nils-herrmann/clean_scirepeval huggingface dataset:
A new experience of online education under the COVID 19 pandemic for occupational therapy students in Palestine
Year: 2020

There are circumstances and conditions that stimulate thinking and research in a scientific and practical way. These conditions fit into our reality under the COVID 19 pandemic. This experience related to myself and my field of work as an instructor of occupational therapy at the Arab American University in Palestine. There are students that I teach that have not been accustomed to distance learning through online courses using social media, especially zoom. This unfamiliar situation provided an opportunity for my students and myself to re think the cases we deal with and the methods that we typically use, especially those people with spinal cord injuries. Here, I started creating an educational unit for students, in order to answer the questions posed by students. How can we provide an occupational therapy service under quarantine because of the COVID 19 pandemic? In what ways are patients suffering in the same way that we are suffering from the coronavirus pandemic? How can I adapt my teaching strategies methods to achieve the goals that I previously set for my students' education before the pandemic?
Wettability of polymers by aqueous solution of binary surfactants mixture with regard to adhesion in polymer solution system II. Critical surface tension of polymers wetting and work of adhesion
Year: 2013

The values of the surface tension of the aqueous solution of CTAB+TX 100, CTAB+TX 114 and TX 100+TX 114 mixtures and those of the contact angle for the same solutions on PTFE, PMMA and nylon 6 surface were applied for the studies of the correlation between the critical surface tension of PTFE, PMMA and nylon 6 wetting and their surface tension as well as the influence of the kind, concentration and composition of the aqueous solutions of binary mixtures of surfactants and the adhesion to those polymers. From these studies it results that the critical surface tension of PTFE wetting by the investigated solutions is somewhat higher than its surface tension determined from the contact angles for n alkanes. However, the critical surface tension of PMMA and nylon 6 wetting is lower than their surface tension determined by using the van Oss et al. approach to the interfacial tension. The critical surface tension of PMMA and nylon 6 wetting was also compared to their surface tension calculated from the Neumann equation. It was found that there is agreement between these values if the surface tension of these polymers was determined on the basis of the solution surface tension and contact angle data corresponding to the concentration equal or higher than the critical micelle concentration.
Exploring Business Process Agility From the Designer's Perspective: The Case of CMMN
Year: 2019

Business process agility remains an intriguing issue for business process management (BPM) when it comes to modeling human centric processes. Several attempts were made from academia to find alternative approaches, with the reputable adaptive case management to be introduced recently as an alternative to BPM methodology and case management modeling and notation (CMMN) standard, as an alternative language of business process management notation (BPMN) targeting the modeling of human centric processes characterized by agility. This chapter identifies the nature of human centric processes, as its main objective is to examine whether using CMMN for the design and modeling of such processes could cover their agility requirements.
Analisis Faktor Dan Kebiasaan Melakukan Pengobatan Tradisional Dengan Keluhan Muskuloskeletal Pada Pekerja Tenun Di Desa Kembang Kerang Daya, Kabupaten Lombok Timur
Year: 2020

Berdasarkan World Health Organization (WHO) bahwa risiko pekerjaan penyebab kesakitan dan kematian berada ditingkat kesepuluh, faktor risiko secara global untuk jumlah kesakitan dan kematian yaitu 37% back pain, 16% hearing loss, 13% chronic obstructive lung disease, 11% asma, 10% cedera, 9% kanker paru, dan 2% leukimia. Pada profil kesehatan di Indonesia tahun 2008, jumlah penderita muskuloskeletal di Rumah Sakit di Indonesia sebesar 175.132 kunjungan (29,8% Hasil studi pendahuluan yang telah dilakukan, diperoleh masalah terkait keluhan muskuloskeletal pada pekerja tenun di Desa Kembang Kerang Daya meliputi: tangan, bahu, pinggang, dan pantat terasa nyeri dan pegal karena melakukan pergerakan tubuh dalam waktu yang lama. Tujuan dari penelitian ini adalah menganalisis faktor penyebab keluhan muskuloskeletal pada pekerja tenun dengan menyajikan data yang telah diukur dengan metode Ovako Working Analysis System dan kuesioner Nordic Body Map yang juga disajikan dalam bentuk tabel dan distribusi frekuensi. Metode yang digunakan dalam penelitian ini adalah observasional analitik dengan rancangan cross sectional. Penelitian ini dilaksanakan pada pekerja tenun di Desa Kembang Kerang Daya, Kabupaten Lombok Timur dengan sampel penelitian berjumlah 68 orang. Data yang diperoleh dari hasil penelitian akan dianalisis secara deskriptif dan statistik meliputi analisis univariate, bivariate, dan multivariate. Hasil uji statistik dengan chi square bahwa faktor sikap kerja (p= 0,000 dan OR= 23,754) umur (p= 0,019 dan OR= 5,517) dan kebiasaan melakukan pengobatan tradisional (p= 0,001 dan OR= 10,855) memiliki hubungan yang signifikan dengan keluhan muskuloskeletal dengan nilai signifikan (a< 0,05) Hasil analisis multivariat menunjukkan bahwa terdapat 2 variabel independen yang memiliki hubungan signifikan dengan keluhan muskuloskeletal, yaitu sikap kerja (p= 0,002) dan kebiasaan melakukan pengobatan tradisional (p= 0,049) Sedangkan variabel umur tidak memiliki hubungan dengan keluhan muskuloskeletal (p= 0,102) Kesimpulan dalam penelitian ini adalah faktor risiko yang paling dominan mempengaruhi keluhan muskuloskeletal adalah sikap kerja dengan nilai odds ratio (OR) sebesar 23,754.
Prediction of Hourly Effect of Land Use on Crime
Venue: ISPRS Int. J. Geo Inf.
Year: 2019

