nils-herrmann/clean_scirepeval huggingface dataset:
The Effects of Interactive Latency on Exploratory Visual Analysis

To support effective exploration, it is often stated that interactive visualizations should provide rapid response times. However, the effects of interactive latency on the process and outcomes of exploratory visual analysis have not been systematically studied. We present an experiment measuring user behavior and knowledge discovery with interactive visualizations under varying latency conditions. We observe that an additional delay of 500ms incurs significant costs, decreasing user activity and data set coverage. Analyzing verbal data from think-aloud protocols, we find that increased latency reduces the rate at which users make observations, draw generalizations and generate hypotheses. Moreover, we note interaction effects in which initial exposure to higher latencies leads to subsequently reduced performance in a low-latency setting. Overall, increased latency causes users to shift exploration strategy, in turn affecting performance. We discuss how these results can inform the design of interactive analysis tools.
Characterization and clinical evaluation of a novel IMRT quality assurance system

Intensity-modulated radiation therapy (IMRT) is a complex procedure that involves the delivery of complex intensity patterns from various gantry angles. Due to the complexity of the treatment plans, the standard-of-care is to perform measurement based patient-specific quality assurance (QA). IMRT QA is traditionally done with film for relative dose in a plane and an ion chamber for absolute dose. This is a laborious and time-consuming process. In this work, we characterized, commissioned, and evaluated the QA capabilities of a novel commercial IMRT device Delta4, (Scandidos, Uppsala, Sweden). This device consists of diode matrices in 2 orthogonal planes inserted in a cylindrical acrylic phantom that is 22 cm in diameter. Although the system has detectors in only 2 planes, it provides a novel interpolation algorithm that is capable of estimating doses at points where no detectors are present. Each diode is sampled per beam pulse so that the dose distribution can be evaluated on segment-by-segment, beam-by-beam, or as a composite plan from a single set of measurements. The end user can calibrate the system to perform absolute dosimetry eliminating the need for additional ion chamber measurements. The patient's IMRT plan is imported into the device over the hospital LAN and the results of measurements can be displayed as gamma profiles, distance-to-agreement maps, dose difference maps, or the measured dose distribution can be superimposed of the patient's anatomy to display an as-delivered plan. We evaluated the system's reproducibility, stability, pulse-rate dependence, dose-rate dependence, angular dependence, linearity of dose response and energy response using carefully planned measurements. We also validated the system's interpolation algorithm by measuring a complex dose distribution from an IMRT treatment. Several simple and complex isodose distributions planned using a treatment planning system were delivered to the QA device; the planned and measured dose distributions were then compared and analyzed. In addition, the dose distributions measured by conventional IMRT QA, which uses an ion chamber and film, were compared. We found that this device is accurate and reproducible and that its interpolation algorithm is valid. In addition the supplied software and network interface allow a streamlined IMRT QA process.
Invariant Features from Interest Point Groups

This paper approaches the problem of finding correspondences between images in which there are large changes in viewpoint, scale and illumination. Recent work has shown that scale-space ‘interest points’ may be found with good repeatability in spite of such changes. Furthermore, the high entropy of the surrounding image regions means that local descriptors are highly discriminative for matching. For descriptors at interest points to be robustly matched between images, they must be as far as possible invariant to the imaging process. In this work we introduce a family of features which use groups of interest points to form geometrically invariant descriptors of image regions. Feature descriptors are formed by resampling the image relative to canonical frames defined by the points. In addition to robust matching, a key advantage of this approach is that each match implies a hypothesis of the local 2D (projective) transformation. This allows us to immediately reject most of the false matches using a Hough transform. We reject remaining outliers using RANSAC and the epipolar constraint. Results show that dense feature matching can be achieved in a few seconds of computation on 1GHz Pentium III machines.
Self-Indexing Inverted Files for Fast Text Retrieval

