nils-herrmann/clean_scirepeval huggingface dataset:
THE STRATEGY OF MANAGERS IN MOVING BUSINESS LEARNING GROUP PROGRAM IN PKBM SRIKANDI CIMAHI CITY
Year: 2018

This research is one way in describing the strategy of managers in moving the business group learning program for street children developed in PKBM Srikandi Kota Cimahi. Business group learning program is one of the containers in building independence for youth and street children who are expected to build entrepreneurial character. The purpose of this study is to describe the strategies undertaken by managers in moving the group learning business program. The theory underlying this research is the Concept of Management Unit Outside School Education, Concept Group Business Learning, Street Children Concept. The method of this research is qualitative descriptive with data collection technique using interview, observation and documentation study. The results obtained show that the group manager of PKBM Srikandi uses a persuasive approach in fostering self confidence in the learning community, using motivation to encourage self reliance in creative and solu tive thinking, and using a test case approach to build resilience in developing a business. This research resulted in the conclusion that humanist, communicative and motivative management strategy able to build and develop self reliance learners to develop their business independently.
MPCE: A Maximum Probability Based Cross Entropy Loss Function for Neural Network Classification
Venue: IEEE Access
Year: 2019

In recent years, multi classifier learning is of significant interest in industrial and economic fields. Moreover, neural network is a popular approach in multi classifier learning. However, the accuracies of neural networks are often limited by their loss functions. For this reason, we design a novel cross entropy loss function, named MPCE, which based on the maximum probability in predictive results. In this paper, we first analyze the difference of gradients between MPCE and the cross entropy loss function. Then, we propose the gradient update algorithm based on MPCE. In the experimental part of this paper, we utilize four groups of experiments to verify the performance of the proposed algorithm on six public datasets. The first group of experimental results show that the proposed algorithm converge faster than the algorithms based on other loss functions. Moreover, the results of the second group show that the proposed algorithm obtains the highest training and test accuracy on the six datasets, and the proposed algorithm perform better than others when class number changing on the sensor dataset. Furthermore, we use the model of convolutional neural network to implement the compared methods on the mnist dataset in the fourth group of experiments. The results show that the proposed algorithm has the highest accuracy among all executed methods.
Evaluating the effect of clinical care pathways on quality of cancer care: analysis of breast, colon and rectal cancer pathways
Venue: Journal of Cancer Research and Clinical Oncology
Year: 2015

PurposeSubstantial gaps exist between clinical practice and evidence based cancer care, potentially leading to adverse clinical outcomes and decreased quality of life for cancer patients. This study aimed to evaluate the usefulness of clinical pathways as a tool for improving quality of cancer care, using breast, colon, and rectal cancer pathways as demonstrations.MethodsNewly diagnosed patients with invasive breast, colon, and rectal cancer were enrolled as pre pathway groups, while patients with the same diagnoses treated according to clinical pathways were recruited for post pathway groups.ResultsCompliance with preoperative core biopsy or fine needle aspiration, utilization of sentinel lymph node biopsy, and proportion of patients whose tumor hormone receptor status was stated in pathology report were significantly increased after implementation of clinical pathway for breast cancer. For colon cancer, compliance with two care processes was significantly improved: surgical resection with anastomosis and resection of at least 12 lymph nodes. Regarding rectal cancer, there was a significant increase in compliance with preoperative evaluation of depth of tumor invasion, total mesorectal excision treatment of middle or low position rectal cancer, and proportion of patients who had undergone rectal cancer surgery whose pathology report included margin status. Moreover, total length of hospital stay was decreased remarkably for all three cancer types, and postoperative complications remained unchanged following implementation of the clinical pathways.ConclusionsClinical pathways can improve compliance with standard care by implementing evidence based quality indicators in daily practice, which could serve as a useful tool for narrowing the gap between clinical practice and evidence based care.
Shari'a as Taboo of Modern Law: Halal Food, Islamophobia, and China
Year: 2018

