nils-herrmann/clean_scirepeval huggingface dataset:
Understanding Telecom Customer Churn with Machine Learning: From Prediction to Causal Inference
Venue: BNAIC/BENELEARN
Year: 2019

Telecommunication companies are evolving in a highly competitive market where attracting new customers is much more expensive than retaining existing ones  Retention campaigns can be used to prevent customer churn, but their effectiveness depends on the availability of accurate prediction models. Churn prediction is notoriously a difficult problem because of the large amount of data, non linearity, imbalance and low separability between the classes of churners and non churners. In this paper, we discuss a real case of churn prediction based on Orange Belgium customer data. In the first part of the paper we focus on the design of an accurate prediction model. The large class imbalance between the two classes is handled with the EasyEnsemble algorithm  using a random forest classifier. The dataset contains 73 variables and about 7.6 million entries, covering a 5 months time window in 2018. The classification model is trained on the first 4 months of data and evaluated on the last month. We also assess the impact of different data preprocessing techniques including feature selection and engineering. Results show that feature selection can be used to reduce computation time and memory requirements, though engineering variables does not necessarily improve performance. In the second part of the paper we explore the application of data driven causal inference, which allows to infer causal relationships between variables purely from observational data. More specifically, we applied 5 different causal inference methods, namely PC [6] Grow shrink (GS)  Incremental Association Markov Blanket (IAMB)  Minimum interaction maximum relevance (mIMR)  and D2C  PC infers the set of causal graphs faithful to the dataset, GS and IAMB infer the Markov blanket of the churn variable, and mIMR and D2C return the direct causes of churn. Two implementations of the mIMR algorithm are used: one based on histograms to estimate mutual information, and another assuming Gaussian variables, thus allowing a closed form formula for the computation of the mutual information. The results of these algorithms (summa
Les interventions realisees par les professionnels de sante aupres des femmes enceintes a propos des perturbateurs endocriniens revue systematique de la litterature
Year: 2019

Les perturbateurs endocriniens regroupent une vaste famille de composes d'origine chimique ou naturelle. Une exposition lors de la vie foetale expose a des effets immediats comme des malformations congenitales ou a des effets a plus long terme comme des troubles de la fertilite, des troubles metaboliques ou des cancers hormono dependants. Les professionnels de sante ont un role d'information et de prevention important a jouer dans ce domaine. L'objectif principal de cette etude etait de realiser une description des interventions realisees par les professionnels de sante aupres des femmes enceintes concernant les perturbateurs endocriniens. L'objectif secondaire etait d'identifier les interventions adaptees a la pratique de la medecine generale. Methode Cette revue de la litterature a ete conduite de fevrier a juin 2019. Les bases de donnees utilisees etaient Medline (Pubmed) et Embase. 2532 articles ont ete selectionnes via l'equation de recherche et leur inclusion s'est faite selon les criteres PRISMA. Resultats Sept articles ont ete identifies. Les interventions etaient de type, de cadre et d'efficacite varies. Il s'agissait du depistage d'une exposition aux PE dans la vie quotidienne ou professionnelle, de conseils, de l'orientation vers un professionnel competent ou un atelier educatif, d'un regime alimentaire, de la distribution de brochures informatives et d'un atelier educatif de groupe avec support informatique.  La plupart des interventions relevees dans cette etude sont adaptees a la pratique de la medecine generale. Ce sujet est peu aborde en consultation pour plusieurs raisons le manque de connaissance des praticiens, le manque de supports informatifs, le manque de temps, l' incertitude scientifique et l'anxiete generee pour la patiente. Une meilleure formation des professionnels de sante ainsi qu'un document de recommandations officielles sont necessaires.
Development of target tunable P22 VLP based delivery nanoplatforms using bacterial superglue
Venue: Biotechnology and bioengineering
Year: 2019

Protein cage nanoparticles are widely used as targeted delivery nanoplatforms, because they have well defined symmetric architectures, high biocompatibility, and enough plasticity to be modified to produce a range of different functionalities. Targeting peptides and ligands are often incorporated on the surface of protein cage nanoparticles. In this research, we adopted the SpyTag/SpyCatcher protein ligation system to covalently display target specific affibody molecules on the exterior surface of bacteriophage P22 virus like particles (VLP) and evaluated their modularity and efficacy of targeted delivery. We genetically introduced the 13 amino acid SpyTag peptide into the C terminus of the P22 capsid protein to construct a target tunable nanoplatform. We constructed two different SpyCatcher fused affibody molecules as targeting ligands, SC EGFRAfb and SC HER2Afb, which selectively bind to EGFR and HER2 surface markers, respectively. We produced target specific P22 VLP based delivery nanoplatforms for the target cell lines by selectively combining SpyTagged P22 VLP and SC fused affibody molecules. We confirmed its target switchable modularity through cell imaging and verified the target specific drug delivery efficacy of the affibody molecules displaying P22 VLP using cell viability assays. The P22 VLP based delivery nanoplatforms can be used as multifunctional delivery vehicles by ligating other functional proteins, as well as affibody molecules. The interior cavity of P22 VLP can be also used to load cargoes like enzymes and therapeutic proteins. We anticipate that the nanoplatforms will provide new opportunities for developing target specific functional protein delivery systems.
Evaluating and Combining Name Entity Recognition Systems
Venue: NEWS@ACM
Year: 2016

