nils-herrmann/clean_scirepeval huggingface dataset:
Applying Model Management to Classical Meta Data Problems

Model management is a new approach to meta data management that offers a higher level programming interface than current techniques. The main abstractions are models (e.g., schemas, interface definitions) and mappings between models. It treats these abstractions as bulk objects and offers such operators as Match, Merge, Diff, Compose, Apply, and ModelGen. This paper extends earlier treatments of these operators and applies them to three classical meta data management problems: schema integration, schema evolution, and round-trip engineering.
Labelling strategies for hierarchical multi-label classification techniques

Many hierarchical multi-label classification systems predict a real valued score for every (instance, class) couple, with a higher score reflecting more confidence that the instance belongs to that class. These classifiers leave the conversion of these scores to an actual label set to the user, who applies a cut-off value to the scores. The predictive performance of these classifiers is usually evaluated using threshold independent measures like precision-recall curves. However, several applications require actual label sets, and thus an automatic labelling strategy. In this article, we present and evaluate different alternatives to perform the actual labelling in hierarchical multi-label classification. We investigate the selection of both single and multiple thresholds. Despite the existence of multiple threshold selection strategies in non-hierarchical multi-label classification, they can not be applied directly to the hierarchical context. The proposed strategies are implemented within two main approaches: optimisation of a certain performance measure of interest (such as F-measure or hierarchical loss), and simulating training set properties (such as class distribution or label cardinality) in the predictions. We assess the performance of the proposed labelling schemes on 10 datasets from different application domains. Our results show that selecting multiple thresholds may result in ∗Corresponding author.  Email addresses: Isaac.Triguero@irc.vib-UGent.be (Isaac Triguero), Celine.Vens@kuleuven-kulak.be (Celine Vens)  2016 an efficient and effective solution for hierarchical multi-label problems.
Parallel Coordinate Descent for L1-Regularized Loss Minimization

We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds for Shotgun which predict near-linear speedups, up to a problemdependent limit. We present a comprehensive empirical study of Shotgun for Lasso and sparse logistic regression. Our theoretical predictions on the potential for parallelism closely match behavior on real data. Shotgun outperforms other published solvers on a range of large problems, proving to be one of the most scalable algorithms for L1.
The Second Dialog State Tracking Challenge

A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step. This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system’s actions. The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking techniques, providing common testbeds and evaluation suites. This paper presents a second challenge, which continues this tradition and introduces some additional features – a new domain, changing user goals and a richer dialog state. The challenge received 31 entries from 9 research groups. The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions. An investigation into ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.
A novel mixed conventional microstrip and composite right/left-handed backward-wave directional coupler with broadband and tight coupling characteristics

A novel backward directional coupler, composed of a conventional microstrip (C/spl mu/S) line and composite right/left-handed (CRLH) line, is proposed. This coupler is functionally backward but it is based on a forward-type coupling phenomenon. The coupler is shown to exhibit broad bandwidth and tight coupling characteristics. The theoretical circuit-model results are supported by full-wave and experimental evidence. A quasi 0-dB prototype with more than 50% bandwidth is demonstrated.
Deep Learning without Poor Local Minima

In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, we prove the following statements under the independence assumption adopted from recent work: 1) the function is non-convex and non-concave, 2) every local minimum is a global minimum, 3) every critical point that is not a global minimum is a saddle point, and 4) the property of saddle points differs for shallow networks (with three layers) and deeper networks (with more than three layers). Moreover, we prove that the same four statements hold for deep linear neural networks with any depth, any widths and no unrealistic assumptions. As a result, we present an instance, for which we can answer to the following question: how difficult to directly train a deep model in theory? It is more difficult than the classical machine learning models (because of the non-convexity), but not too difficult (because of the nonexistence of poor local minima and the property of the saddle points). We note that even though we have advanced the theoretical foundations of deep learning, there is still a gap between theory and practice.
Non-Local Patch-Based Image Inpainting

