nils-herrmann/clean_scirepeval huggingface dataset:
Agenda setting dynamics in the post 2020 cohesion policy reform: the pathway towards the European cross border mechanism as possible policy change
Year: 2019

Cross border areas in Europe are promoted as places of integration, whose cooperation is encouraged by the Interreg programme and the European Grouping for Territorial Cooperation (EGTC) tool. For the 2021 2027  Pack, the European Commission proposed the European Cross Border Mechanism (ECBM) a regulation that would allow one member state to apply the law of a neighbouring member state to facilitate cross border projects. This mechanism represents a paradigm shift, empowering border areas to manage their own integration (functional horizontal) and institutionalise a policy pathway for resolving border specific legal or administrative obstacles (institutional vertical) This paper analyses the dynamics behind the ECBM proposal according to the multiple streams framework (MSF) revealing policy and politics streams' elements to explain agenda setting in EU policy processes. Methodologically, we apply a document analysis with qualitative content and cross case analysis elements, and a combined strategy of deductive inductive codes. This analysis is complemented and validated by expert interviews.
Leadership lessons from the Chilean mine rescue.
Venue: Harvard business review
Year: 2013

Three years ago, when a cave in at the San Jose mine in Chile trapped 33 men under 700,000 metric tons of rock, experts estimated the probability of getting them out alive at less than 1% Yet, after spending a record 69 days underground, all 33 were hoisted up to safety. The inspiring story of their rescue is a case study in how to lead in situations where the stakes, risk, and uncertainty are incredibly high and time pressure is intense. Today executives often find themselves in similar straits. When they do, many feel torn. Should they be directive, taking charge and commanding action? Or should they be empowering, enabling innovation and experimentation? As the successful example of Andre Sougarret, the chief of the mine rescue operation, shows, the answer is yes to both. The choice is a false dichotomy. Implementing this dual approach involves three key tasks. Each has directive and enabling components. The first task is envisioning, which requires instilling both realism and hope. The second task is enrolling, which means setting clear boundaries for who is on and off the team, but inviting in helpful collaborators. The third task is engaging leading disciplined execution while encouraging innovation and experimentation. The authors of this article describe how Sougarret ably juggled all of these tasks, orchestrating the efforts of hundreds of people from different organizations, areas of expertise, and countries in an extraordinary mission that overcame impossible odds.
Investigating intestinal inflammation in DSS induced model of IBD.
Venue: Journal of visualized experiments JoVE
Year: 2012

Inflammatory bowel disease (IBD) encompasses a range of intestinal pathologies, the most common of which are ulcerative colitis (UC) and Crohn's Disease (CD) Both UC and CD, when present in the colon, generate a similar symptom profile which can include diarrhea, rectal bleeding, abdominal pain, and weight loss.(1) Although the pathogenesis of IBD remains unknown, it is described as a multifactorial disease that involves both genetic and environmental components.(2) There are numerous and variable animal models of colonic inflammation that resemble several features of IBD. Animal models of colitis range from those arising spontaneously in susceptible strains of certain species to those requiring administration of specific concentrations of colitis inducing chemicals, such as dextran sulphate sodium (DSS) Chemical induced models of gut inflammation are the most commonly used and best described models of IBD. Administration of DSS in drinking water produces acute or chronic colitis depending on the administration protocol.(3) Animals given DSS exhibit weight loss and signs of loose stool or diarrhea, sometimes with evidence of rectal bleeding.(4,5) Here, we describe the methods by which colitis development and the resulting inflammatory response can be characterized following administration of DSS. These methods include histological analysis of hematoxylin/eosin stained colon sections, measurement of pro inflammatory cytokines, and determination of myeloperoxidase (MPO) activity, which can be used as a surrogate marker of inflammation.(6) The extent of the inflammatory response in disease state can be assessed by the presence of clinical symptoms or by alteration in histology in mucosal tissue. Colonic histological damage is assessed by using a scoring system that considers loss of crypt architecture, inflammatory cell infiltration, muscle thickening, goblet cell depletion, and crypt abscess.(7) Quantitatively, levels of pro inflammatory cytokines with acute inflammatory properties, such as interleukin (IL) 1b, IL 6 and tumour necrosis factor (TNF) a,can be determined using conventional ELISA methods. In addition, MPO activity can be measured using a colorimetric assay and used as an index of inflammation.(8) In experimental colitis, disease severity is often correlated with an increase in MPO activity and higher levels of pro inflammatory cytokines. Colitis severity and inflammation associated damage can be assessed by examining stool consistency and bleeding, in addition to assessing the histopathological state of the intestine using hematoxylin/eosin stained colonic tissue sections. Colonic tissue fragments can be used to determine MPO activity and cytokine production. Taken together, these measures can be used to evaluate the intestinal inflammatory response in animal models of experimental colitis.
Bank earnings management and analyst coverage: evidence from loan loss provisions
Venue: Review of Quantitative Finance and Accounting
Year: 2019

