nils-herrmann/clean_scirepeval huggingface dataset:
Diversity Driven Parallel Data Mining

With increasing availability and power of parallel computational resources, attention is drawn to the question of how best to apply those resources. Instead of simply finding the same answers more quickly, this thesis describes how parallel computational resources are used to explore disparate regions of a solution space by using diversity to steer the solution paths away from each other, thereby discouraging strictly greedy behavior. The formulation of models in a concept/solution space and its relationship to a search space are described as well as common search algorithms with heuristics for time or space computationally prohibitive searches. Measures of diversity are introduced, and the application of a beam search to the solution space for the Krimp algorithm for frequent itemset mining is described. Experimental results show that it is indeed possible to get better results on real-world datasets with these methods.
Quadrotor Helicopter Trajectory Tracking Control

The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), a fleet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. This paper develops an autonomous vehicle trajectory tracking algorithm through cluttered environments for the STARMAC platform. A system relying on a single optimization must trade off the complexity of the planned path with the rate of update of the control input. In this paper, a trajectory tracking controller for quadrotor helicopters is developed to decouple the two problems. By accepting as inputs a path of waypoints and desired velocities, the control input can be updated frequently to accurately track the desired path, while the path planning occurs as a separate process on a slower timescale. To enable the use of planning algorithms that do not consider dynamic feasibility or provide feedforward inputs, a computationally efficient algorithm using space-indexed waypoints is presented to modify the speed profile of input paths to guarantee feasibility of the planned trajectory and minimum time traversal of the planned. The algorithm is an efficient alternative to formulating a nonlinear optimization or mixed integer program. Both indoor and outdoor flight test results are presented for path tracking on the STARMAC vehicles.
Spectral Learning Algorithms for Natural Language Processing

Recent work in machine learning and NLP has developed spectral algorithms for many learning tasks involving latent variables. Spectral algorithms rely on singular value decomposition as a basic operation, usually followed by some simple estimation method based on the method of moments. From a theoretical point of view, these methods are appealing in that they offer consistent estimators (and PAC-style guarantees of sample complexity) for several important latent-variable models. This is in contrast to the EM algorithm, which is an extremely successful approach, but which only has guarantees of reaching a local maximum of the likelihood function. From a practical point of view, the methods (unlike EM) have no need for careful initialization, and have recently been shown to be highly efficient (as one example, in work under submission by the authors on learning of latent-variable PCFGs, a spectral algorithm performs at identical accuracy to EM, but is around 20 times faster).
A general bootstrap performance diagnostic

As datasets become larger, more complex, and more available to diverse groups of analysts, it would be quite useful to be able to automatically and generically assess the quality of estimates, much as we are able to automatically train and evaluate predictive models such as classifiers. However, despite the fundamental importance of estimator quality assessment in data analysis, this task has eluded highly automatic solutions. While the bootstrap provides perhaps the most promising step in this direction, its level of automation is limited by the difficulty of evaluating its finite sample performance and even its asymptotic consistency. Thus, we present here a general diagnostic procedure which directly and automatically evaluates the accuracy of the bootstrap's outputs, determining whether or not the bootstrap is performing satisfactorily when applied to a given dataset and estimator. We show that our proposed diagnostic is effective via an extensive empirical evaluation on a variety of estimators and simulated and real datasets, including a real-world query workload from Conviva, Inc. involving 1.7TB of data (i.e., approximately 0.5 billion data points).
Encoding and decoding the knowledge of association rules over SVM classification trees

This paper presents a constructive method for association rule extraction, where the knowledge of data is encoded into an SVM classification tree (SVMT), and linguistic association rule is extracted by decoding of the trained SVMT. The method of rule extraction over the SVMT (SVMT-rule), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the decision-tree structure of SVMT. Thus, the obtained rules from SVMT-rule have the better comprehensibility of decision-tree rule, meanwhile retains the good classification accuracy of SVM. Moreover, profiting from the super generalization ability of SVMT owing to the aggregation of a group of SVMs, the SVMT-rule is capable of performing a very robust classification on such datasets that have seriously, even overwhelmingly, class-imbalanced data distribution. Experiments with a Gaussian synthetic data, seven benchmark cancers diagnosis, and one application of cell-phone fraud detection have highlighted the utility of SVMT and SVMT-rule on comprehensible and effective knowledge discovery, as well as the superior properties of SVMT-rule as compared to a purely support-vector based rule extraction.
Evolution Strategies as a Scalable Alternative to Reinforcement Learning

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Qlearning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.
Single-wire transmission lines at terahertz frequencies

In this paper, we report results on an original way to excite surface waves on a single-wire transmission line. Although these waves were proposed many decades ago by Goubau, the novelty of our structures is to achieve a broadband planar excitement. This configuration is very well suited for the terahertz frequency range and allows the investigation of biological entities with high spatial resolution with the use of novel biomicroelectromechanical systems, which include microfluidic functions. From experimental results, we compare different types of transitions from coplanar waveguides, and different substrates are also used. We show that the excitation is highly efficient and broadband for structures on a quartz substrate
Causality and Counterfactuals in the Situation Calculus

Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with such concepts as actual (event-to-event) causation. In this paper, we propose a new type of causal model, based on embedding structural considerations in the language of situation calculus. By using situation calculus as a basic language, we leverage its power to express complex, dynamically changing situations and, by relying on structural considerations, we can formulate an effective theory of counterfactuals within the situation-calculus.
The case for depth in cybersecurity education

In his book Outliers, Malcom Gladwell describes the 10,000-Hour Rule, a key to success in any field, as simply a matter of practicing a specific task that can be accomplished with 20 hours of work a week for 10 years [10]. Ongoing changes in technology and national security needs require aspiring excellent cybersecurity professionals to set a goal of 10,000 hours of relevant, hands-on skill development. The education system today is ill prepared to meet the challenge of producing an adequate number of cybersecurity professionals, but programs that use competitions and learning environments that teach depth are filling this void.
A Data Driven Approach for the Science of Cyber Security: Challenges and Directions

This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe that the three aspects addressed in this paper will form the basis for studying the Science of Cyber Security.
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors

In this paper we introduce the simultaneous tracking and activity recognition (STAR) problem, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring. Automatic health monitoring can potentially help the elderly population live safely and independently in their own homes by providing key information to caregivers. Our goal is to perform accurate tracking and activity recognition for multiple people in a home environment. We use a “bottom-up” approach that primarily uses information gathered by many minimally invasive sensors commonly found in home security systems. We describe a Rao-Blackwellised particle filter for roomlevel tracking, rudimentary activity recognition (i.e., whether or not an occupant is moving), and data association. We evaluate our approach with experiments in a simulated environment and in a real instrumented home.
Frequency-Tunable Phase Inverter Based on Slot-Line Resonator

This paper describes a frequency-tunable phase inverter based on a slot-line resonator for the first time. The control circuit is designed and located on the defected ground. None of dc block capacitors are needed in the microstrip line. A wide tuning frequency range is accomplished by the use of the slot-line resonator with two varactors and a single control voltage. A 180-degree phase inverter is achieved by means of reversing electric field with two metallic via holes connecting the microstrip and ground plane. The graphic method is used to estimate the operation frequency. For verification, a frequency-tunable phase inverter is fabricated and measured. The measured results show a wide tuning frequency range from 1.1 GHz to 1.75 GHz with better than 20-dB return loss. The measured results are in good agreement with the simulated ones.
Tecnis Symfony Intraocular Lens with a “ Sweet Spot ” for Tolerance to Postoperative Residual Refractive Errors

To investigate the impact of residual astigmatism on visual acuity (VA) after the implantation of a novel extended range of vision (ERV) intraocular lens (IOL) based on the correction of spherical and chromatic aberration.  The study enrolled 411 patients bilaterally implanted with the ERV IOL Tecnis Symfony. Visual acuity and subjective refraction were analyzed during the 4to 6-month follow-up. The sample of eyes was stratified for four groups according to the magnitude of postoperative refractive astigmatism and postoperative spherical equivalent.  The astigmatism analysis included 386 eyes of 193 patients with both eyes of each patient within the same cylinder range. Uncorrected VAs for distance, intermediate and near were better in the group of eyes with lower level of postoperative astigmatism, but even in eyes with residual cylinders up to 0.75 D, the loss of VA lines was clinically not relevant. The orientation of astigmatism did not seem to have an impact on the tolerance to the residual cylinder. The SE evaluation included 810 eyes of 405 patients, with both eyes of each patient in the same SE range. Uncorrected VAs for distance, intermediate and near, were very similar in all SE groups.  Residual cylinders up to 0.75 D after the implantation of the Tecnis Symfony IOL have a very mild impact on monocular and binocular VA. The Tecnis Symfony IOL shows a good tolerance to unexpected refractive surprises and thus a better “sweet spot”.
Review of benefits and challenges of vehicle-to-grid technology

Plug-in electric vehicles (PEVs) can behave either as loads or as distributed energy sources in a concept known as vehicle-to-grid (V2G). The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural and technical obstacles. Although V2G operation can reduce the lifetime of PEVs, it is projected to be more economical for vehicle owners and grid operators. This paper reviews these benefits and challenges of V2G technology for both individual vehicles and vehicle fleets.
Evaluation of the FICA Tool for Spiritual Assessment.

The National Consensus Project for Quality Palliative Care includes spiritual care as one of the eight clinical practice domains. There are very few standardized spirituality history tools.



The purpose of this pilot study was to test the feasibility for the Faith, Importance and Influence, Community, and Address (FICA) Spiritual History Tool in clinical settings. Correlates between the FICA qualitative data and quality of life (QOL) quantitative data also were examined to provide additional insight into spiritual concerns.



The framework of the FICA tool includes Faith or belief, Importance of spirituality, individual's spiritual Community, and interventions to Address spiritual needs. Patients with solid tumors were recruited from ambulatory clinics of a comprehensive cancer center. Items assessing aspects of spirituality within the Functional Assessment of Cancer Therapy QOL tools were used, and all patients were assessed using the FICA. The sample (n=76) had a mean age of 57, and almost half were of diverse religions.



