nils-herrmann/clean_scirepeval huggingface dataset:
Eliminating the hypervisor attack surface for a more secure cloud

Cloud computing is quickly becoming the platform of choice for many web services. Virtualization is the key underlying technology enabling cloud providers to host services for a large number of customers. Unfortunately, virtualization software is large, complex, and has a considerable attack surface. As such, it is prone to bugs and vulnerabilities that a malicious virtual machine (VM) can exploit to attack or obstruct other VMs -- a major concern for organizations wishing to move to the cloud. In contrast to previous work on hardening or minimizing the virtualization software, we eliminate the hypervisor attack surface by enabling the guest VMs to run natively on the underlying hardware while maintaining the ability to run multiple VMs concurrently. Our NoHype system embodies four key ideas: (i) pre-allocation of processor cores and memory resources, (ii) use of virtualized I/O devices, (iii) minor modifications to the guest OS to perform all system discovery during bootup, and (iv) avoiding indirection by bringing the guest virtual machine in more direct contact with the underlying hardware. Hence, no hypervisor is needed to allocate resources dynamically, emulate I/O devices, support system discovery after bootup, or map interrupts and other identifiers. NoHype capitalizes on the unique use model in cloud computing, where customers specify resource requirements ahead of time and providers offer a suite of guest OS kernels. Our system supports multiple tenants and capabilities commonly found in hosted cloud infrastructures. Our prototype utilizes Xen 4.0 to prepare the environment for guest VMs, and a slightly modified version of Linux 2.6 for the guest OS. Our evaluation with both SPEC and Apache benchmarks shows a roughly 1% performance gain when running applications on NoHype compared to running them on top of Xen 4.0. Our security analysis shows that, while there are some minor limitations with cur- rent commodity hardware, NoHype is a significant advance in the security of cloud computing.
Multi-viewer gesture-based interaction for omni-directional video

Omni-directional video (ODV) is a novel medium that offers viewers a 360º panoramic recording. This type of content will become more common within our living rooms in the near future, seeing that immersive displaying technologies such as 3D television are on the rise. However, little attention has been given to how to interact with ODV content. We present a gesture elicitation study in which we asked users to perform mid-air gestures that they consider to be appropriate for ODV interaction, both for individual as well as collocated settings. We are interested in the gesture variations and adaptations that come forth from individual and collocated usage. To this end, we gathered quantitative and qualitative data by means of observations, motion capture, questionnaires and interviews. This data resulted in a user-defined gesture set for ODV, alongside an in-depth analysis of the variation in gestures we observed during the study.
A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.
Music composition by onomatopoeia

We introduce a simple music notation system based on onomatopoeia. Although many text-based music notation systems have been proposed, most of them are cryptic and much more difficult to understand than standard graphical musical scores. Our music notation system, called the Sutoton notation, is based on onomatopoeia and note names which are easily pronounceable by humans with no extra training. Although being simple, Sutoton notation has been used in a variety of systems and loved by many hobbyists.
Protecting endpoint devices in IoT supply chain

The Internet of Things (IoT), an emerging global network of uniquely identifiable embedded computing devices within the existing Internet infrastructure, is transforming how we live and work by increasing the connectedness of people and things on a scale that was once unimaginable. In addition to increased communication efficiency between connected objects, the IoT also brings new security and privacy challenges. Comprehensive measures that enable IoT device authentication and secure access control need to be established. Existing hardware, software, and network protection methods, however, are designed against fraction of real security issues and lack the capability to trace the provenance and history information of IoT devices. To mitigate this shortcoming, we propose an RFID-enabled solution that aims at protecting endpoint devices in IoT supply chain. We take advantage of the connection between RFID tag and control chip in an IoT device to enable data transfer from tag memory to centralized database for authentication once deployed. Finally, we evaluate the security of our proposed scheme against various attacks.
ARROW: Azimuth-Range ROuting for large-scale Wireless sensor networks