Why is shari'a the taboo of modern law? This article examines the "spread of halal" controversy in China as a window to assess how nativist public opinion influences state law and policy whereas state law is foreclosed to providing protection to rights provided for in shari'a. Among Chinese Muslims (Hui) qingzhen (lit. "pure" plus "true" or "halal" is the kernel of their localized shari'a, and one that prohibits consuming pork. The symbol qingzhen has proliferated in the course of China's economic modernization such that it pervades the public sphere, creating anxiety among Han Chinese that Chinese society and government is becoming "Islamicized." Hui fear that the profusion of qingzhen foments food insecurity and endangers truth in labeling. In response, they have sought greater protection for their diluted core symbol in national legislation attempts which have only exacerbated Hui Han relations. Based on observations from over seven years of field research and interviews with Hui legal entrepreneurs, and drawing from the anthropology of taboo, I explain the debate in China by taking the Hui idea of shari'a as a taboo to reflexively think about how secular non Muslim states regard shari'a as the taboo of modern law.
Advances in Delayed Onset Muscle Soreness (DOMS) Part II: Treatment and Prevention.
Venue: Sportverletzung Sportschaden Organ der Gesellschaft fur Orthopadisch Traumatologische Sportmedizin
Year: 2019

Delayed onset muscle soreness (DOMS) describes an entity of ultrastructural muscle damage. The manifestation of DOMS is caused by eccentric muscle contractions or unaccustomed forms of exercise. Clinical signs include impaired muscular force capacities, painful restriction of movement, stiffness, swelling, and altered biomechanics in adjacent joints. Although DOMS is categorised as a mild type of muscle damage, it is one of the most common reasons for compromised sportive performance. In the last decade, many hypotheses have been developed to explain the aetiology of DOMS, and there are a wide range of different interventions aiming to prevent or alleviate the symptoms. Many studies have evaluated various types of cold or heat therapy, compression, massage, physical therapy or nutritional interventions. Treatment considerations focus on the primary prevention of ultrastructural lesions during exercise, the treatment of the inflammatory response that leads to DOMS, and recovery strategies for manifest DOMS. This narrative review aims to present an overview of the current treatment and preventive strategies in the field of DOMS.
Potential safety, benefits, and influence of the placebo effect in microdosing psychedelic drugs: A systematic review
Venue: Neuroscience Biobehavioral Reviews
Year: 2020

Microdosing psychedelic drugs that is, taking sub behavioral doses of lysergic acid diethylamide (LSD) or psilocybin is a growing practice in Western societies. Taken mainly for creative or mood enhancing purposes, thousands of users are increasingly being exposed to (micro)doses of psychedelic drugs. In this systematic review, we searched the available evidence from human studies, focusing our results in terms of three main axes: efficacy, safety, and the influence of the placebo effect in microdosing practices. While the available evidence has some strengths (e.g. large sample sizes, robust methodologies) there are also remarkable limitations (e.g. gender bias, heterogeneity of dosing schedules and drugs used) Highly contradictory results have been found, showing both the benefits and detriments of microdosing in terms of mood, creative processes, and energy, among other regards. This review provides a general overview of the methods and approaches used, which could be useful for improving future studies.
Sketching landscapes in discourse analysis (1978 2018) A bibliometric study
Year: 2020

John Swales' 1986 article 'Citation analysis and discourse analysis' was the first to apply citation analysis to describe in text citations in the field of discourse analysis. Howard White's 2004 article 'Citation analysis and discourse analysis revisited' was written by an information scientist and primarily focused on citation analysis and discourse analysis. Here, we cast a wider net by conducting a bibliometric analysis of discourse analysis to sketch its scientific landscape between 1978 and 2018. Our findings show that discourse analysis has emerged as a major, and increasingly distinctively sociological, contribution to the analysis of language in social life. It is evident that discourse analysts are increasingly interested in such topics as corpus linguistics, digital conversation analysis, discursive news values approach, membership categorization analysis, multimodal analysis and social media. It is a general trend that the United States is no longer the only dominating publication powerhouse in the field. Countries such as the United Kingdom, Australia and China are wielding a growing share of research output.
Development of Recurrent Neural Networks and Its Applications to Activity Recognition
Year: 2018

Deep learning, including the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) has enjoyed great success in the last decade. They have been widely applied in the areas of image and video related tasks, such as image recognition and action recognition. Despite their great success, there are still some fundamental problems that have not been resolved. This thesis addresses the generic challenges in the CNNs and RNNs as well as challenges specific to their use in human activity recognition. First, an RNN based pooling function is developed to replace the handcrafted and predefined pooling functions. Together with the other layers of the CNNs, this allows all components of the network to be trained from data. Then an independently recurrent neural network (IndRNN) is proposed to solve the gradient vanishing and exploding problem in the conventional RNNs. Unlike the traditional RNNs, IndRNN can learn very long term patterns (over 5000 time steps) and can be stacked to construct very deep networks (over 21 layers) Application of IndRNN to activity recognition is studied where a deep IndRNN based attention model is developed. Finally, applications in complex scenes are explored using camouflaged moving background modelling. Extensive experiments have been conducted to validate the methods proposed in this thesis.
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Venue: IPMI
Year: 2021

Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel wise or feature level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human understandable from the neuroscience perspective as they often do not inform the specific type of morphological changes linked to the brain disorder. Inspired by the image to image translation scheme, we propose to train simulator networks to inject (or remove) patterns of the disease into a given MRI based on a warping operation, such that the classifier increases (or decreases) its confidence in labeling the simulated MRI as diseased. To increase the robustness of training, we propose to couple the two simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of Alzheimer's disease and alcohol dependence. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.
Good Urban Governance and City Resilience: An Afrocentric Approach to Sustainable Development
Venue: Sustainability
Year: 2019

Good urban governance is a multidimensional concept that focuses on the improvement of the quality of living conditions of local citizens, especially those of marginalised and disadvantaged communities. Cities face various adversities and challenges, such as unsustainable use of natural resources, lack of housing and infrastructure, the prevalence of poverty, rapid urbanisation, crime, disasters and effects of climate change. City resilience is an inclusive process that refers to a city's ability to sustainably manage unexpected and expected risk related events. In addition, it includes a city's capacity to adapt to future challenges from a strategic and spatial perspective. This paper aims to analyze the nature of sustainable development in general. More specifically, it sets out to analyze the importance of urban governance in Africa and the interrelationship of good urban governance and city resilience. The purpose is to provide a theoretical underpinning and a practical orientation for the role that urban governance could play in sustainable development. The methodology is based on a document analysis by way of an intensive literature study. The qualitative description of the findings focused on the themes that emerged from the research and the manner in which they were conceptualised. It was established that while African countries have experienced certain successes, there have been many challenges as far as 'good' and 'sustainable' urban governance is concerned. Results indicated that the notion of 'good urban governance' is a prerequisite for African countries to design and execute sustainable development initiatives successfully.
BranchyNet: Fast inference via early exiting from deep neural networks
Venue: 2016 23rd International Conference on Pattern Recognition (ICPR)
Year: 2016

Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer. However, the improved performance of additional layers in a deep network comes at the cost of added latency and energy usage in feedforward inference. As networks continue to get deeper and larger, these costs become more prohibitive for real time and energy sensitive applications. To address this issue, we present BranchyNet, a novel deep network architecture that is augmented with additional side branch classifiers. The architecture allows prediction results for a large portion of test samples to exit the network early via these branches when samples can already be inferred with high confidence. BranchyNet exploits the observation that features learned at an early layer of a network may often be sufficient for the classification of many data points. For more difficult samples, which are expected less frequently, BranchyNet will use further or all network layers to provide the best likelihood of correct prediction. We study the BranchyNet architecture using several well known networks (LeNet, AlexNet, ResNet) and datasets (MNIST, CIFAR10) and show that it can both improve accuracy and significantly reduce the inference time of the network.
Neural News Recommendation with Long and Short term User Representations
Venue: ACL
Year: 2019

Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long term preferences and short term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long and short term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long term user representations from the embeddings of their IDs.In addition, we propose to learn short term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long term and short term user representations. The first one is using the long term user representation to initialize the hidden state of the GRU network in short term user representation. The second one is concatenating both long and short term user representations as a unified user vector. Extensive experiments on a real world dataset show our approach can effectively improve the performance of neural news recommendation.
Influence of process parameters on the microstructural evolution and mechanical characterisations of friction stir welded Al Mg Si alloy
Year: 2020

