nils-herrmann/clean_scirepeval huggingface dataset:
Designing an iPad App to Monitor and Improve Classroom Behavior for Children with ADHD: iSelfControl Feasibility and Pilot Studies
Venue: PloS one

Children with Attention Deficit/Hyperactivity Disorder (ADHD) receive approximately 80% of instruction in the general education classroom, where individualized behavioral management strategies may be difficult for teachers to consistently deliver. Mobile device apps provide promising platforms to manage behavior. This pilot study evaluated the utility of a web based application (iSelfControl) designed to support classroom behavior management. iSelfControl prompted students every 'Center' (30 minutes) to self evaluate using a universal token economy classroom management system focused on compliance, productivity, and positive relationships. Simultaneously, the teacher evaluated each student on a separate iPad. Using Multi Level Modeling, we examined 13 days of data gathered from implementation with 5th grade students (N 12) at a school for children with ADHD and related executive function difficulties. First, an unconditional growth model evaluated the overall amount of change in aggregated scores over time as well as the degree of systematic variation in scores within and across teacher student dyads. Second, separate intercepts and slopes were estimated for teacher and student to estimate degree of congruency between trajectories. Finally, differences between teacher and student scores were tested at each time point in separate models to examine unique 'Center' effects. 51% of the total variance in scores was attributed to differences between dyads. Trajectories of student and teacher scores remained relatively stable across seven time points each day and did not statistically differ from each other. On any given day, students tended to evaluate their behaviors more positively (entered higher scores for themselves) compared to corresponding teacher scores. In summary, iSelfControl provides a platform for self and teacher evaluation that is an important adjunct to conventional classroom management strategies. The application captured teacher/student discrepancies and significant variations across the day. Future research with a larger, clinically diagnosed sample in multiple classrooms is needed to assess generalizability to a wider variety of classroom settings.
The modulation of tumor vessel permeability by thalidomide and its impacts on different types of targeted drug delivery systems in a sarcoma mouse model.
Venue: Journal of controlled release official journal of the Controlled Release Society

The transport of nanocarriers is supposed to be based on EPR effect which is affected by diverse factors, so the modulation of EPR effect seems very significant for nanocarriers including targeted drug delivery systems (TDDSs) Besides, it is extremely unclear how the EPR effect impacts the fate of different types of TDDSs. To make the most advantage of EPR effect for TDDSs, it is definitely necessary to clarify these key issues. Here, we construct and characterize various TDDSs, including sterically stabilized liposomes (SSL) RGD functionalized SSL (RGD SSL) and novel 7PEP functionalized SSL (7PEP SSL) loaded with doxorubicin (DOX) DIR or DID. Here, we modulate the permeability of tumor vessels by thalidomide (THD) in a sarcoma bearing EPR mouse model via monitoring endogenous deoxygenated hemoglobin in circulation, and then we confirm the effect of THD on tumor vessel permeability by vessel density, vessel maturity, VEGF expression and so on. Importantly, we investigate and find the impacts of EPR effect on the antitumor efficacy, in vivo distribution and intratumoral microdistribution of the three TDDSs. Interestingly, the EPR effects affect different TDDSs differently. The elevated EPR effect enhances the tumor accumulation of SSL and RGD SSL but fails to increase their efficacy. The RGD SSL exhibits the best efficacy with the least fluctuation, demonstrating the advantage of angiogenesis targeted systems. 7PEP SSL seems the biggest beneficiary of EPR effect, suggesting the significance of EPR modulation for cells targeted systems. Generally, this study demonstrates the feasibility of modulating EPR effect bidirectionally by THD as well as the impacts of EPR effect on different type of testing TDDSs based on this animal model. It certainly provides novel insight into the design and potential use of TDDSs.
Internet Talent Training Base Construction for Integration of Medical Treatment, Nursing and Elderly Care

