nils-herrmann/clean_scirepeval huggingface dataset:
Arrakis: The Operating System as Control Plane

Recent device hardware trends enable a new approach to the design of network server operating systems. In a traditional operating system, the kernel mediates access to device hardware by server applications to enforce process isolation as well as network and disk security. We have designed and implemented a new operating system, Arrakis, that splits the traditional role of the kernel in two. Applications have direct access to virtualized I/O devices, allowing most I/O operations to skip the kernel entirely, while the kernel is re-engineered to provide network and disk protection without kernel mediation of every operation. We describe the hardware and software changes needed to take advantage of this new abstraction, and we illustrate its power by showing improvements of 2 to 5 × in latency and 9 × throughput for a popular persistent NoSQL store relative to a well-tuned Linux implementation.
Adapting recommender systems to the requirements of personal health record systems

In the future many people in industrialized countries will manage their personal health data electronically in centralized, reliable and trusted repositories - so-called personal health record systems (PHR). At this stage PHR systems still fail to satisfy the individual medical information needs of their users. Personalized recommendations could solve this problem.
 A first approach of integrating recommender system (RS) methodology into personal health records - termed health recommender system (HRS) - is presented. By exploitation of existing semantic networks like Wikipedia a health graph data structure is obtained. The data kept within such a graph represent health related concepts and are used to compute semantic distances among pairs of such concepts.
 A ranking procedure based on the health graph is outlined which enables a match between entries of a PHR system and health information artifacts. This way a PHR user will obtain individualized health information he might be interested in.
The Relationship Among Male Pornography Use , Attachment , and Aggression in Romantic Relationships

The Relationship Among Male Pornography Use, Attachment, and Aggression in Romantic Relationships Andrew P. Brown School of Family Life, BYU Doctor of Philosophy Clinicians and researchers question how pornography might be impacting the people who view it and their partners. In particular, does pornography link to couples attachment and levels of aggression? Using data collected by the RELATE institute a sophisticated structural equation model was set up to answer this question. In particular, an actor partner interdependence model was used to analyze the relationship among male pornography use, insecure attachment behaviors, relational aggression, and physical aggression in 1630 heterosexual couples. Results indicate higher reports of male pornography use are associated with higher reports of insecure attachment behaviors, relational aggression, and physical aggression. Differences between male and female results, including a direct relationship between male pornography use and male and female insecure attachment behaviors as well as female physical aggression, are discussed. Findings add to current literature on the impact pornography has on individuals and their partners. Implications for clinicians are discussed.
Bitsliced High-Performance AES-ECB on GPUs

In order to perform high-performance Monte Carlo simulations of fracture in certain composite materials, we needed fast methods for generating deterministic random numbers. We made several design choices, and due to the fact that the entire simulation was to be done on both CPUs and GPUs, we designed new methods for fast implementation of the AES in the ECB mode on such architectures. This paper describes our algorithms and summarizes the performance results. In our implementation we were able to produce a speed of 78.6 Gbits per second on the GeForce GTX 480, which was 31-62% faster than the fastest implementations reported in the recent literature on similar devices.
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics

Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.
Citation Author Topic Model in Expert Search

This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the querySyntactic Parsing. Our contribution in this paper is two-fold. First, we model the cited author information together with words and paper authors. Such extra contextual information directly models linkage among authors and enhances the author-topic association, thus produces more coherent author-topic distribution. Second, we provide a preliminary solution to the task of expert search when the learning repository contains exclusively research related documents authored by the experts. When compared with a previous proposed model (Johri et al., 2010), the proposed model produces high quality author topic linkage and achieves over 33% error reduction evaluated by the standard MAP measurement.
A DTC-Based Subsampling PLL Capable of Self-Calibrated Fractional Synthesis and Two-Point Modulation

We present an analog subsampling PLL based on a digital-to-time converter (DTC), which operates with almost no performance gap (176/198 fs RMS jitter) between the integer and the worst case fractional operation, achieving -246.6 dB FOM in the worst case fractional mode. The PLL is capable of two-point, 10 Mbit/s GMSK modulation with -40.5 dB EVM around a 10.24 GHz fractional carrier. The analog nonidealities-DTC gain, DTC nonlinearity, modulating VCO bank gain, and nonlinearity-are calibrated in the background while the system operates normally. This results in ~15 dB fractional spur improvement (from -41 dBc to -56.5 dBc) during synthesis and ~15 dB EVM improvement (from -25 dB to -40.5 dB) during modulation. The paper provides an overview of the mechanisms that contribute to performance degradation in DTC-based PLL/phase modulators and presents ways to mitigate them. We demonstrate state-of-the-art performance in nanoscale CMOS for fractional-N synthesis and phase modulation.
Evaluation of Cost Functions for Stereo Matching

Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper we evaluate the insensitivity of different matching costs with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants and measure their performance in the presence of global intensity changes (e.g., due to gain and exposure differences), local intensity changes (e.g., due to vignetting, non-Lambertian surfaces, and varying lighting), and noise. Using existing stereo datasets with ground-truth disparities as well as six new datasets taken under controlled changes of exposure and lighting, we evaluate the different costs with a local, a semi-global, and a global stereo method.
Automatic Identification of Word Translations from Unrelated English and German Corpora

Algorithms for the alignment of words in translated texts are w ell established. However, only recently, new approaches have been proposed to identify word translations f rom non-parallel or even unrelated texts. This task is more difficult, because most statistical clues useful in the processing of parallel texts can not be applied for non-parallel tex ts. For this reason, whereas for parallel texts in some studies up to 99% of the word alignments have been shown to be correct, the accuracy for non-parallel texts has been around 30% up to now. The current study, which is based on the assumption that there is a correlation between the patterns of word cooccurrences in corpora of different languages, makes a significant improvement to about 72% of word translations identified correctly.
Gaussian-Bernoulli deep Boltzmann machine

In this paper, we study a model that we call Gaussian-Bernoulli deep Boltzmann machine (GDBM) and discuss potential improvements in training the model. GDBM is designed to be applicable to continuous data and it is constructed from Gaussian-Bernoulli restricted Boltzmann machine (GRBM) by adding multiple layers of binary hidden neurons. The studied improvements of the learning algorithm for GDBM include parallel tempering, enhanced gradient, adaptive learning rate and layer-wise pretraining. We empirically show that they help avoid some of the common difficulties found in training deep Boltzmann machines such as divergence of learning, the difficulty in choosing right learning rate scheduling, and the existence of meaningless higher layers.
SecondNet: a data center network virtualization architecture with bandwidth guarantees

In this paper, we propose virtual data center (VDC) as the unit of resource allocation for multiple tenants in the cloud. VDCs are more desirable than physical data centers because the resources allocated to VDCs can be rapidly adjusted as tenants' needs change. To enable the VDC abstraction, we design a data center network virtualization architecture called SecondNet. SecondNet achieves scalability by distributing all the virtual-to-physical mapping, routing, and bandwidth reservation state in server hypervisors. Its port-switching based source routing (PSSR) further makes SecondNet applicable to arbitrary network topologies using commodity servers and switches. SecondNet introduces a centralized VDC allocation algorithm for bandwidth guaranteed virtual to physical mapping. Simulations demonstrate that our VDC allocation achieves high network utilization and low time complexity. Our implementation and experiments show that we can build SecondNet on top of various network topologies, and SecondNet provides bandwidth guarantee and elasticity, as designed.
3D Trajectory Recovery for Tracking Multiple Objects and Trajectory Guided Recognition of Actions

A mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. The 3D trajectory, occlusion, and segmentation information are utlized in extracting stabilized views of the moving object. Trajectory-guided recognition (TGR) is proposed as a new and efficient method for adaptive classification of action. The TGR approach is demonstrated using “motion history images” that are then recognized via a mixture of Gaussian classifier. The system was tested in recognizing various dynamic human outdoor activities; e.g., running, walking, roller blading, and cycling. Experiments with synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HOTPOTQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. We show that HOTPOTQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.
Online EM for Unsupervised Models

The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.
Narrating a Knowledge Base

We aim to automatically generate natural language narratives about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we also propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% 72.6% F-
Topology optimization of hyperelastic structures using a level set method

Soft rubberlike materials, due to their inherent compliance, are finding widespread implementation in a variety of applications ranging from assistive wearable technologies to soft material robots. Structural design of such soft and rubbery materials necessitates the consideration of large nonlinear deformations and hyperelastic material models to accurately predict their mechanical behaviour. In this paper, we present an effective level set-based topology optimization method for the design of hyperelastic structures that undergo large deformations. The method incorporates both geometric and material nonlinearities where the strain and stress measures are defined within the total Lagrange framework and the hyperelasticity is characterized by the widely-adopted Mooney-Rivlin material model. A shape sensitivity analysis is carried out, in the strict sense of the material derivative, where the high-order terms involving the displacement gradient are retained to ensure the descent direction. As the design velocity enters into the shape derivative in terms of its gradient and divergence terms, we develop a discrete velocity selection strategy. The whole optimization implementation undergoes a two-step process, where the linear optimization is first performed and its optimized solution serves as the initial design for the subsequent nonlinear optimization. It turns out that this operation could efficiently alleviate the numerical instability and facilitate the optimization process. To demonstrate the validity and effectiveness of the proposed method, three compliance minimization problems are studied and their optimized solutions present significant mechanical benefits of incorporating the nonlinearities, in terms of remarkable enhancement in not only the structural stiffness but also the critical buckling load.
Budgeted Batch Bayesian Optimization With Unknown Batch Sizes

Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially expensive in large parameter spaces, and Bayesian optimization (BO) offers an elegant alternative for global optimization of black box functions. In situations where the black box function can be evaluated at multiple points simultaneously, batch Bayesian optimization is used. Current batch BO approaches are restrictive in that they fix the number of evaluations per batch, and this can be wasteful when the number of specified evaluations is larger than the number of real maxima in the underlying acquisition function. We present the Budgeted Batch Bayesian Optimization (B3O) for hyper-parameter tuning and experimental design we identify the appropriate batch size for each iteration in an elegant way. To set the batch size flexible, we use the infinite Gaussian mixture model (IGMM) for automatically identifying the number of peaks in the underlying acquisition functions. We solve the intractability of estimating the IGMM directly from the acquisition function by formulating the batch generalized slice sampling to efficiently draw samples from the acquisition function. We perform extensive experiments for both synthetic functions and two real world applications machine learning hyper-parameter tuning and experimental design for alloy hardening. We show empirically that the proposed B3O outperforms the existing fixed batch BO approaches in finding the optimum whilst requiring a fewer number of evaluations, thus saving cost and time.
Attribute-Based Access Control Scheme in Federated IoT Platforms

The Internet of Things (IoT) introduced the possibility to connect electronic things from everyday life to the Internet, while making them ubiquitously available. With advanced IoT services, based on a trusted federation among heterogeneous IoT platforms, new security problems (including authentication and authorization) emerge. This contribution aims at describing the main facets of the preliminary security architecture envisaged in the context of the symbIoTe project, recently launched by European Commission under the Horizon 2020 EU program. Our approach features distributed and decoupled mechanisms for authentication and authorization services in complex scenarios embracing heterogeneous and federated IoT platforms, by leveraging Attribute Based Access Control and token-based authorization techniques.
PerFallD: A pervasive fall detection system using mobile phones

Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, fall detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development. To our knowledge, we are the first to do so. We design a detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive fall detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong detection performance and power efficiency.
FastSLAM 2 . 0 : An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges

In , Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM that overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM problems and provide real-world experimental results that i l lustrate an order of magnitude improvement in accuracy over the original FastSLAM algorithm.
Phylogenetic analysis in the anomaly zone.

The concatenation method has been widely used as a means of combining data to estimate phylogenetic trees (Huelsenbeck et al. 1996a, 1996b; Glazko and Nei 2003). However, simulation studies have shown that the maximum likelihood (ML) estimate of the species tree for concatenated sequences may be statistically inconsistent if the gene trees are highly heterogeneous (Kolaczkowski and Thornton 2004; Kubatko and Degnan 2007). Recently, Degnan and Rosenberg (2006) defined an “anomaly zone”—a set of short internal branches in species trees that will generate gene trees that are discordant with the species tree more often than gene trees that are concordant. Kubatko and Degnan (2007) went on to show that when DNA sequences are generated from gene trees simulated from species trees in the anomaly zone, as well as from species trees slightly outside this zone but still with short internal branches, the ML estimate of the species tree for the concatenated sequences can be inconsistent, resulting in increasing certainty in the wrong species tree. These studies were all performed with a molecular clock on rooted gene and species trees within the variation realized in stochastic simulations of DNA sequences under the Jukes and Cantor (1969) model of nucleotide substitution. They applied the ML method with a clock to recover phylogenetic trees from their simulated concatenated data sets. In this paper, we show that phylogenetic methods that solely utilize the relative order of divergences among a set of DNA sequences as a criterion for inferring phylogenies, such as the unweighted pair group method with arithmetic mean (UPGMA), are statistically consistent even when DNA sequences are generated from gene trees simulated from species trees in the anomaly zone. In addition, we use simulation to assess the performance of a variety of tree construction methods when analyzing concatenated sequences generated from 4 and 5-taxon species tree located in the anomaly zone and show that a variety of methods do in fact recover the correct species tree topology, whereas ML, with or without a molecular clock, remains inconsistent. However, the branch lengths of the tree inferred from concatenated data are inevitably overestimated, as predicted by theory. Finally, simulations also suggest that a newly proposed Bayesian approach for estimating species trees from multiple unlinked loci, BEST (Liu and Pearl 2007; Liu et al. 2008), is consistent in both topology and branch lengths on data sets generated from species trees in the anomaly zone.