nils-herrmann/clean_scirepeval huggingface dataset:
Domain-Targeted, High Precision Knowledge Extraction

Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain - in our case, elementary science. To measure the KB’s coverage of the target domain’s knowledge (its “comprehensiveness” with respect to science) we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80% precision and 23% recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources. We have made the KB publicly available.
The impact of DDoS and other security shocks on Bitcoin currency exchanges: evidence from Mt. Gox

We investigate how distributed denial-of-service (DDoS) attacks and other disruptions affect the Bitcoin ecosystem. In particular, we investigate the impact of shocks on trading activity at the leading Mt. Gox exchange between April 2011 and November 2013. We find that following DDoS attacks on Mt. Gox, the number of large trades on the exchange fell sharply. In particular, the distribution of the daily trading volume becomes less skewed (fewer big trades) and had smaller kurtosis on days following DDoS attacks. The results are robust to alternative specifications, as well as to restricting the data to activity prior to March 2013, i.e., the period before the first large appreciation in the price of and attention paid to Bitcoin.
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird’s view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input.
Distributed resource allocation in virtualized wireless cellular networks based on ADMM

In next generation wireless mobile networks, network virtualization will become an important key technology. In this paper, we firstly propose a resource allocation scheme for enabling efficient resource allocation in wireless network virtualization. Then, we formulate the resource allocation strategy as an optimization problem, considering not only the revenue earned by serving end users of virtual networks, but also the cost of leasing infrastructure from infrastructure providers. In addition, we develop an efficient alternating direction method of multipliers (ADMM)-based distributed virtual resource allocation algorithm in virtualized wireless networks. Simulation results are presented to show the effectiveness of the proposed scheme.
9 Self-Conscious Emotions : Where Self and Emotion Meet

In so far as a man amounts to anything, stands for anything, is truly an individual, he has an ego about which his passions cluster. .. We are virtually always in a state of pride or shame. T he centrality of emotion and self to social life is almost axiomatic in the psychological literature. What is less accepted, or at least less frequently discussed, is the essential interconnection between these two domains. Yet, as the quotations above suggest, self and emotion are inextricably linked. The experience of self is shaped by a constant and ever-changing flurry of emotions (i.e., " passions "), and feelings of pride, shame, and other emotions could not exist without perceptions and evaluations of the self (Brown & Marshall, 2001). The traditional disconnect between the self and emotion literatures stems, in part, from their divergent theoretical roots. Emotion researchers have, to a large extent, embraced a biological model of affect. This approach has led to major advances in our understanding of the neural underpinnings and adaptive functions of emotions, their interactions with basic cognitive processes, and their automatically recognized and expressed nonverbal signals also led researchers to neglect psychologically complex emotions, such as pride and shame, which are more closely linked to self-evaluative processes. The disconnect also results from the traditional emphasis in the self literature on cognitive rather than affective processes. When research on self-processes began to flourish in the late 1970s and 1980s, the cognitive perspective was emerging as the dominant paradigm, displacing the previously dominant paradigm of The cognitive approach to studying the self has led to major developments in our understanding of how the self " works " from an information-processing perspective
PRIVACY-PRESERVING PUBLIC AUDITING FOR DATA STORAGE SECURITY IN CLOUD COMPUTING

Cloud Computing is the long dreamed vision of computing as a utility, where users can remotely store their data into the cloud so as to enjoy the on-demand high quality applications and services from a shared pool of configurable computing resources. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the fact that users no longer have physical possession of the possibly large size of outsourced data makes the data integrity protection in Cloud Computing a very challenging and potentially formidable task, especially for users with constrained computing resources and capabilities. Thus, enabling public auditability for cloud data storage security is of critical importance so that users can resort to an external audit party to check the integrity of outsourced data when needed. To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to efficiently audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should bring in no new vulnerabilities towards user data privacy. In this paper, we utilize the public key based homomorphic authenticator and uniquely integrate it with random mask technique to achieve a privacy-preserving public auditing system for cloud data storage security while keeping all above requirements in mind. To support efficient handling of multiple auditing tasks, we further explore the technique of bilinear aggregate signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive security and performance analysis shows the proposed schemes are provably secure and highly efficient.
Labial fusion and asymptomatic bacteriuria

Thirty-three female children with labial fusion were screened for bacteriuria which was defined as the growth of a single micro-organism with ≥100 000 colony-forming units/ml (≥ 100×106 colonies/l) in a properly collected urine specimen. Six girls were found to have bacteriuria. In contrast, none of the 33 girls in a control group had bacteriuria. We recommend that a urine culture be performed in girls with labial fusion and that all girls with bacteriuria should be checked for labial fusion.
Model Predictive Control of Parallel-Connected Inverters for Uninterruptible Power Supplies

