nils-herrmann/clean_scirepeval huggingface dataset:
Tamper Detection in Audit Logs

Audit logs are considered good practice for business systems, and are required by federal regulations for secure systems, drug approval data, medical information disclosure, financial records, and electronic voting. Given the central role of audit logs, it is critical that they are correct and inalterable. It is not sufficient to say, “our data is correct, because we store all interactions in a separate audit log.” The integrity of the audit log itself must also be guaranteed. This paper proposes mechanisms within a database management system (DBMS), based on cryptographically strong one-way hash functions, that prevent an intruder, including an auditor or an employee or even an unknown bug within the DBMS itself, from silently corrupting the audit log. We propose that the DBMS store additional information in the database to enable a separate audit log validator to examine the database along with this extra information and state conclusively whether the audit log has been compromised. We show with an implementation on a high-performance storage engine that the overhead for auditing is low and that the validator can efficiently and correctly determine if the audit log has been compromised.
Robotics & Constructivism in Education : the TERECoP project

This paper presents the European project “Teacher Education on Robotics-Enhanced Constructivist Pedagogical Methods” (TERECoP). A first premise of this project concerns the implementation of constructivist – constructionist methods not only in classroom, but in teacher education as well. A second premise is referred to the technology-enhanced learning as occurred in the implementation of different kinds of curriculum innovation in the classrooms. A third is related with the emerging need for a teaching as a research-based profession and for the creation of a culture in which researchers and teachers can create a shared body of knowledge. Although the role of teacher is crucial for the successful introduction of robotics in classrooms, only few projects have been undertaken to train school teachers in using this, completely new for them, technology. TERECoP project’s aim and ambition is to contribute to fill in this gap suggesting a constructivist model of teacher training in these new technologies. Learning theories, modelling, technology and languages are the main aspects we will have to deal with. The main questions we are currently trying to answer (probably in this order) are: what is “Robotics” at School? Which methodology should we use to apply “Robotics” at school and teacher education? How can we design educational activities (within students’ curricula and teacher training courses) once we have answered to the two previous questions? Our work within the TERECoP project tries to give some answers to these questions. The paper describes the starting point of this project, focusing on the context, on the aims of the project and on the different partners’ countries experiences, and outlines the different stages that are going to be developed to implement the project giving a description of every one. Finally some preliminary conclusions are presented.
Open world classification of printed invoices

A key step in the understanding of printed documents is their classification based on the nature of information they contain and their layout. In this work we consider a dynamic scenario in which document classes are not known a priori and new classes can appear at any time. This open world setting is both realistic and highly challenging. We use an SVM-based classifier based only on image-level features and use a nearest-neighbor approach for detecting new classes. We assess our proposal on a real-world dataset composed of 562 invoices belonging to 68 different classes. These documents were digitalized after being handled by a corporate environment, thus they are quite noisy---e.g., big stamps and handwritten signatures at unfortunate positions and alike. The experimental results are highly promising.
Boosting with early stopping: Convergence and consistency

Boosting is one of the most significant advances in machine learning for classification and regression. In its original and computationally flexible version, boosting seeks to minimize empirically a loss function in a greedy fashion. The resulted estimator takes an additive function form and is built iteratively by applying a base estimator (or learner) to updated samples depending on the previous iterations. An unusual regularization technique, early stopping, is employed based on CV or a test set. This paper studies numerical convergence, consistency, and statistical rates of convergence of boosting with early stopping, when it is carried out over the linear span of a family of basis functions. For general loss functions, we prove the convergence of boosting’s greedy optimization to the infinimum of the loss function over the linear span. Using the numerical convergence result, we find early stopping strategies under which boosting is shown to be consistent based on iid samples, and we obtain bounds on the rates of convergence for boosting estimators. Simulation studies are also presented to illustrate the relevance of our theoretical results for providing insights to practical aspects of boosting. As a side product, these results also reveal the importance of restricting the greedy search step sizes, as known in practice through the works of Friedman and others. Moreover, our results lead to a rigorous proof that for a linearly separable problem, AdaBoost with ǫ → 0 stepsize becomes an L-margin maximizer when left to run to convergence.
Teacher Characteristics and Student Achievement Gains : A Review

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.
Diet eyeglasses: Recognising food chewing using EMG and smart eyeglasses

