nils-herrmann/clean_scirepeval huggingface dataset:
On Demystifying the Android Application Framework: Re-Visiting Android Permission Specification Analysis

In contrast to the Android application layer, Android’s application framework’s internals and their influence on the platform security and user privacy are still largely a black box for us. In this paper, we establish a static runtime model of the application framework in order to study its internals and provide the first high-level classification of the framework’s protected resources. We thereby uncover design patterns that differ highly from the runtime model at the application layer. We demonstrate the benefits of our insights for security-focused analysis of the framework by re-visiting the important use-case of mapping Android permissions to framework/SDK API methods. We, in particular, present a novel mapping based on our findings that significantly improves on prior results in this area that were established based on insufficient knowledge about the framework’s internals. Moreover, we introduce the concept of permission locality to show that although framework services follow the principle of separation of duty, the accompanying permission checks to guard sensitive operations violate it.
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

A significant challenge for the practical application of reinforcement learning in the real world is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appealing, it can be impractically expensive to collect datasets of demonstrations that cover the variation common in the real world (e.g. opening any type of door). Thus in practice, IRL must commonly be performed with only a limited set of demonstrations where it can be exceedingly difficult to unambiguously recover a reward function. In this work, we exploit the insight that demonstrations from other tasks can be used to constrain the set of possible reward functions by learning a “prior” that is specifically optimized for the ability to infer expressive reward functions from limited numbers of demonstrations. We demonstrate that our method can efficiently recover rewards from images for novel tasks and provide intuition as to how our approach is analogous to learning a prior.
A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges

A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.
CAD/CAM monolithic restorations and full-mouth adhesive rehabilitation to restore a patient with a past history of bulimia: the modified three-step technique.

Due to an increasing awareness about dental erosion, many clinicians would like to propose treatments even at the initial stages of the disease. However, when the loss of tooth structure is visible only to the professional eye, and it has not affected the esthetics of the smile, affected patients do not usually accept a full-mouth rehabilitation. Reducing the cost of the therapy, simplifying the clinical steps, and proposing noninvasive adhesive techniques may promote patient acceptance. In this article, the treatment of an ex-bulimic patient is illustrated. A modified approach of the three-step technique was followed. The patient completed the therapy in five short visits, including the initial one. No tooth preparation was required, no anesthesia was delivered, and the overall (clinical and laboratory) costs were kept low. At the end of the treatment, the patient was very satisfied from a biologic and functional point of view.
Dorsal-ventral patterning and neural induction in Xenopus embryos.

We review the current status of research in dorsal-ventral (D-V) patterning in vertebrates. Emphasis is placed on recent work on Xenopus, which provides a paradigm for vertebrate development based on a rich heritage of experimental embryology. D-V patterning starts much earlier than previously thought, under the influence of a dorsal nuclear -Catenin signal. At mid-blastula two signaling centers are present on the dorsal side: The prospective neuroectoderm expresses bone morphogenetic protein (BMP) antagonists, and the future dorsal endoderm secretes Nodal-related mesoderm-inducing factors. When dorsal mesoderm is formed at gastrula, a cocktail of growth factor antagonists is secreted by the Spemann organizer and further patterns the embryo. A ventral gastrula signaling center opposes the actions of the dorsal organizer, and another set of secreted antagonists is produced ventrally under the control of BMP4. The early dorsal -Catenin signal inhibits BMP expression at the transcriptional level and promotes expression of secreted BMP antagonists in the prospective central nervous system (CNS). In the absence of mesoderm, expression of Chordin and Noggin in ectoderm is required for anterior CNS formation. FGF (fibroblast growth factor) and IGF (insulin-like growth factor) signals are also potent neural inducers. Neural induction by anti-BMPs such as Chordin requires mitogen-activated protein kinase (MAPK) activation mediated by FGF and IGF. These multiple signals can be integrated at the level of Smad1. Phosphorylation by BMP receptor stimulates Smad1 transcriptional activity, whereas phosphorylation by MAPK has the opposite effect. Neural tissue is formed only at very low levels of activity of BMP-transducing Smads, which require the combination of both low BMP levels and high MAPK signals. Many of the molecular players that regulate D-V patterning via regulation of BMP signaling have been conserved between Drosophila and the vertebrates.
Lapses in alertness: coherence of fluctuations in performance and EEG spectrum.