With the advent of the global aging society, the demand of the global high quality team of old age care professionals is increasing. Therefore, we constructed an Internet talent training base of integration of medical treatment, nursing and elderly care so as to provide a good platform for students in medical schools with gerontology and geriatric nursing theory teaching and practical training, and as well as standardized professional training platform for the nursing staffs working in nursing institutions for the aged and medical staffs working in elderly medical institutions. By combining theoretical teaching and practical teaching together, practical training base and practice base together, academic education and vocational education together, multiple training ways are applied to develop multilevel elderly medical workers with the Internet background, providing a new thought for the cultivation of elderly medical qualified personnel in the Internet age, and also providing a new way for the employment of higher vocational students majoring in medical science, which enjoys a hopeful prospect.
Convolutional Neural Network based sentiment analysis using Adaboost combination
Venue: 2016 International Joint Conference on Neural Networks (IJCNN)

Sentimental polarity detection has long been a hot task in natural language processing since its applications range from product feedback analysis to user statement understanding. Recently a lot of machine learning approaches have been proposed in the literature, e.g. SVM, Naive Bayes, recursive neural network, auto encoders and etc. Among these different models, Convolutional Neural Network (CNN) architecture have also demonstrated profound efficiency in NLP tasks including sentiment classification. In CNN, the width of convolutional filter functions alike number N in N grams model. Thus, different filter lengths may influence the performance of CNN classifier. In this paper, we want to study the possibility of leveraging the contribution of different filter lengths and grasp their potential in the final polarity of the sentence. We then use Adaboost to combine different classifiers with respective filter sizes. The experimental study on commonly used datasets has shown its potential in identifying the different roles of specific N grams in a sentence respectively and merging their contribution in a weighted classifier.
Characterization of Linear Low Density Polyethylene/Rambutan Peels Flour Blends: Effect of Loading Content

The effect of rambutan peels flour (RPF) content on the tensile properties of linear low density polyethylene filled with rambutan peel flour was studied. RPF was melt blended with linear low density polyethylene (LLDPE) LLDPE/RPF blends were prepared by using twin screw extruder at 150degC with the flour content ranged from 0 to 25 wt% The tensile properties were tested by using a universal testing machine (UTM) according to ASTM D638. The highest tensile strength was pure LLDPE meanwhile the tensile strength LLDPE/RPF decreased gradually with the addition of rambutan peels flour. Young's modulus of rambutan peels flour filled LLDPE increased with increasing fiber loading. The crystallinity of the blends was significantly reduced with increasing RPF content. Instead, the water absorption increased with the addition of RPF content.
Characterization of the Rambutan Peels Fiber (nephelium Lappaceum) as a Lignocellulosic Material for Technological Applications

Characterization of the Rambutan Peel Fiber (Nephelium lappaceum) as a Lignocellulosic Material for Technological Applications Emanoel I. S. Oliveira* Jean B. Santos, Ana Paula B. Goncalves, Silvana Mattedi, Nadia M. Jose Federal University of Bahia, Polytechnic School. Aristides Novis Street, 02, Campus Federacao. Zip code 40210 630 Federal University of Bahia, Institute of Chemistry. Barao de Jeremoabo Street, 147, Campus Ondina. Zip code 40170 115 eigor_oliv@hotmail.com
Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP) The data for analysis and model development was collected at 28 56 and 91 day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.
A review of the influence of source material's oxide composition on the compressive strength of geopolymer concrete

Off late, geopolymer concrete has gained significant attention in the construction industry because of the benefits that it brings via, by product waste utilization, reduction in greenhouse gas emission. Studies reveal that the chemical oxide composition of the raw material (viz. fly ash) strongly influences the mechanical behavior and durability properties of geopolymer concrete. However, not many studies have paid attention towards the influence of an oxide percentage in the raw material on the compressive strength of the geopolymer concrete. In this paper, an attempt has been made to study the compressive strength behavior against the percentage of oxides (viz. SiO2, Al2O3, Fe2O3, CaO etc. present in the raw material which were employed in the production of geopolymer concrete. In this extensive data has been collected from various earlier research publications. Trends for 7 28 day compressive strengths against individual oxide component percentages were developed, and it was observed that the strength of geopolymer concrete differs greatly with the variation in percentage of the individual oxide component. Also, each oxide has shown distinct influence on the compressive strength of geopolymer concrete. Further, it has been noticed that the compressive strength of a sample has been predominantly influenced by the percentage of alumina silicate oxides, and whereas oxides like CaO and Fe2O3 even though lesser in amount compared to alumina silicate oxides have shown a distinctive effect on the strength built up. Also, the oxide molar ratios influence on the compressive strengths has been analysed and it was noticed that compared to individual oxide composition its influence is not major on the compressive strength development. Therefore, knowing the typical range of the major oxides percentage required for achieving superior compressive strength will be beneficial in developing concrete mix proportion.
Test research on the effects of mechanochemically activated iron tailings on the compressive strength of concrete

