Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of cooperative art education in elementary art classrooms

The purpose of this study is to Investigate how elementary art teachers conduct cooperative art experiences in their classrooms and why they consider these experiences important. Qualitative methodology was used to conduct a study of cooperative art activities being taught in pre K-5th grade schools in Georgia, U.S.A. A survey of 135 elementary art teachers was conducted. The survey focused on cooperative art experiences, teachers' attitudes towards the experiences, and how cooperative experiences were included in art education curriculum. Interviews were conducted with nine elementary art teachers and two pre-service art teachers who included cooperative art experiences in their curricula.
Personality Traits and the Expression Area of Synthetic House-Tree-Person Drawings in Early Adolescent Japanese

This study surveyed the expression areas of the Synthetic House-Tree-Person drawing test (S-HTP test, Mikami, 1995) for Japanese early adolescents. The S-HTP test is a projective method in which subjects are asked to draw a house, tree, and person. The expression area is defined as the area of each drawn item, such as the house, tree or person. The participants consisted of 186 Japanese junior high school students and their S-HTP drawings were analyzed using path analysis. The relationships between the expression areas of each item in the test and the students’ personality traits were examined. The personality traits were measured using the Five-Factor Personality Inventory for Children (FFPC, Soga, 1999). The results show that personality traits of high conscientiousness were associated with larger houses (p < .10) and trees (p < .10). In addition, higher scores on openness to experience (p < .01) and on agreeableness (p < .05) correlate with bigger person figures as their size, whereas higher scores on neuroticism correlate with smaller figures as their size (p < .01). The findings also indicate that the total fitness of the model was sufficient (CFI = .984, RMSEA = .021). These findings may aid the development of useful criteria for future psychological assessments.
The future of food and agriculture: Trends and challenges

The purpose of this report is to increase understanding of the nature of the challenges that agriculture and food systems are facing now and will face into the 21st century. Its analysis of 15 global trends provides insights into what is at stake and what needs to be done. Most of the trends are strongly interdependent and, combined, inform a set of 10 challenges to achieving food security and nutrition for all and making agriculture sustainable. ‘Business-as-usual’ is not an option. Major transformations in agricultural systems, rural economies and natural resource management will be needed if we are to realize the full potential of food and agriculture to ensure a secure and healthy future for all people and the entire planet.
Wet-wrap therapy with halometasone cream for severe adult atopic dermatitis

ABSTRACT Background: Currently there is no cure for severe atopic dermatitis (AD). Wet-wrap therapy (WWT) has also been used to treat pediatric AD and shown a satisfactory effectiveness, but clinical evidence supporting the use of WWT on severe adult AD is still insufficient. Methods: Twelve patients (eight men and four women) with severe AD (SCORing Atopic Dermatitis [SCORAD] score ≥ 50) treated between January 2015 and September 2017 in our hospital were included. The patients underwent WWT (daily dose: 15 g halometasone cream+100g Vaseline ointment) twice daily for 2 h/session for 7 days. SCORAD, visual analog scale (VAS) for pruritus, investigator’s global assessment (IGA), dermatology life quality index (DLQI) scores and serum cortisol levels were determined before and after the WWT. Results: The patients (mean age: 58.9 ± 18.9 years; range: 27–85 years) had a median disease duration of 27.5 months. After the WWT, the average scores of SCORAD (28.79 ± 5.16 vs. 68.59 ± 8.61, 95%CI: 35.18–44.42), VAS (2.75 ± 0.62 vs. 7.5 ± 1.17, 95%CI: 4.14–5.36), IGA (1.83 ± 0.39 vs. 4.08 ± 0.51, 95%CI: 1.96–2.54), and DQLI score (8.33 ± 1.83 vs. 13.83 ± 2.79, 95%CI: 4.16–6.84) reduced significantly compared with the scores before the WWT (All P < 0.001). However, serum cortisol levels were not affected significantly by the WWT. Four patients complained of tolerable wet dressing-associated discomforts, which was resolved after the wet dressing was removed when the WWT was completed. For the 85-year-old man, serum cortisol levels were lower than the normal value after the WWT (3.67 μg/dL) but restored to the normal levels (13.44 μg/dL) 2 weeks after the WWT was ended. No other adverse events occurred. Conclusion: WWT can relieve pruritus, reduce skin lesions, and improve quality of life in adult patients with severe AD. Thus, WWT may be effective and safe for severe adult AD. Trial registration No. is ChiCTR1800014909 (http://www.chictr.org.cn/index.aspx).
Modélisation Planaire pour un RGB-D SLAM: Localisation éparse et Cartographie réduite. (Planar modeling for an RGB-D SLAM : sparse localisation and reduced mapping)