Image inpainting is the process of filling in missing regions in an image in a plausible way. In this contribution, we propose and describe an implementation of a patch-based image inpainting algorithm. The method is actually a two-dimensional version of our video inpainting algorithm proposed in . The algorithm attempts to minimise a highly non-convex functional, first introducted by Wexler et al. in . The functional specifies that a good solution to the inpainting problem should be an image where each patch is very similar to its nearest neighbour in the unoccluded area. Iterations are performed in a multi-scale framework which yields globally coherent results. In this manner two of the major goals of image inpainting, the correct reconstruction of textures and structures, are addressed. We address a series of important practical issues which arise when using such an approach. In particular, we reduce execution times by using the PatchMatch  algorithm for nearest neighbour searches, and we propose a modified patch distance which improves the comparison of textured patches. We address the crucial issue of initialisation and the choice of the number of pyramid levels, two points which are rarely discussed in such approaches. We provide several examples which illustrate the advantages of our algorithm, and compare our results with those of state-of-the-art methods.
Limbic abnormalities in affective processing by criminal psychopaths as revealed by functional magnetic resonance imaging

Psychopathy is a complex personality disorder of unknown etiology. Central to the disorder are anomalies or difficulties in affective processing.



Functional magnetic resonance imaging was used to elucidate the neurobiological correlates of these anomalies in criminal psychopaths during performance of an affective memory task.



Compared with criminal nonpsychopaths and noncriminal control participants, criminal psychopaths showed significantly less affect-related activity in the amygdala/hippocampal formation, parahippocampal gyrus, ventral striatum, and in the anterior and posterior cingulate gyri. Psychopathic criminals also showed evidence of overactivation in the bilateral fronto-temporal cortex for processing affective stimuli.



These data suggest that the affective abnormalities so often observed in psychopathic offenders may be linked to deficient or weakened input from limbic structures.
Identifying Relations for Open Information Extraction

Open Information Extraction (IE) is the task of extracting assertions from massive corpora without requiring a pre-specified vocabulary. This paper shows that the output of state-ofthe-art Open IE systems is rife with uninformative and incoherent extractions. To overcome these problems, we introduce two simple syntactic and lexical constraints on binary relations expressed by verbs. We implemented the constraints in the REVERB Open IE system, which more than doubles the area under the precision-recall curve relative to previous extractors such as TEXTRUNNER and WOE. More than 30% of REVERB’s extractions are at precision 0.8 or higher— compared to virtually none for earlier systems. The paper concludes with a detailed analysis of REVERB’s errors, suggesting directions for future
Perceived parental social support and academic achievement: an attachment theory perspective.

The study tested the extent to which parental social support predicted college grade point average among undergraduate students. A sample of 418 undergraduates completed the Social Provisions Scale--Parent Form (C.E. Cutrona, 1989) and measures of family conflict and achievement orientation. American College Testing Assessment Program college entrance exam scores (ACT; American College Testing Program, 1986) and grade point average were obtained from the university registrar. Parental social support, especially reassurance of worth, predicted college grade point average when controlling for academic aptitude (ACT scores), family achievement orientation, and family conflict. Support from parents, but not from friends or romantic partners, significantly predicted grade point average. Results are interpreted in the context of adult attachment theory.
Omega-3 Fatty Acids and Inflammatory Processes

Long chain fatty acids influence inflammation through a variety of mechanisms; many of these are mediated by, or at least associated with, changes in fatty acid composition of cell membranes. Changes in these compositions can modify membrane fluidity, cell signaling leading to altered gene expression, and the pattern of lipid mediator production. Cell involved in the inflammatory response are typically rich in the n-6 fatty acid arachidonic acid, but the contents of arachidonic acid and of the n-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) can be altered through oral administration of EPA and DHA. Eicosanoids produced from arachidonic acid have roles in inflammation. EPA also gives rise to eicosanoids and these often have differing properties from those of arachidonic acid-derived eicosanoids. EPA and DHA give rise to newly discovered resolvins which are anti-inflammatory and inflammation resolving. Increased membrane content of EPA and DHA (and decreased arachidonic acid content) results in a changed pattern of production of eicosanoids and resolvins. Changing the fatty acid composition of cells involved in the inflammatory response also affects production of peptide mediators of inflammation (adhesion molecules, cytokines etc.). Thus, the fatty acid composition of cells involved in the inflammatory response influences their function; the contents of arachidonic acid, EPA and DHA appear to be especially important. The anti-inflammatory effects of marine n-3 PUFAs suggest that they may be useful as therapeutic agents in disorders with an inflammatory component.
Disruption of Large-Scale Brain Systems in Advanced Aging