We utilise smart eyeglasses for dietary monitoring, in particular to sense food chewing. Our approach is based on a 3D-printed regular eyeglasses design that could accommodate processing electronics and Electromyography (EMG) electrodes. Electrode positioning was analysed and an optimal electrode placement at the temples was identified. We further compared gel and dry fabric electrodes. For the subsequent analysis, fabric electrodes were attached to the eyeglasses frame. The eyeglasses were used in a data recording study with eight participants eating different foods. Two chewing cycle detection methods and two food classification algorithms were compared. Detection rates for individual chewing cycles reached a precision and recall of 80%. For five foods, classification accuracy for individual chewing cycles varied between 43% and 71%. Majority voting across intake sequences improved accuracy, ranging between 63% and 84%. We concluded that EMG-based chewing analysis using smart eyeglasses can contribute essential chewing structure information to dietary monitoring systems, while the eyeglasses remain inconspicuous and thus could be continuously used.
An analysis of queuing network simulation using GPU-based hardware acceleration

Queuing networks are used widely in computer simulation studies. Examples of queuing networks can be found in areas such as the supply chains, manufacturing work flow, and internet routing. If the networks are fairly small in size and complexity, it is possible to create discrete event simulations of the networks without incurring significant delays in analyzing the system. However, as the networks grow in size, such analysis can be time consuming, and thus require more expensive parallel processing computers or clusters. We have constructed a set of tools that allow the analyst to simulate queuing networks in parallel, using the fairly inexpensive and commonly available graphics processing units (GPUs) found in most recent computing platforms. We present an analysis of a GPU-based algorithm, describing benefits and issues with the GPU approach. The algorithm clusters events, achieving speedup at the expense of an approximation error which grows as the cluster size increases. We were able to achieve 10-x speedup using our approach with a small error in a specific implementation of a synthetic closed queuing network simulation. This error can be mitigated, based on error analysis trends, obtaining reasonably accurate output statistics. The experimental results of the mobile ad hoc network simulation show that errors occur only in the time-dependent output statistics.
Origamizing Polyhedral Surfaces

This paper presents the first practical method for "origamizing¿ or obtaining the folding pattern that folds a single sheet of material into a given polyhedral surface without any cut. The basic idea is to tuck fold a planar paper to form a three-dimensional shape. The main contribution is to solve the inverse problem; the input is an arbitrary polyhedral surface and the output is the folding pattern. Our approach is to convert this problem into a problem of laying out the polygons of the surface on a planar paper by introducing the concept of tucking molecules. We investigate the equality and inequality conditions required for constructing a valid crease pattern. We propose an algorithm based on two-step mapping and edge splitting to solve these conditions. The two-step mapping precalculates linear equalities and separates them from other conditions. This allows an interactive manipulation of the crease pattern in the system implementation. We present the first system for designing three-dimensional origami, enabling a user can interactively design complex spatial origami models that have not been realizable thus far.
Contrastive auto-encoder for phoneme recognition

Speech data typically contains task irrelevant information lying within features. Specifically, phonetic information, speaker characteristic information, emotional information and noise are always mixed together and tend to impair one another for certain task. We propose a new type of auto-encoder for feature learning called contrastive auto-encoder. Unlike other variants of auto-encoders, contrastive auto-encoder is able to leverage class labels in constructing its representation layer. We achieve this by modeling two autoencoders together and making their differences contribute to the total loss function. The transformation built with contrastive auto-encoder can be seen as a task-specific and invariant feature learner. Our experiments on TIMIT clearly show the superiority of the feature extracted from contrastive auto-encoder over original acoustic feature, feature extracted from deep auto-encoder, and feature extracted from a model that contrastive auto-encoder originates from.
Image analysis by bidimensional empirical mode decomposition

Recent developments in analysis methods on the non-linear and non-stationary data have received large attention by the image analysts. In 1998, Huang introduced the empirical mode decomposition (EMD) in signal processing. The EMD approach, fully unsupervised, proved reliable monodimensional (seismic and biomedical) signals. The main contribution of our approach is to apply the EMD to texture extraction and image filtering, which are widely recognized as a difficult and challenging computer vision problem. We developed an algorithm based on bidimensional empirical mode decomposition (BEMD) to extract features at multiple scales or spatial frequencies. These features, called intrinsic mode functions, are extracted by a sifting process. The bidimensional sifting process is realized using morphological operators to detect regional maxima and thanks to radial basis function for surface interpolation. The performance of the texture extraction algorithms, using BEMD method, is demonstrated in the experiment with both synthetic and natural images. q 2003 Elsevier B.V. All rights reserved.
An Iterative Design Methodology for User-Friendly Natural Language Office Information Applications

