nils-herrmann/clean_scirepeval huggingface dataset:
AUI Story Maker: Animation Generation from Natural Language
Venue: AIED
Year: 2018

Various research works have tried to connect Natural Language Processing NLP to computer graphics, as this connection would lay the ground for an automatic generation of computer animations. In this research we aim to provide a novel approach for connecting graphics to NLP by using OpenNLP and the Unity 3D game engine. We rely on two linguistic approaches Vendler's verb classification and Jackendoff's Lexical Conceptual Structure LCS and present how the technology enablers and the linguistic approaches chosen collaborate to provide the animation generation capability. We describe the overall architecture of AUI Story Maker, a system built to illustrate the feasibility of our approach, and discuss the future work required to make it a reliable tool in a modern classroom setting. We also present some writing samples gathered during field work with 1st graders at Al Akhawayn School in Ifrane (ASI) and provide sample outputs of AUI Story Maker.
Chemical Profile and Antioxidant Activity of the Kombucha Beverage Derived from White, Green, Black and Red Tea
Venue: Antioxidants
Year: 2020

Kombucha is a fermented tea beverage prepared as a result of the symbiotic nature of bacterial cultures and yeast, the so called SCOBY (Symbiotic Cultures of Bacteria and Yeasts) Kombucha is characterised by rich chemical content and healthy properties. It includes organic acids, minerals and vitamins originating mainly from tea, amino acids, and biologically active compounds polyphenols in particular. Kombucha is prepared mainly in the form of black tea, but other tea types are increasingly often used as well, which can significantly impact its content and health benefits. This work shows that the type of tea has a significant influence on the parameters associated with the antioxidant potential, pH, as well as the content of acetic acid, alcohol or sugar. Red tea and green tea on the 1st and 14th day of fermentation are a particularly prominent source of antioxidants, especially polyphenols, including flavonoids. Therefore, the choice of other tea types than the traditionally used black tea and the subjection of these tea types to fermentation seems to be beneficial in terms of the healthy properties of kombucha.
Multi Agent Deep Reinforcement Learning for Large Scale Traffic Signal Control
Venue: IEEE Transactions on Intelligent Transportation Systems
Year: 2020

Reinforcement learning (RL) is a promising data driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, the centralized RL is infeasible for large scale ATSC due to the extremely high dimension of the joint action space. The multi agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now, the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state of the art deep RL agent, advantage actor critic (A2C) within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi agent A2C is compared against independent A2C and independent Q learning algorithms, in both a large synthetic traffic grid and a large real world traffic network of Monaco city, under simulated peak hour traffic dynamics. The results demonstrate its optimality, robustness, and sample efficiency over the other state of the art decentralized MARL algorithms.
Phytochemical Composition and Antioxidant Potential of Oven Heated and Microwave Treated Ginger Zingiber officinale
Year: 2018

Cite this article: Nawaz H, Shad MA, Aziz AR, Muntaha ST, Muzaffar S. Phytochemical Composition and Antioxidant Potential of Oven Heated and Microwave Treated Ginger (Zingiber officinale Roscoe) Free Radicals and Antioxidants. 2018;8(2):89 5.  The study was designed to investigate the effect of heating in the simple oven and microwave treatment on phytochemical composition and antioxidant potential of ginger (Zingiber officinale R. a popular food ingredient of medicinal importance.  Dried ginger was ground to fine powder and subjected to heating in the simple oven and microwave oven in an increasing order of heating time (5, 10, 15, 20 and 25 min) The heat treated samples were extracted in methanol and analyzed for their phytochemical composition and antioxidant potential.  A time dependent significant (p<0.05) negative effect of both treatment methods was observed on total phenolic acids (TPA) total tannins content (TTC) total flavonoids content (TFC) total antioxidant activity (TAOA) 2, 2 diphenyl 1 picrylhydrazyl radical scavenging capacity (DPPH RSC) and hydroxyl radical scavenging capacity (HRSC) Both treatments methods showed the significant positive effect on b carotene bleaching capacity (BCBC) reducing power (RP) and iron chelating activity (ICA) TPA and TAOA of oven heated, TTC and RP of microwave treated and TFC, BCBC, and HRSC of ginger varied exponentially while TTC and RP of the oven heated, TPA and TAOA of microwave treated and ICA and DPPH RSC showed linear variation in response to treatment time.  Both heating methods have a significant time dependent effect on the phytochemical composition and antioxidant activity of ginger. This study provides useful information to the manufacturers and consumers regarding the potential benefits of ginger and effect of various heat treatments methods on its medicinal value.
Factorization machines and deep views based co training for improving answer quality prediction in online health expert question answering services
Venue: J. Biomed. Informatics
Year: 2018

