nils-herrmann/clean_scirepeval huggingface dataset:
A worldwide flock of Condors: Load sharing among workstation clusters

Condor is a distributed batch system for sharing the workload of compute-intensive jobs in a pool of Unix workstations connected by a network. In such a Condor pool, idle machines are spotted by Condor and allocated to queued jobs, thus putting otherwise unutilized capacity to e cient use. When institutions owning Condor pools cooperate, they may wish to exploit the joint capacity of their pools in a similar way. So the need arises to extend the Condor load-sharing and protection mechanisms beyond the boundaries of Condor pools, or in other words, to create a ock of Condors. Such a ock may include Condor pools connected by local-area networks as well as by wide-area networks. In this paper we describe the design and implementation of a distributed, layered Condor ocking mechanism. The main concept in this design is the Gateway Machine that represents in each pool idle machines from other pools in the ock and allows job transfers across pool boundaries. Our ocking design is transparent to the workstation owners, to the users, and to Condor itself. We also discuss our experiences with an intercontinental Condor ock.
Modified Watershed Segmentation withDenoising of Medical Images

De-noising and segmentation are fundamental steps in processing of images. They can be used as preprocessing and post-processing step. They are used to enhance the image quality. Various medical imaging that are used in these days are Magnetic Resonance Images (MRI), Ultrasound, X-Ray, CT Scan etc. Various types of noises affect the quality of images which may lead to unpredictable results. Various noises like speckle noise, Gaussian noise and Rician noise is present in ultrasound, MRI respectively. With the segmentation region required for analysis and diagnosis purpose is extracted. Various algorithm for segmentation like watershed, K-mean clustering, FCM, thresholding, region growing etc. exist. In this paper, we propose an improved watershed segmentation using denoising filter. First of all, image will be de-noised with morphological opening-closing technique then watershed transform using linear correlation and convolution operations is applied to improve efficiency, accuracy and complexity of the algorithm. In this paper, watershed segmentation and various techniques which are used to improve the performance of watershed segmentation are discussed and comparative analysis is done.
Applicability of Fractional Slot Axial Flux Permanent Magnet Synchronous Machines in the Field Weakening Region

In this paper, a complete sensitivity analysis of the optimal parameters for the axial flux permanent magnet synchronous machines working in the field weakening region is implemented. Thanks to the presence of a parameterized accurate analytical model, it is possible to obtain all the required parameters of the machine. The two goals of the ideal design are to maximize the power density: <inline-formula><tex-math notation="LaTeX">$P_{\text{density}}$ </tex-math></inline-formula> and the ratio of maximal to rated speed: <inline-formula><tex-math notation="LaTeX"> $n_{\max}/n_r$</tex-math></inline-formula>, which is an inductance related parameter keeping the efficiency at the target speed above 90<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. Different slots/poles/phases combinations are studied to reveal the optimum combination for each phase. This paper has studied the effect of the ratio of number of stator slots to number of rotor poles on the <inline-formula> <tex-math notation="LaTeX">$P_{\text{density}}$</tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$n_{\max}/n_r$</tex-math></inline-formula>. It is shown that a low value of this parameter results in a better <inline-formula><tex-math notation="LaTeX">$P_{\text{density}}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$n_{\max}/n_r$</tex-math></inline-formula>. The effect of the outer diameter, and the inner to outer diameter ratio are studied with respect to the two design goals. In addition, a comparison between the finite and the theoretical infinite speed designs is implemented. A complete 3D finite element validation has proven the robustness of the analytical model.
Music Personalization at Spotify

