nils-herrmann/clean_scirepeval huggingface dataset:
Randomized controlled trial of yoga and exercise in multiple sclerosis.

To determine the effect of yoga and of aerobic exercise on cognitive function, fatigue, mood, and quality of life in multiple sclerosis (MS).



Subjects with clinically definite MS and Expanded Disability Status Score less than or equal to 6.0 were randomly assigned to one of three groups lasting 6 months: weekly Iyengar yoga class along with home practice, weekly exercise class using a stationary bicycle along with home exercise, or a waiting-list control group. Outcome assessments performed at baseline and at the end of the 6-month period included a battery of cognitive measures focused on attention, physiologic measures of alertness, Profile of Mood States, State-Trait Anxiety Inventory, Multi-Dimensional Fatigue Inventory (MFI), and Short Form (SF)-36 health-related quality of life.



Sixty-nine subjects were recruited and randomized. Twelve subjects did not finish the 6-month intervention. There were no adverse events related to the intervention. There were no effects from either of the active interventions on either of the primary outcome measures of attention or alertness. Both active interventions produced improvement in secondary measures of fatigue compared to the control group: Energy and Fatigue (Vitality) on the SF-36 and general fatigue on the MFI. There were no clear changes in mood related to yoga or exercise.



Subjects with MS participating in either a 6-month yoga class or exercise class showed significant improvement in measures of fatigue compared to a waiting-list control group. There was no relative improvement of cognitive function in either of the intervention groups.
Role-Based Access Control Models

This article introduces a family of reference models for rolebased access control (RBAC) in which permissions are associated with roles, and users are made members of appropriate roles. This greatly simpli es management of permissions. Roles are closely related to the concept of user groups in access control. However, a role brings together a set of users on one side and a set of permissions on the other, whereas user groups are typically de ned as a set of users only. The basic concepts of RBAC originated with early multi-user computer systems. The resurgence of interest in RBAC has been driven by the need for general-purpose customizable facilities for RBAC and the need to manage the administration of RBAC itself. As a consequence RBAC facilities range from simple to complex. This article describes a novel framework of reference models to systematically address the diverse components of RBAC, and their interactions.
Person Re-identification: What Features Are Important?

State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods.
Statistics on Password Re-use and Adaptive Strength for Financial Accounts

Multiple studies have demonstrated that users select weak passwords. However, the vast majority of studies on password security uses password lists that only have passwords for one site, which means that several important questions cannot be studied. For example, how much stronger are password choices for different categories of sites? We use a dataset which we extracted from a large dump of malware records. It contains multiple accounts (and passwords) per user and thus allows us to study both password re-use and the correlation between the value of an account and the strength of the passwords for those accounts. The first contribution of our study shows that users in our sample choose (substantially) stronger passwords for financial accounts than for lowvalue accounts, based on the extracted passwords as well as publicly available lists. This contribution has implications for password research, as some widely-used lists contain passwords much weaker than those used in the real world (for accounts of more than low value). In our second contribution, we measure password re-use taking account values into account. We see that although high-value passwords are stronger, they are re-used more frequently than low-value passwords – valuable passwords are identical to 21% of the remaining passwords of a user. Before our study, little was known about password re-use for different account values.
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices

When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable whilst others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have lead to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract the visual desirability of neighborhoods as proxy variables that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the desirability of London streets.
A Comparative Study of Staff Removal Algorithms

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.
Different patterns of autobiographical memory loss in semantic dementia and medial temporal lobe amnesia: a challenge to consolidation theory.

Temporally graded retrograde memory loss, with a disproportionate impairment of recent relative to remote memories, is considered a hallmark of medial temporal lobe amnesia. According to consolidation theory, the hippocampal complex, which includes the hippocampal formation, parahippocampal gyrus, the entorhinal and perirhinal cortex, plays a time-limited role in memory, needed only until consolidation in the neocortex is complete (Squire, . Recent support for this theory comes from findings of a reverse gradient in people with semantic dementia with neocortical degeneration but a relatively preserved hippocampal complex (Hodges and Graham, Neuropsychologia 1998; 36: 803-25). Consolidation theory is challenged by evidence that remote autobiographical memory is not always spared in amnesia (Nadel and Moscovitch, Current Opinion in Neurobiology 1997; 7: 217-27) and that semantic memory becomes highly personalized in semantic dementia (Snowden et al., Memory 1995; 3: 225-46). According to Nadel and Moscovitch, the hippocampal complex is needed to retain and retrieve detailed memories of autobiographical episodes no matter how old they are. To test consolidation theory against the opposing view, we investigated the role of the hippocampal complex in recent and remote autobiographical and personal semantic memory by contrasting the memory of a semantic dementia patient, EL, with that of an amnesic patient, KC, using family photographs as recall cues. KC demonstrated a complete loss of autobiographical episodes with a sparing of autobiographical facts; EL demonstrated well-preserved memory for episodes with a reverse gradient for personally relevant names. The influence of autobiographical significance on memory for names of public figures was examined further by comparing the effect that familiarity and recollection had on recognition of names of famous people and famous places. EL's memory was influenced by autobiographical significance, whereas KC's was not. We propose that the hippocampal complex plays a permanent role in the storage and retrieval of autobiographical episodes and that autobiographical significance may affect semantic representations.
Visualising Textual Concordances with Word Mosaics and Graphs: Prototypes and Evaluation Methods