Most patients rated faith or belief as very important in their lives (mean 8.4; 0-10 scale). FICA quantitative ratings and qualitative comments were closely correlated with items from the QOL tools assessing aspects of spirituality.



Findings suggest that the FICA tool is a feasible tool for clinical assessment of spirituality. Addressing spiritual needs and concerns in clinical settings is critical in enhancing QOL. Additional use and evaluation by clinicians of the FICA Spiritual Assessment Tool in usual practice settings are needed.
Nonlocal Image and Movie Denoising

Neighborhood filters are nonlocal image and movie filters which reduce the noise by averaging similar pixels. The first object of the paper is to present a unified theory of these filters and reliable criteria to compare them to other filter classes. A CCD noise model will be presented justifying the involvement of neighborhood filters. A classification of neighborhood filters will be proposed, including classical image and movie denoising methods and discussing further a recently introduced neighborhood filter, NL-means. In order to compare denoising methods three principles will be discussed. The first principle, “method noise”, specifies that only noise must be removed from an image. A second principle will be introduced, “noise to noise”, according to which a denoising method must transform a white noise into a white noise. Contrarily to “method noise”, this principle, which characterizes artifact-free methods, eliminates any subjectivity and can be checked by mathematical arguments and Fourier analysis. “Noise to noise” will be proven to rule out most denoising methods, with the exception of neighborhood filters. This is why a third and new comparison principle, the “statistical optimality”, is needed and will be introduced to compare the performance of all neighborhood filters. The three principles will be applied to compare ten different image and movie denoising methods. It will be first shown that only wavelet thresholding methods and NL-means give an acceptable method noise. Second, that neighborhood filters are the only ones to satisfy the “noise to noise” principle. Third, that among them NL-means is closest to statistical optimality. A particular attention will be paid to the application of the statistical optimality criterion for movie denoising methods. It will be pointed out that current movie denoising methods are motion compensated neighborhood filters. This amounts to say that they are neighborhood filters and that the ideal neighborhood of a pixel is its trajectory. Unfortunately the aperture problem makes it impossible to estimate ground true trajectories. It will be demonstrated that computing trajectories and restricting the neighborhood to them is harmful for denoising purposes and that space-time NL-means preserves more movie details.
A Benchmark for Building Footprint Classification Using Orthorectified RGB Imagery and Digital Surface Models from Commercial Satellites

Identifying building footprints is a critical and challenging problem in many remote sensing applications. Solutions to this problem have been investigated using a variety of sensing modalities as input. In this work, we consider the detection of building footprints from 3D Digital Surface Models (DSMs) created from commercial satellite imagery along with RGB orthorectified imagery. Recent public challenges (SpaceNet 1 and 2, DSTL Satellite Imagery Feature Detection Challenge, and the ISPRS Test Project on Urban Classification) approach this problem using other sensing modalities or higher resolution data. As a result of these challenges and other work, most publically available automated methods for building footprint detection using 2D and 3D data sources as input are meant for high-resolution 3D lidar and 2D airborne imagery, or make use of multispectral imagery as well to aid detection. Performance is typically degraded as the fidelity and post spacing of the 3D lidar data or the 2D imagery is reduced. Furthermore, most software packages do not work well enough with this type of data to enable a fully automated solution. We describe a public benchmark dataset consisting of 50 cm DSMs created from commercial satellite imagery, as well as coincident 50 cm RGB orthorectified imagery products. The dataset includes ground truth building outlines and we propose representative quantitative metrics for evaluating performance. In addition, we provide lessons learned and hope to promote additional research in this field by releasing this public benchmark dataset to the community.
An amplitude spectral Capon estimator with a variable filter length

The filter bank methods have been a popular non-parametric way of computing the complex amplitude spectrum. So far, the length of the filters in these filter banks has been set to some constant value independently of the data. In this paper, we take the first step towards considering the filter length as an unknown parameter. Specifically, we derive a very simple and approximate way of determining the optimal filter length in a data-adaptive way. Based on this analysis, we also derive a model averaged version of the forward and the forward-backward amplitude spectral Capon estimators. Through simulations, we show that these estimators significantly improve the estimation accuracy compared to the traditional Capon estimators.
The ground truth about metadata and community detection in networks

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks’ links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.
Semantic Clone Detection Using Machine Learning

If two fragments of source code are identical to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. In this paper, we present a machine learning framework to automatically detect clones in software, which is able to detect Types-3 and the most complicated kind of clones, Type-4 clones. Previously used traditional features are often weak in detecting the semantic clones The novel aspects of our approach are the extraction of features from abstract syntax trees (AST) and program dependency graphs (PDG), representation of a pair of code fragments as a vector and the use of classification algorithms. The key benefit of this approach is that our approach can find both syntactic and semantic clones extremely well. Our evaluation indicates that using our new AST and PDG features is a viable methodology, since they improve detecting clones on the IJaDataset 2.0.