Name entity recognition (NER) is an important subtask in natural language processing. Various NER systems have been developed in the last decade. They may target for different domains, employ different methodologies, work on different languages, detect different types of entities, and support different inputs and output formats. These conditions make it difficult for a user to select the right NER tools for a specific task. Motivated by the need of NER tools in our research work, we select several publicly available and well established NER tools to validate their outputs against both Wikipedia gold standard corpus and a small set of manually annotated documents. All the evaluations show consistent results on the selected tools. Finally, we constructed a hybrid NER tool by combining the best performing tools for the domains of our interest.
Norbert Elias's extended theory of community: From established/outsider relations to the gendered we I balance
Venue: The Sociological Review
Year: 2019

Norbert Elias and John Scotson's analysis of the interconnection between positive and negative community identities in The Established and the Outsiders is well known. Elias's subsequent writing about community offers a more rounded analysis, going beyond established/outsider configurations by exploring community's gendered character and the forces involved in the 'we I balance' that counteract the pervasive process of individualization. Elias's use of personal pronouns to reveal how community identity 'we' relates not only to outsiders 'they' but also to an individual member 'I' of communities is central to his extended theory of community.
BAG: A designer oriented integrated framework for the development of AMS circuit generators
Venue: 2013 IEEE/ACM International Conference on Computer Aided Design (ICCAD)
Year: 2013

We introduce BAG, the Berkeley Analog Generator, an integrated framework for the development of generators of Analog and Mixed Signal (AMS) circuits. Such generators are parameterized design procedures that produce sized schematics and correct layouts optimized to meet a set of input specifications. BAG extends previous work by implementing interfaces to integrate all steps of the design flow into a single environment and by providing helper classes both at the schematic and layout level to aid the designer in developing truly parameterized and technology independent circuit generators. This simplifies the codification of common tasks including technology characterization, schematic and testbench translation, simulator interfacing, physical verification and extraction, and parameterized layout creation for common styles of layout. We believe that this approach will foster design reuse, ease technology migration, and shorten time to market, while remaining close to the classical design flow to ease adoption. We have used BAG to design generators for several circuits, including a Voltage Controlled Oscillator (VCO) and a Switched Capacitor (SC) voltage regulator in a CMOS 65nm process. We also present results from automatic migration of our designs to a 40nm process.
PENINGKATAN HASIL BELAJAR SMASH BOLA VOLI MELALUI GAYA MENGAJAR RESIPROKAL PADA SISWA KELAS XI SMA NURCAHAYA MEDAN TAHUN AJARAN 2018/2019
Year: 2019

Penelitian ini bertujuan untuk mengetahui peningkatan kemampuan smash bola voli melalui penggunaan gaya mengajar resiprokal pada siswa kelas XI SMA Nurcahaya Medan. Untuk memperoleh data dalam penelitian ini dilakukan tes hasil belajar smash diakhiri dengan dipergunakan instrumen penelitian berupa tes kemampuan melakukan smash dalam permainan bola voli menurut.(James S.Bosco,Wiliam F.Gustafson''Measurement and Evalution in Physical Education, Fitness,and Sport) dan yang menjadi subjek penelitian ini kelas XI SMA Nurcahaya Medan berdasarkan rendahnya hasil KKM. Analisa data dimulai dengan paparan data dan analisis data sesuai deskriptif.Hasil penelitian menyimpulkan: (1) dari tes hasil belajar siklus I diperoleh sebanyak 18 orang siswa dengan nilai setelah dikonversikan (64% telah mencapai tingkat ketuntasan belajar, sedangkan 10 orang siswa (36% belum mencapai tingkat ketuntasan belajar.dengan nilai rata rata hasil belajar setelah dikonversikan adalah 73,21. (2) dari tes hasil belajar siklus II diperoleh sebanyak 24 orang siswa dengan nilai setelah dikonversikan (85,71% telah mencapai tingkat ketuntasan belajar, sedangkan 4 orang siswa (14,29% masih belum tuntas. dengan nilai rata rata hasil belajar setelah dikonversikan adalah 80,95.Berdasarkan hasil analisa data dapat disimpulkan bahwa dengan penggunaan gaya resiprokal dapat memberikan peningkatan terhadap hasil belajar smash bola voli melalui penggunaan gaya resiprokal pada siswa kelas XI SMA Nurcahaya Medan 2018/2019.
Cognitive Computing: Building a Smarter Planet
Year: 2016