Thirteen subjects detected noise burst targets presented in a white noise background at a mean rate of 10/min. Within each session, local error rate, defined as the fraction of targets detected in a 33 sec moving window, fluctuated widely. Mean coherence between slow mean variations in EEG power and in local error rate was computed for each EEG frequency and performance cycle length, and was shown by a Monte Carlo procedure to be significant for many EEG frequencies and performance cycle lengths, particularly in 4 well-defined EEG frequency bands, near 3, 10, 13, and 19 Hz, and at higher frequencies in two cycle length ranges, one longer than 4 min and the other near 90 sec/cycle. The coherence phase plane contained a prominent phase reversal near 6 Hz. Sorting individual spectra by local error rate confirmed the close relation between performance and EEG power and its relative within-subject stability. These results show that attempts to maintain alertness in an auditory detection task result in concurrent minute and multi-minute scale fluctuations in performance and the EEG power spectrum.
Gait speed and survival in older adults.

Survival estimates help individualize goals of care for geriatric patients, but life tables fail to account for the great variability in survival. Physical performance measures, such as gait speed, might help account for variability, allowing clinicians to make more individualized estimates.



To evaluate the relationship between gait speed and survival.



Pooled analysis of 9 cohort studies (collected between 1986 and 2000), using individual data from 34,485 community-dwelling older adults aged 65 years or older with baseline gait speed data, followed up for 6 to 21 years. Participants were a mean (SD) age of 73.5 (5.9) years; 59.6%, women; and 79.8%, white; and had a mean (SD) gait speed of 0.92 (0.27) m/s.



Survival rates and life expectancy.



There were 17,528 deaths; the overall 5-year survival rate was 84.8% (confidence interval [CI], 79.6%-88.8%) and 10-year survival rate was 59.7% (95% CI, 46.5%-70.6%). Gait speed was associated with survival in all studies (pooled hazard ratio per 0.1 m/s, 0.88; 95% CI, 0.87-0.90; P < .001). Survival increased across the full range of gait speeds, with significant increments per 0.1 m/s. At age 75, predicted 10-year survival across the range of gait speeds ranged from 19% to 87% in men and from 35% to 91% in women. Predicted survival based on age, sex, and gait speed was as accurate as predicted based on age, sex, use of mobility aids, and self-reported function or as age, sex, chronic conditions, smoking history, blood pressure, body mass index, and hospitalization.



In this pooled analysis of individual data from 9 selected cohorts, gait speed was associated with survival in older adults.
Rigorous dynamics of expectation-propagation-based signal recovery from unitarily invariant measurements

This paper investigates sparse signal recovery based on expectation propagation (EP) from unitarily invariant measurements. A rigorous analysis is presented for the state evolution (SE) of an EP-based message-passing algorithm in the large system limit, where both input and output dimensions tend to infinity at an identical speed. The main result is the justification of an SE formula conjectured by Ma and Ping.
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.
Virtualization in Cloud Computing

Cloud computing is one of the most useful technology that is been widely used all over the world. It generally provides on demand IT services and products. Virtualization plays a major role in cloud computing as it provides a virtual storage and computing services to the cloud clients which is only possible through virtualization. Cloud computing is a new business computing paradigm that is based on the concepts of virtualization, multi-tenancy, and shared infrastructure. This paper discusses about cloud computing, how virtualization is done in cloud computing, virtualization basic architecture, its advantages and effects .
Self-transcendence and depression in middle-age adults.

