experimental work aimed at demonstrating the human-like expressive capability of a humanoid robot by examining whether the effect of motion and vocal content influenced the perception of its facial expressions. The first part of the experiment aimed at studying the recognition capability of two kinds of stimuli related to the six basic expressions (i.e. anger, disgust, fear, happiness, sadness, and surprise): static stimuli, that is, photographs, and dynamic stimuli, that is, video recordings. The second and third parts were focused on comparing the same six basic expressions performed by a virtual avatar and by a physical robot under three different conditions: (1) muted facial expressions, (2) facial expressions with nonlinguistic vocalizations, and (3) facial expressions with an emotionally neutral verbal sentence. The results show that static stimuli performed by a human being and by the robot were more ambiguous than the corresponding dynamic stimuli on which motion and vocalization were associated. This hypothesis has been also investigated with a 3-dimensional replica of the physical robot demonstrating that even in case of a virtual avatar, dynamic and vocalization improve the emotional conveying capability.
The Reciprocal Influences among Motivation, Personality Traits, and Game Habits for Playing Pokémon GO

ABSTRACT This article reports a study exploring motivations of Pokémon Game use, individual differences related to personality traits, and game habits. First, it analyzed Pokémon GO motivations through exploratory factor analysis (EFA) by administering online the Pokémon GO Motivational Scale to a group of Italian gamers (N = 560). Successively, a Confirmatory Factor Analysis (CFA) was conducted testing three factorial models of Pokémon Game motivations on a selected random sample (N = 310). Results showed a three-factor model of Pokémon GO Game motivations (i.e. Personal Needs, Social Needs and Recreation), accounting for 68.9% of total variance plus a general higher order factor that best fits the data. Individual differences in Pokémon GO motivations and personality traits have been explored showing that high involved Pokémon GO players are introverted, low agreeableness, and conscientiousness people, driven by personal social and recreational needs. Reciprocal influences on motivational involvement, personality, and game habits were discussed.
SAPL®: a free software for determining the physiological potential in soybean seeds1

ABSTRACT Obtaining image-based information is a powerful approach to capture and quantify seed vigor data. However, commercial systems that facilitate the processing and acquisition of images are often cost prohibitive. This study aimed to evaluate the efficiency of the Seedling Analysis System (Sistema de Análise de Plântulas - SAPL®), in order to analyze the physiological potential of soybean seeds, in comparison with the information provided by vigor tests which are traditionally recommended for this species. Nine lots of soybean seeds were submitted to germination, germination speed, seedling emergence, cold test and electrical conductivity tests. In the SAPL® analyzes, seedlings of four and six days, counted from the beginning of the germination test, were evaluated, resulting in values for seedling length, growth, development uniformity, vigor index and corrected vigor index. The evaluated lots with emergence greater than 90 % showed a vigor index higher than 600 and 800, respectively in the fourth and sixth days. The indexes generated by SAPL®, except for the uniformity index, presented positive and high correlations with the traditional tests (> 0.80). SAPL® is efficient in identifying differences in the vigor of soybean seed lots.
Causes and countermeasures study on psychiatric disabled adults in Guangdong province

Objective:With the study on the data from Guangdong province,to analysis the main causes of psychiatric disabled adults and to suggest the effective measures in preventing and treating this impairment. Method:The relative data were collected in 2006.The statistic study was taken according to the disabled severity, urban and rural distribution,also the onset of age.Corresponding suggestions on prevention and treatment were raised according to the data analysis,survey studies and medical knowledge.Result:Different risk causes led to significant differences in disabled severity and the onset ages(P0.05),but the distribution between urban and rural had no significant difference(P0.05). The main cause of psychiatric disability was schizophrenia(52.1%). Dementia and other organic mental disorders mainly led to psychiatric disability grade 1.The main cause of psychiatric disability in youth(62.9%)and middle-aged(58.9%)patients was schizophreina,in old patients(43.5%) was dementia.Conclusion:To reduce and alleviate the psychiatric disabilities,more works should be done on mental hygiene,psychological education medical counseling,as well as prevention and treatment measures against dementia.Meanwhile more financial,medical and professional supports in the rural place should be taken.
IDMS Solution for Hybrid Broadcast Broadband Delivery Within the Context of HbbTV Standard

Inter-destination media synchronization (IDMS) is a key requirement to enable successful networked shared media experiences between remote users. This paper presents an adaptive, accurate and standard-compliant IDMS solution for hybrid broadcast and broadband delivery. Apart from providing multi- and cross-technology support, the presented IDMS solution is able to accomplish synchronization when different formats/versions of the same, or even related, contents are being played out in a shared session. It is also able to independently manage the playout processes of different groups of users. The IDMS solution has been integrated within an end-to-end platform, which is compatible with the hybrid broadcast broadband TV standard. It has been applied to digital video broadcasting—terrestrial technology and tested for a Social TV scenario, by also including an ad-hoc chat tool as an interaction channel. The results of the conducted (objective and subjective) evaluations prove the statisfactory behavior and performance of the IDMS solution and platform as well as in terms of the perceived quality of experience.
Nowcasting New Zealand GDP Using Machine Learning Algorithms

