nils-herrmann/clean_scirepeval huggingface dataset:
Numerical integration using sparse grids

We present new and review existing algorithms for the numerical integration of multivariate functions defined over d-dimensional cubes using several variants of the sparse grid method first introduced by Smolyak . In this approach, multivariate quadrature formulas are constructed using combinations of tensor products of suitable one-dimensional formulas. The computing cost is almost independent of the dimension of the problem if the function under consideration has bounded mixed derivatives. We suggest the usage of extended Gauss (Patterson) quadrature formulas as the one‐dimensional basis of the construction and show their superiority in comparison to previously used sparse grid approaches based on the trapezoidal, Clenshaw–Curtis and Gauss rules in several numerical experiments and applications. For the computation of path integrals further improvements can be obtained by combining generalized Smolyak quadrature with the Brownian bridge construction.
Prediction of Heart Disease using Supervised Learning Algorithms

The diagnosis of disease is difficult but critical task in medicine. Data mining is the process of extracting hidden interesting patterns from massive database. In the healthcare industry it plays a significant task for predicting the disease. Heart disease is a single largest cause of death in developed countries and one of the main contributors to disease burden in developing countries. Data mining is a more convenient tool to assist physicians in detecting the diseases by obtaining knowledge and information regarding the disease from patient’s data. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. This paper aims at analyzing the various data mining techniques namely Decision Trees, Naive Bayes, Neural Networks, Random Forest Classification and Support Vector Machine by using the Cleveland dataset for Heart disease prediction. Few of the supervised learning algorithms are used for the prediction of heart disease. It provides a quick and easy understanding of various prediction models in data mining and helps to find the best model for further work.
Approximating the covariance matrix of GMMs with low-rank perturbations

Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d elements of the covariance (in d dimensions) is costly and could result in over-fitting; and the simple diagonal approximation can be over-restrictive. In this work, we present a new model, the Low-Rank Gaussian Mixture Model (LRGMM), for modeling data which can be extended to identifying partitions or overlapping clusters. The curse of dimensionality that arises in calculating the covariance matrices of the GMM is countered by using low-rank perturbed diagonal matrices. The efficiency is comparable to the diagonal approximation, yet one can capture correlations among the dimensions. Our experiments reveal the LRGMM to be an efficient and highly applicable tool for working with large high-dimensional datasets.
Water wave optimization: A new nature-inspired metaheuristic

Nature-inspired computing has been a hot topic in scientific and engineering fields in recent years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic method, named water wave optimization (WWO), for global optimization problems. We show how the beautiful phenomena of water waves, such as propagation, refraction, and breaking, can be used to derive effective mechanisms for searching in a high-dimensional solution space. In general, the algorithmic framework of WWO is simple, and easy to implement with a small-size population and only a few control parameters. We have tested WWO on a diverse set of benchmark problems, and applied WWO to a real-world high-speed train scheduling problem in China. The computational results demonstrate that WWO is very competitive with state-of-the-art evolutionary algorithms including invasive weed optimization (IWO), biogeography-based optimization (BBO), bat algorithm (BA), etc. The new metaheuristic is expected to have wide applications in real-world engineering optimization problems. & 2014 Elsevier Ltd.
Deep Learning the City : Quantifying Urban Perception At A Global Scale

Siamese-like networks, Streetscore-CNN (SS-CNN) and Ranking SS-CNN, to predict pairwise comparisons  User Interface for Crowdsourced Online Game Performance Analysis • SS-CNN: We calculate the % of pairwise comparisons in test set predicted correctly by (1) Softmax of output neurons in final layer (2) comparing TrueSkill scores  obtained from synthetic pairwise comparisons from the CNN (3) extracting features from penultimate layer of CNN and feeding pairwise feature representations to a RankSVM  • RSS-CNN: We compare the ranking function outputs for both images in a test pair to decide which image wins, and calculate the binary prediction accuracy.
Demythologizing the high costs of pharmaceutical research

It is widely claimed that research to discover and develop new pharmaceuticals entails high costs and high risks. High research and development (R&D) costs influence many decisions and policy discussions about how to reduce global health disparities, how much companies can afford to discount prices for lowerand middle-income countries, and how to design innovative incentives to advance research on diseases of the poor. High estimated costs also affect strategies for getting new medicines to the world’s poor, such as the advanced market commitment, which built high estimates into its inflated size and prices. This article takes apart the most detailed and authoritative study of R&D costs in order to show how high estimates have been constructed by industry-supported economists, and to show how much lower actual costs may be. Besides serving as an object lesson in the construction of ‘facts’, this analysis provides reason to believe that R&D costs need not be such an insuperable obstacle to the development of better medicines. The deeper problem is that current incentives reward companies to develop mainly new medicines of little advantage and compete for market share at high prices, rather than to develop clinically superior medicines with public funding so that prices could be much lower and risks to companies lower as well. BioSocieties advance online publication, 7 February 2011; doi:10.1057/biosoc.2010.40
PLS-regression : a basic tool of chemometrics

