nils-herrmann/clean_scirepeval huggingface dataset:
A shape-based approach to the segmentation of medical imagery using level sets

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras (2000), we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.
End-to-End Learning of Action Detection from Frame Glimpses in Videos

In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and when to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.
Microstrip Patch Antennas—Basic Characteristics and Some Recent Advances

The basic geometry of a microstrip patch antenna (MPA) consists of a metallic patch printed on a grounded substrate. Three commonly used feeding methods are coaxial feed, stripline feed, and aperture-coupled feed. The patch antenna idea was first proposed in the early 1950s, but it was not until the late 1970s that this type of antenna attracted serious attention of the antenna community. The microstrip patch antenna offers the advantages of low profile, conformability to a shaped surface, ease of fabrication, and compatibility with integrated circuit technology, but the basic geometry suffers from narrow bandwidth. In the last three decades, extensive studies have been devoted to improve the performance of this antenna and the MPA has found numerous applications in both the military and the commercial sectors. This article begins with a brief description of the modeling techniques and basic characteristics of the MPA. Methods of broadbanding, dual and multiband designs, size-reduction techniques, and design for circular polarization are then reviewed. The paper ends with some concluding remarks.
A Positive and Unlabeled Learning Algorithm for One-Class Classification of Remote-Sensing Data

In remote-sensing classification, there are situations when users are only interested in classifying one specific land-cover type, without considering other classes. These situations are referred to as one-class classification. Traditional supervised learning is inefficient for one-class classification because it requires all classes that occur in the image to be exhaustively assigned labels. In this paper, we investigate a new positive and unlabeled learning (PUL) algorithm, applying it to one-class classifications of two scenes of a high-spatial-resolution aerial photograph. The PUL algorithm trains a classifier on positive and unlabeled data, estimates the probability that a positive training sample has been labeled, and generates binary predictions for test samples using an adjusted threshold. Experimental results indicate that the new algorithm provides high classification accuracy, outperforming the biased support-vector machine (SVM), one-class SVM, and Gaussian domain descriptor methods. The advantages of the new algorithm are that it can use unlabeled data to help build classifiers, and it requires only a small set of positive data to be labeled by hand. Therefore, it can significantly reduce the effort of assigning labels to training data without losing predictive accuracy.
AutoTutor: A simulation of a human tutor

AutoTutor is a computer tutor that simulates the discourse patterns and pedagogical strategies of a typical human tutor. AutoTutor is designed to assist college students in learning the fundamentals of hardware, operating systems, and the Internet in an introductory computer literacy course. Most tutors in school systems are not highly trained in tutoring techniques and have only a modest expertise on the tutoring topic, but they are surprisingly effective in producing learning gains in students. We have dissected the discourse and pedagogical strategies these unskilled tutors exhibit by analyzing approximately 100 hours of naturalistic tutoring sessions. These mechanisms are implemented in AutoTutor. AutoTutor presents questions and problems from a curriculum script, attempts to comprehend learner contributions that are entered by keyboard, formulates dialog moves that are sensitive to the learner’s contributions (such as short feedback, pumps, prompts, elaborations, corrections, and hints), and delivers the dialog moves with a talking head. AutoTutor has seven modules: a curriculum script, language extraction, speech act classification, latent semantic analysis, topic selection, dialog move generation, and a talking head.
Business process verification - finally a reality!