Spotify is the world's largest on-demand music streaming company, with over 75 million active listeners choosing what to listen to among tens of millions songs. Discovery and personalization is a key part of the experience and critical to the success of the creator and consumer ecosystem. In this talk, we'll discuss the state of our current discovery approaches, such as the Discover Weekly playlist that has already streamed billions of new discoveries and Fresh Finds, a scalable platform for brand new music that focuses suggestions on the long end of the popularity tail. We'll discuss the technologies at scale necessary to distill the information about music from our listeners and the world at large we collect outside of Spotify -- with the massive amounts of user-item activity data we collect every day to create highly personalized music experiences. Entire teams at Spotify focus on understanding both the creator and listener through collaborative filtering, machine learning, DSP and NLP approaches -- we crawl the web for artist information, scan each note in every one of our millions of songs for acoustic signals, and model users' taste through a cluster analysis and in a latent space based on their historical and real-time listening patterns. The data generated by these analyses have ensured our discovery products are precise and help our users enjoy music and media across our entire catalog. We'll dive deep into the workings of Discover Weekly, our marquee personalized playlist which updates weekly and reached 1 billion streams within the first 10 weeks from its release. The technology behind Discover Weekly is powered by a scalable factor analysis of Spotify's over two billion user-generated playlists matched to each user's current listening behavior. We'll discuss its innovative genesis and the challenges and opportunities the system faces a year after its launch. We'll also discuss Spotify's home page, seen by each of our users, currently undergoing vast efforts around personalization to ensure each listener gets a targeted list of playlists, shows and music to select throughout their day. We'll discuss the various similarity metrics, ranking approaches and user modeling we're working on to increase precision and optimize for our users' happiness.
Learning Image Representations Tied to Egomotion from Unlabeled Video

Understanding how images of objects and scenes behave in response to specific egomotions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images. We propose a new “embodied” visual learning paradigm, exploiting proprioceptive motor signals to train visual representations from egocentric video with no manual supervision. Specifically, we enforce that our learned features exhibit equivariance i.e., they respond predictably to transformations associated with distinct egomotions. With three datasets, we show that our unsupervised feature learning approach significantly outperforms previous approaches on visual recognition and next-best-view prediction tasks. In the most challenging test, we show that features learned from video captured on an autonomous driving platform improve large-scale scene recognition in static images from a disjoint domain.
A Dialogical Approach to the Creation of New Knowledge in Organizations

Despite several insightful empirical studies on how new knowledge is created in organizations, there is still no satisfactory answer to the question, how is new knowledge created in organizations? The purpose of this paper is to address this question by focusing on direct social interaction, adopting a dialogical approach. The following argument is advanced. From a dialogical perspective, new knowledge in organizations originates in the individual ability to draw new distinctions concerning a task at hand. New distinctions may be developed because practitioners experience their situations in terms of already constituted distinctions, which lend themselves to further articulation. Further articulation develops when organizational members engage in dialogical exchanges. When productive, dialogue leads to self-distanciation, namely, to individuals taking distance from their customary and unreflective ways of acting as practitioners. Dialogue is productive depending on the extent to which participants engage relationally with one another. When this happens, participants are more likely to actively take responsibility for both the joint tasks in which they are involved and for the relationships they have with others. Self-distanciation leads to new distinctions through three processes of conceptual change (conceptual combination, conceptual expansion, and conceptual reframing), which, when intersubjectively accepted, constitute new knowledge. Several organizational examples, as well as findings from organizational knowledge research, are reinterpreted to illustrate the above points.
Accelerated DP based search for statistical translation

In this paper, we describe a fast search algorithm for statistical translation based on dynamic programming (DP) and present experimental results. The approach is based on the assumption that the word alignment is monotone with respect to the word order in both languages. To reduce the search e ort for this approach, we introduce two methods: an acceleration technique to e ciently compute the dynamic programming recursion equation and a beam search strategy as used in speech recognition. The experimental tests carried out on the Verbmobil corpus showed that the search space, measured by the number of translation hypotheses, is reduced by a factor of about 230 without a ecting the translation performance.
Statistical Analysis-Based Error Models for the Microsoft Kinect™ Depth Sensor

The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat surface, and horizontal (x) and vertical (y) errors are measured using a novel 3D checkerboard. Results show that the stochastic nature of the Kinect measurement error is affected mostly by the depth at which the object being sensed is located, though radial factors must be considered, as well. Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view. The resulting models are compared against existing Kinect error models, and through these comparisons, the proposed model is shown to be a more sophisticated and precise characterization of the Kinect error distributions.
Proactive edge computing in latency-constrained fog networks

In this paper, the fundamental problem of distribution and proactive caching of computing tasks in fog networks is studied under latency and reliability constraints. In the proposed scenario, computing can be executed either locally at the user device or offloaded to an edge cloudlet. Moreover, cloudlets exploit both their computing and storage capabilities by proactively caching popular task computation results to minimize computing latency. To this end, a clustering method to group spatially proximate user devices with mutual task popularity interests and their serving cloudlets is proposed. Then, cloudlets can proactively cache the popular tasks' computations of their cluster members to minimize computing latency. Additionally, the problem of distributing tasks to cloudlets is formulated as a matching game in which a cost function of computing delay is minimized under latency and reliability constraints. Simulation results show that the proposed scheme guarantees reliable computations with bounded latency and achieves up to 91% decrease in computing latency as compared to baseline schemes.
Towards Blockchain-based Auditable Storage and Sharing of IoT Data