A six-step, iterative, empirical human factors design methodology was used to develop CAL, a natural language computer application to help computer-naive business professionals manage their personal calendars. Input language is processed by a simple, nonparsing algorithm with limited storage requirements and a quick response time. CAL allows unconstrained English inputs from users with no training (except for a five minute introduction to the keyboard and display) and no manual (except for a two-page overview of the system). In a controlled test of performance, CAL correctly responded to between 86 percent and 97 percent of the storage and retrieval requests it received, according to various criteria. This level of performance could never have been achieved with such a simple processing model were it not for the empirical approach used in the development of the program and its dictionaries. The tools of the engineering psychologist are clearly invaluable in the development of user-friendly software, if tha t software is to accommodate the unruly language of computer-naive, first-time users. The key is to elicit the cooperation of such users as partners in an iterative, empirical development process.
A broad-coverage natural language processing system

Natural language processing systems (NLP) that extract clinical information from textual reports were shown to be effective for limited domains and for particular applications. Because an NLP system typically requires substantial resources to develop, it is beneficial if it is designed to be easily extendible to multiple domains and applications. This paper describes multiple extensions of an NLP system called MedLEE, which was originally developed for the domain of radiological reports of the chest, but has subsequently been extended to mammography, discharge summaries, all of radiology, electrocardiography, echocardiography, and pathology.
Blocks and Fuel: Frameworks for deep learning

We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support (Bastien et al., 2012; Bergstra et al., 2010). It facilitates the training of complex neural network models by providing parametrized Theano operations, attaching metadata to Theano’s symbolic computational graph, and providing an extensive set of utilities to assist training the networks, e.g. training algorithms, logging, monitoring, visualization, and serialization. Fuel provides a standard format for machine learning datasets. It allows the user to easily iterate over large datasets, performing many types of pre-processing on the fly.
Dendroid: A text mining approach to analyzing and classifying code structures in Android malware families

The rapid proliferation of smartphones over the last few years has come hand in hand with and impressive growth in the number and sophistication of malicious apps targetting smartphone users. The availability of reuse-oriented development methodologies and automated malware production tools makes exceedingly easy to produce new specimens. As a result, market operators and malware analysts are increasingly overwhelmed by the amount of newly discovered samples that must be analyzed. This situation has stimulated research in intelligent instruments to automate parts of the malware analysis process. In this paper, we introduce Dendroid, a system based on text mining and information retrieval techniques for this task. Our approach is motivated by a statistical analysis of the code structures found in a dataset of Android OS malware families, which reveals some parallelisms with classical problems in those domains. We then adapt the standard Vector Space Model and reformulate the modelling process followed in text mining applications. This enables us to measure similarity between malware samples, which is then used to automatically classify them into families. We also investigate the application of hierarchical clustering over the feature vectors obtained for each malware family. The resulting dendograms resemble the so-called phylogenetic trees for biological species, allowing us to conjecture about evolutionary relationships among families. Our experimental results suggest that the approach is remarkably accurate and deals efficiently with large databases of malware instances.
Deconvolutional Networks for Feature Learning

Building robust low-level image representations, beyond edge primitives, is a long-standing goal in vision. In its most basic form, an image is a matrix of intensities. How we should progress from this matrix to stable mid-level representations, useful for high-level vision tasks, remains unclear. Popular feature representations such as SIFT or HOG spatially pool edge information to form descriptors that are invariant to local transformations. However, in doing so important cues such as edge intersections, grouping, parallelism and symmetry are lost.
Sensorless Indirect-Rotor-Field-Orientation Speed Control of a Permanent-Magnet Synchronous Motor With Stator-Resistance Estimation

Efficient and precise sensorless speed control of a permanent-magnet synchronous motor (PMSM) requires accurate knowledge of rotor flux, position, and speed. In the literature, many sensorless schemes have been presented, in which the accurate estimation of rotor flux magnitude, position, and speed is guaranteed by detecting the back electromotive force (EMF). However, these schemes show great sensitivity to stator resistance mismatch and system noise, particularly, during low-speed operation. In this paper, an indirect-rotor-field-oriented-control scheme for sensorless speed control of a PMSM is proposed. The rotor-flux position is estimated by direct integration of the estimated rotor speed to reduce the effect of the system noise. The stator resistance and the rotor-flux speed and magnitude are estimated adaptively using stable model reference adaptive system estimators. Simple stability analysis and design of the estimators are performed using linear-control theory applied to an error model of the PMSM in a synchronous rotating reference frame. The convergence of rotor position- and speed-estimation errors to zero is guaranteed. Experimental results show excellent performance
Floe: A Continuous Dataflow Framework for Dynamic Cloud Applications