We investigate the role of loan loss provisions in analysts' decision to follow banks. We find that abnormal loan loss provisions (ALLP) regardless of whether it is income increasing or income decreasing, reduce analyst coverage. We interpret this effect with the finding that the greater magnitude of ALLPs decreases the accuracy and increases the dispersion of analysts' forecasts. In addition, the volatility in ALLPs leads to the decrease in analyst coverage as well. We also find a pecking order for lead analysts' decisions in a noisy information environment. Lead analysts prefer to follow financial institutions with more accurate loan loss provisions first, then with more positive (incoming decreasing) ALLPs, and are less likely to follow those with negative (income increasing) ALLPs. Our findings are robust to endogeneity concerns and indicate that lead analysts are deterred from more aggressive bank earnings management.
High Level Topology Oblivious Optimization of MPI Broadcast Algorithms on Extreme Scale Platforms
Venue: Euro Par Workshops
Year: 2014

There has been a significant research in collective communication operations, in particular in MPI broadcast, on distributed memory platforms. Most of the research works are done to optimize the collective operations for particular architectures by taking into account either their topology or platform parameters. In this work we propose a very simple and at the same time general approach to optimize legacy MPI broadcast algorithms, which are widely used in MPICH and OpenMPI. Theoretical analysis and experimental results on IBM BlueGene/P and a cluster of Grid'5000 platform are presented.
Theoretical study of the impact of adaptation on cell fate heterogeneity and fractional killing
Venue: Scientific reports
Year: 2020

Fractional killing illustrates the cell propensity to display a heterogeneous fate response over a wide range of stimuli. The interplay between the nonlinear and stochastic dynamics of biochemical networks plays a fundamental role in shaping this probabilistic response and in reconciling requirements for heterogeneity and controllability of cell fate decisions. The stress induced fate choice between life and death depends on an early adaptation response which may contribute to fractional killing by amplifying small differences between cells. To test this hypothesis, we consider a stochastic modeling framework suited for comprehensive sensitivity analysis of dose response curve through the computation of a fractionality index. Combining bifurcation analysis and Langevin simulation, we show that adaptation dynamics enhances noise induced cell fate heterogeneity by shifting from a saddle node to a saddle collision transition scenario. The generality of this result is further assessed by a computational analysis of a detailed regulatory network model of apoptosis initiation and by a theoretical analysis of stochastic bifurcation mechanisms. Overall, the present study identifies a cooperative interplay between stochastic, adaptation and decision intracellular processes that could promote cell fate heterogeneity in many contexts.
Adaptive Design for Educational Hypermedia Environments and Bio Educational Adaptive Design for 3D Virtual Learning Environments
Year: 2018