Uninterruptible power supplies (UPSs) have been used in many installations for critical loads that cannot afford power failure or surge during operation. It is often difficult to upgrade the UPS system as the load grows over time. Due to lower cost and maintenance, as well as ease of increasing system capacity, the parallel operation of modularized small-power UPS has attracted much attention in recent years. In this paper, a new scheme for parallel operation of inverters is introduced. A multiple-input-multiple-output state-space model is developed to describe the parallel-connected inverters system, and a model-predictive-control scheme suitable for paralleled inverters control is proposed. In this algorithm, the control objectives of voltage tracking and current sharing are formulated using a weighted cost function. The effectiveness and the hot-swap capability of the proposed parallel-connected inverters system have been verified with experimental results.
Collaborative ranking

Typical recommender systems use the root mean squared error (RMSE) between the predicted and actual ratings as the evaluation metric. We argue that RMSE is not an optimal choice for this task, especially when we will only recommend a few (top) items to any user. Instead, we propose using a ranking metric, namely normalized discounted cumulative gain (NDCG), as a better evaluation metric for this task. Borrowing ideas from the learning to rank community for web search, we propose novel models which approximately optimize NDCG for the recommendation task. Our models are essentially variations on matrix factorization models where we also additionally learn the features associated with the users and the items for the ranking task. Experimental results on a number of standard collaborative filtering data sets validate our claims. The results also show the accuracy and efficiency of our models and the benefits of learning features for ranking.
Socket Shield Technique-A Neoteric Approach in Ridge Preservation

Resorption of alveolar bone subsequent to extraction resulting in loss of alveolar bone height and width is sequelae that pose difficulty in placement of an implant especially in esthetically important area like maxillary anterior region. Several approaches have been described in the literature to overcome the complications of alveolar ridge resorption and to preserve the ridge like the hard and soft tissue augmentation with GBR, bone substitutes with or without immediate implant placement. An ideal method should always be cost effective and minimally invasive. Socket shield technique is one such new method where buccal root segment is retained as a shield which prevents resorption and achieves complete alveolar ridge preservation.
Advanced Image Classification Using Wavelets and Convolutional Neural Networks

Image classification is a vital technology many people in all arenas of human life utilize. It is pervasive in every facet of the social, economic, and corporate spheres of influence, worldwide. This need for more accurate, detail-oriented classification increases the need for modifications, adaptations, and innovations to Deep learning algorithms. This paper uses Convolutional Neural Networks (CNN) to classify handwritten digits in the MNIST database, and scenes in the CIFAR-10 database. Our proposed method preprocesses the data in the wavelet domain to attain greater accuracy and comparable efficiency to the spatial domain processing. By separating the image into different subbands, important feature learning occurs over varying low to high frequencies. The fusion of the learned low and high frequency features, and processing the combined feature mapping results in an increase in the detection accuracy. Comparing the proposed methods to spatial domain CNN and Stacked Denoising Autoencoder (SDA), experimental findings reveal a substantial increase in accuracy.
Recognizing and measuring self-regulated learning in a mobile learning environment

With the realization that more research is needed to explore external factors (e.g., pedagogy, parental involvement in the context of K-12 learning) and internal factors (e.g., prior knowledge, motivation) underlying student-centered mobile learning, the present study conceptually and empirically explores how the theories and methodologies of self-regulated learning (SRL) can help us analyze and understand the processes of mobile learning. The empirical data collected from two elementary science classes in Singapore indicates that the analytical SRL model of mobile learning proposed in this study can illuminate the relationships between three aspects of mobile learning: students’ self-reports of psychological processes, patterns of online learning behavior in the mobile learning environment (MLE), and learning achievement. Statistical analyses produce three main findings. First, student motivation in this case can account for whether and to what degree the students can actively engage in mobile learning activities metacognitively, motivationally, and behaviorally. Second, the effect of students’ self-reported motivation on their learning achievement is mediated by their behavioral engagement in a pre-designed activity in the MLE. Third, students’ perception of parental autonomy support is not only associated with their motivation in school learning, but also associated with their actual behaviors in self-regulating their learning. ! 2011
Automatically discovering local visual material attributes

Shape cues play an important role in computer vision, but shape is not the only information available in images. Materials, such as fabric and plastic, are discernible in images even when shapes, such as those of an object, are not. We argue that it would be ideal to recognize materials without relying on object cues such as shape. This would allow us to use materials as a context for other vision tasks, such as object recognition. Humans are intuitively able to find visual cues that describe materials. Previous frameworks attempt to recognize these cues (as visual material traits) using fully-supervised learning. This requirement is not feasible when multiple annotators and large quantities of images are involved. In this paper, we derive a framework that allows us to discover locally-recognizable material attributes from crowdsourced perceptual material distances. We show that the attributes we discover do in fact separate material categories. Our learned attributes exhibit the same desirable properties as material traits, despite the fact that they are discovered using only partial supervision.
FastSLAM 2.0 running on a low-cost embedded architecture

