nils-herrmann/clean_scirepeval huggingface dataset:
Performance driven instrumentation and mapping strategies using the LARA aspect oriented programming approach
Venue: Softw. Pract. Exp.
Year: 2016

The development of applications for high performance embedded systems is a long and error prone process because in addition to the required functionality, developers must consider various and often conflicting nonfunctional requirements such as performance and/or energy efficiency. The complexity of this process is further exacerbated by the multitude of target architectures and mapping tools. This article describes LARA, an aspect oriented programming language that allows programmers to convey domain specific knowledge and nonfunctional requirements to a toolchain composed of source to source transformers, compiler optimizers, and mapping/synthesis tools. LARA is sufficiently flexible to target different tools and host languages while also allowing the specification of compilation strategies to enable efficient generation of software code and hardware cores (using hardware description languages) for hybrid target architectures a unique feature to the best of our knowledge not found in any other aspect oriented programming language. A key feature of LARA is its ability to deal with different models of join points, actions, and attributes. In this article, we describe the LARA approach and evaluate its impact on code instrumentation and analysis and on selecting critical code sections to be migrated to hardware accelerators for two embedded applications from industry.
The Effect of Digital Stories on Academic Achievement: A Meta Analysis
Year: 2020

The purpose of this study is to determine the effect of digital stories on academic achievement. In order to achieve this purpose, meta analysis method was used in the study. Within the scope of the study, ERIC, Google Academic, YOK Thesis Center, ProQuest, Science Direct and ULAKBIM databases were scanned and 23 studies (10 theses, 13 articles) were included in the meta analysis using the criteria determined by the researchers. Cochran's X (Q 285,155, p .05) test was conducted to test whether the studies included in the study were heterogeneous. Random effects model was used to calculate effect size since heterogeneity was determined between studies. At the end of the study, it was seen that the general effect size (Hedge's g 1.081) regarding the effect of digital stories on academic achievement was strong, that there was a positive effect in all areas according to the lessons which was higher for Science. It was also observed that the effect was positive in all dimensions according to the education level, there was a difference between the education levels and the highest effect occurred at university and middle school levels.
Lean startup orientation empirical evidence on venture success
Year: 2018

The LSM has reached a high popularity and wide use in the startup world. However, it's unknown if LSM is positively related to the performance of a new venture. Existing research on the effectiveness of LSM is either scarce or indirect. Therefore, the popularity across entrepreneurs using LSM is in contrast with the lack of its empirical validation. First qualitative research and operationalisation to reveal the essence of LSM exists but an empirical validation is still missing. This thesis advances the academic discourse on LSM by providing direct evidence that supports the effectiveness claim of LSM. First, the degree to which startups use Lean Startup (Lean Startup Orientation, LSO) is presented. The quantitative analysis of data collected by 100 Berlin based software startups revealed a strong, robust and highly significant relationship between LSO and performance. Therefore, LSO delivers on its promise for new venture performance. The relevance for research lay in the proposed operationalisation of LSO that future research can build on and refine. Moreover, evidence for the positive performance impact of experiential entrepreneurship is provided. The empirical validation of LSO activities contributes to existing management strategies by providing strong justification for lean startup capabilities leading to higher likelihood of success.
Hybrid Mobile Edge Computing: Unleashing the Full Potential of Edge Computing in Mobile Device Use Cases
Venue: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
Year: 2017

Many different technologies fostering and supporting distributed and decentralized computing scenarios emerged recently. Edge computing provides the necessary on demand computing power for Internet of Things (IoT) devices where it is needed. Computing power is moved closer to the consumer, with the effect of reducing latency and increasing fail safety due to absent centralized structures. This is an enabler for applications requiring high bandwidth uplinks and low latencies to computing units. In this paper, a new use case for edge computing is identified. Mobile devices can overcome their battery limitations and performance constraints by dynamically using the edge computing provided computational power. We call this new technology Hybrid Mobile Edge Computing. We present a general architecture and framework, which targets the mobile device use case of hybrid mobile edge computing, not only considering the improvement of performance and energy consumption, but also providing means to protect user privacy, sensitive data and computations. The achieved results are backed by the results of our analysis, targeting the energy saving potentials and possible performance improvements.
Colon cancer, version 3.2014.
Venue: Journal of the National Comprehensive Cancer Network JNCCN
Year: 2014

The NCCN Guidelines for Colon Cancer address diagnosis, pathologic staging, surgical management, perioperative treatment, posttreatment surveillance, management of recurrent and metastatic disease,and survivorship. This portion of the guidelines focuses on the use of systemic therapy in metastatic disease. The management of metastatic colorectal cancer involves a continuum of care in which patients are exposed sequentially to a variety of active agents, either in combinations or as single agents. Choice of therapy is based on the goals of treatment, the type and timing of prior therapy, the different efficacy and toxicity profiles of the drugs, the mutational status of the tumor, and patient preference.
Learning Loss for Active Learning
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