Query-processing costs on large text databases are dominated by the need to retrieve and scan the inverted list of each query term. Retrieval time for inverted lists can be greatly reduced by the use of compression, but this adds to the CPU time required. Here we show that the CPU component of query response time for conjunctive Boolean queries and for informal ranked queries can be similarly reduced, at little cost in terms of storage, by the inclusion of an internal index in each compressed inverted list. This method has been applied in a retrieval system for a collection of nearly two million short documents. Our experimental results show that the self-indexing strategy adds less than 20% to the size of the compressed inverted file, which itself occupies less than 10% of the indexed text, yet can reduce processing time for Boolean queries of 5-10 terms to under one fifth of the previous cost. Similarly, ranked queries of 40-50 terms can be evaluated in as little as 25% of the previous time, with little or no loss of retrieval effectiveness.
An Analysis of Multi-Criteria Decision Making Methods

 Multi-Criteria Decision Making (MCDM) methods have evolved to accommodate various types of applications. Dozens of methods have been developed, with even small variations to existing methods causing the creation of new branches of research. This paper performs a literature review of common Multi-Criteria Decision Making methods, examines the advantages and disadvantages of the identified methods, and explains how their common applications relate to their relative strengths and weaknesses. The analysis of MCDM methods performed in this paper provides a clear guide for how MCDM methods should be used in particular situations.
Information and Communication Technologies in Education

Education in Latin America is an important part of social policy. Although huge strides were made in the last decade, a region of disparity between the rich and· poor needs to focus on the reduction of inequality of access and provision if it is to hope for qualitative change. Detailed achievements and challenges are presented, with an emphasis on improving school enrolment and a change to curriculum relevant for the future and local community and business involvement. Change will be achieved by a combination of new teachers, new management and leadership, and the involvement of all society.
A Compositional Distributional Model of Meaning

We propose a mathematical framework for a unification of the distributional theory of meaning in terms of vector space models, and a compositional theory for grammatical types, namely Lambek’s pregroup semantics. A key observation is that the monoidal category of (finite dimensional) vector spaces, linear maps and the tensor product, as well as any pregroup, are examples of compact closed categories. Since, by definition, a pregroup is a compact closed category with trivial morphisms, its compositional content is reflected within the compositional structure of any non-degenerate compact closed category. The (slightly refined) category of vector spaces enables us to compute the meaning of a compound well-typed sentence from the meaning of its constituents, by ‘lifting’ the type reduction mechanisms of pregroup semantics to the whole category. These sentence meanings live in a single space, independent of the grammatical structure of the sentence. Hence we can use the inner-product to compare meanings of arbitrary sentences. A variation of this procedure which involves constraining the scalars of the vector spaces to the semiring of Booleans results in the well-known Montague semantics.
A Platform Independent Game Technology Model for Model Driven Serious Games Development.

Game-based learning (GBL) combines pedagogy and interactive entertainment to create a virtual learning environment in an effort to motivate and regain the interest of a new generation of ‘digital native’ learners. However, this approach is impeded by the limited availability of suitable ‘serious’ games and high-level design tools to enable domain experts to develop or customise serious games. Model Driven Engineering (MDE) goes some way to provide the techniques required to generate a wide variety of interoperable serious games software solutions whilst encapsulating and shielding the technicality of the full software development process. In this paper, we present our Game Technology Model (GTM) which models serious game software in a manner independent of any hardware or operating platform specifications for use in our Model Driven Serious Game Development Framework.
THE LYAPUNOV AND DIMENSION SPECTRA OF EQUILIBRIUMMEASURES FOR CONFORMAL EXPANDING MAPS