Predicting the exact urban places where crime is most likely to occur is one of the greatest interests for Police Departments. Therefore, the goal of the research presented in this paper is to identify specific urban areas where a crime could happen in Manhattan, NY for every hour of a day. The outputs from this research are the following: (i) predicted land uses that generates the top three most committed crimes in Manhattan, by using machine learning (random forest and logistic regression) (ii) identifying the exact hours when most of the assaults are committed, together with hot spots during these hours, by applying time series and hot spot analysis, (iii) built hourly prediction models for assaults based on the land use, by deploying logistic regression. Assault, as a physical attack on someone, according to criminal law, is identified as the third most committed crime in Manhattan. Land use (residential, commercial, recreational, mixed use etc. is assigned to every area or lot in Manhattan, determining the actual use or activities within each particular lot. While plotting assaults on the map for every hour, this investigation has identified that the hot spots where assaults occur were 'moving' and not confined to specific lots within Manhattan. This raises a number of questions: Why are hot spots of assaults not static in an urban environment? What makes them 'move' is it a particular urban pattern? Is the 'movement' of hot spots related to human activities during the day and night? Answering these questions helps to build the initial frame for assault prediction within every hour of a day. Knowing a specific land use vulnerability to assault during each exact hour can assist the police departments to allocate forces during those hours in risky areas. For the analysis, the study is using two datasets: a crime dataset with geographical locations of crime, date and time, and a geographic dataset about land uses with land use codes for every lot, each obtained from open databases. The study joins two datasets based on the spatial location and classifies data into 24 classes, based on the time range when the assault occurred. Machine learning methods reveal the effect of land uses on larceny, harassment and assault, the three most committed crimes in Manhattan. Finally, logistic regression provides hourly prediction models and unveils the type of land use where assaults could occur during each hour for both day and night.
DUM: Diversity Weighted Utility Maximization for Recommendations
Venue: ArXiv
Year: 2014

The need for diversification of recommendation lists manifests in a number of recommender systems use cases. However, an increase in diversity may undermine the utility of the recommendations, as relevant items in the list may be replaced by more diverse ones. In this work we propose a novel method for maximizing the utility of the recommended items subject to the diversity of user's tastes, and show that an optimal solution to this problem can be found greedily. We evaluate the proposed method in two online user studies as well as in an offline analysis incorporating a number of evaluation metrics. The results of evaluations show the superiority of our method over a number of baselines.
ESTHER: Extremely Simple Image Translation Through Self Regularization
Venue: BMVC
Year: 2018

Image translation between two domains is a class of problems where the goal is to learn the mapping from an input image in the source domain to an output image in the target domain. It has important applications such as data augmentation, domain adaptation, and unsupervised training. When paired training data are not accessible, the mapping between the two domains is highly under constrained and we are faced with an ill posed task. Existing approaches tackling this challenge usually make assumptions and introduce prior constraints. For example, CycleGAN [59] assumes cycle consistency while UNIT  assumes shared latent space between the two domains. We argue that none of these assumptions explicitly guarantee that the learned mapping is the desired one. We, taking a step back, observe that most image translations are based on the intuitive requirement that the translated image needs to be perceptually similar to the original image and also appear to come from the new domain. On the basis of such observation, we propose an extremely simple yet effective image translation approach, which consists of a single generator and is trained with a self regularization term and an adversarial term. We further propose an adaptive method to search for the best weight between the two terms. Extensive experiments and evaluations show that our model is significantly more cost effective and can be trained under budget, yet easily achieves better performance than other methods on a broad range of tasks and applications.
A Bayesian method for discriminative context dependent fusion of GPR based detection algorithms
Venue: Other Conferences
Year: 2012

