nils-herrmann/clean_scirepeval huggingface dataset:
Fine Classification & Recognition of Hand Written Devnagari Characters with Regular Expressions & Minimum Edit Distance Method

Regular expressions are extremely useful, because they allow us to work with text in terms of patterns. They are considered the most sophisticated means of performing operations such as string searching, manipulation, validation, and formatting in all applications that deal with text data. Character recognition problem scenarios in sequence analysis that are ideally suited for the application of regular expression algorithms. This paper describes a use of regular expressions in this problem domain, and demonstrates how the effective use of regular expressions that can serve to facilitate more efficient and more effective character recognition.
Social Fusion: Integrating Twitter and Instagram for Event Monitoring

This paper describes the implementation of a service to identify and geo-locate real world events that may be present as social activity signals in two different social networks. Specifically, we focus on content shared by users on Twitter and Instagram in order to design a system capable of fusing data across multiple networks. Past work has demonstrated that it is indeed possible to detect physical events using various social network platforms. However, many of these signals need corroboration in order to handle events that lack proper support within a single network. We leverage this insight to design an unsupervised approach that can correlate event signals across multiple social networks. Our algorithm can detect events and identify the location of the event occurrence. We evaluate our algorithm using both simulations and real world datasets collected using Twitter and Instagram. The results indicate that our algorithm significantly improves false positive elimination and attains high precision compared to baseline methods on real world datasets.
Reconocimiento de patrones numéricos para vuelo controlado de un AR Drone utilizando redes neuronales artificiales

In this paper the letter segmentation of photographs was used, taken from a Parrot AR Drone’s camera with the aim of establishing a stimulusresponse, where the original picture formed by Red, Green and Blue (RGB) colors was segmented by color (choosing the red channel). Once the character is recognized, the Drone executes the corresponding action. Noise-free number patterns were initially used and then some pixels were added in the image in order to make a set of patterns more robust, which provided the training set for neural network and thus are able to interpolate new patterns. Edge techniques detection were used for image segmentation including Sobel filter and filters for noise removal based on the median filtering, that is a low pass filter. All this took place in a closed environment, expecting to extend this to different environments. 61 Research in Computing Science 107 (2015) pp. 61–71; rec. 2015-08-04; acc. 2015-10-19
Getting Humanoids to Move and Imitate

Mataric builds on two inspirations from biology in designing a humanoid robot motor control system: spinal fields and mirror neurons. Spinal fields code primitive motor behaviors that serve as building blocks for more complex behaviors in the organism. As such they somewhat resemble Mataric's "basis behaviors" which act as similar building blocks in complex robot behavior patterns. Mirror neurons are brain cell constructs that correspond to both perception and activation of motor activity, playing a central role in imitation.
Path-Loss Modeling for Wireless Sensor Networks: A review of models and comparative evaluations.

Propagation models are used to abstract the actual propagation characteristics of electromagnetic waves utilized for conveying information in a compact form (i.e., a model with a small number of parameters). The correct modeling of propagation and path loss is of paramount importance in wireless sensor network (WSN) system design and analysis . Most of the important performance metrics commonly employed for WSNs, such as energy dissipation, route optimization, reliability, and connectivity, are affected by the utilized propagation model. However, in many studies on WSNs, overly simplistic and unrealistic propagation models are used. One of the reasons for the utilization of such impractical propagation models is the lack of awareness of experimentally available WSN-specific propagation and path-loss models. In this article, necessary succint background information is given on general wireless propagation modeling, and salient WSN-specific constraints on path-loss modeling are summarized. Building upon the provided background, an overview of the experimentally verified propagation models for WSNs is presented, and quantitative comparisons of propagation models employed in WSN research under various scenarios and frequency bands are provided.
Not-So-CLEVR: learning same-different relations strains feedforward neural networks.

The advent of deep learning has recently led to great successes in various engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural network, now approach human accuracy on visual recognition tasks like image classification and face recognition. However, here we will show that feedforward neural networks struggle to learn abstract visual relations that are effortlessly recognized by non-human primates, birds, rodents and even insects. We systematically study the ability of feedforward neural networks to learn to recognize a variety of visual relations and demonstrate that same-different visual relations pose a particular strain on these networks. Networks fail to learn same-different visual relations when stimulus variability makes rote memorization difficult. Further, we show that learning same-different problems becomes trivial for a feedforward network that is fed with perceptually grouped stimuli. This demonstration and the comparative success of biological vision in learning visual relations suggests that feedback mechanisms such as attention, working memory and perceptual grouping may be the key components underlying human-level abstract visual reasoning.
Artificial intelligence: Learning to see and act

An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529 I mprovements in our ability to process large amounts of data have led to progress in many areas of science, not least artificial intelligence (AI). With advances in machine learning has come the development of machines that can learn intelligent behaviour directly from data, rather than being explicitly programmed to exhibit such behaviour. For instance, the advent of 'big data' has resulted in systems that can recognize objects or sounds with considerable precision. On page 529 of this issue, Mnih et al. 1 describe an agent that uses large data sets to teach itself how to play 49 classic Atari 2600 computer games by looking at the pixels and learning actions that increase the game score. It beat a professional games player in many instances — a remarkable example of the progress being made in AI. In machine learning, systems are trained to infer patterns from observational data. A particularly simple type of pattern, a mapping between input and output, can be learnt through a process called supervised learning. A supervised-learning system is given training data consisting of example inputs and the corresponding outputs, and comes up with a model to explain those data (a process called function approximation). It does this by choosing from a class of model specified by the system's designer. Designing this class is an art: its size and complexity should reflect the amount of training data available, and its content should reflect 'prior knowledge' that the designer of the system considers useful for the problem at hand. If all this is done well, the inferred model will then apply not only for the training set, but also for other data that adhere to the same underlying pattern. The rapid growth of data sets means that machine learning can now use complex model classes and tackle highly non-trivial inference problems. Such problems are usually characterized by several factors: the data are multi dimensional; the underlying pattern is complex (for instance, it might be nonlinear or changeable); and the designer has only weak prior knowledge about the problem — in particular , a mechanistic understanding is lacking. The human brain repeatedly solves non-trivial inference problems as we go about our daily lives, interpreting high-dimensional sensory …
THE BØIFOT ORTHOMODE JUNCTION