In this note, we nd an explicit relationship between the dimension spectrum for equilibrium measures and the Lyapunov spectrum for conformal repellers. We explicitly compute the Lyapunov spectrum and show that it is a delta function. We observe that while the Lyapunov exponent exists for almost every point with respect to an ergodic measure, the set of points for which the Lyapunov exponent does not exist has positive Hausdorr dimension if the SRB measure does not coincide with the measure of maximal entropy. It follows that for such conformal repellers, the set of points for which the pointwise dimension of the measure of maximal entropy does not exist has positive Hausdorr dimension. In EP], Eckmann and Procaccia suggested an analysis of the spectrum of Lyapunov exponents for chaotic dynamical systems which is similar to the multifractal analysis of measures invariant under chaotic dynamical systems. This suggestion was further investigated on a physical level by Sz epfalusy and T el ST] and by T el T]. In this note, we nd an explicit relationship between the dimension spectrum for equilibrium measures  and the Lyapunov spectrum for conformal expanding maps. We also explicitly compute the Lyapunov spectrum and show that it is a delta function. Using the multifractal analysis of equilibrium measures for conformal repellers in PW1], we show that while the Lyapunov exponent exists for almost every point with respect to an ergodic measure, the set of points for which the Lyapunov exponent does not exist has positive Hausdorr dimension if the SRB measure (Sinai-Ruelle-Bowen) B] does not coincide with the measure of maximal entropy. It follows that for such conformal repellers, the set of points for which the pointwise dimension of the measure of maximal entropy does not exist has positive Hausdorr dimension. The dimension spectrum is one of the principle components in the multifractal analysis of measures. In PW1] the authors eeected a complete multifractal analysis of equilibrium measures for conformal expanding maps. Examples of conformal expanding maps include Markov one-dimensional maps, rational maps whose Julia sets are hyperbolic, and conformal endomorphisms of the torus. See PW1] for deenitions and examples. Let M be a smooth manifold and g : M ! M a C 2 map. Suppose that is a compact invariant subset of M and consider the map g restricted to. We say that g is a conformal
Modelling and characterization of NAND flash memory channels

The threshold voltage distribution after ideal programming in NAND flash memory cells is usually distorted by a combination of the random telegraph noise (RTN), cell-to-cell Interference (CCI), and the retention process. To decide the original bits more accurately in this scenario, a precise channel model shall be utilized on the basis of the measured threshold voltages. This paper aims to characterize these various distortions occurring in multi-level cell (MLC) flash memories. A mathematical description of the overall distribution for the total flash channel distortion is presented. The final threshold voltage distribution for each symbol of MLC flash is also characterized, which is important for calculating the exact soft decisions of cell bits and the application of advanced flash error correction. The results of the theoretical analysis have been validated through Monte Carlo simulations of the flash channel.
Intelligent Market-Making in Artificial Financial Markets

Progress in machine learning techniques has led to the development of various techniques well suited to online estimation and rapid aggregation of information. Theoretical models of marketmaking have led to price-setting equations for which solutions cannot be achieved in practice, whereas empirical work on algorithms for market-making has so far focused on sets of heuristics and rules that lack theoretical justification. We are developing algorithms that are theoretically justified by results in finance, and at the same time flexible enough to be easily extended by incorporating modules for dealing with considerations like portfolio risk and competition from other market-makers.
Twitter sentiment classification using stanford NLP

Twitter is a micro blogging site where users review or tweet their approach i.e., opinion towards the service providers twitter page in words and it is useful to analyze the sentiments from it. Analyze means finding approach of users or customers where it is positive, negative, neutral, or in between positive-neutral or in between negative-neutral and represent it. In such a system or tool tweets are fetch from twitter regarding shopping websites, or any other twitter pages like some business, mobile brands, cloth brands, live events like sport match, election etc. get the polarity of it. These results will help the service provider to find out about the customers view toward their products.
Mathematical Models of Flow Shop and Job Shop Scheduling Problems

In this paper, mathematical models for permutation flow shop scheduling and job shop scheduling problems are proposed. The first problem is based on a mixed integer programming model. As the problem is NP-complete, this model can only be used for smaller instances where an optimal solution can be computed. For large instances, another model is proposed which is suitable for solving the problem by stochastic heuristic methods. For the job shop scheduling problem, a mathematical model and its main representation schemes are presented. Flow shop, job shop, mixed integer model, representation scheme.
Development and Validation of Extrinsic Motivation Scale for Crowdsourcing Micro-task Platforms

In this paper, we introduce a scale for measuring the extrinsic motivation of crowd workers. The new questionnaire is strongly based on the Work Extrinsic Intrinsic Motivation Scale (WEIMS)  and theoretically follows the Self-Determination Theory (SDT) of motivation. The questionnaire has been applied and validated in a crowdsourcing micro-task platform. This instrument can be used for studying the dynamics of extrinsic motivation by taking into account individual differences and provide meaningful insights which will help to design a proper incentives framework for each crowd worker that eventually leads to a better performance, an increased well-being, and higher overall quality.
Teaching English Discrete Sounds through Minimal Pairs