During the past few years, the development of wireless sensor network technologies has spurred the design of novel protocol paradigms capable of meeting the needs of a wide broad of applications while taking into account the inherent constraints of the underlying network technologies, e.g. limited energy and computational capacities. Geographic routing is one of such paradigms whose principles of operation are based on the geographic location of the network nodes. Even though the large number of works already reported in the literature, there are still many open issues towards the design of robust and scalable geographic routing algorithms. In this study, after an analysis of the most relevant solutions reported in the literature, we introduce Azimuth-Range ROuting for largescale Wireless (ARROW) sensor networks. ARROW goes a step further on the design of geographic routing protocols by defining a simple and robust routing protocol whose operation principles completely free the network nodes of the burden of keeping routing records. Under ARROW, nodes carry out all routing decisions exclusively using the information imbedded in the data packets while avoiding the risk of routing loops, a major challenge when designing routing protocols for large-scale networks. Moreover, ARROW is supplemented with a simple yet effective forwarder resolution protocol, also introduced in this study, allowing the fast and loop-free selection of the forwarding node in a hop-to-hop basis. Both protocols, ARROW and the proposed forwarder resolution protocol, are validated by extensive computer simulations. Our results show that both protocols exhibit excellent scalability properties by limiting the overhead.
A Compact Broadband Direct Coaxial Line to SIW Transition

In this letter, a novel coaxial line to substrate integrated waveguide (SIW) broadband transition is presented. The transition is designed by connecting the inner conductor of a coaxial line to an open-circuited SIW. The configuration directly transforms the TEM mode of a coaxial line to the fundamental TE10 mode of the SIW. A prototype back-to-back transition is fabricated for X-band operation using a 0.508 mm thick RO 4003C substrate with dielectric constant 3.55. Comparison with other reported transitions shows that the present structure provides lower passband insertion loss, wider bandwidth and most compact. The area of each transition is 0.08λg2 where λg is the guided wavelength at passband center frequency of f0 = 10.5 GHz. Measured 15 dB and 20 dB matching bandwidths are over 48% and 20%, respectively, at f0.
The Impacts of Social Media on Bitcoin Performance

As the world's first completely decentralized digital payment system, the emergence of Bitcoin represents a revolutionary phenomenon in financial markets. This study examines the dynamic relationships between social media and bitcoin performance. We consider the distinct effects of different social media platforms and different user groups subdivided by posting volume. The results suggest that more bullish forum posts have a positive effect on bitcoin returns, and the effect is stronger when we only include the posts by users who are less likely to contribute. In addition, messages on Internet forum have stronger impacts on future bitcoin market measures at a daily frequency, but microblogs’ effects are more significant at an hourly frequency.
HFirst: A Temporal Approach to Object Recognition

This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired asynchronous address event representation (AER) vision sensors. The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems. Freedom from rigid timing constraints opens the possibility of using true timing to our advantage in computation. We show not only how timing can be used in object recognition, but also how it can in fact simplify computation. Specifically, we rely on a simple temporal-winner-take-all rather than more computationally intensive synchronous operations typically used in biologically inspired neural networks for object recognition. This approach to visual computation represents a major paradigm shift from conventional clocked systems and can find application in other sensory modalities and computational tasks. We showcase effectiveness of the approach by achieving the highest reported accuracy to date (97.5% ± 3.5%) for a previously published four class card pip recognition task and an accuracy of 84.9% ± 1.9% for a new more difficult 36 class character recognition task.
Modified bagging of maximal information coefficient for genome-wide identification

A new method, modified Bagging (mBagging) of Maximal Information Coefficient (mBoMIC), was developed for genome-wide identification. Traditional Bagging is inadequate to meet some requirements of genome-wide identification, in terms of statistical performance and time cost. To improve statistical performance and reduce time cost, an mBagging was developed to introduce Maximal Information Coefficient (MIC) into genomewide identification. The mBoMIC overcame the weakness of original MIC, i.e., the statistical power is inadequate and MIC values are volatile. The three incompatible measures of Bagging, i.e. time cost, statistical power and false positive rate, were significantly improved simultaneously. Compared with traditional Bagging, mBagging reduced time cost by 80%, improved statistical power by 15%, and decreased false positive rate by 31%. The mBoMIC has sensitivity and university in genome-wide identification. The SNPs identified only by mBoMIC have been reported as SNPs associated with cardiac disease.
Finding Overlapping Communities Using Disjoint Community Detection Algorithms

