nils-herrmann/clean_scirepeval huggingface dataset:
Methods for Evaluating Text Extraction Toolkits: An Exploratory Investigation

Text extraction tools are vital for obtaining the textual content of computer files and for using the electronic text in a wide variety of applications, including search and natural language processing. However, when extraction tools fail, they convert once reliable electronic text into garbled text, or no text at all. The techniques and tools for validating the accuracy of these text extraction tools are conspicuously absent from academia and industry. This paper contributes to closing this gap. We discuss an exploratory investigation into a method and a set of tools for evaluating a text extraction toolkit. Although this effort focuses on the popular open source Apache Tika toolkit and the govdocs1 corpus, the method generally applies to other text extraction toolkits and corpora.
CS Teacher Experiences with Educational Technology, Problem-BasedLearning, and a CS Principles Curriculum

Little is known about how K-12 Computer Science (CS) teachers use technology and problem-based learning (PBL) to teach CS content in the context of CS Principles curricula. Significantly, little qualitative research has been conducted in these areas in computer science education, so we lack an in-depth understanding of the complicated realities of CS teachers' experiences. This paper describes the practices and experiences of six teachers' use of technology that was implemented to support PBL in the context of a dual enrollment CS Principles course.
 Results from an early offering of this course suggest that (1) while CS teachers used technology, they did not appear to use it to support student inquiry, (2) local adaptations to the curriculum were largely teacher-centric, and (3) the simultaneous adoption of new instructional practices, technologies, and curricula was overwhelming to teachers. This paper then describes how these results were used to modify the curriculum and professional development, leading to increased teacher satisfaction and student success in the course.
A novel sensor for wrist based optical heart rate monitor

The growing demands of continuous healthcare and hence physiological monitoring necessitates a system with high reliability and accuracy. Wearable used for continuous cardiological parameter estimation from wrist use reflective photoplethysmography technique that has certain limitations which are imperative. One such constraint is skin pigmentation of the subject. In the present work a sensor module design is proposed addressing to the anomalies due to optical properties of skin. A novel 590 nm (yellow-orange) wavelength based optical system is tailored suitably to maximize the signal quality acquired. The proposed setup is validated on a conglomeration of subjects in terms of age, gender and skin tone. A generous agreement between coherent measures for signal quality shows that the proposed wavelength holds an advantage over its shorter counterpart when subjected to varied skin pigmentation levels. A maximum improvement factor of 71 is observed in case of perfusion index, 31 for pulsatile strength and 3 for SNR. The details of sensor design, experimental setup, validation protocol, observations and inferences drawn from the study are presented.
Better streaming algorithms for clustering problems

We study clustering problems in the streaming model, where the goal is to cluster a set of points by making one pass (or a few passes) over the data using a small amount of storage space. Our main result is a randomized algorithm for the k--Median problem which produces a constant factor approximation in one pass using storage space O(k poly log n). This is a significant improvement of the previous best algorithm which yielded a 2O(1/ε) approximation using O(nε) space. Next we give a streaming algorithm for the k--Median problem with an arbitrary distance function. We also study algorithms for clustering problems with outliers in the streaming model. Here, we give bicriterion guarantees, producing constant factor approximations by increasing the allowed fraction of outliers slightly.
Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis

Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We describe in detail the interplay between objective and constraints in a number of single-period variants, including semivariance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multiperiod models based on scenario trees. A key property is the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.
Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors

Inertial and magnetic measurement systems (IMMSs) are a new generation of motion analysis systems which may diffuse the measurement of upper-limb kinematics to ambulatory settings. Based on the MT9B IMMS (Xsens Technologies, NL), we therefore developed a protocol that measures the scapulothoracic, humerothoracic and elbow 3D kinematics. To preliminarily evaluate the protocol, a 23-year-old subject performed six tasks involving shoulder and elbow single-joint-angle movements. Criteria for protocol validity were limited cross-talk with the other joint-angles during each task; scapulohumeral-rhythm close to literature results; and constant carrying-angle. To assess the accuracy of the MT9B when measuring the upper-limb kinematics through the protocol, we compared the MT9B estimations during the six tasks, plus other four, with the estimations of an optoelectronic system (the gold standard), in terms of RMS error, correlation coefficient (r), and the amplitude ratio (m). Results indicate that the criteria for protocol validity were met for all tasks. For the joint angles mainly involved in each movement, the MT9B estimations presented RMS errors <3.6°, r > 0.99 and 0.9 < m < 1.09. It appears therefore that (1) the protocol in combination with the MT9B is valid for, and (2) the MT9B in combination with the protocol is accurate when, measuring shoulder and elbow kinematics, during the tasks tested, in ambulatory settings.
Mathematical applications of inductive logic programming