High silicon iron tailings, which have been mechanochemically activated, was used for the preparation of concrete as a supplementary cementing material to substitute cement (by 10% 20% 30% and 40% respectively) based on their composition, particle sizes and pozzolanic activity. With the aid of tests, this paper discusses the effects of the tailings on the compressive strength of concrete. Tests show that with an increase in substitution rate of cement in the tailings, the compressive strength of concrete tends to decrease, and when the substitution rate is at 10% 20% and 30% respectively, the compressive strength of concrete is measured up to the design requirement; if an appropriate amount of water reducing admixture is added while cement is substituted by tailings with a substitution rate of 10% 20% 30% and 40% respectively, the compressive strength of concrete is also measured up to the design requirement. The research result demonstrates that as far as the compressive strength concerned, it is feasible to use mechanochemically activated high silicon iron tailings as a supplementary cementing material to partly substitute cement in concrete.
ASPEK PERKEMBANGAN MOTORIK DAN HUBUNGANNYA DENGAN ASPEK FISIK DAN INTELEKTUAL ANAK

Perkembangan motorik adalah proses tumbuh kembang kemampuan gerak seorang anak. Pada dasarnya, perkembangan ini berkembang sejalan dengn kematangan saraf dan otot anak. Sehingga, setiap gerakan sesederhana apapun, adalah merupakan hasil pola interaksi yang kompleks dari berbagai bagian dan system dalam tubuh yang dikontrol oleh otak. Adapun tujuan penulisan ini adalah agar orang tua memahami dan dapat meningkatkan perhatiannya kepada anak. Setelah dikaji secara teori, maka dapat disimpulkan bahwa motorik anak perlu dilatih agar dapat berkembang dengan baik. Perkembangan motorik anak berhubungan erat dengan kondisi fisik dan intelektual anak.  Perkembangan Motorik, Fisik dan Intelektual Anak.
Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures
Venue: IEEE Transactions on Vehicular Technology

With the emergence of ever growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs) and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC) which is an architecture that utilizes a collaborative multitude of end user clients or near user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.
Antimicrobial susceptibility pattern and plasmid mediated antibacterial resistance in Staphylococcus aureus and coagulase negative staphylococci

Staphylococcal infections constitute problems to health care institutions. Its resistance to antibiotic has been associated with resistant plasmids (R plasmid) that have the ability to mediate the production of drug inactivated enzymes such as a lactamase.  Forty five Staphylococcus aureus (S. aureus) and 15 Coagulase negative Staphylococci (CoNS) were isolated from clinical samples and isolates subjected to antibiotic susceptibility testing, plasmid curing and plasmid DNA isolation.  The highest percentages isolates were recovered from urine samples and the least from high vagina swab and wound swab for S. aureus and CoNS respectively. The antibiogram showed that majority of S. aureus (95.6% was resistant to cefuroxime and ceftazidime, while CoNS (93.3% were more resistance to cefuroxime and gentamycin. (p=0.17) 24.4% S. aureus and 20% CoNS were resistant to more than 5 antibiotics with multiple antibiotic resistances (MAR) index of 0.33 to 0.89 for S. aureus and 0.56 to 0.78 for CoNS. Most of the S. aureus and CoNS were cured of their resistant markers showing that their R markers may have been borne on plasmid. However, resistance to ciprofloxacin and ceftriaxone for CoNS may have been borne on chromosome as all the resistant markers were not cured. The percentage of S. aureus and CoNS cured of their R plasmid were similar (p=0.25) Plasmids ranging from 2.03kbp to 23.13kbp were harboured by both S. aureus and CoNS.  There is widespread antimicrobial resistance patterns and diverse plasmid profile of S. aureus and CoNS in this study. This data will be a useful baseline for further epidemiological investigations.  Plasmid, Staphylococcus susceptibility, antimicrobials, resistance, curing
JOB SATISFACTION IN HOTEL EMPLOYEES: A SYSTEMATIC REVIEW OF THE LITERATURE