Many algorithms have been designed to discover com munity structure in networks. Most of these detect disjoint communities , while a few can find communities that overlap. We propose a new, two-phase, method of detecting overlapping communities. In the first phase, a network is transformed to a new one by splitting vertices, using the idea of split betweenness ; in the second phase, the transformed network is processed by a disjoint comm unity detection algorithm. This approach has the potential to convert any disj oint community detection algorithm into an overlapping community detection algor ithm. Our experiments, using several “disjoint” algorithms, demonstrate that the m thod works, producing solutions, and execution times, that are often better t han those produced by specialized “overlapping” algorithms.
First-order Meta-Learned Initialization for Faster Adaptation in Deep Reinforcement Learning

Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. Recent works have proposed training a meta-learner on a distribution of similar tasks, in the hope of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. The hypothesis is that is should be possible to learn something about the optimization process while learning on a specific task that generalizes across tasks. However, recent meta-learning approaches are hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. While there do exist modelagnostic meta-learning methods, they often contain gradient-of-gradient (second order derivative) terms in the parameter update rules. We aim to characterize the effect of first-order approximations to recently proposed meta-learning algorithms that work around this by using the gradient updates of the learner itself. We also evaluate these first-order methods (which have hitherto only been tested in supervised settings) for reinforcement learning problems.
Continuum: designing timelines for hierarchies, relationships and scale

Temporal events, while often discrete, also have interesting relationships within and across times: larger events are often collections of smaller more discrete events (battles within wars; artists' works within a form); events at one point also have correlations with events at other points (a play written in one period is related to its performance over a period of time). Most temporal visualisations, however, only represent discrete data points or single data types along a single timeline: this event started here and ended there; this work was published at this time; this tag was popular for this period. In order to represent richer, faceted attributes of temporal events, we present Continuum. Continuum enables hierarchical relationships in temporal data to be represented and explored; it enables relationships between events across periods to be expressed, and in particular it enables user-determined control over the level of detail of any facet of interest so that the person using the system can determine a focus point, no matter the level of zoom over the temporal space. We present the factors motivating our approach, our evaluation and implementation of this new visualisation which makes it easy for anyone to apply this interface to rich, large-scale datasets with temporal data.
Recognizing Pathogenic Empathy in Social Media

Empathy is an integral part of human social life, as people care about and for others who experience adversity. However, a specific “pathogenic” form of empathy, marked by automatic contagion of negative emotions, can lead to stress and burnout. This is particularly detrimental for individuals in caregiving professions who experience empathic states more frequently, because it can result in illness and high costs for health systems. Automatically recognizing pathogenic empathy from text is potentially valuable to identify at-risk individuals and monitor burnout risk in caregiving populations. We build a model to predict this type of empathy from social media language on a data set we collected of users’ Facebook posts and their answers to a new questionnaire measuring empathy. We obtain promising results in identifying individuals’ empathetic states from their social media (Pearson r = 0.252,
Progressive graph matching: Making a move of graphs via probabilistic voting

Graph matching is widely used in a variety of scientific fields, including computer vision, due to its powerful performance, robustness, and generality. Its computational complexity, however, limits the permissible size of input graphs in practice. Therefore, in real-world applications, the initial construction of graphs to match becomes a critical factor for the matching performance, and often leads to unsatisfactory results. In this paper, to resolve the issue, we propose a novel progressive framework which combines probabilistic progression of graphs with matching of graphs. The algorithm efficiently re-estimates in a Bayesian manner the most plausible target graphs based on the current matching result, and guarantees to boost the matching objective at the subsequent graph matching. Experimental evaluation demonstrates that our approach effectively handles the limits of conventional graph matching and achieves significant improvement in challenging image matching problems.
Overcoming beta-agonist tolerance: high dose salbutamol and ipratropium bromide. Two randomised controlled trials

Asthmatics treated with long-acting beta-agonists have a reduced bronchodilator response to moderate doses of inhaled short acting beta-agonists during acute bronchoconstriction. It is not known if the response to higher doses of nebulised beta-agonists or other bronchodilators is impaired. We assessed the effect of long-acting beta-agonist treatment on the response to 5 mg nebulised salbutamol and to ipratropium bromide.



