nils-herrmann/clean_scirepeval huggingface dataset:
Silent Bias: Challenges, Obstacles, and Strategies for Leadership Development in Academic Medicine Lessons From Oral Histories of Women Professors at the University of Kansas
Venue: Academic medicine journal of the Association of American Medical Colleges

Despite dramatic increases in female learners and junior faculty, a significant gap remains in female leadership in academic medicine. To assess challenges and obstacles encountered, strategies for academic success, and lessons learned for leadership development, the authors conducted an in depth study of women full professors.  The authors used a qualitative oral history approach, interviewing 87% of the cohort of female full professors at one Midwestern medical school in 2013 using a pretested, open ended, semistructured interview guide. Interviews were videotaped and the audio recordings transcribed. Content was sorted into categories and key themes identified within each category.  Participants described significant challenges: being treated with "silent bias," "being ignored," and being seen as an "other." Coping strategies included downplaying, keeping a distance, employing humor, and using symbols (e.g. white coat) to carefully present themselves. Explanations for success included intelligence, meritocracy, being even tempered, and carefully constructing femininity. The participants recommended individual skills and actions to prepare for leadership development. Virtually all women could describe an individual mentor (sponsor) usually male, who provided essential assistance for their career success. At the same time, they stressed the importance of institutional support for diversity, especially with child care.  Attaining "full professor" status is the pinnacle of academic success. Women who successfully navigated this academic ladder describe significant external and internal challenges that require multiple strategies to overcome. Leadership development entails a combination of individual support through mentors and sponsors, self education and reflection, and organizational structural support to promote diversity.
Underground Pipeline Management Based on Road Information Modeling to Assist in Road Management

AbstractThe excavation of underground pipelines in Taiwan has led to widespread complaints and, in some cases, claims for national compensation. This issue has also caused significant challenges for road authorities, directly impacting the citizens' satisfaction with the government's policy of the so called Smooth Roads Project. This study interviewed executives from road authorities, pipeline agencies, and engineering consulting companies to obtain a thorough understanding of current underground pipeline management, public utility databases, and common duct policies in Taiwan. In addition, building information modeling (BIM) is currently applied mostly to buildings, rarely to road infrastructure. Therefore, in this study, BIM was used as a basis for developing the concept of road information modeling (RIM) According to the interview surveys, respondents generally agreed that RIM would be tremendously beneficial to underground pipeline management. This study examined frameworks and strategies for the pro.
Understanding and Managing Public Organizations, Fifth Edition

In today's society, the slight mention of the term management may cause someone to sub divide the concept into the roles of management, such as information, marketing, operations, per son nel, production, strategic, and financial manage ment. At the same time, someone else may sub divide the term into types of organizations, such as small business, sports, or university manage ment. Then at another level, it may subdivide into theoretical frameworks such as scientific management or management by objectives.
Managing innovation in eldercare: A glimpse into what and how public organizations are planning to deliver healthcare services for their future elderly

Eldercare policies are being dramatically reshaped due to demographic shifts worldwide. The elderly are living longer and healthier, and their infrastructural impacts on society are well known among researchers. It is known that most countries will be experiencing unprecedented growths in their elder population, but what is less known is what and how public entities are going to meet these upcoming challenges. The aim of this paper is to analyze eldercare innovations in light of the increasing numbers of elderly, with the support of eldercare theory. This empirical study will deepen the understanding of eldercare by showing the current strategic direction of leaders in this field. We found a lack of innovation strategy in formal innovation training, recruitment of workers, and knowledge sharing channels between municipalities. It's clear that Norway's innovation strategy is to facilitate healthy aging for the elderly in their own homes as long as possible. Most developments have been smart, in home technology. Accordingly, eldercare theory would urge Norwegian municipalities to strive for more balance in their eldercare system, inter alia, by developing innovation processes, improving recruitment, or reshaping social responsibility. We have enumerated, in the conclusion, how municipalities and other public entities can learn from this study.
Information Technology Governance in Public Organizations: Understanding the Expectations of its Adoption through the Lens of Organizational Citizenship
Venue: ECIS

The evolution of Information Technology (IT) stimulates several changes in citizen's profiles and government openness. It generates new demands whose operationalization depends on IT solutions. Managing IT is no longer enough; it is necessary to go one step further in governance process. IT Governance (ITG) can support the organizational decision making on its IT initiatives, increasing the alignment IT/business. The goal is to understand how ITG influences the behaviour of individuals within organizations. We selected Organizational Citizenship Behaviour (OCB) concept to analyse individuals' voluntary commitment within an organization that is not part of their contractual tasks. Our premise is that ITG acts on the antecedents of the organizational citizenship behaviour such as job satisfaction and rewards perception. The research was conducted through a focus group made up of the members of an IT State Governance Committee in Brazil. Questions based on OCB were discussed in order to understand the behavioural changes expectations along the ITG mechanisms adoption. The preliminary results showed that citizenship behaviour could improve because of the ITG process. Based on the results, we proposed a preliminary conceptual model, which will be tested in the next steps through a survey research in all Brazilian states.
Spatial distribution and temporal trends of rainfall erosivity in mainland China for 1951 2010