The application of Inductive Logic Programming to scientific datasets has been highly successful. Such applications have led to breakthroughs in the domain of interest and have driven the development of ILP systems. The application of AI techniques to mathematical discovery tasks, however, has largely involved computer algebra systems and theorem provers rather than machine learning systems. We discuss here the application of the HR and Progol machine learning programs to discovery tasks in mathematics. While Progol is an established ILP system, HR has historically not been described as an ILP system. However, many applications of HR have required the production of first order hypotheses given data expressed in a Prolog-style manner, and the core functionality of HR can be expressed in ILP terminology. In Colton (2003), we presented the first partial description of HR as an ILP system, and we build on this work to provide a full description here. HR performs a novel ILP routine called Automated Theory Formation, which combines inductive and deductive reasoning to form clausal theories consisting of classification rules and association rules. HR generates definitions using a set of production rules, interprets the definitions as classification rules, then uses the success sets of the definitions to induce hypotheses from which it extracts association rules. It uses third party theorem provers and model generators to check whether the association rules are entailed by a set of user supplied axioms. HR has been applied successfully to a number of predictive, descriptive and subgroup discovery tasks in domains of pure mathematics. We survey various applications of HR which have led to it producing number theory results worthy of journal publication, graph theory results rivalling those of the highly successful Graffiti program and algebraic results leading to novel classification theorems. To further promote mathematics as a challenge domain for ILP systems, we present the first application of Progol to an algebraic domain—we use Progol to find algebraic properties of quasigroups, semigroups and magmas (groupoids) of varying sizes which differentiate pairs of non-isomorphic objects. This development is particularly interesting because algebraic domains have been an important proving ground for both deduction systems and constraint solvers. We believe that AI programs written for discovery tasks will need to simultaneously employ a variety of reasoning techniques such as induction, abduction, deduction, calculation and invention. We argue that mathematics is not only a challenging domain for the application of ILP systems, but that mathematics could be a good domain in which to develop a new generation of systems which integrate various reasoning techniques.
Neural foundations of imagery

Mental imagery has, until recently, fallen within the purview of philosophy and cognitive psychology. Both enterprises have raised important questions about imagery, but have not made substantial progress in answering them. With the advent of cognitive neuroscience, these questions have become empirically tractable. Neuroimaging studies, combined with other methods (such as studies of brain-damaged patients and of the effects of transcranial magnetic stimulation), are revealing the ways in which imagery draws on mechanisms used in other activities, such as perception and motor control. Because of its close relation to these basic processes, imagery is now becoming one of the best understood 'higher' cognitive functions.
Rise and fall patterns of information diffusion: model and implications

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law?
 In this paper, we propose SpikeM, a concise yet flexible analytical model for the rise and fall patterns of influence propagation. Our model has the following advantages: (a) unification power: it generalizes and explains earlier theoretical models and empirical observations; (b) practicality: it matches the observed behavior of diverse sets of real data; (c) parsimony: it requires only a handful of parameters; and (d) usefulness: it enables further analytics tasks such as fore- casting, spotting anomalies, and interpretation by reverse- engineering the system parameters of interest (e.g. quality of news, count of interested bloggers, etc.).
 Using SpikeM, we analyzed 7.2GB of real data, most of which were collected from the public domain. We have shown that our SpikeM model accurately and succinctly describes all the patterns of the rise-and-fall spikes in these real datasets.
Optical eye simulator for laser dazzle events.

