nils-herrmann/clean_scirepeval huggingface dataset:
Rectification of QR-code images using the parametric cylindrical surface model

In recent years, QR codes have found a wide range of applications in business and industry. Moreover, their popularity and function are still growing (fast). Therefore, the processing of the images of QR codes is an important task. In this paper, we try to solve a problem encountered in QR decoding when the QR code is printed near the binding edge inside a book or on the surface of a cylindrical container. When a picture is taken on such a QR code, the picture is no longer in a rectangular shape, which is supposedly the “right” shape that any QR code should take, due to warping from the image capturing mechanism by the camera. We refer to such warping as cylinder-to-screen (C2S) warping. This warping translates into serious distortion of the original QR-code image (also referred to as QR image, for short) and thus causes failure in its decoding. In this paper, a mathematical model called the parametric cylindrical surface model (PCSM) is proposed. Then, based on it, a scheme is proposed to rectify C2S-warped QR images. Experiments show that the proposed scheme is effective, in the sense that many failed QR decodings become successful after the rectification.
A Contingency Approach To Data Governance

Enterprises need data quality management (DQM) to respond to strategic and operational challenges demanding high-quality corporate data. Hitherto, companies have assigned accountabilities for DQM mostly to IT departments. They have thereby ignored the organizational issues that are critical to the success of DQM. With data governance, however, companies implement corporate-wide accountabilities for DQM that encompass professionals from business and IT. This paper outlines a data governance model comprised of three components that build a matrix comparable to an RACI chart: data quality roles, decision areas, and responsibilities. The data governance model documents the data quality roles and their type of interaction with DQM activities. In addition, the paper identifies contingency factors that impact the model configuration. Companies can structure their company-specific data governance model based on these findings.
Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks

In many real-world scenarios, labeled data for a specific machine learning task is costly to obtain. Semi-supervised training methods make use of abundantly available unlabeled data and a smaller number of labeled examples. We propose a new framework for semi-supervised training of deep neural networks inspired by learning in humans. Associations are made from embeddings of labeled samples to those of unlabeled ones and back. The optimization schedule encourages correct association cycles that end up at the same class from which the association was started and penalizes wrong associations ending at a different class. The implementation is easy to use and can be added to any existing end-to-end training setup. We demonstrate the capabilities of learning by association on several data sets and show that it can improve performance on classification tasks tremendously by making use of additionally available unlabeled data. In particular, for cases with few labeled data, our training scheme outperforms the current state of the art on SVHN.
CAES Cryptosystem: Advanced Security Tests and Results

A robust and secure cryptosystem is an encrypting system that resists against all practical cryptanalysis methods such as statistical attacks, differential cryptanalysis and linear cryptanalysis. To prove the resistance against these attacks, the cryptosystem designer must carry out a list of robustness tests. Considering these constraints, we present in the current paper results of robustness and security tests conducted on the CAES (Cellular automata Encryption System) cryptosystem published in a previous article. The presented tests focus on randomness tests and on differential cryptanalysis. As results of these tests, we concluded that the cryptosystem CAES gives a pseudo-random output regardless the input. Also the differential attack needs huge number of chosen plaintexts which make it impractical.
Online Risk-Based Security Assessment

The work was motivated by a perceived increase in the frequency at which power system operators are encountering high stress in bulk transmission systems and the corresponding need to improve security monitoring of these networks. Online risk-based security assessment provides rapid online quantification of the security level associated with an existing or forecasted operating condition. One major advantage of this approach over deterministic online security assessment is that it condenses contingency likelihood and severity into indices that reflect probabilistic risk. Use of these indices in control room decision-making leads to increased understanding of potential network problems, including overload, cascading overload, low voltages, and voltage instability, resulting in improved security related decision-making. Test results on large-scale transmission models retrieved from the energy management system of a U. S. utility company are described.
A RESIZABLE MINI-BATCH GRADIENT DESCENT

Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit for achieving best performance in grid search by selecting an appropriate batch size at each epoch with a probability defined as a function of its previous success/failure. This probability encourages exploration of different batch size and then later exploitation of batch size with history of success. At each epoch, the RMGD samples a batch size from its probability distribution, then uses the selected batch size for mini-batch gradient descent. After obtaining the validation loss at each epoch, the probability distribution is updated to incorporate the effectiveness of the sampled batch size. The RMGD essentially assists the learning process to explore the possible domain of the batch size and exploit successful batch size. Experimental results show that the RMGD achieves performance better than the best performing single batch size. Furthermore, it, obviously, attains this performance in a shorter amount of time than grid search. It is surprising that the RMGD achieves better performance than grid search.
Self-calibration and visual SLAM with a multi-camera system on a micro aerial vehicle

The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. An accurate calibration is a necessary prerequisite if vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system. On our MAV, we set up each camera pair in a stereo configuration. We propose a novel vSLAM-based self-calibration method for a multi-sensor system that includes multiple calibrated stereo cameras and an inertial measurement unit (IMU). Our selfcalibration estimates the transform with metric scale between each camera and the IMU. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses inertial information to recover the relative motion of the MAV with metric scale. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated realtime on-board vSLAM with loop closures. We show experimental results in both indoor and outdoor environments. The code for both the self-calibration and vSLAM is available as a set of ROS packages at https://github.com/hengli/vmav-ros-pkg.
Determinization in Monte-Carlo Tree Search for the card game

Monte-Carlo Tree Search (MCTS) is a class of game tree search algorithms that have recently proven success ful for deterministic games of perfect information, particu larly the game of Go. Determinization is an AI technique for making decisions in stochastic games of imperfect information by analys ing several instances of the equivalent deterministic game of p erfect information. In this paper we combine determinizati on techniques with MCTS for the popular Chinese card game Dou Di Zh u. In determinized MCTS, there is a trade-off between the number of determinizations searched and the time spent search ing each one; however, we show that this trade-off does not signi ficantly affect the performance of determinized MCTS, as long as bot h quantities are sufficiently large. We also show that the abili ty to see opponents’ hidden cards in Dou Di Zhu is a signific ant advantage, which suggests that inference techniques could pote ntially lead to much stronger play.
Generative Adversarial Self-Imitation Learning

This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework. Instead of directly maximizing rewards, GASIL focuses on reproducing past good trajectories, which can potentially make long-term credit assignment easier when rewards are sparse and delayed. GASIL can be easily combined with any policy gradient objective by using GASIL as a learned shaped reward function. Our experimental results show that GASIL improves the performance of proximal policy optimization on 2D Point Mass and MuJoCo environments with delayed reward and stochastic dynamics.
Heart Disease Prediction Using Data Mining preprocessing and Hierarchical Clustering

The diagnosis of diseases is a crucial and difficult job in medicine. The recognition of heartdisease from diverse features or signs is a major issue which is not free from false presumptions often accompanied by unpredictable effects. The healthcare industry gathers enormous amounts of heart disease data that unfortunately, are not mined to determine concealed information for effective diagnosing. Due to this rapid growth is the main motivation for researchers to mine useful information from these medical databases. As the volume of stored data increases, data mining techniques play an important role in finding patterns and extracting knowledge to provide better patient care and effective diagnostic capabilities. Heart disease prediction suffers from the problem of missing data, statistical tests will lose power, results may be based, or analysis may not be feasible at all. There are several ways to handle the problem, for example through imputation. To overcome this problem initially, the data set containing 13 medical attributes were obtained from the Cleveland heart disease database missing attributes data is replaced with the help of imputation method. With imputation, missing values are replaced with estimated values according to an imputation method or model. In this paper, preprocessed dataset from EM is given as input to clustering method for heart disease prediction. In this paper, an efficient approach non negative matrix factorization with hierarchical clustering methods (NMF-HC) is proposed for the intelligent heart disease prediction. The dataset is clustered with the aid of NMF-HC clustering algorithm. The NMF-HC is trained using the preprocessed data sets. The proposed NMF-HC works as promising tool for prediction of heart disease.
Predicting the Demographics of Twitter Users from Website Traffic Data