In online health expert question answering (HQA) services, it is significant to automatically determine the quality of the answers. There are two prominent challenges in this task. First, the answers are usually written in short text, which makes it difficult to absorb the text semantic information. Second, it usually lacks sufficient labeled data but contains a huge amount of unlabeled data. To tackle these challenges, we propose a novel deep co training framework based on factorization machines (FM) and deep textual views to intelligently and automatically identify the quality of HQA systems. More specifically, we exploit additional domain specific semantic information from domain specific word embeddings to expand the semantic space of short text and apply FM to excavate the non independent interaction relationships among diverse features within individual views for improving the performance of the base classifier via co training. Our learned deep textual views, the convolutional neural networks (CNN) view which focuses on extracting local features using convolution filters to locally model short text and the dependency sensitive convolutional neural networks (DSCNN) view which focuses on capturing long distance dependency information within the text to globally model short text, can then overcome the challenge of feature sparseness in the short text answers from the doctors. The developed co training framework can effectively mine the highly non linear semantic information embedded in the unlabeled data and expose the highly non linear relationships between different views, which minimizes the labeling effort. Finally, we conduct extensive empirical evaluations and demonstrate that our proposed method can significantly improve the predictive performance of the answer quality in the context of HQA services.
Endoscopic Removal of Metallic Airway Stents
Venue: Journal of bronchology interventional pulmonology
Year: 2011

BackgroundMetallic airway stents must be removed when stent related complications cause airway injury or obstruction. This study compared the techniques and complications associated with endoscopic removal of covered metallic stents (CMSs) and uncovered metallic stents (UCMS) MethodsA retrospective analysis of data from 48 patients with airway disorders treated with 40 CMSs and 18 UCMSs, in whom endoscopic stent removal was indicated between January 2005 and January 2010 was carried out. Procedures were performed under general anesthesia with a rigid bronchoscope or local anesthesia with a flexible bronchoscope. The airway wall of 54 stents was pretreated with argon plasma coagulation and/or cryoablation before stent removal. Balloon dilation, argon plasma coagulation, CO2 cryoablation, or chemotherapy was performed to prevent restenoses after the stent removal in selected patients. ResultsOf the stents, 84.5% (49 of 58) were eventually removed, including 36 CMSs and 13 UCMSs; 4 CMSs and 5 UCMSs could not be removed. Strut fracture was more common in UCMSs (77.8% than in CMSs (5% P<0.001) Of patients with CMS, 91.7% underwent successful removal of intact stents, whereas 92.3% of patients with UCMS were removed damaged or piecemeal. In CMS or UCMS, major complications of stent removal included mucosal tear with bleeding and unveiling of significant granulation tissue from behind the stent. No deaths occurred during the procedure. ConclusionsCMSs can be effectively and safely removed by a flexible bronchoscope with a retrieval hook without major sequelae, whereas UCMSs are very difficult to remove, and complications are very common. Metallic stents should be used only as a last resort in patients with airway disorders.
LGBTQ+ EXISTENCE IN INDONESIA: INVESTIGATING INDONESIAN YOUTH'S PERSPECTIVES TOWARDS THE LGBTQ+ COMMUNITY
Year: 2020

The presence of lesbian, gay, bisexual, transgender, and queer communities is a mandatory requirement. Even though the LGBTQ+ community is a group of people concerned about religious belief, Indonesians even now regard LGBTQ+ as transgression and sin. Those who do not consequently approve of same sex marriage. Interestingly, this perception has been disputed because, in reality, others may have started to be open minded and fully accept the prevalence of LGBTQ+ people in LGBTQ+ communities. This study used qualitative research to investigate the perspective of Indonesian younger generations towards the LGBTQ+ community in Indonesia. The researcher used the questionnaire as an instrument for data collection with ten questions required to fulfilled by Indonesian youth to investigate their perspectives. The study's data is collected from Indonesian participants, with a total of was eighty three Indonesian youths between the ages of 15 26 in all around Indonesia. Since the issue of the study is quite sensitive in Indonesia, there are sure of positive and negative perspectives that are also apparent in the result of questionnaires. In a nutshell, the study's aim will further help the authorities take precautions to be incorporated in the future. Also, it is to investigate the Indonesian youths from a different background of beliefs and perspectives toward the LGBTQ+ community. The implication of this research informed young people of the LGBTQ+ subculture to Indonesian parents and teachers as those responsible for educating young kids so that they would not be adversely affected by this social phenomenon.
An Investigation of the Interdependence between Writing in Persian as L1 and English as L2 on Female Intermediate Students: Language Problem or Writing Problem
Year: 2016