Self-transcendence has been found to be an important correlate of mental health in older adults and adults facing the end of life. This study extends current theory by examining the relationship of transcendence and other transcendence variables to depression in middle-age adults (N = 133). Reed's Self-Transcendence Scale, the Center for Epidemiological Studies-Depression Scale, and measures of parenting, acceptance and spirituality were administered. Findings indicating significant inverse correlations between self-transcendence and depression, as well as between other measures of transcendence and depression support Reed's (1991b) theory. Multiple regression analysis indicated that acceptance may be another significant correlate of depression. Significant gender differences and age-related patterns of increased levels of self-transcendence were found. Study results illuminate the need to continue research into developmentally based transcendence variables related to various experiences of health and well-being across the life span.
The Affordances of Business Analytics for Strategic Decision-Making and Their Impact on Organisational Performance

Increasingly, business analytics is seen to provide the possibilities for businesses to effectively support strategic decision-making, thereby to become a source of strategic business value. However, little research exists regarding the mechanism through which business analytics supports strategic decisionmaking and ultimately organisational performance. This paper draws upon literature on IT affordances and strategic decision-making to (1) understand the decision-making affordances provided by business analytics, and (2) develop a research model linking business analytics, data-driven culture, decision-making affordances, strategic decision-making, and organisational performance. The model is empirically tested using structural equation modelling based on 296 survey responses collected from UK businesses. The study produces four main findings: (1) business analytics has a positive effect on decision-making affordances both directly and indirectly through the mediation of a data-driven culture; (2) decision-making affordances significantly influence strategic decision comprehensiveness positively and intuitive decision-making negatively; (3) data-driven culture has a significant and positive effect on strategic decision comprehensiveness; and (4) strategic decision comprehensiveness has a positive effect on organisational performance but a negative effect on intuitive decision-making.
Comparison of Dimension Reduction Methods for Automated Essay Grading

Automatic Essay Assessor (AEA) is a system that utilizes information retrieval techniques such as Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) for automatic essay grading. The system uses learning materials and relatively few teacher-graded essays for calibrating the scoring mechanism before grading. We performed a series of experiments using LSA, PLSA and LDA for document comparisons in AEA. In addition to comparing the methods on a theoretical level, we compared the applicability of LSA, PLSA, and LDA to essay grading with empirical data. The results show that the use of learning materials as training data for the grading model outperforms the k-NN-based grading methods. In addition to this, we found that using LSA yielded slightly more accurate grading than PLSA and LDA. We also found that the division of the learning materials in the training data is crucial. It is better to divide learning materials into sentences than paragraphs.
Relay technologies for WiMax and LTE-advanced mobile systems

Relay technologies have been actively studied and considered in the standardization process of next-generation mobile broadband communication systems such as 3GPP LTE-Advanced, IEEE 802.16j, and IEEE 802.16m. This article first introduces and compares different relay types in LTE-Advanced and WiMAX standards. Simulation results show that relay technologies can effectively improve service coverage and system throughput. Three relay transmission schemes are then summarized and evaluated in terms of transmission efficiency under different radio channel conditions. Finally, a centralized pairing scheme and a distributed pairing scheme are developed for effective relay selection. Simulation results show that the proposed schemes can maximize the number of served UE units and the overall throughput of a cell in a realistic multiple-RS-multiple-UE scenario.
Processing: a programming handbook for visual designers and artists

A Programming Handbook for Visual Designers and Artists With this completely revised edition, Casey Reas and Ben Fry show readers how.  A Programming Handbook for Visual Designers and Artists by Ben Fry, Casey Reas, John Maeda download pdf book. Jun 28, 2010 – All right,. With Ben Fry, Reas initiated Processing in 2001. Reas and Fry published Processing: A Programming Handbook for Visual Designers and Artists, a comprehensive introduction to programming within the context of visual media (MIT Press.
Partially-Ranked Choice Models for Data-Driven Assortment Optimization