Ground penetrating radar (GPR) is a very useful technology for buried threat detection applications which is capable of identifying both metallic and non metallic objects with moderate false alarm rates. Several pattern classication algorithms have been proposed and evaluated which enable GPR systems to achieve robust per formance. However, comparisons of these algorithms have shown that their relative performance varies with respect to the environmental context under which the GPR is operating. Context dependent fusion has been proposed as a technique for algorithm fusion and has been shown to improve performance by exploiting the dierences in algorithm performance under dierent environmental and operating conditions. Early approaches to context dependent fusion clustered observations in the joint condence space of all algorithms and applied fusion rules within each cluster (i.e. discriminative learning) Later approaches exploited physics based fea tures extracted from the background data to leverage more environmental information, but decoupled context learning from algorithm fusion (i.e. generative learning) In this work, a Bayesian inference technique which combines the generative and discriminative approaches is proposed for physics based context dependent fusion of detection algorithms for GPR. The method uses a Dirichlet process (DP) mixture as a model for context, and relevance vector machines (RVMs) as models for algorithm fusion. Variational Bayes is used as an approximate inference technique for joint learning of the context and fusion models. Experimental results compare the pro posed Bayesian discriminative technique to generative techniques developed in past work by investigating the similarities and dierences in the contexts learned as well as overall detection performance.
Design of an EEG Acquisition System Based on Front End ADS1292
Year: 2019

The signals obtained through electroencephalography are used to the identification of several pathologies, neurological and psychiatric disturbs. The present work describes the hardware, firmware and software of an EEG acquisition module based on the analog front end ADS1292, with two channels and ADC conversion with a resolution of 268 nV. The system has a composition of filters and amplifiers capable of extracting signals in the amplitude and frequency range for EEG signals and the acquired data is recorded and presented online with a software developed in the LabVIEW 2016 environment and post processed with software Matlab. The system showed to be adequate to acquire EEG signals and present the results in an online software, along with the possibility of storing the data for offline analysis. To conclude, this EEG system acquisition can be a useful tool for engineering students to develop their abilities and build a practical device in which several fields of knowledge are related, therefore presenting a possibility to develop a complete project with real applications.
Persistence of beak and feather disease virus (BFDV) infection in wild Crimson Rosellas (Platycercus elegans)
Venue: Emu Austral Ornithology
Year: 2019

Beak and feather disease virus (BFDV) is a globally distributed pathogen of psittacine birds. In Australia, it is thought to be the dominant pathogen of Psittaciformes, and it is listed as a 'key threatening process to biodiversity' Infection with BFDV can cause serious and often fatal disease. However, knowledge on BFDV infection persistence in wild hosts is scarce. We investigated this in 55 wild Crimson Rosellas (Platycercus elegans) in Victoria, Australia, which were trapped and tested two to five times during 14 years of study. We analysed blood and feather samples for BFDV and antibodies. We then quantified infection persistence in 17 individuals that were BFDV positive (BFDV+ during at least one capture, and that were recaptured at intervals of 0.5 months to 5.9 years. Birds that were BFDV+ when first caught were likely to also be BFDV+ if recaptured within the following five months (80% 4/5) but this rate declined to only 8.3% of birds (1/12) that were recaptured more than five months after initial capture. BFDV was detected in feathers of 44.4% (4/9) of birds that were BFDV+ in blood upon initial capture, and 88.9% (8/9) of these individuals showed an immunological response to infection upon recapture. Viral load varied between captures and individuals. Our data indicate that P. elegans can clear BFDV from their blood, and provide new estimates for the duration of chronic BFDV infection in wild parrots. These findings can aid wildlife disease management and species conservation.
m6A RNA methylation impacts fate choices during skin morphogenesis
Venue: eLife
Year: 2020

N6 methyladenosine is the most prominent RNA modification in mammals. Here, we study mouse skin embryogenesis to tackle m6A's functions and physiological importance. We first landscape the m6A modifications on skin epithelial progenitor mRNAs. Contrasting with in vivo ribosomal profiling, we unearth a correlation between m6A modification in coding sequences and enhanced translation, particularly of key morphogenetic signaling pathways. Tapping physiological relevance, we show that m6A loss profoundly alters these cues and perturbs cellular fate choices and tissue architecture in all skin lineages. By single cell transcriptomics and bioinformatics, both signaling and canonical translation pathways show significant downregulation after m6A loss. Interestingly, however, many highly m6A modified mRNAs are markedly upregulated upon m6A loss, and they encode RNA methylation, RNA processing and RNA metabolism factors. Together, our findings suggest that m6A functions to enhance translation of key morphogenetic regulators, while also destabilizing sentinel mRNAs that are primed to activate rescue pathways when m6A levels drop.
Covid 19: a remote assessment in primary care
Venue: BMJ
Year: 2020