Cette these traite du probleme de la Localisation et Cartographie Simultanees (SLAM) dans les environnements d’interieur. Dans ce contexte, nous avons choisi un SLAM visuel en utilisant les donnees d’un capteur RGB-D de type Kinect pour estimer la trajectoire de la camera et construire une carte 3D de l’environnement en temps reel. Malgre les avantages des cameras RGB-D (faible cout, images couleurs et cartes de profondeur), les donnees de profondeur issues de ce genre de capteur peuvent etre de mauvaise qualite ce qui affecte l’estimation de la pose. En outre, la taille des nuages de points engendre une carte globale lourde et contenant de nombreux points 3D redondants. Afin de diminuer l’impact de ces faiblesses sur la resolution du SLAM, nous proposons d’utiliser des plans 3D, majoritaires dans les scenes d’interieur, dans le processus d’estimation de poses de la camera pour construire des cartes 3D basees-plans.Les plans 3D servent alors a generer des points d’interet 3D planaires moins bruites que les points bruts deduits directement des nuages de points. En rectifiant les valeurs de profondeur des points d’interet 3D bruts appartenant a ces plans, nous ameliorons ainsi l’estimation de pose quand la scene est composee essentiellement de plans. Par la suite, les plans 3D detectes sont utilises pour construire une carte 3D globale legere. La carte est elaboree en fusionnant iterativement les regions planaires detectees dans la scene avec celles deja presentes dans la carte ou en ajoutant de nouveaux plans. Contrairement a la representation classique basee point,nous reduisons ainsi la taille de la carte 3D et construisons des cartes compactes. Ces cartes sont exploitables par des applications de robotique mobile et de navigation. Pour montrer les benefices des travaux proposes dans cette these, les experimentations realisees evaluent la precision de la localisation, l’influence de l’echantillonnage des donnees RGB-D sur la detection des plans ainsi que la qualite de la carte basee-plans 3D par rapport a la scene reelle. La carte ainsi constituee de plans presente une premiere etape vers une carte plus semantique.
EuroGames16: Evaluating Change Detection in Online Conversation

We introduce the challenging task of detecting changes from an online conversation. Our goal is to detect signiﬁcant changes in, for example, sentiment or topic in a stream of messages that are part of an ongoing conversation. Our approach relies on ﬁrst applying linguistic preprocessing or collecting simple statistics on the messages in the conversation in order to build a time series. Change point detection algorithms are then applied to identify the location of signiﬁcant changes in the distribution of the underlying time series. We present a collection of sport events on which we can evaluate the performance of our change detection method. Our experiments, using several change point detection algorithms and several types of time series, show that it is possible to detect salient changes in an on-line conversation with relatively high accuracy.
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events

Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from heterogeneous, high-volume text streams remains a significant challenge. This work describes a corpus of German-language documents which has been annotated with fine-grained geo-entities, such as streets, stops and routes, as well as standard named entity types. It has also been annotated with a set of 15 traffic- and industry-related n-ary relations and events, such as accidents, traffic jams, acquisitions, and strikes. The corpus consists of newswire texts, Twitter messages, and traffic reports from radio stations, police and railway companies. It allows for training and evaluating both named entity recognition algorithms that aim for fine-grained typing of geo-entities, as well as n-ary relation extraction systems.
Improving CAD performance in detecting masses depicted on prior images