Rainfall erosivity is an important factor for estimating soil erosion rates. Understanding the spatial distribution and temporal trends of rainfall erosivity is especially critical for soil erosion risk assessment and soil conservation planning in mainland China. However, reports on the spatial distribution and temporal trends of rainfall erosivity for China, especially of its eight soil erosion regions, are still lacking, which reduces the accuracy of predicting soil losses, assessing soil erosion risks and evaluating the effects of soil conservation measures. Additionally, the lack of the most suitable spatial interpolation method in mainland China, to some degree, has reduced the applicability and reliability of the interpolation results. In this study, long term (1951 2010) daily rainfall data from 756 national weather stations were assembled to characterize the spatial and temporal patterns of annual rainfall erosivity across mainland China. Sixteen spatial interpolation methods were compared to select the most suitable one for accurately mapping the spatial distribution of rainfall erosivity, and the Mann Kendall test was employed to detect the temporal trends. The results indicated that 1) the universal co kriging method with the aid of elevation was superior to the other spatial interpolation methods; 2) long term average rainfall erosivity increased from the northwest to the southeast, ranging from 31 to 30,051 MJ mm ha 1 h 1 a 1; 3) overall, rainfall erosivity across China and water erosion regions experienced an insignificant increasing trend over the study period. Significant decreasing trends were observed in the northwest Loess Plateau region (0.01 level) the northeast black soil region and the north earth and gravel mountain region (0.05 level) Significant increasing trends (0.05 level) were found in the southern red soil hilly region and the southwest Karst region; and 4) two lines were identified according to the temporal trends of rainfall erosivity from the east to the west. In total, this study offers useful information both for soil erosion prediction and land management practices of mainland China.
Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks
Venue: USENIX Annual Technical Conference

Human chosen text passwords, today's dominant form of authentication, are vulnerable to guessing attacks. Unfortunately, existing approaches for evaluating password strength by modeling adversarial password guessing are either inaccurate or orders of magnitude too large and too slow for real time, client side password checking. We propose using artificial neural networks to model text passwords' resistance to guessing attacks and explore how different architectures and training methods impact neural networks' guessing effectiveness. We show that neural networks can often guess passwords more effectively than state of the art approaches, such as probabilistic context free grammars and Markov models. We also show that our neural networks can be highly compressed to as little as hundreds of kilobytes without substantially worsening guessing effectiveness. Building on these results, we implement in JavaScript the first principled client side model of password guessing, which analyzes a password's resistance to a guessing attack of arbitrary duration with sub second latency. Together, our contributions enable more accurate and practical password checking than was previously possible.
On the position analysis of a new spherical parallel robot with orientation applications

In this paper, we propose a new spherical parallel robot for celestial orientation, and rehabilitation applications (TV satellite dish, tracking systems, solar panels, cameras, telescopes, table of the machine tools, ankle, shoulder, wrist and etc. The proposed robot can completely rotate about an axis. After describing the robot and its inverse position analysis, using the genetic algorithm, the dimensional optimization to maximize the workspace of the robot is performed. The workspace analysis shows that the proposed robot has a relatively large workspace. Also, singularity analysis represents that the manipulator is a singularity free workspace. It is a great advantage of the proposed robot. Next, an optimal approach is proposed for solving the direct position problem of the robot. According to the geometry of the robot, two coupled trigonometric equations are obtained through using a special form of Rodrigues' rotation formula. Next, the two coupled equations are transformed to a 8 degrees polynomial using the Sylvester's Dialytic elimination method. Finally, a numerical example for the robot with an asymmetric structure is given with eight real solutions. Therefore, the polynomial being minimal and the proposed approach is optimal. This greatly decreases computational time, which is necessary for dynamics, control and simulation. A new spherical parallel robot for orientation applications is proposed.The proposed robot can completely rotate about an axis.The dimensional optimization to maximize the workspace of the robot is performed.An innovative approach is proposed for solving the direct position problem.
Biomimetic flexible robot arm design and kinematic analysis of a novel flexible robot arm
Venue: 2016 Moratuwa Engineering Research Conference (MERCon)