The performance of deep neural networks improves with more annotated data. The problem is that the budget for annotation is limited. One solution to this is active learning, where a model asks human to annotate data that it perceived as uncertain. A variety of recent methods have been proposed to apply active learning to deep networks but most of them are either designed specific for their target tasks or computationally inefficient for large networks. In this paper, we propose a novel active learning method that is simple but task agnostic, and works efficiently with the deep networks. We attach a small parametric module, named `loss prediction module, to a target network, and learn it to predict target losses of unlabeled inputs. Then, this module can suggest data that the target model is likely to produce a wrong prediction. This method is task agnostic as networks are learned from a single loss regardless of target tasks. We rigorously validate our method through image classification, object detection, and human pose estimation, with the recent network architectures. The results demonstrate that our method consistently outperforms the previous methods over the tasks.
The Dynamics between State Control and Metropolitan Governance Capacity
Year: 2020

Due to the national importance of metropolitan areas, central governments seek to make them as attractive and competitive as possible. In many cases, this is done through state control over metropolitan areas and their governance arrangements. This article examines the relationship between state control and metropolitan governance capacity based on a comparative case study of four metropolitan areas: Auckland, Dublin, Montreal and Oslo. These areas represent different models of metropolitan governance, as well as different forms of state control. We studied state control by focusing on political and administrative decentralization: how independently can the metropolitan government make decisions and perform its tasks? The conclusion is that the different dimensions of decentralization are connected to capacity, but not straightforwardly. This study indicates that state involvement through different forms of control can increase metropolitan governance capacity, but only if it is combined with mandate and supportive policy actions.
An Ensemble based Modulation Recognition using Feature Extraction
Venue: 2021 International Conference on Intelligent Technologies (CONIT)
Year: 2021

Due to the increase in wireless devices, there has been an increasing demand for spectrum utilization and intelligent radios. This is done by sensing the spectrum to collect important information regarding the received signal and is generally used in military applications to identify or decode enemy signals. Software defined radio (SDR) and Cognitive radio (CR) are important civilian applications for this field. These types of task fall into the broader study of signal recognition, which is quite difficult due to many different parts of the signal to be identified just by using the received signal. In this paper, we explore the viability of different hand crafted features from various domains such as Time, Frequency, and Statistics for the task of Automatic Modulation Classification (AMC) For the classification we are using an ensemble learning approach with Random Forests (RF) model, and comparing it with Gini based Decision tree (DT) algorithm. We found that the accuracy of RF model is 10% higher than the DT model for medium to high SNR values and also one feature is dominating the prediction in case of DT approach. Using the scikit learn machine learning library, we are able to get \approx$75% accuracy without using any complex Deep Learning architectures or expensive hardware.
Does non hard sell content really work? Leveraging the value of branded content marketing in brand building
Venue: Journal of Product Brand Management
Year: 2019

This study aims to test the role of branded content marketing on YouTube in brand building and explicates the mechanism through which brand content influences brand loyalty and purchase intentions.  This study uses a quantitative, Web based, three step randomized intervention design and recruits YouTube users through the Amazon Mechanical Turk (n 925) Findings Post intervention results (n 596) show that consumers' repeated exposure to branded content facilitates their social learning processes. Consumers derive value from the relevant content and subsequently form more favorable brand attitudes, greater brand loyalty and heightened purchase intentions. Brand loyalty mediates the effect of perceived brand content value on purchase intentions.  This study's findings support the advantages of investing in the creation and dissemination of valuable brand content through a brand's own social media channel(s) While informative content and entertaining content can both drive brand loyalty, high product involvement brands are advised to emphasize on informative content to precipitate brand consumer attachment. Low product involvement brands, on the other hand, are advised to feature more enticing and captivating content to stimulate consumer devotion.  This study reveals the positive impact of branded content marketing within social media on consumers' brand attitudes, brand loyalty and purchase intentions. It also explicates the mechanism through which content marketing influences brand evaluation and purchase intentions by coordinating consumer learning and value derivation.
Navigation referencee multi capteurs d'un robot mobile en environnement encombre
Year: 2012