Hotel employees' job satisfaction is very important to their performance. In a systematic review of the previous literature on hotel employees, it was found that the most studied variable was job satisfaction; there were no other systematic reviews on the topic. In this review, performed in the Web of Knowledge, Web of Science ( Medline) and Science Direct between 2000 and 2014, 51 studies were found. These studies indicated that satisfaction is crucial to the financial performance and prosperity of hotels and acts a mediator in customer satisfaction. Contributing to greater satisfaction are factors such as greater autonomy and independence, greater power of decision making, flexible schedules, better working conditions, and training. The factors that promote dissatisfaction are wages and reduced benefits. The studies also indicated that a higher level of job satisfaction can have a direct impact on increasing the financial performance of the hotel. The implications of this study for hoteliers and directors relates to the creation of adequate working conditions to increase job satisfaction and provide hotel employees with a greater sense of subjective well being.
Generating Abstractive Summaries of Sports Games from Japanese Tweets
Venue: 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI AAI)

Microblogs are one of the most important resources for natural language processing. In this paper, we deal with a summarization task of sports events on Twitter. Although many researchers have proposed methods to summarize tweets about sports events, the methods were based on extractive approaches. We focus on an abstractive approach to generate a better summary as compared with the extractive approaches. First, our method detects burst situations in which many users post tweets when a sub event in a game occurs. Tweets in the burst situations are the inputs of our method. Next, it extracts sub event elements (SEEs) that contain actions in a game, such as "Player A made a pass to Player B" and "Player B made a shot on goal." Then, it identifies the optimal order of the extracted SEEs by using a scoring method. Finally, it generates an abstractive summary on the basis of the ordered SEEs, such as "Playler B made a shot on goal from the Player A's pass." In the experiment, we show the effectiveness of our method as compared with related work based on an extractive approach.
Summarizing Situational and Topical Information During Crises
Venue: ArXiv

The use of microblogging platforms such as Twitter during crises has become widespread. More importantly, information disseminated by affected people contains useful information like reports of missing and found people, requests for urgent needs etc. For rapid crisis response, humanitarian organizations look for situational awareness information to understand and assess the severity of the crisis. In this paper, we present a novel framework (i) to generate abstractive summaries useful for situational awareness, and (ii) to capture sub topics and present a short informative summary for each of these topics. A summary is generated using a two stage framework that first extracts a set of important tweets from the whole set of information through an Integer linear programming (ILP) based optimization technique and then follows a word graph and concept event based abstractive summarization technique to produce the final summary. High accuracies obtained for all the tasks show the effectiveness of the proposed framework.
Coherent continuous wave dual frequency high Q external cavity semiconductor laser for GHz THz applications.
Venue: Optics letters

We report a continuous wave highly coherent and tunable dual frequency laser emitting at two frequencies separated by 30 GHz to 3 THz, based on compact III V diode pumped quantum well surface emitting semiconductor laser technology. The concept is based on the stable simultaneous operation of two Laguerre Gauss transverse modes in a single axis short cavity, using an integrated sub wavelength thick metallic mask. Simultaneous operation is demonstrated theoretically and experimentally by recording intensity noises and beat frequency, and time resolved optical spectra. We demonstrated a >80 mW output power, diffraction limited beam, narrow linewidth of <300 kHz, linear polarization state >45 dB) and low intensity noise class A dynamics of <0.3% rms, thus opening the path to a compact low cost coherent GHz to THz source development.
Sequence Level Training with Recurrent Neural Networks
Venue: ICLR

Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous words and some context such as an image. However, at test time the model is expected to generate the entire sequence from scratch. This discrepancy makes generation brittle, as errors may accumulate along the way. We address this issue by proposing a novel sequence level training algorithm that directly optimizes the metric used at test time, such as BLEU or ROUGE. On three different tasks, our approach outperforms several strong baselines for greedy generation. The method is also competitive when these baselines employ beam search, while being several times faster.