Ž . Ž PLS-regression PLSR is the PLS approach in its simplest, and in chemistry and technology, most used form two-block . predictive PLS . PLSR is a method for relating two data matrices, X andY, by a linear multivariate model, but goes beyond traditional regression in that it models also the structure of X andY. PLSR derives its usefulness from its ability to analyze data with many, noisy, collinear, and even incomplete variables in both X andY. PLSR has the desirable property that the precision of the model parameters improves with the increasing number of relevant variables and observations. This article reviews PLSR as it has developed to become a standard tool in chemometrics and used in chemistry and engineering. The underlying model and its assumptions are discussed, and commonly used diagnostics are reviewed together with the interpretation of resulting parameters. Ž . Two examples are used as illustrations: First, a Quantitative Structure–Activity Relationship QSAR rQuantitative StrucŽ . ture–Property Relationship QSPR data set of peptides is used to outline how to develop, interpret and refine a PLSR model. Second, a data set from the manufacturing of recycled paper is analyzed to illustrate time series modelling of process data by means of PLSR and time-lagged X-variables. q2001 Elsevier Science B.V. All rights reserved.
Protecting Intellectual Property of Deep Neural Networks with Watermarking

Deep learning technologies, which are the key components of state-of-the-art Artificial Intelligence (AI) services, have shown great success in providing human-level capabilities for a variety of tasks, such as visual analysis, speech recognition, and natural language processing and etc. Building a production-level deep learning model is a non-trivial task, which requires a large amount of training data, powerful computing resources, and human expertises. Therefore, illegitimate reproducing, distribution, and the derivation of proprietary deep learning models can lead to copyright infringement and economic harm to model creators. Therefore, it is essential to devise a technique to protect the intellectual property of deep learning models and enable external verification of the model ownership.
 In this paper, we generalize the "digital watermarking'' concept from multimedia ownership verification to deep neural network (DNNs) models. We investigate three DNN-applicable watermark generation algorithms, propose a watermark implanting approach to infuse watermark into deep learning models, and design a remote verification mechanism to determine the model ownership. By extending the intrinsic generalization and memorization capabilities of deep neural networks, we enable the models to learn specially crafted watermarks at training and activate with pre-specified predictions when observing the watermark patterns at inference. We evaluate our approach with two image recognition benchmark datasets. Our framework accurately (100%) and quickly verifies the ownership of all the remotely deployed deep learning models without affecting the model accuracy for normal input data. In addition, the embedded watermarks in DNN models are robust and resilient to different counter-watermark mechanisms, such as fine-tuning, parameter pruning, and model inversion attacks.
DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable computation, significantly less training time, and flexibility in modeling dependencies. We propose a novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise). A light-weight neural net, “Directional Self-Attention Network (DiSAN)”, is then proposed to learn sentence embedding, based solely on the proposed attention without any RNN/CNN structure. DiSAN is only composed of a directional self-attention with temporal order encoded, followed by a multi-dimensional attention that compresses the sequence into a vector representation. Despite its simple form, DiSAN outperforms complicated RNN models on both prediction quality and time efficiency. It achieves the best test accuracy among all sentence encoding methods and improves the most recent best result by 1.02% on the Stanford Natural Language Inference (SNLI) dataset, and shows stateof-the-art test accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK), Customer Review, MPQA, TREC question-type classification and Subjectivity (SUBJ) datasets.
Improved solutions for the traveling purchaser problem

ÐThe traveling purchaser problem (TPP) is an interesting generalization of the well-known traveling salesman problem (TSP), in which a list of commodity items have to be purchased at some markets selling various commodities with dierent prices, and the total travel and purchase costs must be minimized. Applications include the purchase of raw materials for the manufacturing factories in which the total cost has to be minimized, and the scheduling of jobs over some machines with dierent set-up and job processing costs in which the total costs for completing the jobs has to be minimized. The TPP has been shown to be computationally intractable. Therefore, many heuristic solution procedures, including the Search algorithm, the Generalized-Savings algorithm, the Tour-Reduction algorithm, and the Commodity-Adding algorithm have been proposed to solve the TPP approximately. In this paper, we consider some variations of these algorithms to improve the solutions. The proposed variations are compared with the existing solution procedures. The results indicate that the proposed variations signi®cantly improve the existing solutions.
A 143$\,\times\,$ 81 Mutual-Capacitance Touch-Sensing Analog Front-End With Parallel Drive and Differential Sensing Architecture