The purpose of this paper is to demonstrate that process verification has matured to a level where it can be used in practice. This paper reports on new verification techniques that can be used to assess the correctness of real-life models.  The proposed approach relies on using formal methods to determine the correctness of business processes with cancellation and OR-joins. The paper also demonstrates how reduction rules can be used to improve the efficiency. These techniques are presented in the context of the workflow language yet another workflow language (YAWL) that provides direct support for 20 most frequently used patterns found today (including cancellation and OR-joins). But the results also apply to other languages with these features (e.g. BPMN, EPCs, UML activity diagrams, etc.). An editor has been developed that provides diagnostic information based on the techniques presented in this paper.  The paper proposes four properties for business processes with cancellation and OR-joins, namely: soundness, weak soundness, irreducible cancellation regions and immutable OR-joins and develop new techniques to verify these properties. Reduction rules have been used as a means of improving the efficiency of the algorithm. The paper demonstrates the feasibility of this verification approach using a realistic and complex business process, the visa application process for general skilled migration to Australia, modelled as a YAWL workflow with cancellation regions and OR-joins.  Business processes sometimes require complex execution interdependencies to properly complete a process. For instance, it is possible that certain activities need to be cancelled mid-way though the process. Some parallel activities may require complex “wait and see” style synchronisation depending on a given context. These types of business processes can be found in various domains, such as application integration, B2B commerce, web service composition and workflow systems. Even though cancellation and sophisticated join structures are present in many business processes, existing verification techniques are unable to deal with such processes. Hence, this paper plays an important role in making process verification a reality.
Corona Discharge Surface Treater Without High Voltage Transformer

A corona discharge generator for surface treatment without the use of a step-up transformer with a high-voltage secondary is presented. The oil bath for high-voltage components is eliminated and still a reasonable volume, efficiency, and reliability of the generator are obtained. The voltage multiplication is achieved by an LC series resonant circuit. The resonant circuit is driven by a bridge type voltage-source resonant inverter. First, feasibility of the proposed method is proved by calculations. Closed form design expressions for key components of the electronic generator are provided. Second, a prototype of the electronic generator is built and efficiency measurements are performed. For power measurement, Lissajous figures and direct averaging of the instantaneous voltage-current product are used. The overall efficiency achieved is in the range between 80% and 90%.
From real-time attention assessment to “with-me-ness” in human-robot interaction

Measuring “how much the human is in the interaction” - the level of engagement - is instrumental in building effective interactive robots. Engagement, however, is a complex, multi-faceted cognitive mechanism that is only indirectly observable. This article formalizes with-me-ness as one of such indirect measures. With-me-ness, a concept borrowed from the field of Computer-Supported Collaborative Learning, measures in a well-defined way to what extent the human is with the robot over the course of an interactive task. As such, it is a meaningful precursor of engagement. We expose in this paper the full methodology, from real-time estimation of the human's focus of attention (relying on a novel, open-source, vision-based head pose estimator), to on-line computation of with-me-ness. We report as well on the experimental validation of this approach, using a naturalistic setup involving children during a complex robot-teaching task.
Generating Impact-Based Summaries for Scientific Literature

In this paper, we present a study of a novel summarization problem, i.e., summarizing the impact of a scientific publication. Given a paper and its citation context, we study how to extract sentences that can represent the most influential content of the paper. We propose language modeling methods for solving this problem, and study how to incorporate features such as authority and proximity to accurately estimate the impact language model. Experiment results on a SIGIR publication collection show that the proposed methods are effective for generating impact-based summaries.
Modeling MOOC Dropouts

In this project, we model MOOC dropouts using user activity data. We have several rounds of feature engineering and generate features like activity counts, percentage of visited course objects, and session counts to model this problem. We apply logistic regression, support vector machine, gradient boosting decision trees, AdaBoost, and random forest to this classification problem. Our best model is GBDT, achieving AUC of 0.8763, about 3% off the KDD winner.
A robust method for speech signal time-delay estimation in reverberant rooms

Conventional time-delay estimators exhibit dramatic performance degradations in the presence of multipath signals. This limits their application in reverberant enclosures, particularly when the signal of interest is speech and it may not possible to estimate and compensate for channel effects prior to time-delay estimation. This paper details an alternative approach which reformulates the problem as a linear regression of phase data and then estimates the time-delay through minimization of a robust statistical error measure. The technique is shown to be less susceptible to room reverberation effects. Simulations are performed across a range of source placements and room conditions to illustrate the utility of the proposed time-delay estimation method relative to conventional methods.
Platener: Low-Fidelity Fabrication of 3D Objects by Substituting 3D Print with Laser-Cut Plates