An optical simulator of the human eye and its application to laser dazzle events are presented. The simulator combines optical design software (ZEMAX) with a scientific programming language (MATLAB) and allows the user to implement and analyze a dazzle scenario using practical, real-world parameters. Contrary to conventional analytical glare analysis, this work uses ray tracing and the scattering model and parameters for each optical element of the eye. The theoretical background of each such element is presented in relation to the model. The overall simulator's calibration, validation, and performance analysis are achieved by comparison with a simpler model based uponCIE disability glare data. Results demonstrate that this kind of advanced optical eye simulation can be used to represent laser dazzle and has the potential to extend the range of applicability of analytical models.
Semantic Linking of Learning Object Repositories to DBpedia

Large-sized repositories of learning objects (LOs) are difficult to create and also to maintain. In this paper we propose a way to reduce this drawback by improving the classification mechanisms of the LO repositories. Specifically, we present a solution to automate the LO classification of the Universia repository, a collection of more than 15 million of LOs represented according to the IEEE LOM standard. Although a small part of these LOs is correctly classified, most are unclassified and therefore searching and accessing information is difficult. Our solution makes use of the categories provided by DBpedia, a linked data repository, to automatically improve its classification through a graph-based filtering algorithm, which selects the most suitable categories for describing a LO. Once selected, these categories will classify the LO, linking its classification metadata with a set of DBpedia categories.
Walking Robots and the Central and Peripheral Control of Locomotion in Insects

This paper outlines aspects of locomotor control in insects that may serve as the basis for the design of controllers for autonomous hexapod robots. Control of insect walking can be considered hierarchical and modular. The brain determines onset, direction, and speed of walking. Coordination is done locally in the ganglia that control leg movements. Typically, networks of neurons capable of generating alternating contractions of antagonistic muscles (termed central pattern generators, or CPGs) control the stepping movements of individual legs. The legs are coordinated by interactions between the CPGs and sensory feedback from the moving legs. This peripheral feedback provides information about leg load, position, velocity, and acceleration, as well as information about joint angles and foot contact. In addition, both the central pattern generators and the sensory information that feeds them may be modulated or adjusted according to circumstances. Consequently, locomotion in insects is extraordinarily robust and adaptable.
A circularly polarized planar antenna modified for passive UHF RFID

The majority of RFID tags are linearly polarized dipole antennas, but a few use a planar, dual-dipole antenna that facilitates circular polarization, but requires a three-terminal IC. In this paper, we present a novel way to achieve circular polarization with a planar antenna using a two-terminal IC. We present an intuitive methodology for design, and perform experiments that validate circular polarization. The results show that the tag exhibits strong circular polarization, but the precise axial ratio of the tag remains uncertain due to lack of precision in the experimental system.
A computer vision system to detect diving cases in soccer

Recently, motion analysis systems have been getting a lot of attention due to their potential in human motion analysis, which has a wide range of applications. One of these applications is analyzing tackle scenes in soccer games. In a tackle scene, players occasionally tend to deceive the referee by intentionally falling to get a free or penalty kick. In this paper, we propose a system to program human body tracking in order to analyze tackle scenes in soccer games. The main idea behind this system is to determine whether the falling player in the tackle scene is attempting to deceive the referee (diving) or not. In this system, the tackle scene goes through five main stages of processing; identification of the falling player, extraction of tracking points, motion tracking, features extraction and scene classification. The tracking component is implemented using Kanade-Lucas-Tomasi optical flow with the aid of pyramid levels and forward-backward error algorithm, while the classification is carried out using Weka software with Naive Bayes tree (NB tree) classifier. The proposed system is implemented and its performance is experimentally tested. The results show a potential to detect diving cases (deceiving in falling), with a classification accuracy of 84%.
Fading correlation and its effect on the capacity of multielement antenna systems

We investigate the effects of fading correlations in multielement antenna (MEA) communication systems. Pioneering studies showed that if the fades connecting pairs of transmit and receive antenna elements are independently, identically distributed, MEA’s offer a large increase in capacity compared to single-antenna systems. An MEA system can be described in terms of spatial eigenmodes, which are single-input single-output subchannels. The channel capacity of an MEA is the sum of capacities of these subchannels. We will show that the fading correlation affects the MEA capacity by modifying the distributions of the gains of these subchannels. The fading correlation depends on the physical parameters of MEA and the scatterer characteristics. In this paper, to characterize the fading correlation, we employ an abstract model, which is appropriate for modeling narrow-band Rayleigh fading in fixed wireless systems.
Beyond simple pessimism: effects of sadness and anger on social perception.