Dans ce travail, nous nous interessons a la navigation referencee vision d'un robot mobile equipe d'une camera dans un environnement encombre d'obstacles possiblement occultants. Pour realiser cette tache, nous nous sommes appuyes sur l'asservissement visuel 2D. Cette technique consiste a synthetiser une loi de commande basee sur les informations visuelles renvoyees par la camera embarquee. Le robot atteint la situation desiree lorsque les projections dans l'image de l'amer d'interet, appeles indices visuels, atteignent des valeurs de consigne predefinies. La navigation par asservissement visuel 2D necessite de s'interesser a trois problemes garantir l'integrite du robot vis a vis des obstacles, gerer les occultations des amers d'interets et realiser de longs deplacements. Nos contributions portent sur les deux derniers problemes mentionnes. Dans un premier temps nous nous sommes interesses a l'estimation des indices visuels lorsque ceux ci ne sont plus disponibles a cause d'une occultation. La profondeur etant un parametre determinant dans ce processus, nous avons developpe une methode permettant de l'estimer. Celle ci est basee sur une paire predicteur/correcteur et permet d'obtenir des resultats exploitables malgre la presence de bruits dans les mesures. Dans un second temps, nous nous sommes attaches a la realisation de longs deplacements par asservissement visuel. Cette technique necessitant de percevoir l'amer d'interet des le debut de la tache, la zone de navigation est limitee par la portee de la camera. Afin de relaxer cette contrainte, nous avons elabore un superviseur que nous avons ensuite couple a une carte topologique integrant un ensemble d'amers caracteristiques de l'environnement. La tache de navigation globale peut alors etre decomposee sous la forme d'une sequence d'amers a atteindre successivement, la selection et l'enchainement des mouvements necessaires etant effectues au sein du superviseur. Les travaux ont ete valides par le biais de simulations et d'experimentations, demontrant la pertinence et l'efficacite de l'approche retenue.
Strategies Adopted in Adventure Tourism Product Diversification in Sustainable Tourism Development in Elgeyo Marakwet County, Kenya
Year: 2018

Tourism as a sector of the economy is used by most countries in the advocacy of economic development. Most of the adventure tourists/visitors travel to rural areas to experience natural wonders of the world which are prominently features such as great waterfalls, volcanic phenomena, and geological phenomena like caves or spectacular views. This study investigated the strategies adopted in adventure tourism product diversification for sustainable tourism development in Elgeyo Marakwet County, Kenya. The study employed descriptive research design in which the target population included 2 County government officials, 1 national government representative, 1 Kerio Valley Development Authority (KVDA) official, 1,250 Athletes (High Altitude camp) 3 hotel managers, 4,876 tourists, 3 private investors and 24,450 community members. The sample size for each category was determined and distributed proportionately as follows: Community Members were 316, tourists 63 and athletes 16. Census approach was used for County Government Officials, National Government official (Tourism Regulatory Officer) KVDA (tourism officer) hotel managers and private investors since they were few in number. Therefore, the total sample size was 405 respondents. Purposive sampling strategy was utilized to choose the respondents for every one of the Community Members, tourists and athletes. Questionnaires and interview guides were used to collect the data. Data analysis was done using descriptive and inferential statistics techniques. Results revealed that adventure products influenced tourism diversification in Elgeyo Marakwet County. Some of the prominent adventure products included paragliding sports on the escarpments of Elgeyo Marakwet County, cycling, trekking, camping (Rimoi National reserve) caves (Kiplachoch cave in Kapyego Marakwet East Sub county) Torok falls situated at high cliff, Teren rapids with pot like shapes, Kipkabus dam, Chebloch George, Kipteber hills, hanging valley and birds migratory. It was recommended that there was need to focus on new adventure products should be encouraged in order to increase tourism diversity. Policies on adventure tourism should be formulated and implemented to increase the diversification of tourism.  Strategies adopted, Adventure products, Tourism diversification, Tourism development and Elgeyo Marakwet County
Articulated Robot Motion for Simultaneous Localization and Mapping (ARM SLAM)
Venue: IEEE Robotics and Automation Letters
Year: 2016

A robot with a hand mounted depth sensor scans a scene. When the robot's joint angles are not known with certainty, how can it best reconstruct the scene? In this work, we simultaneously estimate the joint angles of the robot and reconstruct a dense volumetric model of the scene. In this way, we perform simultaneous localization and mapping in the configuration space of the robot, rather than in the pose space of the camera. We show using simulations and robot experiments that our approach greatly reduces both 3 D reconstruction error and joint angle error over simply using the forward kinematics. Unlike other approaches, ours directly reasons about robot joint angles, and can use these to constrain the pose of the sensor. Because of this, it is more robust to missing or ambiguous depth data than approaches that are unconstrained by the robot's kinematics.
On the Robustness of Self Attentive Models
Venue: ACL
Year: 2019