Conventional robot manipulators have singularities in their workspaces and constrained spatial movements. Flexible and soft robots provide a unique solution to overcome this limitation. Flexible robot arms have biologically inspired characteristics as flexible limbs and redundant degrees of freedom. From these special characteristics, flexible manipulators are able to develop abilities such as bend, stretch and adjusting stiffness to traverse a complex maze. Many researchers are working to improve capabilities of flexible arms by improving the number of degrees of freedoms and their methodologies. The proposed flexible robot arm is composed of multiple sections and each section contains three similar segments and a base segment. These segments act as the backbone of the basic structure and each section can be controlled by changing the length of three control wires. These control wires pass through each segment and are held in place by springs. This design provides each segment with 2 DOF. The proposed system single section can be bent 90deg with respective to its centre axis. Kinematics of the flexible robot is derived with respect to the base segment.
IMPACT OF THREE ALTERNATIVE SURFACING PROCESSES ON WEATHERING PERFORMANCE OF AN EXTERIOR WATER BASED COATING

Oblique cutting, face milling, and helical planing were used to surface black spruce wood prior to the application of an exterior acrylic water based coating. Surface characteristics were assessed using an environment scanning electron microscope and coating performance was evaluated through pull off measurements before and after a 3 year natural weathering exposure. Microscopically, oblique cut surfaces were smooth with plateau like areas, had a low level of fibrillation and few open lumens. Face milled surfaces were characterized by a high level of fibrillation and numerous open lumens that favor coating spreading and penetration. Helical planed specimens had an intermediate level of fibrillation and number of open lumens. After coating application, oblique cut and helical planed surfaces presented similar overall visual quality, whereas face milled samples had an irregular appearance that degraded their quality. As a result, the latter were subjected to erosion during weathering exposure which further degraded their overall quality as well as pull off strength. More specifically, face milled samples had a significant inferior pull off strength both before and after weathering. Oblique cut specimens yielded higher initial pull off strength but were associated with higher adhesion loss. According to the results, helical planing reduces adhesion loss of the coating studied over a 3 year exposure to yield a superior pull off strength after weathering. Therefore, surfaces having a certain level of fibrillation still firmly attached to the surface and open lumens are desirable to increase mechanical anchorage of coating on black spruce wood surfaces.
Solution approach of a laser plane based on Plucker matrices of the projective lines on a flexible 2D target.
Venue: Applied optics

A calibration method adopting Plucker matrices is proposed to explore the laser plane in a structured light measurement. The calibration model establishes the geometrical relationship among the camera, 2D target, and laser plane. The laser plane is constructed by multiple Plucker matrices of the dual 3D crossing lines between the laser plane and target planes in the camera coordinate system. Moreover, the validity of this calibration method is experimentally analyzed through the impact factors of noise magnitude and number of images. The mean errors of three directional angles of the normal vector to the laser plane are 0.174deg,  The variances of the errors of three directional angles are 0.069deg,  The maximal absolute errors of three directional angles are 1.362deg, 1.351deg, and 1.347deg, respectively. The experiments prove that the calibration method is available to provide an accurate calibration for the laser plane.
Fine grained sketch based image retrieval: The role of part aware attributes
Venue: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

We study the problem of fine grained sketch based image retrieval. By performing instance level (rather than category level) retrieval, it embodies a timely and practical application, particularly with the ubiquitous availability of touchscreens. Three factors contribute to the challenging nature of the problem: (i) free hand sketches are inherently abstract and iconic, making visual comparisons with photos more difficult, (ii) sketches and photos are in two different visual domains, i.e. black and white lines vs. color pixels, and (iii) fine grained distinctions are especially challenging when executed across domain and abstraction level. To address this, we propose to detect visual attributes at part level, in order to build a new representation that not only captures fine grained characteristics but also traverses across visual domains. More specifically, (i) we propose a dataset with 304 photos and 912 sketches, where each sketch and photo is annotated with its semantic parts and associated part level attributes, and with the help of this dataset, we investigate (ii) how strongly supervised deformable part based models can be learned that subsequently enable automatic detection of part level attributes, and (iii) a novel matching framework that synergistically integrates low level features, mid level geometric structure and high level semantic attributes to boost retrieval performance. Extensive experiments conducted on our new dataset demonstrate value of the proposed method.
Task Driven Progressive Part Localization for Fine Grained Object Recognition
Venue: IEEE Transactions on Multimedia