Cognitive decline is commonly observed in advanced aging even in the absence of disease. Here we explore the possibility that normal aging is accompanied by disruptive alterations in the coordination of large-scale brain systems that support high-level cognition. In 93 adults aged 18 to 93, we demonstrate that aging is characterized by marked reductions in normally present functional correlations within two higher-order brain systems. Anterior to posterior components within the default network were most severely disrupted with age. Furthermore, correlation reductions were severe in older adults free from Alzheimer's disease (AD) pathology as determined by amyloid imaging, suggesting that functional disruptions were not the result of AD. Instead, reduced correlations were associated with disruptions in white matter integrity and poor cognitive performance across a range of domains. These results suggest that cognitive decline in normal aging arises from functional disruption in the coordination of large-scale brain systems that support cognition.
The case for RAMClouds: scalable high-performance storage entirely in DRAM

Disk-oriented approaches to online storage are becoming increasingly problematic: they do not scale gracefully to meet the needs of large-scale Web applications, and improvements in disk capacity have far outstripped improvements in access latency and bandwidth. This paper argues for a new approach to datacenter storage called RAMCloud, where information is kept entirely in DRAM and large-scale systems are created by aggregating the main memories of thousands of commodity servers. We believe that RAMClouds can provide durable and available storage with 100-1000x the throughput of disk-based systems and 100-1000x lower access latency. The combination of low latency and large scale will enable a new breed of dataintensive applications.
Hardware assisted control flow obfuscation for embedded processors

With more applications being deployed on embedded platforms, software protection becomes increasingly important. This problem is crucial on embedded systems like financial transaction terminals, pay-TV access-control decoders, where adversaries may easily gain full physical accesses to the systems and critical algorithms must be protected from being cracked. However, as this paper points out that protecting software with either encryption or obfuscation cannot completely preclude the control flow information from being leaked. Encryption has been widely studied and employed as a traditional approach for software protection, however, the control flow information is not 100% hidden with solely encrypting the code. On the other hand, pure software-based obfuscation has been proved inefficient to protect software due to its lack of theoretical foundation and considerable performance overhead introduced by complicated transformations. Moreover, even though obfuscation can prevent static reverse engineering, attacker can still successfully bypass the obfuscation by monitoring the dynamic program execution.To address all of these shortcomings, this paper presents a hardware assisted obfuscation technique that is capable of obfuscating the control flow information dynamically. Dynamic obfuscation changes memory access sequence on-the-fly and conceals recurrent instruction access sequences from being identified. Our scheme makes it provably difficult for the attacker to extract any useful information. Our results show that a high-level security protection is possible with only minor performance penalty. Finally, we show that our scheme can be implemented on embedded systems with very little hardware overhead.
Dataset Distillation

Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep the model fixed and instead attempt to distill the knowledge from a large training dataset into a small one. The idea is to synthesize a small number of data points that do not need to come from the correct data distribution, but will, when given to the learning algorithm as training data, approximate the model trained on the original data. For example, we show that it is possible to compress 60, 000 MNIST training images into just 10 synthetic distilled images (one per class) and achieve close to original performance with only a few steps of gradient descent, given a particular fixed network initialization. We evaluate our method in a wide range of initialization settings and with different learning objectives. Experiments on multiple datasets show the advantage of our approach compared to alternative methods in most settings.
Authenticated Append-only Skip Lists