English is the leading foreign language enjoying a prestigious position in many countries, including Iran. Many Iranian learners start learning English from first grade of junior high school; some other people send their children to English institutes as early as primary school or even pre school, yet the problem is that most of them have problems in obtaining the satisfactory level of proficiency either in receptive skills (Listening and Reading) or productive skills (Speaking and Writing) or in both. Among the four skills, writing is of great importance. Hence, the current study examined the interdependence between writing in Persian (L1) and English (L2) For this purpose, 30 Iranian EFL learners majoring in English Translation at Islamic Azad University of Qaemshahr were selected. First of all, a TOEFL test was used to homogenize the learners. Then, the participants were asked to write English and Persian compositions on the same topic in narrative, descriptive and expository genres in two separate sessions, first L2 compositions then L1 composition after a two week delay. These writings were scored according to the ESL Composition Profile (Jacobs et al. 1981) by two scorers for each language. Using Pearson product moment correlation, the correlation between L2 proficiency and L1 writing to L2 writing was examined. The outcomes displayed large correlations between L2 proficiency and L2 writing but no correlation between L1 writing and L2 writing. The findings entail some pedagogical implications for improving EFL learners' L2 writing ability through getting more knowledge in English rather than focusing on Persian writing.
Rumor Detection with Hierarchical Recurrent Convolutional Neural Network
Venue: NLPCC
Year: 2019

Automatic rumor detection for events on online social media has attracted considerable attention in recent years. Usually, the events on social media are divided into several time segments, and for each segment, corresponding text will be converted as vectors for various neural network models to detect rumors. During this process, however, only sentence level embedding has been considered, while the contextual information at the word level has been largely ignored. To address that issue, in this paper, we propose a novel rumor detection method based on a hierarchical recurrent convolutional neural network, which integrates contextual information for rumor detection. Specifically, with dividing events on social media into time segments, recurrent convolution neural network is adapted to learn the contextual representation information. Along this line, a bidirectional GRU network with attention mechanism is integrated to learn the time period information via combining event feature vectors. Experiments on real world data sets validate that our solution could outperform several state of the art methods.
Inside Rehab: The Surprising Truth About Addiction Treatment and How to Get Help That Works; and Clean: Overcoming Adiction and Ending America's Greatest Tragedy
Year: 2014

A pair of books that are sharply critical of the addiction treatment industry were released by the popular press in 2013. Clean by David Sheff (2013) and Inside Rehab by Anne M. Fletcher (2013) both address America's addiction epidemic and its treatment. The two books complement each other in several ways. While Sheff presents a sweeping polemic aimed at reforming the way we think about and treat addiction, Fletcher offers a more dispassionate critique of the treatment landscape. As is so often the case for those interested in addiction, both authors were motivated to write about the subject, at least in part, by personal experience: Sheff through being the parent of an addict and Fletcher through her own struggles with alcohol. As Sheff captured so poignantly in his 2008 memoir Beautiful Boy, he is the father of a drug addicted son. Although Fletcher discussed her own struggle with problematic drinking in her 2001 book Sober for Good, she leaves out her personal experience in Inside Rehab and rather maintains the perspective of an objective investigator. Perhaps this is because she never was "inside rehab" during her own recovery. Nevertheless, both she and Sheff offer equally critical descriptions of our current system of care for those struggling with addictive disorders. Both authors use clear, straightforward prose that reveals their anger toward current addiction policy and treatment in the United States. Sheff's primary thesis is that addiction should move swiftly away from being seen as a legal issue toward one that ought to be addressed purely as a medical problem. While many treatment centers, as well as Alcoholics Anonymous (AA) have long subscribed to the "disease model" of addiction treatment and in more recent years the National Institute on Drug Abuse has termed addiction a "brain disease," Sheff does much to help us remove the quotation marks around the word disease. Fletcher, on the other hand, uses such quotation marks, signaling her own skepticism about the legitimacy of
Video enhancement leveraging high quality depth maps
Venue: VRCAI '12
Year: 2012

Using an image based rendering (IBR) algorithm, we can reconstruct and enhance a low quality video by extracting colors from several high quality photographs according to the correspondences. In this paper, we present several improvements for the IBR algorithm to improve the reconstruction faithfulness and temporal coherence of the resultant video. Concretely, we propose to use another state of the art multiview stereo (MVS) method to recover more accurate and highly consistent depths for the video, and adapt it to handle the sparse photographs. Leveraging the consistent video depths, we further propose a simple but effective method to generate more faithful photograph candidates, which also helps to maintain the temporal coherence. The effectiveness of our improvements is verified by achieving the effect of video super resolution. Besides, we improve the structure from motion (SfM) system to process the heterogeneous dataset containing several unordered photographs and a video clip more efficiently for our application.
Selecting Data Augmentation for Simulating Interventions
Venue: ICML
Year: 2021