The visualisation of textual concordances has seen little change since the proposal of the keyword-in-context indexing technique in the 1950s. This report details an investigation of the current state of the art in concordance visualisation. The investigation lead to the the expansion of context trees to a new graph based representation of concordance lists. This representation is explained in detail and two visualisations, of the graph, are explored. It is proposed that one of these visualisations may offer significant advantages over existing concordance visualisations for the investigation of keyword collocation frequencies. An evaluation plan to test this hypothesis is outlined as future work in this project.
Image quality measures and their performance

A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers’ response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.
JOINT IMAGE SHARPENING AND DENOISING BY 3 D TRANSFORM-DOMAIN COLLABORATIVE FILTERING

In order to simultaneously sharpen image details and attenuate noise, we propose to combine the recent blockmatching and 3D Þltering (BM3D) denoising approach, based on 3D transform-domain collaborative Þltering, with alpha-rooting, a transform-domain sharpening technique. The BM3D exploits grouping of similar image blocks into 3D arrays (groups) on which collaborative Þltering (by hard-thresholding) is applied. We propose two approaches of sharpening by alpha-rooting; the Þrst applies alpharooting individually on the 2D transform spectra of each grouped block; the second applies alpha-rooting on the 3D-transform spectra of each 3D array in order to sharpen Þne image details shared by all grouped blocks and further enhance the interblock differences. The conducted experiments with the proposed method show that it can preserve and sharpen Þne image details and effectively attenuate noise.
Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments

In this report we provide an overview of the open source Armadillo C++ linear algebra library (matrix maths). The library aims to have a good balance between speed and ease of use, and is useful if C++ is the language of choice (due to speed and/or integration capabilities), rather than another language like Matlab or Octave. In particular, Armadillo can be used for fast prototyping and computationally intensive experiments, while at the same time allowing for relatively painless transition of research code into production environments. It is distributed under a license that is applicable in both open source and proprietary software development contexts. The library supports integer, floating point and complex numbers, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK, or one its high-performance drop-in replacements, such as MKL from Intel or ACML from AMD. A delayed evaluation approach is employed (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. This is accomplished through C++ template meta-programming. Performance comparisons suggest that the library is considerably faster than Matlab and Octave, as well as previous C++ libraries such as IT++ and Newmat. This report reflects a subset of the functionality present in Armadillo v2.4. Armadillo can be downloaded from: • http://arma.sourceforge.net If you use Armadillo in your research and/or software, we would appreciate a citation to this document.  An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments. Technical Report,   An Open Source C++
Generating Natural Language from Linked Data: Unsupervised template extraction

We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.
Simultaneous Localization and Mapping for Forest Harvesters

A real-time SLAM (simultaneous localization and mapping) approach to harvester localization and tree map generation in forest environments is presented in this paper. The method combines 2D laser localization and mapping with GPS information to form global tree maps. Building an incremental map while also using it for localization is the only way a mobile robot can navigate in large outdoor environments. Until recently SLAM has only been confined to small-scale, mostly indoor, environments. We try to addresses the issues of scale for practical implementations of SLAM in extensive outdoor environments. Presented algorithms are tested in real outdoor environments using an all-terrain vehicle equipped with the navigation sensors and a DGPS receiver.
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires more effort to implement and execute compared to maximumlikelihood methods. In this paper, we propose new natural-gradient algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms can be implemented within the Adam optimizer by perturbing the network weights during gradient evaluations, and uncertainty estimates can be cheaply obtained by using the vector that adapts the learning rate. This requires lower memory, computation, and implementation effort than existing VI methods, while obtaining uncertainty estimates of comparable quality. Our empirical results confirm this and further suggest that the weight-perturbation in our algorithm could be useful for exploration in reinforcement learning and stochastic optimization.
Power without wires