Cognitive co mputing is the simulat ion of human thought processes in a co mputerized model. Cognitive systems are co mplex informat ion processing ones, capable of acquiring info rmation, putting it in to action and transmitting knowledge. Cognitiv e computing involves self learning systems that use data min ing, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive Systems will require innovation breakthroughs at every layer of info rmation technology, starting with nanotechnology and progressing through computing systems design, information management, programming and mach ine learning, and, finally, the interfaces between mach ines and humans. Advances on this scale will require remarkable efforts and collaboration, calling forth the best minds and the combined resources of academia, government and industry.
Implementation of 5S Practices in a Small Scale Organization: A Case Study
Year: 2015

A small scale industry plays an important role in Indian economy. In an organization the prime importance is given to the quality and productivity. Since a problem come across due to the defects in materials, down time in production, working conditions, housekeeping etc. This case study deals with the 5S implementation in an industry, Implementation of 5S can result in considerable improvements in environmental performance beside with improved housekeeping and health and safety.[5]5S can improve the quality, productivity and working conditions in organizations.[5] Keywords 5S, Strategic Planning, Down Time, Change
Clozapine Combination and Augmentation Strategies in Patients With Schizophrenia Recommendations From an International Expert Survey Among the Treatment Response and Resistance in Psychosis (TRRIP) Working Group.
Venue: Schizophrenia bulletin
Year: 2020

Evidence for the management of inadequate clinical response to clozapine in treatment resistant schizophrenia is sparse. Accordingly, an international initiative was undertaken with the aim of developing consensus recommendations for treatment strategies for clozapine refractory patients with schizophrenia.  We conducted an online survey among members of the Treatment Response and Resistance in Psychosis (TRRIP) working group. An agreement threshold of =75% (responses "agree" "strongly agree" was set to define a first round consensus. Questions achieving agreement or disagreement proportions of >50% in the first round, were re presented to develop second round final consensus recommendations.  Forty four (first round) and 49 (second round) of 63 TRRIP members participated. Expert recommendations at =75% agreement included raising clozapine plasma levels to =350 ng/ml for refractory positive, negative, and mixed symptoms. Where plasma level guided dose escalation was ineffective for persistent positive symptoms, waiting for a delayed response was recommended. For clozapine refractory positive symptoms, combination with a second antipsychotic (amisulpride and oral aripiprazole) and augmentation with ECT achieved consensus. For negative symptoms, waiting for a delayed response was recommended, and as an intervention for clozapine refractory negative symptoms, clozapine augmentation with an antidepressant reached consensus. For clozapine refractory suicidality, augmentation with antidepressants or mood stabilizers, and ECT met consensus criteria. For clozapine refractory aggression, augmentation with a mood stabilizer or antipsychotic medication achieved consensus. Generally, cognitive behavioral therapy and psychosocial interventions reached consensus.  Given the limited evidence from randomized trials of treatment strategies for clozapine resistant schizophrenia (CRS) this consensus based series of recommendations provides a framework for decision making to manage this challenging clinical situation.
Patterns of long term swallowing dysfunction after definitive radiotherapy or chemoradiation.
Venue: Radiotherapy and oncology journal of the European Society for Therapeutic Radiology and Oncology
Year: 2015

To identify patterns of long term, radiation induced swallowing dysfunction after definitive radiotherapy with or without chemotherapy (RT or CHRT) and to determine which factors may explain these patterns over time.  The study population consisted of 238 consecutive head and neck cancer patients treated with RT or CHRT. The primary endpoint was grade 2 swallowing dysfunction at 6, 12, 18 and 24months after treatment. Cluster analysis was used to identify different patterns over time. The differences between the mean dose to the swallowing organs at risk for each pattern were determined by using dose maps.  The cluster analysis revealed five patterns of swallowing dysfunction: low persistent, intermediate persistent, severe persistent, transient and progressive. Patients with high dose to the upper pharyngeal, laryngeal and lower pharyngeal region had the highest risk of severe persistent swallowing dysfunction. Transient problems mainly occurred after high dose to the laryngeal and lower pharyngeal regions, combined with moderate dose to the upper pharyngeal region. The progressive pattern was mainly seen after moderate dose to the upper pharyngeal region.  Various patterns of swallowing dysfunction after definitive RT or CHRT can be identified over time. This could reflect different underlying biological processes.
Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method
Year: 2017