In keeping with cognitive appraisal models of emotion, it was hypothesized that sadness and anger would exert different influences on causal judgments. Two experiments provided initial support for this hypothesis. Sad Ss perceived situationally caused events as more likely (Experiment 1) and situational forces more responsible for an ambiguous event (Experiment 2) than angry Ss, who, in contrast, perceived events caused by humans as more likely and other people as more responsible. Experiments 3, 4, and 5 showed that the experience of these emotions, rather than their cognitive constituents, mediates these effects. The nonemotional exposure to situational or human agency information did not influence causal judgments (Experiment 3), whereas the induction of sadness and anger without explicit agency information did (Experiments 4 and 5). Discussion is focused on the influence of emotion on social judgment.
Select-and-Evaluate: A Learning Framework for Large-Scale Knowledge Graph Search

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph search to meet the response-time requirements of the applications. To address these scalability challenges, we develop a learning framework for graph search called Select-and-Evaluate (SCALE). The key insight is to select a small part of the data graph that is sufficient to answer a given query in order to satisfy the specified constraints on time or accuracy. We formulate the problem of generating the candidate subgraph as a computational search process and induce search control knowledge from training queries using imitation learning. First, we define a search space over candidate selection plans, and identify target selection plans corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target selection plans. Our experiments on large-scale knowledge graphs including DBpedia, YAGO, and Freebase show that using the learned selection plans, we can significantly improve the computational-efficiency of graph search to achieve high accuracy.
DCFont: an end-to-end deep chinese font generation system

Building a complete personalized Chinese font library for an ordinary person is a tough task due to the existence of huge amounts of characters with complicated structures. Yet, existing automatic font generation methods still have many drawbacks. To address the problem, this paper proposes an end-to-end learning system, DCFont, to automatically generate the whole GB2312 font library that consists of 6763 Chinese characters from a small number (e.g., 775) of characters written by the user. Our system has two major advantages. On the one hand, the system works in an end-to-end manner, which means that human interventions during offline training and online generating periods are not required. On the other hand, a novel deep neural network architecture is designed to solve the font feature reconstruction and handwriting synthesis problems through adversarial training, which requires fewer input data but obtains more realistic and high-quality synthesis results compared to other deep learning based approaches. Experimental results verify the superiority of our method against the state of the art.
Deep learning in fluid dynamics

It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area of high-dimensional, complex dynamical systems. In the last decade, DNNs have become a dominant data mining tool for big data applications. Although neural networks have been applied previously to complex fluid flows, the article featured here (Ling et al.,  2016, pp. 155–166) is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models. As one often expects with modern DNNs, performance gains are achieved over competing state-of-the-art methods, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.
Parametric analysis for adaptive computation offloading

Many programs can be invoked under different execution options, input parameters and data files. Such different execution contexts may lead to strikingly different execution instances. The optimal code generation may be sensitive to the execution instances. In this paper, we show how to use parametric program analysis to deal with this issue for the optimization problem of computation offloading.Computation offloading has been shown to be an effective way to improve performance and energy saving on mobile devices. Optimal program partitioning for computation offloading depends on the tradeoff between the computation workload and the communication cost. The computation workload and communication requirement may change with different execution instances. Optimal decisions on program partitioning must be made at run time when sufficient information about workload and communication requirement becomes available.Our cost analysis obtains program computation workload and communication cost expressed as functions of run-time parameters, and our parametric partitioning algorithm finds the optimal program partitioning corresponding to different ranges of run-time parameters. At run time, the transformed program self-schedules its tasks on either the mobile device or the server, based on the optimal program partitioning that corresponds to the current values of run-time parameters. Experimental results on an HP IPAQ handheld device show that different run-time parameters can lead to quite different program partitioning decisions.