This paper presents Platener, a system that allows quickly fabricating intermediate design iterations of 3D models, a process also known as low-fidelity fabrication. Platener achieves its speed-up by extracting straight and curved plates from the 3D model and substituting them with laser cut parts of the same size and thickness. Only the regions that are of relevance to the current design iteration are executed as full-detail 3D prints. Platener connects the parts it has created by automatically inserting joints. To help fast assembly it engraves instructions. Platener allows users to customize substitution results by (1) specifying fidelity-speed tradeoffs, (2) choosing whether or not to convert curved surfaces to plates bent using heat, and (3) specifying the conversion of individual plates and joints interactively. Platener is designed to best preserve the fidelity of func-tional objects, such as casings and mechanical tools, all of which contain a large percentage of straight/rectilinear elements. Compared to other low-fab systems, such as faBrickator and WirePrint, Platener better preserves the stability and functionality of such objects: the resulting assemblies have fewer parts and the parts have the same size and thickness as in the 3D model. To validate our system, we converted 2.250 3D models downloaded from a 3D model site (Thingiverse). Platener achieves a speed-up of 10 or more for 39.5% of all objects.
Circle Packing for Origami Design Is Hard

Over the last 20 years, the world of origami has been changed by the introduction of design algorithms that bear a close relationship to, if not outright ancestry from, computational geometry. One of the first robust algorithms for origami design was the circle/river method (also called the tree method) developed independently by Lang [7–9] and Meguro [12, 13]. This algorithm and its variants provide a systematic method for folding any structure that topologically resembles a graph theoretic weighted tree. Other algorithms followed, notably one by Tachi  that gives the crease pattern to fold an arbitrary 3D surface. Hopes of a general approach for efficiently solving all origami design problems were dashed early on, when Bern and Hayes showed in 1996 that the general problem of crease assignment — given an arbitrary crease pattern, determine whether each fold is mountain or valley — was NP-complete . In fact, they showed more: given a complete crease assignment, simply determining the stacking order of the layers of paper was also NP-complete. Fortunately, while crease assignment in the general case is hard, the crease patterns generated by the various design algorithms carry with them significant extra information associated with each crease, enough extra information that the problem of crease assignment is typically only polynomial in difficulty. This is certainly the case for the tree method of design . Designing a model using the tree method (or one of its variants) is a two-step process: the first step involves solving an optimization problem where one solves for certain key vertices of the crease pattern. The second step constructs creases following a geometric prescription and assigns their status as mountain, valley, or unfolded. The process of constructing the creases and assigning them is definitely polynomial in complexity; but, up to now, the computational complexity of the optimization was not established. There were reasons for believing that the optimization was, in principle, computationally intractable. The conditions on the vertex coordinates in the
Dynamic ad layout revenue optimization for display advertising

Display advertising has been growing rapidly in recent years, with revenue generated from display ads placed on spaces allocated on publisher's web pages. Traditionally, the design and layout of ad spaces on a web page are predetermined and fixed for the publisher. The objective of this work is to investigate the revenue opportunities of changing the ad layout dynamically for the publisher. A dynamic ad layout revenue optimization framework is developed for display advertising, in terms of both guaranteed and non-guaranteed advertising. The system automatically selects the ad layout template with the highest potential revenue yield for each single web page presented to the user. Forecasting algorithms are developed to predict the revenue of each ad opportunity. Two objectives are explored for the forecasting algorithms of ad layout optimization, the expected revenue and actual revenue. Promising results are obtained in offline simulation on real data collected from a Yahoo! property. The dynamic ad layout optimization system is further tested on real-time traffic and a significant revenue gain is observed compared with a static ad layout serving method.
Data-augmentation for reducing dataset bias in person re-identification

In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.
Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.
Forecasting daily streamflow using hybrid ANN models