Applications in cyber-physical systems are increasingly coupled with online instruments to perform long-running, continuous data processing. Such “always on” dataflow applications are dynamic, where they need to change the applications logic and performance at runtime, in response to external operational needs. F`oε is a continuous dataflow framework that is designed to be adaptive for dynamic applications on Cloud infrastructure. It offers advanced dataflow patterns like BSP and MapReduce for flexible and holistic composition of streams and files, and supports dynamic recomposition at runtime with minimal impact on the execution. Adaptive resource allocation strategies allow our framework to effectively use elastic Cloud resources to meet varying data rates. We illustrate the design patterns of F`oε by running an integration pipeline and a tweet clustering application from the Smart Power Grids domain on a private Eucalyptus Cloud. The responsiveness of our resource adaptation is validated through simulations for periodic, bursty and random workloads.
Dynamic Stability of a Rocker Bogie Vehicle: Longitudinal Motion

This paper describes a unified measure of stability of a Rocker Bogie vehicle that accounts for the tendency to slide, tipover, or lose contact with the ground considering both static equilibrium and dynamic effects. The measure of stability is computed by solving for the range of acceptable velocities and accelerations that satisfy a set of dynamic constraints. The maximum acceptable velocity serves as a dynamic stability measure, whereas the maximum acceptable acceleration at zero velocity serves as a static stability measure. The utility of the static and dynamic stability margins are demonstrated for both two dimensional and longitudinal quasi-3D motion in several examples.
Augmented Reality Interactive Storytelling Systems Using Tangible Cubes for Edutainment

Augmented reality has been used in many areas and it has proved to be useful for storytelling. These systems are adequate for edutainment. In this paper, we present two augmented reality interactive storytelling systems that use tangible cubes. The first shows the story in only one face of the cube and the second presents the story in all visible faces of the cube. The progress of the story can be chosen using two different tangible interfaces. We have tested the systems with the children of the Summer School of the Technical University of Valencia. In these tests we have analysed if the use of a head-mounted display or a typical monitor influence in the experience of the user. Results do not offer statistical significant differences using both visualization systems and confirm that children enjoyed playing with the systems.
Unlocking the black box: exploring the link between high-performance work systems and performance.

With a growing body of literature linking systems of high-performance work practices to organizational performance outcomes, recent research has pushed for examinations of the underlying mechanisms that enable this connection. In this study, based on a large sample of Welsh public-sector employees, we explored the role of several individual-level attitudinal factors--job satisfaction, organizational commitment, and psychological empowerment--as well as organizational citizenship behaviors that have the potential to provide insights into how human resource systems influence the performance of organizational units. The results support a unit-level path model, such that department-level, high-performance work system utilization is associated with enhanced levels of job satisfaction, organizational commitment, and psychological empowerment. In turn, these attitudinal variables were found to be positively linked to enhanced organizational citizenship behaviors, which are further related to a second-order construct measuring departmental performance.
Adaptive reservoir computing through evolution and learning

The development of real-world, fully autonomous agents would require mechanisms that would offer generalization capabilities from experience, suitable for a large range of machine learning tasks, like those from the areas of supervised and reinforcement learning. Such capacities could be offered by parametric function approximators that could either model the environment or the agent’s policy. To expert input. Towards this goal, we propose an adaptive function approximator method for developing appropriate neural networks in the form of reservoir computing systems through evolution and learning. Our neuro-evolution of augmenting reservoirs approach comprises of several ideas, successful on their own, in an effort to develop an algorithm that could handle a large range of problems, more efficiently. In particular, we use the neuro-evolution of augmented topologies algorithm as a metasearch method for the adaptation of echo state networks for handling problems to be encountered by autonomous entities. We test our approach on several test-beds from the realms of time series prediction and reinforcement learning. We compare our methodology against similar state-of-the-art algorithms with promising results. & 2012 Elsevier B.V. All rights reserved.