Minimal pairs bear great benefits in pronunciation teaching and learning which have long been of fruitful use. However, the full use of these pairs has not yet been made in the setting of Hung Vuong University. This paper sought to examine possible problems facing English non-majored students at Hung Vuong University in recognizing and producing English discrete sounds as well as in what way and to what extent do minimal pairs facilitate the teaching and learning of English discrete sounds. The data were collected both quantitatively and qualitatively from various sources: questionnaires for and interviews with both the teacher and student subjects, tests of students’ sound recognitions, regular real-time observations, audio recordings of students’ sound productions, and spectrogram-based analyses of these recordings. The findings revealed that virtually all of the student subjects face the six pronunciation problems: omitting the word-final consonant, adding the word-final /s/ to English words not ending in /s/, adding the schwa /6/ in the middle of a consonant cluster, mispronouncing strange sounds to Vietnamese people, e.g. /t/ and /d/, failing to differentiate between long and short vowels, and failing to differentiate between voiced and voiceless consonants. Both the student and the teacher subjects also show high appreciation of the pedagogical effectiveness of minimal pairs when employed either as a teaching or learning tool within the extent to which English discrete sounds are concerned.
Do Informed Voters Make Better Choices ? Experimental Evidence from Urban India

Do informational deficits on the part of voters sustain poor quality of governance in low income countries? We provide experimental evidence on the role of public disclosures on candidate quality and incumbent performance in enhancing electoral accountability. Slum dwellers who were randomly exposed to newspaper report cards on politician performance responded by increasing turnout and rewarding incumbents who spent more in slums and attended fair price shop oversight committee meetings. We also find evidence of yardstick competition – incumbent’s vote share is sensitive to the wealth and education qualifications of his challengers. ∗The authors are from MIT (Banerjee), Yale (Kumar) and Harvard University (Pande and Su). We thank our partners Satark Nagrik Sangathan, Delhi NGO Network and Hindustan times and especially Anjali Bharadwaj, Amrita Johri and Mrinal Pande for enabling this study and Shobhini Mukherji for providing field oversight.We also thank Hewlett Foundation for financial support, and Tim Besley and Esther Duflo for helpful comments.
Outlier detection for patient monitoring and alerting

We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25% to 66% for a variety of patient-management actions, with 66% corresponding to the strongest outliers.
A deep reinforcement learning framework for optimizing fuel economy of hybrid electric vehicles

Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with conventional ICE vehicles. However, the relatively complicated powertrain structures of HEVs necessitate an effective power management policy to determine the power split between ICE and EM. In this work, we propose a deep reinforcement learning framework of the HEV power management with the aim of improving fuel economy. The DRL technique is comprised of an offline deep neural network construction phase and an online deep Q-learning phase. Unlike traditional reinforcement learning, DRL presents the capability of handling the high dimensional state and action space in the actual decision-making process, making it suitable for the HEV power management problem. Enabled by the DRL technique, the derived HEV power management policy is close to optimal, fully model-free, and independent of a prior knowledge of driving cycles. Simulation results based on actual vehicle setup over real-world and testing driving cycles demonstrate the effectiveness of the proposed framework on optimizing HEV fuel economy.
Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment

Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
Collaborative Memory Network for Recommendation Systems

Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learning has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep learning architectures with the latent factor model ignoring a major class of CF models, neighborhood or memory-based approaches. We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion. Motivated by the success of Memory Networks, we fuse a memory component and neural attention mechanism as the neighborhood component. The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood. Finally, the output module jointly exploits the neighborhood with the user and item memories to produce the ranking score. Stacking multiple memory modules together yield deeper architectures capturing increasingly complex user-item relations. Furthermore, we show strong connections between CMN components, memory networks and the three classes of CF models. Comprehensive experimental results demonstrate the effectiveness of CMN on three public datasets outperforming competitive baselines. Qualitative visualization of the attention weights provide insight into the model's recommendation process and suggest the presence of higher order interactions.