Today the cloud plays a central role in storing, processing, and distributing data. Despite contributing to the rapid development of IoT applications, the current IoT cloud-centric architecture has led into a myriad of isolated data silos that hinders the full potential of holistic data-driven analytics within the IoT. In this paper, we present a blockchain-based design for the IoT that brings a distributed access control and data management. We depart from the current trust model that delegates access control of our data to a centralized trusted authority and instead empower the users with data ownership. Our design is tailored for IoT data streams and enables secure data sharing. We enable a secure and resilient access control management, by utilizing the blockchain as an auditable and distributed access control layer to the storage layer. We facilitate the storage of time-series IoT data at the edge of the network via a locality-aware decentralized storage system that is managed with the blockchain technology. Our system is agnostic of the physical storage nodes and supports as well utilization of cloud storage resources as storage nodes.
TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation

We address the challenge of personalized recommendation of high quality content producers in social media. While some candidates are easily identifiable (say, by being "favorited" many times), there is a long-tail of potential candidates for whom we have little evidence. Through careful modeling of contextual factors like the geo-spatial, topical, and social preferences of users, we propose a tensor-based personalized expert recommendation framework that integrates these factors for revealing latent connections between homogeneous entities (e.g., users and users) and between heterogeneous entities (e.g., users and experts). Through extensive experiments over geo-tagged Twitter data, we find that the proposed framework can improve the quality of recommendation by over 30% in both precision and recall compared to the state-of-the-art.
American Finance Association On the Existence of an Optimal Capital Structure : Theory and Evidence

On the Existence of an Optimal Capital Structure: Theory and Evidence Author(s): Michael Bradley, Gregg A. Jarrell, E. Han Kim Source: The  Forty-Second Annual Meeting, American Finance Association, San Francisco, CA, December 28-30, 1983 ( 1984), pp. 857-878  Blackwell Publishing for the American Finance Association Stable URL: http://www.jstor.org/stable/2327950 Accessed: 22/01/2009 14:04
From neural PCA to deep unsupervised learning

A network supporting deep unsupervised learning is present d. The network is an autoencoder with lateral shortcut connections from the enc oder to decoder at each level of the hierarchy. The lateral shortcut connections al low the higher levels of the hierarchy to focus on abstract invariant features. Wher eas autoencoders are analogous to latent variable models with a single layer of st ochastic variables, the proposed network is analogous to hierarchical latent varia bles models. Learning combines denoising autoencoder and denoising sou rces separation frameworks. Each layer of the network contributes to the cos t function a term which measures the distance of the representations produce d by the encoder and the decoder. Since training signals originate from all leve ls of the network, all layers can learn efficiently even in deep networks. The speedup offered by cost terms from higher levels of the hi erarchy and the ability to learn invariant features are demonstrated in exp eriments.
Semantics-based Question Generation and Implementation

This paper presents a question generation system based on the approach of semantic rewriting. State-of-the-art deep linguistic parsing and generation tools are employed to map natural language sentences into their meaning representations in the form of Minimal Recursion Semantics (mrs) and vice versa. By carefully operating on the semantic structures, we obtain a principled way of generating questions which avoids ad-hoc manipulation of syntactic structures. Based on the (partial) understanding of the sentence meaning, the system generates questions that are semantically grounded and purposeful. As the generator uses a deep linguistic grammar, the grammaticality of the generation results is licensed by the grammar. With a specialized ranking model, the linguistic realizations from the general purpose generation model are further refined for the question generation task. The evaluation results from QGSTEC2010 show promising results for the proposed approach.
An Empirical Study of the Evolution of PHP Web Application Security

Web applications are increasingly subject to mass attacks, with vulnerabilities found easily in both open source and commercial applications as evinced by the fact that approximately half of reported vulnerabilities are found in web applications. In this paper, we perform an empirical investigation of the evolution of vulnerabilities in fourteen of the most widely used open source PHP web applications, finding that vulnerabilities densities declined from 28.12 to 19.96 vulnerabilities per thousand lines of code from 2006 to 2010. We also investigate whether complexity metrics or a security resources indicator (SRI) metric can be used to identify vulnerable web application showing that average cyclomatic complexity is an effective predictor of vulnerability for several applications, especially for those with low SRI scores.
Recommendations on Queue Management and Congestion Avoidance in the Internet