Adaptive learning environments design has been originally influenced by the adaptive learning environments model, which has been recently re shaped by the bio educational adaptive approach. Adaptive and bio educational models share the common main idea that education should be adaptive. Since the 90's the adoption of an adaptive educational point of view have been at the base of adaptive educational hypermedia systems, which design joins interest towards learners' individual differences with adaptive learning environments research. Educational hypermedia systems have been overcome by the technology of 3D Virtual Learning Environments. Some emerging questions are related to the design criteria of adaptive learning environments. Which lessons learned from adaptive hypermedia systems design could be now applied to VLEs' design? How a virtual learning environment should be designed to be adaptive? This research tries to answer to these questions by describing Federico 3DSU, an educational University 3D Virtual Learning Environment which has been designed with adaptive criteria, according to bio educational model
Increased Pathway Complexity Is a Prognostic Biomarker in Metastatic Castration Resistant Prostate Cancer
Venue: Cancers
Year: 2021

Simple  Circulating tumour DNA profiling can cost efficiently accelerate biomarker discovery within oncology trials. However, biomarker identification in metastatic castration resistant prostate cancer is confounded by a heterogeneous genomic landscape with few commonly perturbed genes and a large number of infrequently mutated, yet potentially biologically relevant, cancer drivers. Hence, large sample sizes are required for the stratified evaluation of these infrequent perturbations. To circumvent this issue, we investigated whether grouping genomic alterations with other events within the same cellular pathways would offer increased precision for biomarker discovery. We undertook an individual patient level pooled analysis of 342 patients with metastatic castration resistant prostate cancer initiating abiraterone acetate or enzalutamide. We found that the total number of altered pathways, which we termed the pathway complexity index (PCI) was associated with a poor prognosis. Since genomic profiling is now standard practice in interventional oncology trials, our findings highlight the importance of comprehensive genomic profiling for biomarker discovery and utilization.  Metastatic castration resistant prostate cancer (mCRPC) is a heterogeneous disease, characterized by common and rare driver gene alterations that provide a selective growth advantage for progressing tumour cells. We hypothesized that the number of distinct gene driver alteration affected pathways or gene classes was associated with poor prognosis in patients initiating androgen receptor signalling inhibitors (ARSi) We performed a post hoc analysis of an amalgamated baseline circulating tumour DNA (ctDNA) mutational landscape dataset of ARSi treated men with mCRPC (n 342) We associated the detected hotspot, pathogenic, and/or high impact protein function affecting perturbations in 39 genes into 13 pathways. Progression free (PFS) and overall survival (OS) were analysed using Kaplan Meier curves and multivariate Cox regression models. Driver gene alterations were detected in 192/342 (56.1% evaluable patients. An increased number of affected pathways, coined pathway complexity index (PCI) resulted in a decremental PFS and OS, and was independently associated with prognosis once =3 pathway or gene classes were affected (PFS HR (95%CI) 1.7 (1.02 2.84) p 0.04, and OS HR (95%CI) 2.5 (1.06 5.71) p 0.04) Additionally, visceral disease and baseline PSA and plasma ctDNA levels were independently associated with poor prognosis. Elevated PCI is associated with poor ARSi outcome and supports comprehensive genomic profiling to better infer mCRPC prognosis.
A feature preserving framework for point cloud denoising
Venue: Comput. Aided Des.
Year: 2020

Point cloud denoising has been an attractive problem in geometry processing. The main challenge is to eliminate noise while preserving different levels of features and preventing unnatural effects (such as over sharpened artifacts on smoothly curved faces and cross artifacts on sharp edges) In this paper, we propose a novel feature preserving framework to achieve these goals. Firstly, we newly define some discrete operators on point clouds, which can be used to construct a second order regularization for restoring a point normal field. Then, based on the filtered normals, we perform a feature detection step by a bi tensor voting scheme. As will be seen, it is robust against noise and can locate underlying geometric features accurately. Finally, we reposition points with a multi normal strategy by using a simple yet effective RANSAC based algorithm. Intensive experimental results show that the proposed method performs favorably compared to other state of the art approaches.
Data to Text Generation with Attention Recurrent Unit
Venue: 2019 International Joint Conference on Neural Networks (IJCNN)
Year: 2019