This study proposes a multiple sources and multiple measures based traffic flow prediction algorithm using the chaos theory and support vector regression method. In particular, first, the chaotic characteristics of traffic flow associated with the speed, occupancy, and flow are identified using the maximum Lyapunov exponent. Then, the phase space of multiple measures chaotic time series are reconstructed based on the phase space reconstruction theory and fused into a same multi dimensional phase space using the Bayesian estimation theory. In addition, the support vector regression (SVR) model is designed to predict the traffic flow. Numerical experiments are performed using the data from multiple sources. The results show that, compared with the single measure, the proposed method has better performance for the short term traffic flow prediction in terms of the accuracy and timeliness.
Social responsibility dimension in Islamic investment: a survey of investors' perspective in Malaysia
Year: 2015

This paper seeks to explore the level of support investors attach to the social responsibility dimension in Islamic investment. The call to address this 'neglected' dimension as observed in the practice of Islamic finance is emphasized in the face of the rapidly growing Socially Responsible Investment (SRI) industry in the West and other developed markets, where various social, ethical, and environmental considerations are central to the investment decision. The empirical analysis of the paper is based on a survey conducted on Malaysian investors of Islamic funds from three leading fund management companies. The overall result reveals that the investors in general place a hierarchy in the elements they considered important; the observance of fiqh injunctions is ranked as the most important consideration, followed by economic and social responsibility dimensions. The findings are based on both the descriptive analysis and the composite scales constructed as a result of an exploratory factor analysis, which provide statistical evidence on the nature of social responsibility dimensions being acknowledged by the respondents as part of an important underlying factor in their investment decision. Despite being overpowered by the economic aspect, the findings suggest that social responsibility criteria in investment are perceived as important by a majority of the investors, and substantial proportion of the respondents consider the dimension to be as equally or more important than the economic dimension. Further analysis reveals that the level of importance investors attached to the social responsibility dimension is determined by ethnicity, religion, level of commitment to Shariah principles in investment, income, age, level of SRI awareness, as well as gender, marital status and participation in pro social activities. The favorable attitude of the respondents on the importance of social responsibility issues can be an encouraging precursor to the incorporation of these concerns into Islamic investment practices.
Integrating strategic human capital and strategic human resource management
Year: 2018

Human capital is an important construct in a variety of fields spanning from micro scholarship in psychology to macro scholarship in economics. Within the various disciplinary perspectives, research focuses on slightly different aspects and levels of human capital within organizations, which may give opportunities for integration. The current paper aims to increase knowledge about human capital within organizations by integrating two streams of research which focus directly on human capital, but have approached human capital in different ways: strategic human capital (SHC) and strategic HRM. We describe both SHC and strategic HRM research streams and propose areas of integration, and directions for future research on human capital in organizations.
Implementing the scholarship of teaching and learning in the Community College Office Administration classroom a faculty learning community initiative
Year: 2016

The community college enrolls over 40 percent of all higher education undergraduate students (Mullin, 2012) Recent calls for educational reform and demands for accountability within higher education have placed increasing pressure on community colleges to produce learning and increase student success. Professional development has become necessary to assist faculty in improving teaching practice. Faculty learning communities are a faculty development initiative that can contribute to changes in instruction that improve student learning. Those who engage in the scholarship of teaching and learning (SoTL) movement can help improve teaching and/or learning through systematic inquiry into student learning, application of findings to practice, dissemination of results, self reflection, and peer review (Cambridge, 2001) In addition, those who engage in the SoTL seek to improve teaching and learning in the classroom and advance the practice of teaching. This study implemented the scholarship of teaching and learning in the Office Administration Department of a large metropolitan area community college through the use of a faculty learning community. This mixed methods case study captured qualitative data from faculty learning community members through dialogue, audio recordings and surveys. Student participants completed a pretest/post test assignment through a skills assessment manager instructional technology tool and the results provided quantitative data for the study. The qualitative data were analyzed for common themes and the major findings were that class size and course delivery method had little effect on student learning. Quantitative data were analyzed using Microsoft Excel and SPSS software and the findings showed that student learning was impacted through the use of the instructional technology implemented for the study.
Playing with Duality: An Overview of Recent Primal Dual Approaches for Solving Large Scale Optimization Problems
Year: 2018

Optimization methods are at the core of many problems in signal/image proce ssing, computer vision, and machine learning. For a long time, it has been recognized that looking at the dual o f an ptimization problem may drastically simplify its solution. Deriving efficient strategies which jointly brings into play th e primal and the dual problems is however a more recent idea which has generated many important new contributions in the last years. These novel developments are grounded on recent advances in convex an alysis, discrete optimization, parallel processing, and nonsmooth optimization with emphasis on sparsity issues. In this paper, we aim at presenting the principles of primal dual approaches, while giving an overview of numerical me thods which have been proposed in different contexts. We show the benefits which can be drawn from primal dual algo rithms both for solving large scale convex optimization problems and discrete ones, and we provide various applicatio n examples to illustrate their usefulness.