Understanding the demographics of users of online social networks has important applications for health, marketing, and public messaging. In this paper, we predict the demographics of Twitter users based on whom they follow. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50% of visitors to gizmodo.com are estimated to have a bachelor’s degree). We then fit a regression model to predict these demographics using information about the followers of each website on Twitter. The resulting average heldout correlation is .77 across six different variables (gender, age, ethnicity, education, income, and child status). We additionally validate the model on a smaller set of Twitter users labeled individually for ethnicity and gender, finding performance that is surprisingly competitive with a fully supervised approach.
Adversarial Balancing for Causal Inference

Biases in observational data pose a major challenge to estimation methods for the effect of treatments. An important technique that accounts for these biases is reweighting samples to minimize the discrepancy between treatment groups. Inverse probability weighting, a popular weighting technique, models the conditional treatment probability given covariates. However, it is overly sensitive to model misspecification and suffers from large estimation variance. Recent methods attempt to alleviate these limitations by finding weights that minimize a selected discrepancy measure between the reweighted populations. We present a new reweighting approach that uses classification error as a measure of similarity between datasets. Our proposed framework uses bi-level optimization to alternately train a discriminator to minimize classification error, and a balancing weights generator to maximize this error. This approach borrows principles from generative adversarial networks (GANs) that aim to exploit the power of classifiers for discrepancy measure estimation. We tested our approach on several benchmarks. The results of our experiments demonstrate the effectiveness and robustness of this approach in estimating causal effects under different data generating settings.
Enhancing Students ’ Engagement by Increasing Teachers ’ Autonomy Support

Engagement refers to the behavioral intensity and emotional quality of a person’s active involvement during a task. Recognizing the benefits highly engaged people experience, we tested whether classroom teachers could incorporate the relatively foreign concept of autonomy support into their motivating styles as a way to promote their students’ engagement during instruction. Teachers in an experimental group and teachers in a delayed-treatment control group received information and guidance consistent with self-determination theory on how to support students’ autonomy. Over a series of 3 classroom observations, trained raters scored each teacher’s autonomy support and 2 measures of their students’ engagement–task involvement and influence attempts. Trained teachers displayed significantly more autonomy-supportive behaviors than did nontrained teachers. Further, the more teachers used autonomy support during instruction, the more engaged were their students. This was true on both measures of engagement.
The vehicle routing problem: State of the art classification and review

Over the past decades, the Vehicle Routing Problem (VRP) and its variants have grown ever more popular in the academic literature. Yet, the problem characteristics and assumptions vary widely and few literature reviews have made an effort to classify the existing articles accordingly. In this article, we present a taxonomic review of the VRP literature published between 2009 and 2013. Based on an adapted version of the comprehensive taxonomy suggested by Eksioglu et al. (2009), we classify 144 articles and analyze the trends in the VRP literature. This classification is the first to categorize the articles to this level of detail.
Reflect , and React : A Culturally Responsive Model for Pre-service Secondary Social Studies Teachers

The purpose of this qualitative study was to design and implement a model of cultural-responsiveness within a social studies teacher education program. Specifically, we sought to understand how pre-service grades 6-12 social studies practitioners construct culturally responsive teaching (CRT) in their lesson planning. In addition, we examined the professional barriers that prevented teacher-candidates from actualizing culturally responsive pedagogy. Incorporating a conceptual model of Review, Reflect, and React, 20 teacher candidates in a social studies methods course engaged CRT theory and practice. Thematic analysis of lesson plans and clinical reflections indicated successful proponents of CRT critically analyzed their curriculum, explored the diverse needs of their students, and engaged learners in culturally appropriate social studies pedagogy. Findings also showed that unsuccessful CRT was characterized by a lack of content knowledge, resistance from the cooperating teacher, and a reliance on the textbook materials.
Steps toward Artificial Intelligence