This paper analyses the real-time nowcasting performance of machine learning algorithms estimated on New Zealand data. Using a large set of real-time quarterly macroeconomic indicators, we train a range of popular machine learning algorithms and nowcast real GDP growth for each quarter over the 2009Q1-2018Q1 period. We compare the predictive accuracy of these nowcasts with that of other traditional univariate and multivariate statistical models. We find that the machine learning algorithms outperform the traditional statistical models. Moreover, combining the individual machine learning nowcasts further improves the performance than in the case of the individual nowcasts alone.
Bayesian Adjustment for Insurance Misrepresentation in Heavy-Tailed Loss Regression

In this paper, we study the problem of misrepresentation under heavy-tailed regression models with the presence of both misrepresented and correctly-measured risk factors. Misrepresentation is a type of fraud when a policy applicant gives a false statement on a risk factor that determines the insurance premium. Under the regression context, we introduce heavy-tailed misrepresentation models based on the lognormal, Weibull and Pareto distributions. The proposed models allow insurance modelers to identify risk characteristics associated with the misrepresentation risk, by imposing a latent logit model on the prevalence of misrepresentation. We prove the theoretical identifiability and implement the models using Bayesian Markov chain Monte Carlo techniques. The model performance is evaluated through both simulated data and real data from the Medical Panel Expenditure Survey. The simulation study confirms the consistency of the Bayesian estimators in large samples, whereas the case study demonstrates the necessity of the proposed models for real applications when the losses exhibit heavy-tailed features.
Performance, Power, and Area Design Trade-Offs in Millimeter-Wave Transmitter Beamforming Architectures

Millimeter wave (mmW) communications is viewed as the key enabler of 5G cellular networks due to vast spectrum availability that could boost peak rate and capacity. Due to increased propagation loss in mmW band, transceivers with massive antenna array are required to meet a link budget, but their power consumption and cost become limiting factors for commercial systems. Radio designs based on hybrid digital and analog array architectures and the usage of radio frequency (RF) signal processing via phase shifters have emerged as potential solutions to improve radio energy efficiency and deliver performances close to the conventional digital antenna arrays. In this paper, we provide an overview of the state-of-the-art mmW massive antenna array designs and comparison among three array architectures, namely digital array, partially-connected hybrid array (sub-array), and fully-connected hybrid array. The comparison of performance, power, and area for these three architectures is performed for three representative 5G downlink use cases, which cover a range of pre-beamforming signal-to-noise-ratios (SNR) and multiplexing regimes. This is the first study to comprehensively model and quantitatively analyze all design aspects and criteria including: 1) optimal linear precoder, 2) impact of quantization error in digital-to-analog converter (DAC) and phase shifters, 3) RF signal distribution network, 4) power and area estimation based on state-of-the-art mmW circuits including baseband digital precoding, digital signal distribution network, high-speed DACs, oscillators, mixers, phase shifters, RF signal distribution network, and power amplifiers. Our simulation results show that the fully-digital array architecture is the most power and area efficient compared against optimized designs for sub-array and hybrid array architectures. Our analysis shows that digital array architecture benefits greatly from multi-user multiplexing. The analysis also reveals that sub-array architecture performance is limited by reduced beamforming gain due to array partitioning, while the system bottleneck of the fully-connected hybrid architecture is the excessively complicated and power hungry RF signal distribution network.
Increased expression of autoantibody protein and soluble IL-6 receptor in serum of patients with Tourette's syndrome

Objective To explore the mechanism of immunologic injury in patients with Tourette's syndrome(TS). Methods The serum level of anti-brain antibody(ABAb),antinuclear antibody(ANAb),soluble IL-6 receptor(sIL-6R) and soluble gp130(sgp130) in patients with TS were analyzed by commercially available ELISA kits,and the titer of anti-streptolysin O(ASO) in TS and in control was examined with latex enhanced immunoturbidimetry.Results The level of sIL-6R and sgp130 was significantly elevated in TS compared with control group[(44.1±15.8)ng/mL vs(30.3±9.0)ng/mL and(69.0±24.6)ng/mL vs(47.3±14.1)ng/mL,P0.01 respectively]. ASO titer(250 IU/mL) was found higher in TS than that in control(P0.01). The positive rates of anti-brain antibody and antinuclear antibody in TS were higher than that in control group(66% vs 4% and 53% vs 25%,P0.01 respectively). The level of ABAb negatively correlated with sgp130 concentration(r=0.375,P0.05). Conclusion IL-6 signal transduction might be involved in Tourette's syndrome to lead the function enhancementand start cascade of feedback inhibition,because of elevated levels of sIL-6R,sgp130,ANAb and ABAb in serum.Autoimmune-related injuries may potentially explain the pathogenesis of the disease.
The integrating of teaching and learning through technology in high school : learning management systems (LMS) and alternatives