We investigated a new approach to improve the performance of a computer-aided detection (CAD) scheme in identifying masses depicted on images acquired earlier ("prior"). The scheme was trained using a dataset with simulated mass features. From a database with images acquired during two consecutive examinations, 100 locations matched pairs of malignant mass regions were selected in both the “current” and the most recent “prior” images. While reviewing the current images, mass regions were identified and as a result biopsies were ultimately performed. Prior images were not identified as suspicious by radiologists during the original interpretation. The same number of false-positive regions was also selected in both current and prior images. The selected regions were then randomly divided into training and testing datasets with 50 true-positive and 50 false-positive regions in each. For each selected region, five features; area, contrast, circularity, normalized standard deviation of radial length, and conspicuity; were computed. The ratios of the average difference of five feature values between current and prior mass regions in the training datasets were also computed. Multiplying these ratios by the computed values in current mass regions, we generated a new dataset of simulated features of “prior” mass regions. Three artificial neural networks (ANN) were trained. ANN-1 and ANN-2 were trained using training datasets of current and prior regions, respectively. ANN-3 was trained using simulated “prior” dataset. The performance of three ANNs was then evaluated using the testing dataset of prior images. Areas under ROC curves (Az) were 0.613 ± 0.026 for ANN-1, 0.678 ± 0.029 for ANN-2, and 0.667 ± 0.029 for ANN-3, respectively. This preliminary study demonstrated that one could estimate an average change of feature values over time and "adjust" CAD performance for better detection of masses at an earlier stage.
Correlative Users travel Pattern Discovery from Smart card Data

: Smart card transactions capture rich information of human mobility and urban dynamics, therefore are of particular interest to urban planners and location-based service providers. This provides the opportunity to discover valuable knowledge from these transaction records. In recent years, research on measuring user similarity for behaviour analysis has attracted a lot of attention in applications such as recommendation systems, crowd behavior analysis applications, and numerous data mining tasks. In this paper, our goal is to estimate the similarity between users’ travel patterns according to their travel smart card data. The core of our proposal is a novel user similarity measurement, namely, Travel Spatial-Temporal Similarity (TST), which measures the spatial range and temporal similarity between users. Moreover, we also propose a hybrid index structure, which integrates inverted files and cluster-based partitioning, to allow for efficient retrieval of the top-K most similar users. Through experimental evaluation, our proposed approach is shown to deliver scalable performance.
Investigating the Effect of Momentum Strategies on Investment Success in the Iran Stock Market

One of the most challenging observations in the capital market is that in contrary to the work market hypothesis, the regular portfolio’s returns shows specific behaviors in different time periods and therefore it’s possible to acquire higher returns than the market by following the investment strategy compatible with the intended time horizon. George and Hwang (2004) show that a stock’s 52-week high price explains the momentum effect and that a strategy based on closeness to the 52- week high has better forecasting power for future returns than those strategies based on past returns. Cahan shows that absolute 52high price is better than 52high momentum for forecasting power for future. We demonstrate that the 52-week high and absolute 52high momentum strategies are robust in Iran Stock Market(ISM) over the period 2004–2008. Our sample exhibit statistically significant profits when implementing this 52-week high for 3, 6 and 12 month holding periods and 6 month holding period for absolute 52high momentum strategy. Then we measure its investment performance on the basis of the Fama and French 3-Factor to measure incremental performance. Our findings show that the 52-week high strategy generates significant, positive risk-adjusted returns within the framework of the Fama/French 3-Factor Model.
Importance of the surface size distribution of erodible material: an improvement on the Dust Entrainment And Deposition (DEAD) Model