Two double-blind, placebo-controlled, crossover studies of inhaled formoterol 12 mug twice daily in patients with asthma.High-dose salbutamol: 36 hours after the last dose of 1 week of formoterol or placebo treatment, 11 subjects inhaled methacholine to produce a 20% fall in FEV1. Salbutamol 5 mg was then administered via nebuliser and the FEV1 was monitored for 20 minutes. Ipratropium: 36 hours after the last dose of 1 week of formoterol or placebo treatment, 11 subjects inhaled 4.5% saline to produce a 20% fall in FEV1. Salbutamol 200 mug or ipratropium bromide 40 mug was then inhaled and the FEV1 was monitored for 30 minutes. Four study arms compared the response to each bronchodilator after formoterol and placebo. Analyses compared the area under the bronchodilator response curves, adjusting for changes in pre-challenge FEV1, dose of provocational agent and FEV1 fall during the challenge procedure.



The response to nebulised salbutamol was 15% lower after formoterol therapy compared to placebo (95% confidence 5 to 25%, p = 0.008). The response to ipratropium was unchanged.



Long-acting beta-agonist treatment induces tolerance to the bronchodilator effect of beta-agonists, which is not overcome by higher dose nebulised salbutamol. However, the bronchodilator response to ipratropium bromide is unaffected.
A comparison between decision trees and decision tree forest models for software development effort estimation

Accurate software effort estimation has been a challenge for many software practitioners and project managers. Underestimation leads to disruption in the project's estimated cost and delivery. On the other hand, overestimation causes outbidding and financial losses in business. Many software estimation models exist; however, none have been proven to be the best in all situations. In this paper, a decision tree forest (DTF) model is compared to a traditional decision tree (DT) model, as well as a multiple linear regression model (MLR). The evaluation was conducted using ISBSG and Desharnais industrial datasets. Results show that the DTF model is competitive and can be used as an alternative in software effort prediction.
Studying the values of hard-to-reach populations: content analysis of tweets by the 21st century homeless

This paper describes a content analysis of a corpus of 5,313 tweets from 32 individuals collected during a three-week period in March/April 2011. The corpus comprised two study groups: Group H -- Twitter users who self-identified as homeless or formerly homeless in their Twitter profiles, and Group NH -- a random, stratified sample of Twitter users who did not self-identify as homeless and who shared similar Twitter characteristics with those in Group H. The study uses the Meta-Inventory of Human Values for Informal Communication (MIHV-IC) to study value expression in tweets. Two rounds of inter-coder reliability testing demonstrated the challenges of reliably detecting human values in tweets. Analysis of categories with substantial inter-coder agreement indicated significant differences between the two groups for helpfulness and wealth. This approach provides a promising opportunity for detecting the values of hard-to-reach populations such as the 21st century homeless.
An Accurate Pseudoempirical Model of Winding Loss Calculation in HF Foil and Round Conductors in Switchmode Magnetics

When operating higher up in frequency, the copper losses in transformer windings will significantly rise due to enhanced skin and proximity effect. This leads to a high need to develop new methods to accurately evaluate winding losses at higher frequencies. This paper investigates the effect of different geometrical parameters at a wide range of frequencies in order to propose a pseudoempirical formula for winding loss calculation in high-frequency transformers. A comprehensive analysis of the edge effect and ac resistance is done by performing more than 12 300 2-D finite element simulations on foil and round conductors. Unlike previous studies which mostly focused on specific case studies with limited applications, this model provides very high accuracy, especially where the most common analytical models drastically underestimate the winding losses, with a wide-range applicability which could be of interest for designers to avoid time consuming FEM simulation without compromising with the accuracy. Several transformers are built and the model is experimentally verified with a good agreement.
Grid-enabling data mining applications with DataMiningGrid: An architectural perspective

The DataMiningGrid system has been designed to meet the requirements of modern and distributed data mining scenarios. Based on the Globus Toolkit and other open technology and standards, the DataMiningGrid system provides tools and services facilitating the grid-enabling of data mining applications without any intervention on the application side. Critical features of the system include flexibility, extensibility, scalability, efficiency, conceptual simplicity and ease of use. The system has been developed and evaluated on the basis of a diverse set of use cases from different sectors in science and technology. The DataMiningGrid software is freely available under Apache License 2.0.