nils-herrmann/clean_scirepeval huggingface dataset:
HHCART: An Oblique Decision Tree

Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using axis parallel splits. When the true decision boundaries are not aligned with the feature axes, this approach can produce a complicated boundary structure. Oblique decision trees use oblique decision boundaries to potentially simplify the boundary structure. The major limitation of this approach is that the tree induction algorithm is computationally expensive. In this article we present a new decision tree algorithm, called HHCART. The method utilizes a series of Householder matrices to reflect the training data at each node during the tree construction. Each reflection is based on the directions of the eigenvectors from each classes’ covariance matrix. Considering axis parallel splits in the reflected training data provides an efficient way of finding oblique splits in the unreflected training data. Experimental results show that the accuracy and size of the HHCART trees are comparable with some benchmark methods in the literature. The appealing feature of HHCART is that it can handle both qualitative and quantitative features in the same oblique split.
Experimental Evaluation of Relative Pose Estimation Algorithms

We give an extensive experimental comparison of four popular relative pose (epipolar geometry) estimation algorithms: the eight, seven, six and five point algorithms. We focus on the practically important case that only a single solution may be returned by automatically selecting one of the solution candidates, and investigate the choice of error measure for the selection. We show that the five point algorithm gives very good results with automatic selection. As sometimes the eight point algorithm is better, we propose a combination algorithm which selects from the solutions of both algorithms and thus combines their strengths. We further investigate the behavior in the presence of outliers by using adaptive RANSAC, and give practical recommendations for the choice of the RANSAC parameters. Finally, we verify the simulation results on real data.
Burnout among U.S. medical students, residents, and early career physicians relative to the general U.S. population.

To compare the prevalence of burnout and other forms of distress across career stages and the experiences of trainees and early career (EC) physicians versus those of similarly aged college graduates pursuing other careers.



In 2011 and 2012, the authors conducted a national survey of medical students, residents/fellows, and EC physicians (≤ 5 years in practice) and of a probability-based sample of the general U.S. population. All surveys assessed burnout, symptoms of depression and suicidal ideation, quality of life, and fatigue.



Response rates were 35.2% (4,402/12,500) for medical students, 22.5% (1,701/7,560) for residents/fellows, and 26.7% (7,288/27,276) for EC physicians. In multivariate models that controlled for relationship status, sex, age, and career stage, being a resident/fellow was associated with increased odds of burnout and being a medical student with increased odds of depressive symptoms, whereas EC physicians had the lowest odds of high fatigue. Compared with the population control samples, medical students, residents/fellows, and EC physicians were more likely to be burned out (all P < .0001). Medical students and residents/fellows were more likely to exhibit symptoms of depression than the population control samples (both P < .0001) but not more likely to have experienced recent suicidal ideation.



Training appears to be the peak time for distress among physicians, but differences in the prevalence of burnout, depressive symptoms, and recent suicidal ideation are relatively small. At each stage, burnout is more prevalent among physicians than among their peers in the U.S. population.
Moving beyond linearity and independence in top-N recommender systems

This paper suggests a number of research directions in which the recommender systems can improve their quality, by moving beyond the assumptions of linearity and independence that are traditionally made. These assumptions, while producing effective and meaningful results, can be suboptimal, as in lots of cases they do not represent the real datasets. In this paper, we discuss three different ways to address some of the previous constraints. More specifically, we focus on the development of methods capturing higher-order relations between the items, cross-feature interactions and intra-set dependencies which can potentially lead to a considerable enhancement of the recommendation accuracy.
PASCAL Boundaries: A Semantic Boundary Dataset with a Deep Semantic Boundary Detector

In this paper, we address the task of instance-level semantic boundary detection. To this end, we generate a large database consisting of more than 10k images (which is 20 bigger than existing edge detection databases) along with ground truth boundaries between 459 semantic classes including instances from both foreground objects and different types of background, and call it the PASCAL Boundaries dataset. This is a timely introduction of the dataset since results on the existing standard dataset for measuring edge detection performance, i.e. BSDS500, have started to saturate. In addition to generating a new dataset, we propose a deep network-based multi-scale semantic boundary detector and name it Multi-scale Deep Semantic Boundary Detector (M-DSBD). We evaluate M-DSBD on PASCAL boundaries and compare it to baselines. We test the transfer capabilities of our model by evaluating on MS-COCO and BSDS500 and show that our model transfers well.
High frequency data in financial markets : Issues and applications

The development of high frequency data bases allows for empirical investigations of a wide range of issues in the financial markets. In this paper, we set out some of the many important issues connected with the use, analysis, and application of high-frequency data sets. These include the effects of market structure on the availability and interpretation of the data, methodological issues such as the treatment of time, the effects of intra-day seasonals, and the effects of time-varying volatility, and the information content of various market data. We also address using high frequency data to determine the linkages between markets and to determine the applicability of temporal trading rules. The paper concludes with a discussion of the issues for future research.
ePluribus: Ethnicity on Social Networks

We propose an approach to determine the ethnic breakdown of a population based solely on people’s names and data provided by the U.S. Census Bureau. We demonstrate that our approach is able to predict the ethnicities of individuals as well as the ethnicity of an entire population better than natural alternatives. We apply our technique to the population of U.S. Facebook users and uncover the demographic characteristics of ethnicities and how they relate. We also discover that while Facebook has always been diverse, diversity has increased over time leading to a population that today looks very similar to the overall U.S. population. We also find that different ethnic groups relate to one another in an assortative manner, and that these groups have different profiles across demographics, beliefs, and usage of site features.
Design and Implementation of Dual-Frequency Dual-Polarization Slotted Waveguide Antenna Array for Ka-Band Application