Abstract. This paper is based on dust aerosol cycle modelling in the atmospheric model ALADIN (Aire Limitee Adaptation dynamique Developpement InterNational) coupled with the EXternalised SURFace scheme SURFEX. Its main goal is to create an appropriate mineral dust emission parameterization compatible with the global database of land surface parameters ECOCLIMAP, and the Food and Agriculture Organization (FAO) soil type database in SURFEX. An improvement on the Dust Entrainment And Deposition scheme (DEAD) is proposed in this paper by introducing the geographical variation of surface soil size distribution, the Marticorena and Bergametti (1995) formulation of horizontal saltation flux and the Shao et al. (1996) formulation of sandblasting efficiency α. To show the importance of the modifications introduced in the DEAD, both sensitivity and comparative studies are conducted in 0 dimensions (0-D) and then in 3 dimensions (3-D) between the old DEAD and the new DEAD. The results of the 0-D simulations indicate that the revised DEAD scheme represents the dust source emission better, particularly in the Bodele depression, and provides a reasonable friction threshold velocity. In 3-D simulations, small differences are found between the DEAD and the revised DEAD for the simulated Aerosol Optical Depth (AOD) compared with the AErosol RObotic NETwork (AERONET) photometer measurements available in the African Monsoon Multidisciplinary Analyses (AMMA) databases. For the surface concentration, a remarkable improvement is noted for the revised DEAD scheme.
Recognition method of lung nodules using blood vessel extraction techniques and 3D object models

In this paper, we propose a method for reducing false positives in X-ray CT images using ridge shadow extraction techniques and 3D geometric object models. Suspicious shadows are detected by our variable N-quoit (VNQ) filter, which is a type of mathematical morphology filter. This filter can detect lung cancer shadows with the sensitivity over 95[%], but it also detects many false positives which are mainly related to blood vessel shadows. We have developed two algorithms to distinguish lung nodule shadows from blood vessel shadows. In the first algorithm, the ridge shadows, which come from blood vessels, are emphasized by our Tophat by Partial Reconstruction filter which is also a type of mathematical morphology filter. And then, the region of the ridge shadow is extracted using binary distance transformation. In the second algorithm, we propose a recognition method of nodules using 3D geometric lung nodule and blood vessel models. The anatomical knowledge about the 3D structures of nodules and blood vessels can be reflected in recognition process. By applying our new method to actual CT images (37 patient images), a good result has been acquired.
Annotating If the Authors of a Tweet are Located at the Locations They Tweet About

The locations in a tweet do not always indicate spatial information involving the author of the tweet. In this paper, we investigate whether authors are located or not located in the locations they tweet about, and temporally anchor this spatial information in the tweet timestamp. Speciﬁcally, we work with temporal tags centered around the tweet timestamp: longer than 24 hours before or after tweeting, within 24 hours before or after tweeting, and at the time of tweeting. We introduce a corpus of 1,200 location mentions from 1,062 tweets, discuss several annotation samples, and analyze annotator disagreements.
Functional feature subspace mapping of fMRI data in the spectral domain

We propose a new method for the analysis of functional magnetic resonance imaging (fMRI) which is called functional feature subspace mapping (FFSM). We mainly focused on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the spectral domain. Then the subspace is obtained through the dimension reduction technique. Finally, the presence of activated time series is identified by the clustering method. Experiments with simulated data and the real human experiments are conducted to demonstrate that the algorithm we proposed is feasible. Although we focus on analyzing periodic fMRI data, the approach could be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the spectral analysis with a wavelet analysis.
Tumor-associated intronic editing of HNRPLL generates a novel splicing variant linked to cell proliferation