The problems of heuristic programming-of making computers solve really difficult problems-are divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction. A computer can do, in a sense, only what it is told to do. But even when we do not know how to solve a certain problem, we may program a machine (computer) to Search through some large space of solution attempts. Unfortunately, this usually leads to an enormously inefficient process. With Pattern-Recognition techniques, efficiency can often be improved, by restricting the application of the machine's methods to appropriate problems. Pattern-Recognition, together with Learning, can be used to exploit generalizations based on accumulated experience, further reducing search. By analyzing the situation, using Planning methods, we may obtain a fundamental improvement by replacing the given search with a much smaller, more appropriate exploration. To manage broad classes of problems, machines will need to construct models of their environments, using some scheme for Induction. Wherever appropriate, the discussion is supported by extensive citation of the literature and by descriptions of a few of the most successful heuristic (problem-solving) programs constructed to date.
Crossing the finish line faster when paddling the Data Lake with Kayak

Paddling in a data lake is strenuous for a data scientist. Being a loosely-structured collection of raw data with little or no meta-information available, the difficulties of extracting insights from a data lake start from the initial phases of data analysis. Indeed, data preparation, which involves many complex operations (such as source and feature selection, exploratory analysis, data profiling, and data curation), is a long and involved activity for navigating the lake before getting precious insights at the finish line. In this framework, we demonstrate kayak, a framework that supports data preparation in a data lake with ad-hoc primitives and allows data scientists to cross the finish line sooner. kayak takes into account the tolerance of the user in waiting for the primitives’ results and it uses incremental execution strategies to produce informative previews of these results. The framework is based on a wise management of metadata and on features that limit human intervention, thus scaling smoothly when the data lake evolves.
Visual inertial odometry for quadrotors on SE(3)

The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.
Rigid-Body Dynamics with Friction and Impact

Rigid-body dynamics with unilateral contact is a good approximation for a wide range of everyday phenomena, from the operation of car brakes to walking to rock slides. It is also of vital importance for simulating robots, virtual reality, and realistic animation. However, correctly modeling rigid-body dynamics with friction is difficult due to a number of discontinuities in the behavior of rigid bodies and the discontinuities inherent in the Coulomb friction law. This is particularly crucial for handling situations with large coefficients of friction, which can result in paradoxical results known at least since Painlevé [C. R. Acad. Sci. Paris, 121 (1895), ]. This single example has been a counterexample and cause of controversy ever since, and only recently have there been rigorous mathematical results that show the existence of solutions to his example. The new mathematical developments in rigid-body dynamics have come from several sources: “sweeping processes” and the measure differential inclusions of Moreau in the 1970s and 1980s, the variational inequality approaches of Duvaut and J.-L. Lions in the 1970s, and the use of complementarity problems to formulate frictional contact problems by Lötstedt in the early 1980s. However, it wasn’t until much more recently that these tools were finally able to produce rigorous results about rigid-body dynamics with Coulomb friction and impulses.
Sensorized pneumatic muscle for force and stiffness control

This paper presents the design and experimental validation of a soft pneumatic artificial muscle with position and force sensing capabilities. Conductive liquid-based soft sensors are embedded in a fiber-reinforced contractile actuator to measure two modes of deformation — axial strain and diametral expansion — which, together, are used to determine the stroke length and contractile force generated under internal pressure. We validate the proposed device by using data from the embedded sensors to estimate the force output of the actuator at fixed lengths and the stiffness and force output of a one degree-of-freedom hinge joint driven by an antagonist pair of the sensorized pneumatic muscles.
Spatial working memory in humans as revealed by PET

THE concept of working memory is central to theories of human cognition because working memory is essential to such human skills as language comprehension and deductive reasoning1–4. Working memory is thought to be composed of two parts, a set of buffers that temporarily store information in either a phonological or visuospatial form, and a central executive responsible for various computations such as mental arithmetic5,6. Although most data on working memory come from behavioural studies of normal and brain-injured humans7, there is evidence about its physiological basis from invasive studies of monkeys8–10. Here we report positron emission tomography (PET) studies of regional cerebral blood flow in normal humans that reveal activation in right-hemisphere prefrontal, occipital, parietal and premotor cortices accompanying spatial working memory processes. These results begin to uncover the circuitry of a working memory system in humans.