Recurrent Neural Networks (RNNs) have shown promising results in many text generation tasks with their ability in modeling complex data distribution. However, the text generation model in their encoder or decoder RNNs still can not use the context efficiently. In this paper, we propose a novel Attention Recurrent Unit (ARU) to generate short descriptive texts conditioned on database records. Different from conventional approaches Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) ARU allows the context information from the encoder to be aligned first inside the unit, which can improve the ability of content selection and surface realization for the model. And we also design a method called DoubleAtten to enhance the attention distribution when computing the generation probabilities. On the recently released ROTOWIRE dataset, extensive experimental results demonstrate that the ARU and DoubleAtten can efficiently improve the model performance for data to text generation task.
The SEMAINE API: Towards a Standards Based Framework for Building Emotion Oriented Systems
Venue: Adv. Hum. Comput. Interact.
Year: 2010

This paper presents the SEMAINE API, an open source framework for building emotion oriented systems. By encouraging and simplifying the use of standard representation formats, the framework aims to contribute to interoperability and reuse of system components in the research community. By providing a Java and C+ wrapper around a message oriented middleware, the API makes it easy to integrate components running on different operating systems and written in different programming languages. The SEMAINE system 1.0 is presented as an example of a full scale system built on top of the SEMAINE API. Three small example systems are described in detail to illustrate how integration between existing and new components is realised with minimal effort.
Email Classification Using Machine Learning Algorithms
Year: 2017

Email has become one of the frequently used forms of communication. Everyone has at least one email account. Inflow of spam messages is a major problem faced by email users. Currently there are many spam filtering techniques. As the spam filtering techniques came up, spammers improved their methods of spamming. Thus, an effective spam filtering technique is the timely requirement. In this paper email classification is done using machine learning algorithms. Two of the important algorithms namely, Naive Bayes and J48 Decision Tree are tested for their efficiency in classifying emails as spam or ham. The experiment focused on classification in combination with pre processing techniques and concepts of text categorization. The dataset used is Enron Corpus. TF IDF value is used as the weight score of text. The classifiers are also tested for different feature size. The test results show that J48 is more accurate in classifying emails as spam or ham with a minimum feature size and classification time. Keyword Spam, Machine learning algorithm, Naive Bayes, J48 Decision Tree, Pre processing, Enron, TFIDF, Feature size.
A Hexagon Slotted Circular Monopole UWB Antenna for Cognitive Radio Applications
Venue: 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic ETITE)
Year: 2020

The paper emphasizes on Ultra Wide Band (UWB) technology, which gained popularity since nineteenth century. This technology is still proliferating to new branches of research due to its key features like secure, reliable, low power consumption and high speed data transfer over smaller distances. The paper presents a hexagon slotted circular monopole UWB antenna of dimensions 27mmx20mmx1.6mm printed on a FR4 substrate. It achieves less than 10dB return loss performance in the frequencies starting from 2.36GHz to 12.69GHz that covers the FCC unlicensed UWB band 3.1GHz to 10.6GHz. The maximum efficiency of proposed antenna is 97% at 3.1GHz and maximum peak gain is 3dBi at 7.18GHz. This antenna is simulated with HFSS EM simulation tool. The fabricated prototype is tested using VNA. The measured and simulated return loss characteristics have good agreement. This antenna is a good design for spectrum sensing applications in cognitive radio and RF energy harvesting in 4G, LTE, Wi Fi, Wi MAX and 5G bands.
Intersectional power dynamics and extended households: Elderly and widowed women's international migration from Armenia
Year: 2019