What you need to know A 37 year old healthcare assistant develops a cough. Next day, she wakes with a fever (which she measures at 37.4degC) and shortness of breath. She manages her condition at home for several days, experiencing increasing tiredness, loss of appetite, and a persistent dry cough. On the fifth day of her illness, she develops mild diarrhoea, and her chest feels quite tight. She takes her temperature, which has gone up to 38.1degC. Feeling unwell, she contacts her GP surgery for advice. She would like someone to listen to her chest, but the receptionist tells her not to come to the surgery and offers her the choice of a telephone or video consultation. She was previously well apart from mild asthma (on occasional salbutamol) Five years ago, she took citalopram for anxiety. She is a single parent of three children. Novel coronavirus disease 2019 (covid 19) is an urgent and spreading threat whose clinical and epidemiological characteristics are still being documented.12 With a view to containing covid 19, a shift from in person to remote consulting is occurring. Clinicians are thus faced with a new disease and a new way of interacting with patients. This article will present some guiding principles on how to choose between telephone and video appointments, how to conduct a "query covid" consultation remotely, and considerations when arranging follow up and next steps. It does not cover remote triage or how to set up
The Effect of Cooperative Learning Activities on Enhancing the Writing Skills of Syrian EFL Learners at Arab International University
Year: 2020

This paper aims at enhancing writing skills by using cooperative learning strategies since recent methodologies in foreign language teaching have deviated the shift of focus from traditional teacher oriented classes to more learner centered contexts. Among the methods endorsed in teaching writing is the application of Cooperative Learning (CL) The use of CL has become an increasingly popular method to improve the learner's linguistic, social and communicative competence. This research investigated the effects of CL to improve the writing skill of AIU students. A questionnaire was done to spot their problems and weak points. The quasi experimental design was used, with pre test and post test of two different kinds of essay as an instrument. Two different kinds of studies were employed qualitatively and quantitatively to analyze the data. The students' writing was scored on the five writing components which were content, vocabulary, organization, grammar and mechanics see (Yusuf, Jusoh, Yusuf, 2019) After analyzing the results of the post test, it was noted that there was a significant increase in the scores of students' writing in comparison with their marks of the pretest as a result of implementing cooperative learning. Subsequently, the results lent credence to the positive effects of cooperative learning in enhancing writing performance and teachers were able to involve more students to participate and the class environment was very encouraging and intriguing. EFL participants in a cooperative learning ambience managed to foster the linguistic competence that they need to acquire which was also tackled in the paper.
GRPCA21 for recovering a corrupted low rank matrix
Venue: Int. J. Mach. Learn. Cybern.
Year: 2020

Robust principal component analysis (RPCA) based methods via decomposition into low rank plus sparse matrices offer a wide range of applications for image processing, video processing and 3D computer vision. Most of the time the observed imagery data is often arbitrarily corrupted by anything such as large sparse noise, small dense noise and other unknown fraction, which we call mixed noise in this paper. However, low rank matrix recovery by RPCA is born for the existence of large sparse noise, so its performance and applicability are limited in the presence of mixed noise. In this paper, a generalized robust principal component analysis with norm $l_{2,1} l 2 1 model is proposed to solve the problem of low rank matrix recovery under mixed large sparse noise and small dense noise. The corrupted matrix is written as a combination that minimizes the nuclear norm, the 1 norm and the norm $l_{2,1} l 2 1 which has high efficiency, flexibility and robustness for low rank matrix recovery from mixed noise. Then a novel and efficient algorithm called random permutation alternative direction of multiplier method is applied to solve the model. Experiments with simulations and real datasets demonstrate efficiency and robustness of this model and algorithm.
Introducing machine learning concepts using hands on Android based exercises
Venue: 2019 IEEE Frontiers in Education Conference (FIE)
Year: 2019

In this innovative practice work in progress paper, we discuss novel methods to teach machine learning concepts to undergraduate students. Teaching machine learning involves introducing students to complex concepts in statistics, linear algebra, and optimization. In order for students to better grasp concepts in machine learning, we provide them with hands on exercises. These types of immersive experiences will expose students to the different stages of the practical uses of machine learning. The data collection apparatus is based on applications (apps) developed for the Android platform. Due to the accessible nature of the app and the exercises based on the app, this approach is useful for students across all majors.We provide the students with three different sets of activities, the first of which will introduce the basics of machine learning with specially designed artificial datasets. The second and third activities involve data collection, modeling, training, and testing, as applied to machine learning algorithms. The second activity will involve collecting touch/swipe data on mobile devices from students as they use a touch logger app. The third activity uses the Reflections app to collect cross correlation data from rooms with different purposes. These hands on activities guide the students through every step of the machine learning process. Student learning is assessed for each activity by holding workshops for undergraduate students. A workshop with the first activity outlining the basics of machine learning was given in the fall of 2018 and significant student learning was demonstrated. Workshops for the second and third activities are planned for the fall semester of 2019. Results from these workshops will be presented at the conference.