Machine learning models trained with purely observational data and the principle of empirical risk minimization (Vapnik 1992) can fail to generalize to unseen domains. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels. We find that many domain generalization methods do not explicitly take this spurious correlation into account. Instead, especially in more application oriented research areas like medical imaging or robotics, data augmentation techniques that are based on heuristics are used to learn domain invariant features. To bridge the gap between theory and practice, we develop a causal perspective on the problem of domain generalization. We argue that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels. We demonstrate that data augmentation can serve as a tool for simulating interventional data. We use these theoretical insights to derive a simple algorithm that is able to select data augmentation techniques that will lead to better domain generalization.
Assessing the Measurement Properties of the Principal Instructional Management Rating Scale
Year: 2013

In a recent article, Hallinger (2011b) reviewed 135 empirical studies that had employed the Principal Instructional Management Rating Scale (PIMRS) over the prior three decades. The author concluded that the PIMRS appeared to have attained a consistent record of yielding reliable and valid data on principal instructional leadership. However, given the focus of the review, little detail was provided on measurement results related to the PIMRS instrument.  In light of the extensive past and current use of the PIMRS, the current article seeks to provide a comprehensive and detailed picture of reliability results. More specifically, the authors present a meta analysis of reliability results derived from 52 data sets derived from 43 independent empirical studies in which the PIMRS had been employed for data collection.  The main contribution of this article lies in elaborating on and updating information regarding the measurement properties of the PIMRS, among the most widely used scales in the study and assessment of principal leadership. This information will be useful for researchers who are planning empirical studies of leadership and learning as well as practitioners who have a need for reliable measures of principal instructional leadership.
Current Gender Trends in Belarusian Labor Market: Wage Gap, Child Penalty and Marriage Premium
Year: 2016

The issue of gender equality in Belarus labor market is still unclear. On the one hand the rate of female participation is high. At the same time there is evidence of the rising gender pay gap that grew from 16.5% in 2005 to 24% in 2014. The decomposition of the wage gap during 2005 2014 time period using Oaxaca Blinder, Juhn Murphy Pierce and Machado Mata techniques revealed that differences in income function (the difference in a way efforts of men and women are remunerated) are the main factors affecting the growth of gender inequality, while the personal characteristics are losing the influencing power. The role of the factors differs depending on the quantile of income distribution. I also find no association between wages and marriage for females, while there is a 10.5% wage premium in case of married males. The parenthood wage penalty is attributed to women and equals approximately 14.4% Moreover, children from 0 to 6 provide the most severe drop in wages. In addition, the paper reveals that educated women are suffering the most and face 20.4% decline in wages, while penalty for women with the secondary school or lower amounts to just 8%
A program evaluation of an in school daily physical activity initiative for children and youth
Venue: BMC Public Health
Year: 2018

BackgroundThe school system is one setting in which children's physical activity levels may be increased through daily physical activity (DPA) policies and initiatives. Adherence to DPA policies is typically poor and results are limited in regard to the associated benefits for participating children. Therefore, the purpose of this study was to evaluate a range of psychosocial outcomes following a community led, in school DPA initiative for 9 14 year old children and youth.MethodsThis program evaluation examined the impact of a DPA program consisting of 20 min of teacher led DPA for 20 consecutive weeks. Student outcomes were measured using a questionnaire administered at three time points: baseline, mid intervention, and post intervention. A teacher questionnaire regarding program adherence and student behaviour was completed at post intervention. Mixed effects models were used to test for intervention effects, with random intercepts for students, classes, and schools, as well as fixed effects for age and sex. Due to the large number of outcomes measured, we first conducted an omnibus test of the intervention effect followed by three exploratory analyses examining each outcome separately, associations between outcomes and program adherence, and results from the teacher survey.ResultsThirty classes (N 19 experimental) from 7 schools participated in the study, with a total of 362 children (n 265 experimental) There was no significant overall effect of the intervention (z 0.89, p 0.38) and the exploratory analyses demonstrated significant differences only for self esteem and subjective happiness, with the control group slightly increasing relative to the experimental group. Teacher reported adherence to the program was poor with only 21% of teachers adhering to the program. There was no association between overall adherence and student reported outcomes; however, positive correlations were present between adherence and teacher reported student behaviour.ConclusionsThe DPA program evaluated here did not improve the psychosocial well being of elementary school aged children more than usual practices. However, adherence to the program was poor and it did not have a negative effect on the students. Future work is needed on how best to support DPA implementation in the context of the school day and how student well being may be positively impacted through school based physical activity.