In this letter, a dual-frequency dual-polarization slotted waveguide antenna array at Ka-band is proposed. A 9 ×10 inclined slot array working at 35 GHz, cut in the narrow wall of rectangular waveguides, is employed for horizontal polarization (HP). An 8 ×10 longitudinal slot array working at 30 GHz, etched on the broadside of ridged waveguides, is employed for vertical polarization (VP). The two arrays are interlaced with each other and fed from the opposite sides. Two novel bend interconnected structures are invented to connect the radiating waveguides and feeding networks, which simplify and compact the antenna structure and improve the aperture efficiency. The measured -10-dB reflection coefficient bandwidth of HP array is 688 MHz, and 658 MHz for VP array. The maximum gain is 25.4 and 24.8 dB for HP array and VP array, which correspond to the aperture efficiency of 36.3% and 43.05%, respectively. The sidelobe levels of both arrays are below -17.6 dB.
Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing field
A decision cognizant Kullback-Leibler divergence

In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback-Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback-Leibler divergence (DC-KL), to reduce the contribution of the minority classes, which obscure the true degree of classifier incongruence. We investigate the properties of the novel divergence measure analytically and by simulation studies. The proposed measure is demonstrated to be more robust to minority class clutter. Its sensitivity to estimation noise is also shown to be considerably lower than that of the classical KL divergence. These properties render the DC-KL divergence a much better statistic for discriminating between classifier congruence and incongruence in pattern recognition systems.
A new design method based on artificial bee colony algorithm for digital IIR filters

Digital filters can be broadly classified into two groups: recursive (infinite impulse response (IIR)) and non-recursive (finite impulse response (FIR)). An IIR filter can provide a much better performance than the FIR filter having the same number of coefficients. However, IIR filters might have a multi-modal error surface. Therefore, a reliable design method proposed for IIR filters must be based on a global search procedure. Artificial bee colony (ABC) algorithm has been recently introduced for global optimization. The ABC algorithm simulating the intelligent foraging behaviour of honey bee swarm is a simple, robust, and very flexible algorithm. In this work, a new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm. r 2009 The Franklin Institute.
Following High-level Navigation Instructions on a Simulated Quadcopter with Imitation Learning

We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAGGERFM, a modified variant of DAGGER that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.
Fine-grained metadata journaling on NVM

Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.
Evolutionary-based heuristic generators for checkers and give-away checkers

Two methods of genetic evolution of linear and non-linear heuristic evaluation functions for the game of checkers and give-away checkers are presented in the paper. The first method is based on the simplistic assumption that a relation ‘close’ to partial order can be defined over the set of the evaluation functions. Hence explicit fitness function is not necessary in this case and direct comparison between heuristics (a tournament) can be used instead. In the other approach heuristic is developed step-bystep based on the set of training games. First, the end-game positions are considered and then the method gradually moves “backwards” in the game tree up to the starting position and at each step the best fitted specimen from the previous step (previous game tree depth) is used as the heuristic evaluation function in the alpha-beta search for the current step. Experimental results confirm that both approaches lead to quite strong heuristics and give hope that more sophisticated and more problem-oriented evolutionary process might ultimately provide heuristics of quality comparable to those of commercial programs.
System Implementation Success Factors ; It ’ s not just the Technology

System implementation efforts offer extraordinary challenges to information technology professionals and the organizations impacted by the implementations. A successful implementation can reap vast rewards in organizational strengths and efficiencies. A failure can drain an organization of people, funds and vitality. Consequently, many people have puzzled over the reasons for the successes and failures experienced with these implementations. This paper examines these puzzlings as offered through scholarly research and first-hand reports of system implementations within institutions of higher education and discovers that many answers lie outside the bounds of technology.
An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.
Event detection over twitter social media streams

In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.
Optimizing Hierarchical Visualizations with the Minimum Description Length Principle

In this paper we examine how the Minimum Description Length (MDL) principle can be used to efficiently select aggregated views of hierarchical datasets that feature a good balance between clutter and information. We present MDL formulae for generating uneven tree cuts tailored to treemap and sunburst diagrams, taking into account the available display space and information content of the data. We present the results of a proof-of-concept implementation. In addition, we demonstrate how such tree cuts can be used to enhance drill-down interaction in hierarchical visualizations by implementing our approach in an existing visualization tool. Validation is done with the feature congestion measure of clutter in views of a subset of the current DMOZ web directory, which contains nearly half million categories. The results show that MDL views achieve near constant clutter level across display resolutions. We also present the results of a crowdsourced user study where participants were asked to find targets in views of DMOZ generated by our approach and a set of baseline aggregation methods. The results suggest that, in some conditions, participants are able to locate targets (in particular, outliers) faster using the proposed approach.
The ecology of human performance: a framework for considering the effect of context.

In theory and in practice, context (as an area of concern to occupational therapists) has not received the same attention as performance components and performance areas. The Ecology of Human Performance serves as a framework for considering the effect of context. Context is described as a lens from which persons view their world. The interrelationship of person and context determines which tasks fall within the person's performance range. The Ecology of Human Performance framework provides guidelines for encompassing context in occupational therapy theory, practice, and research.
Automatic Face Image Quality Prediction

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcherindependent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.
Structural Topic Model for Latent Topical Structure Analysis

Topic models have been successfully applied to many document analysis tasks to discover topics embedded in text. However, existing topic models generally cannot capture the latent topical structures in documents. Since languages are intrinsically cohesive and coherent, modeling and discovering latent topical transition structures within documents would be beneficial for many text analysis tasks. In this work, we propose a new topic model, Structural Topic Model, which simultaneously discovers topics and reveals the latent topical structures in text through explicitly modeling topical transitions with a latent first-order Markov chain. Experiment results show that the proposed Structural Topic Model can effectively discover topical structures in text, and the identified structures significantly improve the performance of tasks such as sentence annotation and sentence ordering.