This work examines the robustness of self attentive neural networks against adversarial input perturbations. Specifically, we investigate the attention and feature extraction mechanisms of state of the art recurrent neural networks and self attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks. We also propose a novel attack algorithm for generating more natural adversarial examples that could mislead neural models but not humans. Experimental results show that, compared to recurrent neural models, self attentive models are more robust against adversarial perturbation. In addition, we provide theoretical explanations for their superior robustness to support our claims.
WISC V Canadian Norms: Relevance and Use in the Assessment of Canadian Children
Year: 2018

Following debate over the use of the WAIS IV Canadian adult norms that has been suitably addressed, this study examines the normative differences in the WISC V child and adolescent data in three ways: (a) presenting a comparison of country normative data across subtest and composite scores for demographically matched and unmatched samples, (b) providing a review of Canadian U.S. standard FSIQ score differences across ability level, and (c) comparing the case detection accuracy of the WISC V CDN versus U.S. norms for children with intellectual disability and giftedness. Results demonstrate small mean differences overall in the WISC V norms when comparing the standardization samples in the two countries; however, there are significant observable differences in the sensitivity of the norms in identifying case status. This is vital for accurate identification, and supports the use of Canadian norms in clinical and gifted decision making. These results support the use of Canadian norms when assessing these exceptional children following best clinical assessment practices and testing guidelines. A la suite du debat concernant l'utilisation des normes adultes canadiennes WAIS IV, lequel a a ete relativement bien adresse, la presente etude examine les ecarts normatifs au niveau des donnees sur les enfants et les adolescents de la WISC V de trois facons (a) en presentant une comparaison des donnees normatives par pays sur un ensemble de scores de sous tests et composites pour des echantillons apparies et non apparies sur le plan demographique, (b) en fournissant un examen des ecarts de scores FSIQ standard entre le Canada et les E. U. sur l'ensemble des niveaux de capacites, et (c) en comparant la precision en ce qui a trait a la detection des cas de la WISC V par rapport aux normes americaines chez les enfants atteints de deficience intellectuelle et de douance. Les resultats montrent de faibles ecarts moyens dans l'ensemble au niveau des normes WISC V lorsque l'on compare les echantillons de normalisation dans les deux pays; or, il existe des differences notables dans la sensibilite des normes en ce qui a trait a l'identification de l'etat des cas. Cela s'avere capital pour une identification adequate et soutient l'utilisation des normes canadiennes pour la prise de decisions cliniques et celles de personnes douees. Ces resultats appuient l'utilisation des normes canadiennes dans l'evaluation de ces enfants exceptionnels conformement aux pratiques d'evaluation clinique exemplaires et aux lignes directrices de mise a l'essai.
DIFFERENT FAILURE MODES ASSESSMENT TO IMPROVE THE SANDWICH COMPOSITE PANEL STIFFNESS WITH HONEYCOMB CORE FOR MARINE STRUCTURES APPLICATION
Year: 2016

This research paper focuses on the prediction of different failure modes to improve the sandwich composite panel with honeycomb core for application in marine structures. Marine, automotive and aerospace industries are continually trying to optimize material performance in terms of strength and weight. Success has been achieved through the growth of high performance materials, including fibrous composites such as ceramics, new alloys, and carbon fiber composites and through the use of structural concepts such as sandwich composite panel construction. Sandwich composite panel construction with honeycomb core consists of three components: two facing sheets, the core that fill the space between the facing sheet and the core to facing bonding adhesives. The facing sheets of a sandwich panel can be compared to the flanges of an I beam element, as they carry the bending stresses to which the beam is subjected. With one facing sheet in compression, the other is in tension. Similarly the honeycomb core corresponds to the web of the I beam that resists the shear loads and vertical compressive load to the face sheet. This paper presents a model for prediction of different failure mode of face sheet and core material. The obtained results of this model were compared with experimental results and presents that it is a simple and good model.
AIRPORT CAPACITY REPRESENTATION BASED ON AIRSIDE SIMULATIONS
Year: 2012

Airport capacity as key indicator of airport system performance, measures a variable with an increasingly constraining effect on air traffic development, so that airport capacity optimization is becoming one of the main objectives for future developments of airport infrastructure and aircraft alike. Computer simulations are commonly used to perform "what if studies" to evaluate alternative future development options in this field, while there is still no established practice for the presentation of meaningful airport airside capacity values based on simulation output. In this paper an approach is presented to establish robust capacity values based on fast time simulation results. It is based on previous work by E. Gilbo  adapted to ensure robust results while taking the requirements in the context of the simulation based approach into account and enhanced to produce not only ultimate capacity figures but to also evaluate the practical capacity of an airport system, which has direct practical relevance especially in the context of flight scheduling. The results of an exemplary application demonstrate that the capacity representation provides a good understanding of the capacity impact for different development alternatives while it is also possible to aggregate the results to a single relevant performance indicator value.