Drawing upon interviews and fieldwork conducted in Armenia and Turkey with 25 Armenian migrant women and their non accompanying family members, the present article examines how gendered norms intersecting with age, marital and motherhood statuses have structured the migration decision making process as it occurs at the household level. These migrant women were mostly elderly, widowed and from extended households, where male income support to the family was either insufficient or wholly absent for a variety of reasons. Building on the Household Survival Strategies (HSS) approach, this article examines the dynamism and complex kinship norms in extended households and how these have led some women to assume the role of migrant labourers in a patriarchal context that would ordinarily deny them mobility. While empirically this study sheds light on women's migration from an understudied geography, it also deepens our understanding of the interplay between patriarchy, intersectionality and women's agency outside of the traditional nuclear household.
Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests
Venue: WWW
Year: 2021

Randomized experiments, or "A/B" tests, remain the gold standard for evaluating the causal effect of a policy intervention or product change. However, experimental settings, such as social networks, where users are interacting and influencing one another, may violate conventional assumptions of no interference for credible causal inference. Existing solutions to the network setting include accounting for the fraction or count of treated neighbors in a user's network, yet most current methods do not account for the local network structure beyond simply counting the number of neighbors. Our study provides an approach that accounts for both the local structure in a user's social network via motifs as well as the treatment assignment conditions of neighbors. We propose a two part approach. We first introduce and employ "causal network motifs" which are network motifs that characterize the assignment conditions in local ego networks; and then we propose a tree based algorithm for identifying different network interference conditions and estimating their average potential outcomes. Our approach can account for social network theories, such as structural diversity and echo chambers, and also can help specify network interference conditions that are suitable to each experiment. We test our method on a synthetic network setting and on a real world experiment on a large scale network, which highlight how accounting for local structures can better account for different interference patterns in networks.
Supporting Self efficacy and Learner Autonomy in Relation to Academic Success in EFL Classrooms (A Case Study)
Year: 2011

This research was conducted for revealing the missing point of the discussions related to foreign language teaching and learning in Turkey. This study intended to investigate the relationship between self efficacy and learner autonomy, self efficacy and academic success, learner autonomy and academic success, and these two concepts and academic success. Also, it was aimed to explore the effect of self efficacy on academic success, the effect of learner autonomy on academic success and the effect of self efficacy and learner autonomy on academic success. The study was applied to 250 preparatory level students at Gaziantep, Zirve, Inonu, Selcuk and Karatay Universities in 2010 2011 academic year. The data were collected through Self Efficacy Questionnaire (SEQ) and Autonomous Learner Questionnaire (ALQ) and analyzed by SPSS 19.0. The analysis of the data revealed that there was a positive relationship between self efficacy and learner autonomy (r= .667 p>.01) self efficacy and academic success (r=.597 p>.01) and learner autonomy and academic success (r=.506 p>.01)
Leaders of Tomorrow on the Future of Software Engineering: A Roundtable
Venue: IEEE Softw.
Year: 2016

Nine rising stars in software engineering describe how software engineering research will evolve, highlighting emerging opportunities and groundbreaking solutions. They predict the rise of end user programming, the monitoring of developers through neuroimaging and biometrics sensors, analysis of data from unstructured documents, the mining of mobile marketplaces, and changes to how we create and release software.
Integrated cost, quality, risk and schedule control through earned value management (EVM)
Venue: Journal of Engineering, Design and Technology
Year: 2019

PurposeThe purpose of this paper is to propose an integrated earned value management (EVM) approach to control quality, cost, schedule and risk of projects. study represents a new EVM framework by considering a quality control index. Particularly, some control indices and cumulative buffers are defined by two proposed, methods, namely the linear and Taguchi based methods. These methods are implemented in three different projects in different industries.FindingsAccording to the results, integration of the quality index creates a better control situation by providing more accurate information. Hence, project managers could comprehensively monitor the status of important factors to make more precise decisions while maintaining the simplicity of their analysis. the methodological and theoretical features, this paper offers new visions because, to the best of authors' knowledge, no comparable study has been conducted before.