The first method that was developed to deal with the SLAM problem is based on the extended Kalman filter, EKF SLAM. However this approach cannot be applied to a large environments because of the quadratic complexity and data association problem. The second approach to address the SLAM problem is based on the Rao-Blackwellized Particle filter FastSLAM, which follows a large number of hypotheses that represent the different possible trajectories, each trajectory carries its own map, its complexity increase logarithmically with the number of landmarks in the map. In this paper we will present the result of an implementation of the FastSLAM 2.0 on an open multimedia applications processor, based on a monocular camera as an exteroceptive sensor. A parallel implementation of this algorithm was achieved. Results aim to demonstrate that an optimized algorithm implemented on a low cost architecture is suitable to design an embedded system for SLAM applications.
Visual gesture recognition

This paper presents a method for recognizing human-hand gestures using a model-based approach. A nite state machine is used to model four qualitatively distinct phases of a generic gesture. Fingertips are tracked in multiple frames to compute motion trajectories. The trajectories are then used for nding the start and stop position of the gesture. Gestures are represented as a list of vectors and are then matched to stored gesture vector models using table lookup based on vector displacements. Results are presented showing recognition of seven gestures using images sampled at 4Hz on a SPARC-1 without any special hardware. The seven gestures are representatives for
Prognostics of Machine Health Condition using an Improved ARIMA-based Prediction method

Prognostics is very useful to predict the degradation trend of machinery and to provide an alarm before a fault reaches critical levels. This paper proposes an ARIMA approach to predict the future machine status with accuracy improvement by an improved forecasting strategy and an automatic prediction algorithm. Improved forecasting strategy increases the times of model building and creates datasets for modeling dynamically to avoid using the previous values predicted to forecast and generate the predictions only based on the true observations. Automatic prediction algorithm can satisfy the requirement of real-time prognostics by automates the whole process of ARIMA modeling and forecasting based on the Box-Jenkins's methodology and the improved forecasting strategy. The feasibility and effectiveness of the approach proposed is demonstrated through the prediction of the vibration characteristic in rotating machinery. The experimental results show that the approach can be applied successfully and effectively for prognostics of machine health condition.
In-season strength maintenance training increases well-trained cyclists’ performance

We investigated the effects of strength maintenance training on thigh muscle cross-sectional area (CSA), leg strength, determinants of cycling performance, and cycling performance. Well-trained cyclists completed either (1) usual endurance training supplemented with heavy strength training twice a week during a 12-week preparatory period followed by strength maintenance training once a week during the first 13 weeks of a competition period (E + S; n = 6 [♂ = 6]), or (2) usual endurance training during the whole intervention period (E; n = 6 [♂ = 5, ♀ = 1]). Following the preparatory period, E + S increased thigh muscle CSA and 1RM (p < 0.05), while no changes were observed in E. Both groups increased maximal oxygen consumption and mean power output in the 40-min all-out trial (p < 0.05). At 13 weeks into the competition period, E + S had preserved the increase in CSA and strength from the preparatory period. From the beginning of the preparatory period to 13 weeks into the competition period, E + S increased peak power output in the Wingate test, power output at 2 mmol l−1 [], maximal aerobic power output (W max), and mean power output in the 40-min all-out trial (p < 0.05). The relative improvements in the last two measurements were larger than in E (p < 0.05). For E, W max and power output at 2 mmol l−1 [] remained unchanged. In conclusion, in well-trained cyclists, strength maintenance training in a competition period preserved increases in thigh muscle CSA and leg strength attained in a preceding preparatory period and further improved cycling performance determinants and performance.
The prolongation of the lifespan of rats by repeated oral administration of [60]fullerene.

Countless studies showed that [60]fullerene (C(60)) and derivatives could have many potential biomedical applications. However, while several independent research groups showed that C(60) has no acute or sub-acute toxicity in various experimental models, more than 25 years after its discovery the in vivo fate and the chronic effects of this fullerene remain unknown. If the potential of C(60) and derivatives in the biomedical field have to be fulfilled these issues must be addressed. Here we show that oral administration of C(60) dissolved in olive oil (0.8 mg/ml) at reiterated doses (1.7 mg/kg of body weight) to rats not only does not entail chronic toxicity but it almost doubles their lifespan. The effects of C(60)-olive oil solutions in an experimental model of CCl(4) intoxication in rat strongly suggest that the effect on lifespan is mainly due to the attenuation of age-associated increases in oxidative stress. Pharmacokinetic studies show that dissolved C(60) is absorbed by the gastro-intestinal tract and eliminated in a few tens of hours. These results of importance in the fields of medicine and toxicology should open the way for the many possible -and waited for- biomedical applications of C(60) including cancer therapy, neurodegenerative disorders, and ageing.
Perceptual expertise in forensic facial image comparison.

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.