This paper presents an analog front-end (AFE) IC for mutual capacitance touch sensing with 224 sensor channels in 0.18 μm CMOS with 3.3 V drive voltage. A 32-in touch sensing system and a 70-in one having 37 dB SNR for 1 mm diameter stylus at 240 Hz reporting rate are realized with the AFE. The AFE adopts a parallel drive method to achieve the large format and the high SNR simultaneously. With the parallel drive method, the measured SNRs of the AFE stay almost constant at a higher level regardless of the number of sensor channels, which was impossible by conventional sequential drive methods. A novel differential sensing scheme which enhances the immunity against the noise from a display device is also realized in the AFE. While the coupled LCD is on and off, the differences between the measured SNRs are less than 2 dB.
Duct fan based wall climbing robot for concrete surface crack inspection

Degradation inspection of inaccessible parts of concrete structures is always a difficult task. An efficient and low cost solution is the use of wall climbing robots in such areas. In this paper, we propose a duct fan based wall climbing robot for the crack inspection on concrete wall surfaces. The thrust force provided by the duct fan holds the robot on to the wall. This wireless robot moves like a four wheeled vehicle on the wall surface and captures images of the wall under inspection using a camera mounted on it. Cracks are detected from these images using percolation method of image processing.
Semi-supervised Word Sense Disambiguation with Neural Models

Determining the intended sense of words in text – word sense disambiguation (WSD) – is a longstanding problem in natural language processing. Recently, researchers have shown promising results using word vectors extracted from a neural network language model as features in WSD algorithms. However, a simple average or concatenation of word vectors for each word in a text loses the sequential and syntactic information of the text. In this paper, we study WSD with a sequence learning neural net, LSTM, to better capture the sequential and syntactic patterns of the text. To alleviate the lack of training data in all-words WSD, we employ the same LSTM in a semi-supervised label propagation classifier. We demonstrate state-of-the-art results, especially on verbs.
Orchestrating Virtualized Network Functions

Middleboxes or network appliances like firewalls, proxies, and WAN optimizers have become an integral part of today's ISP and enterprise networks. Middlebox functionalities are usually deployed on expensive and proprietary hardware that require trained personnel for deployment and maintenance. Middleboxes contribute significantly to a network's capital and operation costs. In addition, organizations often require their traffic to pass through a specific sequence of middleboxes for compliance with security and performance policies. This makes the middlebox deployment and maintenance tasks even more complicated. Network function virtualization (NFV) is an emerging and promising technology that is envisioned to overcome these challenges. It proposes to move packet processing from dedicated hardware middleboxes to software running on commodity servers. In NFV terminology, software middleboxes are referred to as virtualized network functions (VNFs). It is a challenging problem to determine the required number and placement of VNFs that optimizes network operational costs and utilization, without violating service level agreements. We call this the VNF orchestration problem (VNF-OP) and provide an integer linear programming formulation with implementation in CPLEX. We also provide a dynamic programming-based heuristic to solve larger instances of VNF-OP. Trace driven simulations on realworld network topologies demonstrate that the heuristic can provide solutions that are within 1.3 times of the optimal solution. Our experiments suggest that a VNF-based approach can provide more than 4× reduction in the operational cost of a network.
RNA-Guided RNA Cleavage by a CRISPR RNA-Cas Protein Complex

Compelling evidence indicates that the CRISPR-Cas system protects prokaryotes from viruses and other potential genome invaders. This adaptive prokaryotic immune system arises from the clustered regularly interspaced short palindromic repeats (CRISPRs) found in prokaryotic genomes, which harbor short invader-derived sequences, and the CRISPR-associated (Cas) protein-coding genes. Here, we have identified a CRISPR-Cas effector complex that is comprised of small invader-targeting RNAs from the CRISPR loci (termed prokaryotic silencing (psi)RNAs) and the RAMP module (or Cmr) Cas proteins. The psiRNA-Cmr protein complexes cleave complementary target RNAs at a fixed distance from the 3' end of the integral psiRNAs. In Pyrococcus furiosus, psiRNAs occur in two size forms that share a common 5' sequence tag but have distinct 3' ends that direct cleavage of a given target RNA at two distinct sites. Our results indicate that prokaryotes possess a unique RNA silencing system that functions by homology-dependent cleavage of invader RNAs.
On predicting learning styles in conversational intelligent tutoring systems using fuzzy classification trees