This dissertation is a case study about how teachers use Learning Management Systems (LMS) and teaching technologies in their classes. Research was conducted through interviews, observations, and document analysis. Data analysis yielded fourteen themes and thirty-two codes about teachers’ beliefs and experiences. Codes were validated using triangulation, rich, thick description, and member checking. This research builds on Fishbein and Ajzen’s Theory of Reasoned Action (TRA) and the theory of Diffusion of Innovation (DOI). Data was collected in two high schools and a career center situated within a school district that provided and supported, but did not require the use of, a commercial LMS. All participants use the LMS and other technologies. They were motivated by their positive attitude, subject expertise, longevity in the field, and previous experiences. They also believed that regular use of technology prepares students for the future. Participants self-identified as a minority among teachers in their early adoption of technology confirming the theory of DOI. This study found differences between the LMS use of the extensive and limited users of technology, with the extensive users incorporating a wide variety of tools in unsupported Open Source LMSs. The most extensive users of technology were in science, humanities and IT; limited users, who rely mostly on the district-supported LMS, were in business and health, suggesting different needs in an LMS across the curriculum. This research found that new technologies require time to learn and to use effectively, suggesting that teachers need more time to prepare during school hours as well as more peer mentoring to use the LMS to its fullest extent.
Study on Blood Lead Levels of Children and High-risk Factors in Shenzhen City

Objective To investigate the blood lead levels of children and to identify related risk factors in Shenzhen City. Methods A total number of 834 children aged 1～6 years were selected to test the blood lead levels. Potentially related risk factors of 611 children were collected using questionnaires. 1 568 food from 6 categories were randomly sampled to measure lead contents. A multi-variable linear regression was used to analyze the correlation between blood lead levels and risk factors. Results Blood lead contents of 834 children were from 22 0 μg/L to 197 4 μg/L, with an average value of 90 6 μg/L, of which 317 children(38 01%) were found with a blood lead level≥100 μg/L. The average value of lead contents in food was 0.41 mg/kg and 1 15% of the samples were above the standard. New decoration in family or kindergarten, metal or painted toys, parent smoking, food and commodities with high lead contents were positive correlated with blood lead contents. Conclusion Blood lead Levels of children are correlated with the lead pollution in living environments and undesirable living habits. A comprehensive Intervention program is necessary to control and prevent lead pollution problem in children.
Text Document Classification by using WordNet Ontology and Neural Network

Every day the mass of information available, merely finding the relevant information is not the only task of automatic text classification systems. The main problem is to classify which documents are relevant and which are irrelevant. The Automated text classification consists of automatically organizing clustered data. We propose a method of automatic text classification using Convolutional Neural Network based on the disambiguation of the meaning of the word we use the WordNet ontology and word embedding algorithm to eliminate the ambiguity of words so that each word is replaced by its meaning in suitable context. The closest ancestors of the senses of all the words in a given document are selected as folders for the specified document.
Prompt Image Search with Deep Convolutional Neural Network via Efficient Hashing Code and Addictive Latent Semantic Layer

As we know that the nearest neighbor search is a good and effective method for good-sized image search. This paper indicates a vision learning framework to generate compact binary hash codes for quick vision search after knowing the recent benefits of convolution neural networks (CNN). Our concept is that binary codes can be obtained using a hidden layer to present some latent concepts dominating the class labels with usable data labels. CNN also can be used to learn image representations. Binary code learning is required for other supervised methods. However, our method is effective in obtaining hash codes and image representations and we use pretrained model from googlenet for incremental learning so it is suitable for good-sized dataset. It is demonstrated in our experiment that this method is better than some most advanced hashing algorithms in MINIST, NUS-WIDE and CIFAR-10 dataset. The scalability and efficiency still needs to be further investigated in a good-sized dataset.
Value of three dimensional electroanatomic mapping system in guiding radiofrequency catheter ablation of atrial arrhythmias

Objective To investigate the value and advantage of electroanatomic mapping system(CARTO) in guiding ablation of atrial arrhythmias. Methods Linear ablation was performed in 15 patients with paroxysmal atrial fibrillation(Af) and 10 with persistent Af after reconstruction of left atrium with CARTO. Ablation was performed after left or right atrial activation sequence mapping guided by CARTO in 4 patients with right atrial tachycardia and 3 with left atrial tachycardia. Results Electrical isolation was achieved in 14 patients with paroxysmal Af under sinus rhythm, and in 10 patients with persistent Af and one patient with paroxysmal Af under AF. Atrial tachycardia was converted to sinus rhythm during ablation procedure in all 7 patients. Mean procedure and X-exposure time were 220 min and 48 min for AF ablation, and 86 min and 20 min for atrial tachycardia ablation, respectively. No complication occured in all patients. Conclusion It is safe and effective to treat AF with circumferential isolation of pulmonary veins guided by CARTO. CARTO is useful in designing ablation line of atrial tachycardia, shortening X-ray exposure time and improving successful rate.