In this work we describe, design and analyze the security of a tamper-evident, append-only data structure for maintaining secure data sequences in a loosely coupled distributed system where individual system components may be mutually distrust-ful. The resulting data structure, called an Authenticated Append-Only Skip List (AASL), allows its maintainers to produce one-way digests of the entire data sequence, which they can publish to others as a commitment on the contents and order of the sequence. The maintainer can produce efficiently succinct proofs that authenticate a particular datum in a particular position of the data sequence against a published digest. AASLs are secure against tampering even by malicious data structure maintain-ers. First, we show that a maintainer cannot " invent " and authenticate data elements for the AASL after he has committed to the structure. Second, he cannot equivocate by being able to prove conflicting facts about a particular position of the data sequence. This is the case even when the data sequence grows with time and its maintainer publishes successive commitments at times of his own choosing. AASLs can be invaluable in reasoning about the integrity of system logs maintained by untrusted components of a loosely-coupled distributed system .
Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets

We present attribute bagging (AB), a technique for improving the accuracy and stability of classi#er ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classi#ers are built. The induced classi#ers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classi#cation accuracy and voting using only the best subsets further improves the resulting performance of the ensemble. ? 2002 Pattern Recognition Society.
Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit

Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Therefore, users usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in context. This problem drives the need for better privacy control which can adaptively filter the members in a selected social circle to satisfy users' requirements while maintaining users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e. Potential privacy risks) and incentives of sharing (i.e. Potential social benefits), and quantifies users' requirements as a trade-off between these two types of utilities. By balancing these two metrics, our framework suggests a subset of a selected circle that aims to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.
Theoretical Results on Reinforcement Learning with Temporally Abstract Options

We present new theoretical results on planning within the framework of temporally abstract reinforcement learning (Precup & Sutton, 1997; Sutton, 1995). Temporal abstraction is a key step in any decision making system that involves planning and prediction. In temporally abstract reinforcement learning, the agent is allowed to choose among ”options”, whole courses of action that may be temporally extended, stochastic, and contingent on previous events. Examples of options include closed-loop policies such as picking up an object, as well as primitive actions such as joint torques. Knowledge about the consequences of options is represented by special structures called multi-time models. In this paper we focus on the theory of planning with multi-time models. We define new Bellman equations that are satisfied for sets of multi-time models. As a consequence, multi-time models can be used interchangeably with models of primitive actions in a variety of well-known planning methods including value iteration, policy improvement and policy iteration.
Practical graph isomorphism, II

We report the current state of the graph isomorphism problem from the practical point of view. After describing the general principles of the refinement-individualization paradigm and proving its validity, we explain how it is implemented in several of the key programs. In particular, we bring the description of the best known program nauty up to date and describe an innovative approach called Traces that outperforms the competitors for many difficult graph classes. Detailed comparisons against saucy, Bliss and conauto are presented.  bdm@cs.anu.edu.au (Brendan D. McKay), piperno@di.uniroma1.it (Adolfo Piperno). 1 Supported by the Australian Research Council.  ar X iv :1 30 1. 14 93 v1 [ cs .D M ] 8 J an 2 01 3
An end-to-end voting-system based on bitcoin

In this work we re-adapt the Bitcoin e-payment system and propose it as a decentralised end-to-end voting platform (from voters to candidates). We describe the main architectural choices behind the implementation, which consists of the pre-voting, voting, and post-voting phases. The resulting implementation is completely decentralised: it is possible to directly cast a vote in the block-chain without any collecting intermediate-level. All the votes can be verified by anyone reading such a public ledger. We also exploit digital asset coins to directly keep track of votes (through the Open Asset Protocol), and we show the election cost for n voters.
Iterative multi - view plane fitting

We present a method for the reconstruction of 3D planes from calibrated 2D images. Given a set of pixels Ω in a reference image, our method computes a plane which best approximates that part of the scene which has been projected to Ω by exploiting additional views. Based on classical image alignment techniques we derive linear matching equations minimally parameterized by the three parameters of an object-space plane. The resulting iterative algorithm is highly robust because it is able to integrate over large image regions due to the correct object-space approximation and hence is not limited to comparing small image patches. Our method can be applied to a pair of stereo images but is also able to take advantage of the additional information provided by an arbitrary number of input images. A thorough experimental validation shows that these properties enable robust convergence especially under the influence of image sensor noise and camera calibration errors.