This article presents the history of WPT and the various technologies and applications of this exciting technology. In the near future, standardization and regulation will be of importance to realizing WPT based products for commercial applications. The WPC has defined a standard for inductive coupling and members of this group have released conforming products. There are, as yet, no standards or regulation for resonant coupling and MPT technologies. In Japan, a technical forum known as Broadband Wireless Forum has been established to discuss the future of WPT. SPS researchers have also submitted a proposal for WPT to the International Telecommunication Union (ITU). In the IEEE MTT Society, the Technical Committee MTT-26 Wireless Energy Transfer and Conversion was established in June, 2011 to discuss the future of WPT.
Cluster Ensembles for High Dimensional Clustering : An Empirical Study

This paper studies cluster ensembles for high dimensional data clustering. We examine three different approaches to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on two popular dimension reduction techniques, random projection and principal component analysis (PCA). We present evidence showing that ensembles generated by random projection perform better than those by PCA and further that this can be attributed to the capability of random projection to produce diverse base clusterings. We also examine four different consensus functions for combining the clusterings of the ensemble. We compare their performance using two types of ensembles, each with different properties. In both cases, we show that a recent consensus function based on bipartite graph partitioning achieves the best performance.
3D Object Recognition with Deep Belief Nets

We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB database (normalized-uniform version), which contains stereo-pair images of objects under different lighting conditions and viewpoints. Our model achieves 6.5% error on the test set, which is close to the best published result for NORB (5.9%) using a convolutional neural net that has built-in knowledge of translation invariance. It substantially outperforms shallow models such as SVMs (11.6%). DBNs are especially suited for semi-supervised learning, and to demonstrate this we consider a modified version of the NORB recognition task in which additional unlabeled images are created by applying small translations to the images in the database. With the extra unlabeled data (and the same amount of labeled data as before), our model achieves 5.2% error.
Towards Information Diffusion in Mobile Social Networks

The emerging of mobile social networks opens opportunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the <italic>diffusion minimization</italic> problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric <inline-formula> <tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="lu-ieq1-2451624.gif"/></alternatives> </inline-formula>-center problem which is NP-hard, and the best known approximation algorithm for the asymmetric <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="lu-ieq2-2451624.gif"/></alternatives></inline-formula>-center problem has approximation ratio of <inline-formula><tex-math notation="LaTeX">$\log ^*n$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="lu-ieq3-2451624.gif"/></alternatives></inline-formula> and time complexity <inline-formula> <tex-math notation="LaTeX">$O(n^5)$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="lu-ieq4-2451624.gif"/> </alternatives></inline-formula>. Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace compared to existing algorithms, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time.
An Electrically Large Metallic Cavity Antenna With Circular Polarization for Satellite Applications

An electrically large metallic cavity antenna with circular polarization (CP) is proposed in this letter. The aim is to increase the size of satellite antennas working in the Ku-band and other higher-frequency bands through low cost and easy fabrication. A brass C-band antenna prototype is designed, fabricated, and measured for concept verification. The overall dimensions of the antenna are 0.67λ<sub>0</sub> × 0.67λ<sub>0</sub> × 0.83λ<sub>0</sub> at the working frequency. The resonant mode of the proposed cavity antenna is excited by a coaxial feeding probe, and circular polarization is realized by an inserted perturbation screw. The measured impedance bandwidth for | <i>S</i><sub>11</sub>| ≤ -10 dB and 3-dB axial-ratio bandwidth are 1.02 GHz (6.07-7.09 GHz) and 320 MHz (6.59-6.91 GHz), respectively. The measured gain is 8.5 dBic across the whole 3-dB axial-ratio bandwidth. The measured 3-dB axial-ratio beamwidths at the central frequency are 150<sup>°</sup> and 148<sup>°</sup> in the <i>xoz</i>- and the <i>yoz</i>-planes, respectively, which is wide enough to cover the >; 0-dBi gain beamwidths of 120<sup>°</sup> for practical engineering applications. The measured and the simulated results show good agreement.
An ERP study on the time course of emotional face processing.

Using event-related brain potentials (ERPs), we investigated the time course of facial expression processing in human subjects watching photographs of fearful and neutral faces. Upright fearful faces elicited a frontocentral positivity within 120 ms after stimulus presentation, which was followed by a broadly distributed sustained positivity beyond 250 ms post-stimulus. Emotional expression effects were delayed and attenuated when faces were inverted. In contrast, the face-specific N170 component was completely unaffected by facial expression. We conclude that emotional expression analysis and the structural encoding of faces are parallel processes. Early emotional ERP modulations may reflect the rapid activation of prefrontal areas involved in the analysis of facial expression.