Processing of the eukaryotic transcriptome is a dynamic regulatory mechanism that confers genetic diversity, and splicing and adenosine to inosine (A-to-I) RNA editing are well-characterized examples of such processing. Growing evidence reveals the cross-talk between the splicing and RNA editing, but there is a paucity of substantial evidence for its mechanistic details and contribution in a physiological context. Here, our findings demonstrate that tumor-associated differential RNA editing, in conjunction with splicing machinery, regulates the expression of variants of HNRPLL, a gene encoding splicing factor. We discovered an HNRPLL transcript variant containing an additional exon 12A (E12A), which is a substrate of ADAR1 and ADAR2. Adenosine deaminases acting on RNA (ADAR) direct deaminase-dependent expression of the E12A transcript, and ADAR-mediated regulation of E12A is largely splicing-based, and does not affect the stability or nucleocytoplasmic distribution of the transcript. Furthermore, ADAR-mediated modification of exon 12A generates an enhancer for the oncogenic splicing factor SRSF1 and consequently promotes the frequency of alternative splicing. Gene expression profiling by RNA-seq revealed that E12A acts distinctly from HNRPLL and regulates a set of growth-related genes, such as cyclin CCND1 and growth factor receptor TGFBR1. Accordingly, silencing E12A expression leads to impaired clonogenic ability and enhanced sensitivity to doxorubicin, thus highlighting the significance of this alternative isoform in tumor cell survival. In summary, we present the interplay of RNA editing and splicing as a regulatory mechanism of gene expression and also its physiological relevance. These findings extend our understanding of transcriptional dynamics and provide a mechanistic explanation to the link of RNA editors to tumorigenesis.
A Legal Perspective on Training Models for Natural Language Processing

A significant concern in processing natural language data is the often unclear legal status of the input and output data/resources. In this paper, we investigate this problem by discussing a typical activity in Natural Language Processing: the training of a machine learning model from an annotated corpus. We examine which legal rules apply at relevant steps and how they affect the legal status of the results, especially in terms of copyright and copyright-related rights.
On the performance of actuators with harmonic drive speed reducers

Harmonic Drives (HD) are commonly used in robotics because of zero-backlash and compactness. This makes them ideal for several mechatronic applications. Despite their wide use, there is no compelling literature on their performance, and to date it is not clear how they fare in a complete actuation setup. In this work we evaluate the joint structure we currently install on the iCub humanoid robot comprising an HD speed reducer showing that their efficiency is not optimal.
Biomimetic Superhydrophobic Hollowed-Out Pyramid Surface Based on Self-Assembly

In this paper, we present a periodic hollowed-out pyramid microstructure with excellent superhydrophobicity. In our approach, T-topping pillars and capillary-induced self-assembly methods were combined with the photolithography process to fabricate a hollowed-out pyramid structure. First, a wideband ultraviolet source without a filter was used to fabricate the T-topping pillars during the exposure process; then, the evaporation-induced assembly collapsed the pillars and formed the hollowed-out pyramid structure. Scanning electron microscopy images showed the microstructures of the prepared surface. The contact angle of the surface was 154°. The surface showed excellent high temperature and ultraviolet irradiation tolerance, and the contact angle of the surface barely changed when the temperature dropped. This excellent environmental durability of our superhydrophobic surface has potential applications for self-cleaning and friction drag reduction under water.
High-accuracy time-variant recommender system with Number-Score curve

Recommender systems are becoming more effective as information becomes extensive. Conventional recommender systems show a list of recommended items, but the number of recommended items shown must be limited in some systems. For example, the recommender system for TV programs in a hard disk recorder has only a limited user interface and hardware resources, such as a remote controller, low display resolution, and limited hard disk capacity. Moreover, recommended TV programs must be changed in accordance with the time and day in this system. Thus, for these “time-variant” recommender systems, items must be recommended as accurately as possible within the limited number of recommended items. In this paper, we propose a high-accuracy time-variant recommender system with a “Number-Score curve”, which indicates the relationship between the recommendation score and the number of recommended items. We examine a recommender system for TV programs in the hard disk recorder that can achieve 82% precision and 78% recall for the recommended results with the Number-Score curve.