Oscar is a conversational intelligent tutoring system (CITS) which dynamically predicts and adapts to a student's learning style throughout the tutoring conversation. Oscar aims to mimic a human tutor to improve the effectiveness of the learning experience by leading a natural language tutorial and adapting material to suit an individual's learning style. Prediction of learning style is undertaken through capturing independent variables during the conversation. The variable with the highest value determines the individuals learning style. This paper proposes a new method which uses a fuzzy classification tree to build a fuzzy predictive model using these variables which are captured through natural language dialogue Experiments have been undertaken on two of the learning style dimensions: perception (sensory-intuitive) and understanding (sequential-global). Early results show the model has substantially increased the predictive accuracy of the Oscar CITS and discovered some interesting relationships amongst these variables.
Developing a standard for TV white space coexistence: technical challenges and solution approaches

TV white space refers to TV channels that are not used by any licensed services at a particular location and at a particular time. To exploit this unused TVWS spectrum for improved spectrum efficiency, regulatory agencies have begun developing regulations to permit its use this TVWS by unlicensed wireless devices as long as they do not interfere with any licensed services. In the future many heterogeneous, and independently operated, wireless networks may utilize the TVWS. Coexistence between these networks is essential in order to provide a high level of QoS to end users. Consequently, the IEEE 802 LAN/MAN standards committee has approved the P802.19.1 standardization project to specify radio-technology-independent methods for coexistence among dissimilar or independently operated wireless devices and networks. In this article we provide a detailed overview of the regulatory status of TVWS in the United States and Europe, analyze the coexistence problem in TVWS, and summarize existing coexisting mechanisms to improve coexistence in TVWS. The main focus of the article is the IEEE P802.19.1 standardization project, including its requirements and system design, and the major technical challenges ahead.
Traveling time prediction in scheduled transportation with journey segments

Urban mobility impacts urban life to a great extent. To enhance urban mobility, much research was invested in traveling time prediction: given an origin and destination, provide a passenger with an accurate estimation of how long a journey lasts. In this work, we investigate a novel combination of methods from Queueing Theory and Machine Learning in the prediction process. We propose a prediction engine that, given a scheduled bus journey (route) and a ‘source/destination’ pair, provides an estimate for the traveling time, while considering both historical data and real-time streams of information that are transmitted by buses. We propose a model that uses natural segmentation of the data according to bus stops and a set of predictors, some use learning while others are learning-free, to compute traveling time. Our empirical evaluation, using bus data that comes from the bus network in the city of Dublin, demonstrates that the snapshot principle, taken from Queueing Theory works well yet suffers from outliers. To overcome the outliers problem, we use machine learning techniques as a regulator that assists in identifying outliers and propose prediction based on historical data.
CatchUp! Capturing and replaying refactorings to support API evolution

Library developers who have to evolve a library to accommodate changing requirements often face a dilemma: Either they implement a clean, efficient solution but risk breaking client code, or they maintain compatibility with client code, but pay with increased design complexity and thus higher maintenance costs over time.We address this dilemma by presenting a lightweight approach for evolving application programming interfaces (APIs), which does not depend on version control or configuration management systems. Instead, we capture API refactoring actions as a developer evolves an API. Users of the API can then replay the refactorings to bring their client software components up to date.We present catchup!, an implementation of our approach that captures and replays refactoring actions within an integrated development environment semi-automatically. Our experiments suggest that our approach could be valuable in practice.
A Fluxgate Current Sensor With a U-Shaped Magnetic Gathering Shell

This paper presents a new current sensor based on fluxgate principle. The sensor consists of a U-shaped magnetic gathering shell. In the designed sensor, the exciting winding and the secondary winding are arranged orthogonally, so that the magnetic fields produced by the two windings are mutually orthogonal and decoupled. Introducing a magnetic gathering shell into the sensor is to concentrate the detected magnetic field and to reduce the interference of an external stray field. Based on the theoretical analysis and the simulation results, a prototype was designed. Test results show that the proposed sensor can measure currents up to 25 A, and has an accuracy of 0.6% and a remarkable resolution.
Echo State Networks-Based Reservoir Computing for MNIST Handwritten Digits Recognition

Reservoir Computing is an attractive paradigm of recurrent neural network architecture, due to the ease of training and existing neuromorphic implementations. Successively applied on speech recognition and time series forecasting, few works have so far studied the behavior of such networks on computer vision tasks. Therefore we decided to investigate the ability of Echo State Networks to classify the digits of the MNIST database. We show that even if ESNs are not able to outperform state-of-the-art convolutional networks, they allow low error thanks to a suitable preprocessing of images. The best performance is obtained with a large reservoir of 4,000~neurons, but committees of smaller reservoirs are also appealing and might be further investigated.