The assortment of products carried by a store has a crucial impact on its success. However, finding the right mix of products to attract a large portion of the customers is a challenging task. Several mathematical models have been proposed to optimize assortments. In particular, rank-based choice models have been acknowledged for representing well high-dimensional product substitution effects, and therefore reflect customer preferences in a reasonably realistic manner. In this work, we extend the concept of (strictly) fully-ranked choice models to models with partial ranking that additionally allow for indifference among subsets of products, i.e., on which the customer does not have a strict preference. We show that partially-ranked choice models are theoretically equivalent to fully-ranked choice models, but a partially-ranked preference sequence would require a factorial number of fully-ranked sequences to represent the same buying behavior. We then show how partially-ranked choice models can be learned efficiently from historical transaction and assortment data. The embedded column generation procedure involves subproblems that can be efficiently solved by using a growing decision tree that represents partially-ranked preferences, enabling us to learn preferences and optimize assortments for thousands of products. Computational experiments on artificially generated data and case studies on real industrial retail data suggest a significant potential to increase profits when performing data-driven assortment optimization and provide useful insights on customer segmentations to the decision makers, in our real case, the store managers. When comparing to existing algorithms, our method increases by one order of magnitude the scale of problems that can be learned by non-parametric choice models.
The OWL Reasoner Evaluation (ORE) 2015 Competition Report

The OWL Reasoner Evaluation competition is an annual competition (with an associated workshop) that pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we discuss the design, execution and results of the 2015 competition with particular attention to lessons learned for benchmarking, comparative experiments, and future competitions.
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks

Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images that resemble real images. Recently, GANs have been extended from generating images to generating sequences (e.g., poems, music and codes). Existing GANs on sequence generation mainly focus on general sequences, which are grammar-free. In many real-world applications, however, we need to generate sequences in a formal language with the constraint of its corresponding grammar. For example, to test the performance of a database, one may want to generate a collection of SQL queries, which are not only similar to the queries of real users, but also follow the SQL syntax of the target database. Generating such sequences is highly challenging because both the generator and discriminator of GANs need to consider the structure of the sequences and the given grammar in the formal language. To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar. The discriminator uses a tree-structured RNN to distinguish the generated trees from real trees. We show that TreeGAN can generate sequences for any CFG and its generation fully conforms with the given syntax. Experiments on synthetic and real data sets demonstrated that TreeGAN significantly improves the quality of the sequence generation in context-free languages.
Carbetocin for prevention of postcesarean hemorrhage in women with severe preeclampsia: a before-after cohort comparison with oxytocin.

The aim of the study was to compare the incidence of the use of additional uterotonics before and after the change of carbetocin to oxytocin for the prevention of postpartum hemorrhage after cesarean delivery in women with severe preeclampsia.



This was an observational retrospective before-and-after study.



Operating room, postoperative recovery area.



Sixty women with severe preeclampsia undergoing cesarean delivery under spinal anesthesia; American Society of Anesthesiologists 3.



Observational study.



Blood pressure, heart rate, and biological data (hemoglobin, platelets, haptoglobin, prothrombin time index, activated partial thromboplastin time ratio, blood uric acid, aspartate aminotransferase, alanine aminotransferase, serum urea, serum creatinine, and albumin).



The incidence of additional uterotonic administration in the carbetocin and oxytocin groups was 15% and 10%, respectively (P=.70).



As carbetocin appears to be as effective and safe as oxytocin in preeclamptic women, its advantages make it a good uterotonic option in this particular setting.
Eye tracking the user experience - An evaluation of ontology visualization techniques

Various ontology visualization techniques have been developed over the years, offering essential interfaces to users for browsing and interacting with ontologies, in an effort to assist with ontology understanding. Yet few studies have focused on evaluating the usability of existing ontology visualization techniques. This paper presents an eye-tracking user study that evaluates two commonly used ontology visualization techniques, namely, indented list and graph. The eye-tracking experiment and analysis presented in this paper complements the set of existing evaluation protocols for ontology visualization. In addition, the results found from this study contribute to a greater understanding of the strengths and weaknesses of the two visualization techniques, and in particular, how and why one is more effective than the other. Based on approximately 500MB of eye movement data containing around 30 million rows of gaze data generated by a Tobii eye tracker, we found evidence suggesting indented lists are more efficient at supporting information searches and graphs are more efficient at supporting information processing.  eye tracking, ontology visualization evaluation, usability study, gaze data processing, statistical analysis *	  Corresponding author.  bofu@uvic.ca. †	  Current affiliation: Google Inc.