Heat generation and plastic deformation during Friction Stir Welding (FSW) produce profound changes in the microstructure and structural properties of welded joints. Strengthening precipitate, grain size and crystallographic texture evolution are the most important microstructural changes in the case of welding aluminium alloys. An interaction relationship has been developed in this study to understand the evolution of microstructure during FSW of Al Mg Si alloy for a wide range of welding temperatures and plastic deformations by controlling two important process parameters (tool rotation and welding speeds) Electron backscattered diffraction (EBSD) and transmission electron microscopy (TEM) have been used for microstructural characterisation. The mechanical properties were evaluated using microhardness, tensile and low cycle fatigue tests. In all cases, fine recrystallised equiaxed grains with a partial fibre texture was evolved in the nugget zone. Microstructural development was found to be significantly influenced by the weld pitch (welding speed/rotation speed) as it is controlled the heat input, cooling rate, exposure time and plastic deformations. The strength of the FSW joints is improved by increasing the welding speed because of grain refinement, incomplete dissolution or re precipitate of strengthening precipitates M g 5 S i 6 b and the introduction of edge dislocations. The fatigue performance of FSW joints is dependent on grain size with a remarkable improvement in the fatigue life observed for smaller grain sizes.
Ssn_nlp at SemEval 2020 Task 12: Offense Target Identification in Social Media Using Traditional and Deep Machine Learning Approaches
Venue: SEMEVAL
Year: 2020

Offensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group. Due to immense growth and usage of social media, it has an extensive reach and impact on the society. OLI is helpful for hate speech detection, flame detection and cyber bullying, hence it is used to avoid abuse and hurts. In this paper, we present state of the art machine learning approaches for OLI. We follow several approaches which include classifiers like Naive Bayes, Support Vector Machine(SVM) and deep learning approaches like Recurrent Neural Network(RNN) and Masked LM (MLM) The approaches are evaluated on the OffensEval@SemEval2020 dataset and our team ssn_nlp submitted runs for the third task of OffensEval shared task. The best run of ssn_nlp that uses BERT (Bidirectional Encoder Representations from Transformers) for the purpose of training the OLI model obtained F1 score as 0.61. The model performs with an accuracy of 0.80 and an evaluation loss of 1.0828. The model has a precision rate of 0.72 and a recall rate of 0.58.
Biomedicaux,tradipraticiens et confessionels face au sida
Year: 2014

Resume Tout en se demarquant du courant de pensee qui tend a instaurer des oppositions rigides entre le savoir scientifique et le savoir profane (malgre la reconnaissance de leurs differences et de leurs specificites) l'auteur se place dans l'orientation des travaux de Coreil et Mull (1988) et de F. Laplantine (1986) en tachant de relever les interstices de jonction eventuelle, les elements de convergence (et de divergence) dans les discours et les pratiques lies au vih/sida chez les biomedicaux, les tradipraticiens et les confessionnels a Bangui, capitale de la Centrafrique. L'etude se situe dans la perspective des travaux en anthropologie qui ont pour objet d'etude le pluralisme medical et tend a allier harmonieusement et pertinemment les approches ethno historique synchronique, prospective, biologique et anthropologique des representations et des pratiques de sante et de maladie liees au vih/sida. L'ouvrage centre l'analyse sur trois principaux points L'intelligibilite des systemes medicaux en presence dans leur rapport avec le vih/sida est saisie en prenant en compte le contexte social global des conceptions et des pratiques de sante et de maladie en Centrafrique.
Simplified 3D model for the uplift analysis of liquid storage tanks
Venue: Engineering Structures
Year: 2019

Vertical uplifting of the tank base is perhaps the most relevant characteristic of the seismic behaviour of unanchored cylindrical liquid storage tanks made of steel. When an unanchored tank is subjected to lateral loads due to the earthquake induced hydrodynamic pressures, a portion of the base plate tends uplift locally. Analysis of the base plate uplifting of unanchored tanks implies many sources of nonlinear behaviour mechanisms such as the varying contact of the base plate with the foundation, large deflections, the material yielding and the plastic rotation of the base plate. This paper presents a comprehensive analysis of the partially uplifted base plate of two thin walled cylindrical tanks (one slender and one broad) First, a nonlinear quasi static cyclic analysis with a finite element model and a simplified model is carried out for each tank. Next, an efficient method for three dimensional dynamic analysis is proposed and used in order to obtain the nonlinear time history response of each tank. The simplified non linear model proposed in this paper was capable to provide a good estimation of both the rocking resistance of the liquid loaded base plate and the stress distribution on the tank wall in contact with the foundation. A set of seven seismic ground motions was used for the time history analysis. Finally, the distribution of the tank wall stresses and the critical responses of the tanks are discussed. Both anchored and unanchored tanks may present a high compressive axial stress due to the seismic effect.