In this paper, the classic ‘divide and conquer (DAC)’ paradigm is applied as a top-down black-box technique for the forecasting of daily streamflows from the streamflow records alone, i.e. without employing exogenous variables of the runoff generating process such as rainfall. To this end, three forms of hybrid artificial neural networks (ANNs) are used as univariate time series models, namely, the threshold-based ANN (TANN), the cluster-based ANN (CANN), and the periodic ANN (PANN). For the purpose of comparison of forecasting efficiency, the normal multi-layer perceptron form of ANN (MLP–ANN) is selected as the baseline ANN model. Having first applied the MLP–ANN models without any data-grouping procedure, the influence of various data preprocessing procedures on the MLP–ANN model forecasting performance is then investigated. The preprocessing procedures considered are: standardization, log-transformation, rescaling, deseasonalization, and combinations of these. In the context of the single streamflow series considered, deseasonalization without rescaling was found to be the most effective preprocessing procedure. Some discussions are presented (i) on data preprocessing and (ii) on selection of the best ANN model. Overall, among the three variations of hybrid ANNs tested, the PANN model performed best. Compared with the MLP–ANN fitted to the deseasonalized data, the PANN based on the soft seasonal partitioning performed better for short lead times (%3 days), but the advantage vanishes for longer lead times. q 2005
Comparing Different Moving Target Defense Techniques

Moving Target Defense techniques have been proposed to increase uncertainty and apparent complexity for attackers. When more than one Moving Target Defense techniques are effective to limit opportunities of an attack, it is required to compare these techniques and select the best defense choice. In this paper, we propose a three-layer model to evaluate and compare effectiveness of different Moving Target Defenses. This model is designed as an attempt to fill a gap among existing evaluation methods and works as a systematic framework for Moving Target Defense comparison.
Challenges in Teaching Test Driven Development

Test-driven development (TDD) has been proposed as a solution to improve testing in Industry and in academia. The purpose of this poster is to outline the challenges of teaching a novel Test-First approach in a Level 8 course on Software Testing. Traditionally, introductory programming and software testing courses teach a test-last approach. After the introduction of the Extreme Programming version of AGILE, industry and academia have slowly shifted their focus to the Test-First approach. This poster paper is a pedagogical insight into this shift from the test-last to the test-first approach known as Test Driven Development (TDD).
Mechanical Design of a 6-DOF Aerial Manipulator for assembling bar structures using UAVs

The aim of this paper is to show a methodology to perform the mechanical design of a 6-DOF lightweight manipulator for assembling bar structures using a rotary-wing UAV. The architecture of the aerial manipulator is based on a comprehensive performance analysis, a manipulability study of the different options and a previous evaluation of the required motorization. The manipulator design consists of a base attached to the UAV landing gear, a robotic arm that supports 6-DOF, and a gripper-style end effector specifically developed for grasping bars as a result of this study. An analytical expression of the manipulator kinematic model is obtained.
AILA - design of an autonomous mobile dual-arm robot

This paper presents the design of the robot AILA, a mobile dual-arm robot system developed as a research platform for investigating aspects of the currently booming multidisciplinary area of mobile manipulation. The robot integrates and allows in a single platform to perform research in most of the areas involved in autonomous robotics: navigation, mobile and dual-arm manipulation planning, active compliance and force control strategies, object recognition, scene representation, and semantic perception. AILA has 32 degrees of freedom, including 7-DOF arms, 4-DOF torso, 2-DOF head, and a mobile base equipped with six wheels, each of them with two degrees of freedom. The primary design goal was to achieve a lightweight arm construction with a payload-to-weight ratio greater than one. Besides, an adjustable body should sustain the dual-arm system providing an extended workspace. In addition, mobility is provided by means of a wheel-based mobile base. As a result, AILA's arms can lift 8kg and weigh 5.5kg, thus achieving a payload-to-weight ratio of 1.45. The paper will provide an overview of the design, especially in the mechatronics area, as well as of its realization, the sensors incorporated in the system, and its control software.