The problem of fine grained object recognition is very challenging due to the subtle visual differences between different object categories. In this paper, we propose a task driven progressive part localization (TPPL) approach for fine grained object recognition. Most existing methods follow a two step approach that first detects salient object parts to suppress the interference from background scenes and then classifies objects based on features extracted from these regions. The part detector and object classifier are often independently designed and trained. In this paper, our major finding is that the part detector should be jointly designed and progressively refined with the object classifier so that the detected regions can provide the most distinctive features for final object recognition. Specifically, we develop a part based SPP net (Part SPP) as our baseline part detector. We then establish a TPPL framework, which takes the predicted boxes of Part SPP as an initial guess, and then examines new regions in the neighborhood using a particle swarm optimization approach, searching for more discriminative image regions to maximize the objective function and the recognition performance. This procedure is performed in an iterative manner to progressively improve the joint part detection and object classification performance. Experimental results on the Caltech UCSD 200 2011 dataset demonstrate that our method outperforms state of the art fine grained categorization methods both in part localization and classification, even without requiring a bounding box during testing.
Object Detection via End to End Integration of Aspect Ratio and Context Aware Part based Models and Fully Convolutional Networks
Venue: ArXiv

This paper presents a framework of integrating a mixture of part based models and region based convolutional networks for accurate and efficient object detection. Each mixture component consists of a small number of parts accounting for both object aspect ratio and contextual information explicitly. The mixture is category agnostic for the simplicity of scaling up in applications. Both object aspect ratio and context have been extensively studied in traditional object detection systems such as the mixture of deformable part based models [13] They are, however, largely ignored in deep neural network based detection systems[17, 16, 39, 8] The proposed method addresses this issue in two fold: (i) It remedies the wrapping artifact due to the generic RoI (region of interest) pooling (e.g. a 3x 3 grid) by taking into account object aspect ratios. (ii) It models both global (from the whole image) and local (from the surrounding of a bounding box) context for improving performance. The integrated framework is fully convolutional and enjoys end to end training, which we call the aspect ratio and context aware fully convolutional network (ARCFCN) In experiments, ARC FCN shows very competitive results on the PASCAL VOC datasets, especially, it outperforms both Faster R CNN [39] and R FCN [8] with significantly better mean average precision (mAP) using larger value for the intersection over union (IoU) threshold (i.e. 0.7 in the experiments) ARC FCN is still sufficiently efficient with a test time speed of 380ms per image, faster than the Faster R CNN but slower than the R FCN. Tianfu Wu is the corresponding author. This work was done when Shuai Shao was an Intern at YunOS BU, Alibaba Group. Feature Warp RoI Pooling Fast /Faster RCNN R CNN
A large scale video codec comparison of x264, x265 and libvpx for practical VOD applications
Venue: Optical Engineering Applications

Over the last years, we have seen exciting improvements in video compression technology, due to the introduction of HEVC and royalty free coding specifications such as VP9. The potential compression gains of HEVC over H.264/AVC have been demonstrated in different studies, and are usually based on the HM reference software. For VP9, substantial gains over H.264/AVC have been reported in some publications, whereas others reported less optimistic results. Differences in configurations between these publications make it more difficult to assess the true potential of VP9. Practical open source encoder implementations such as x265 and libvpx (VP9) have matured, and are now showing high compression gains over x264. In this paper, we demonstrate the potential of these encoder imple mentations, with settings optimized for non real time random access, as used in a video on demand encoding pipeline. We report results from a large scale video codec comparison test, which includes x264, x265 and libvpx. A test set consisting of a variety of titles with varying spatio temporal characteristics from our catalog is used, resulting in tens of millions of encoded frames, hence larger than test sets previously used in the literature. Re sults are reported in terms of PSNR, SSIM, MS SSIM, VIF and the recently introduced VMAF quality metric. BD rate calculations show that using x265 and libvpx vs. x264 can lead to significant bitrate savings for the same quality. x265 outperforms libvpx in most cases, but the performance gap narrows (or even reverses) at the higher resolutions.
Sparse flow sensitive pointer analysis for multithreaded programs
Venue: 2016 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

For C programs, flow sensitivity is important to enable pointer analysis to achieve highly usable precision. Despite significant recent advances in scaling flow sensitive pointer analysis sparsely for sequential C programs, relatively little progress has been made for multithreaded C programs. In this paper, we present FSAM, a new Flow Sensitive pointer Analysis that achieves its scalability for large Multithreaded C programs by performing sparse analysis on top of a series of thread interference analysis phases. We evaluate FSAM with 10 multithreaded C programs (with more than 100K lines of code for the largest) from Phoenix 2.0, Parsec 3.0 and open source applications. For two programs, raytrace and x2 64, the traditional data flow based flow sensitive pointer analysis is unscalable (under two hours) but our analysis spends just under 5 minutes on raytrace and 9 minutes on x264. For the rest, our analysis is 12x faster and uses 28x less memory.