Status of Memo This memo provides information for the Internet community. It does not specify an Internet standard of any kind. Distribution of this memo is unlimited.  This memo presents two recommendations to the Internet community concerning measures to improve and preserve Internet performance. It presents a strong recommendation for testing, standardization, and widespread deployment of active queue management in routers, to improve the performance of today's Internet. It also urges a concerted effort of research, measurement, and ultimate deployment of router mechanisms to protect the Internet from flows that are not sufficiently responsive to congestion notification.
Planning to teach with digital tools: Introducing the interactive whiteboard to pre-service secondary mathematics teachers

Teaching is a complex endeavour that requires teachers to meld knowledge about the nature of learners, pedagogical strategies and discipline content. In recent years an increasing variety of educational technologies are finding their way into the school classroom, including the widespread acceptance of interactive whiteboards (IWBs). The emerging literature on IWB use is mixed, with no clear verdict on the merits of this technology in relation to teaching or student learning outcomes. However, as this equipment is fast becoming standard in a growing number of classrooms, it is important for pre-service teachers to be familiar with its features, potential difficulties and to have had experience in developing lesson activities that utilise the technology.
Scalable proximity estimation and link prediction in online social networks

Proximity measures quantify the closeness or similarity between nodes in a social network and form the basis of a range of applications in social sciences, business, information technology, computer networks, and cyber security. It is challenging to estimate proximity measures in online social networks due to their massive scale (with millions of users) and dynamic nature (with hundreds of thousands of new nodes and millions of edges added daily). To address this challenge, we develop two novel methods to efficiently and accurately approximate a large family of proximity measures. We also propose a novel incremental update algorithm to enable near real-time proximity estimation in highly dynamic social networks. Evaluation based on a large amount of real data collected in five popular online social networks shows that our methods are accurate and can easily scale to networks with millions of nodes.
 To demonstrate the practical values of our techniques, we consider a significant application of proximity estimation: link prediction, i.e., predicting which new edges will be added in the near future based on past snapshots of a social network. Our results reveal that (i) the effectiveness of different proximity measures for link prediction varies significantly across different online social networks and depends heavily on the fraction of edges contributed by the highest degree nodes, and (ii) combining multiple proximity measures consistently yields the best link prediction accuracy.
Fully Convolutional Architectures for Multiclass Segmentation in Chest Radiographs

The success of deep convolutional neural networks (NNs) on image classification and recognition tasks has led to new applications in very diversified contexts, including the field of medical imaging. In this paper, we investigate and propose NN architectures for automated multiclass segmentation of anatomical organs in chest radiographs (CXRs), namely for lungs, clavicles, and heart. We address several open challenges including model overfitting, reducing number of parameters, and handling of severely imbalanced data in CXR by fusing recent concepts in convolutional networks and adapting them to the segmentation problem task in CXR. We demonstrate that our architecture combining delayed subsampling, exponential linear units, highly restrictive regularization, and a large number of high-resolution low-level abstract features outperforms state-of-the-art methods on all considered organs, as well as the human observer on lungs and heart. The models use a multiclass configuration with three target classes and are trained and tested on the publicly available Japanese Society of Radiological Technology database, consisting of 247 X-ray images the ground-truth masks for which are available in the segmentation in CXR database. Our best performing model, trained with the loss function based on the Dice coefficient, reached mean Jaccard overlap scores of 95% for lungs, 86.8% for clavicles, and 88.2% for heart. This architecture outperformed the human observer results for lungs and heart.
The past and future of the past tense

What is the interaction between storage and computation in language processing? What is the psychological status of grammatical rules? What are the relative strengths of connectionist and symbolic models of cognition? How are the components of language implemented in the brain? The English past tense has served as an arena for debates on these issues. We defend the theory that irregular past-tense forms are stored in the lexicon, a division of declarative memory, whereas regular forms can be computed by a concatenation rule, which requires the procedural system. Irregulars have the psychological, linguistic and neuropsychological signatures of lexical memory, whereas regulars often have the signatures of grammatical processing. Furthermore, because regular inflection is rule-driven, speakers can apply it whenever memory fails.