Wideband dual-polarization performance is desired for low-noise receivers and radiom eters at cent imete r and m illimeter wavelengths. The use of a waveguide orthomode transducer (OMT) can increase spectral coverage and sensitivity while reducing exit aperture size, optical spill, and instrumental polarization offsets. For these reasons, an orthomode junction is favored over a traditional quasi-op tical wire grid for focal plane imaging arrays from a systems perspective. The fabrication and pe rformance o f wideban d symm etric Bøifot OM T junctions at K -, Ka-, Q-, and W-bands are described. Typical WR10.0 units have an insertion loss of <0.2 dB , return loss ~20dB, and >40dB isolation over a >75-to-110 GHz band. The OMT operates with reduced ohmic losses at cryogenic temperatures.
An Infinite Hidden Markov Model for Short-term Interest Rates ∗

The time-series dynamics of short-term interest rates are important as they are a key input into pricing models of the term structure of interest rates. In this paper we extend popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian nonparametric model that allows for changes in the unknown conditional distribution over time. Applied to weekly U.S. data we find significant parameter change over time and strong evidence of non-Gaussian conditional distributions. Our new model with an hierarchical prior provides significant improvements in density forecasts as well as point forecasts. We find evidence of recurring regimes as well as structural breaks in the empirical application. key words: hierarchical Dirichlet process prior, beam sampling, Markov switching,  ∗We are grateful for helpful comments from Yong Song and seminar participants at the University of Toronto. Maheu thanks the SSHRC for financial support. †DeGroote School of Business, McMaster University, 1280 Main Street W., Hamilton, ON, Canada, L8S4M4 and University of Toronto, Canada and RCEA, Italy, maheujm@mcmaster.ca ‡Department of Economics, University of Toronto, bill.yang@mail.utoronto.ca
A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings

How should we measure metacognitive ("type 2") sensitivity, i.e. the efficacy with which observers' confidence ratings discriminate between their own correct and incorrect stimulus classifications? We argue that currently available methods are inadequate because they are influenced by factors such as response bias and type 1 sensitivity (i.e. ability to distinguish stimuli). Extending the signal detection theory (SDT) approach of Galvin, Podd, Drga, and Whitmore (2003), we propose a method of measuring type 2 sensitivity that is free from these confounds. We call our measure meta-d', which reflects how much information, in signal-to-noise units, is available for metacognition. Applying this novel method in a 2-interval forced choice visual task, we found that subjects' metacognitive sensitivity was close to, but significantly below, optimality. We discuss the theoretical implications of these findings, as well as related computational issues of the method. We also provide free Matlab code for implementing the analysis.
Long short-term memory model for traffic congestion prediction with online open data

Traffic congestion in metropolitan areas has become more and more serious. Over the past decades, many academic and industrial efforts have been made to alleviate this problem, among which providing accurate, timely and predictive traffic conditions is a promising approach. Nowadays, online open data have rich traffic related information. Typical such resources include official websites of traffic management and operations, web-based map services (like Google map), weather forecasting websites, and local events (sport games, music concerts, etc.) websites. In this paper, online open data are discussed to provide traffic related information. Traffic conditions collected from web based map services are used to demonstrate the feasibility. The stacked long short-term memory model, a kind of deep architecture, is used to learn and predict the patterns of traffic conditions. Experimental results show that the proposed model for traffic condition prediction has superior performance over multilayer perceptron model, decision tree model and support vector machine model.
Remote Sensing and GIS Applications in Environmental Sciences – A Review

Remote sensing and Geographic Information System play a pivotal role in environmental mapping, mineral exploration, agriculture, forestry, geology, water, ocean, infrastructure planning and management, disaster mitigation and management etc. Remote Sensing and GIS has grown as a major tool for collecting information on almost every aspect on the earth for last few decades. In the recent years, very high spatial and spectral resolution satellite data are available and the applications have multiplied with respect to various purpose. Remote sensing and GIS has contributed significantly towards developmental activities for the four decades in India. In the present paper, we have discussed the remote sensing and GIS applications of few environmental issues like Mining environment, Urban environment, Coastal and marine environment and Wasteland environment.
A job recommendation method optimized by position descriptions and resume information

With the development of Internet technology, online job-hunting plays an increasingly important role in job-searching. It is difficult for job hunters to solely rely on keywords retrieving to find positions which meet their needs. To solve this issue, we adopted item-based collaborative filtering algorithm for job recommendations. In this paper, we optimized the algorithm by combining position descriptions and resume information. Specifically, job preference prediction formula is optimized by historical delivery weight calculated by position descriptions and similar user weight calculated by resume information. The experiments tested on real data set have shown that our methods have a significant improvement on job recommendation results.
Classification : Naive Bayes , Logistic Regression , Sentiment

Numquam ponenda est pluralitas sine necessitate 'Plurality should never be proposed unless needed' William of Occam Classification lies at the heart of both human and machine intelligence. Deciding what letter, word, or image has been presented to our senses, recognizing faces or voices, sorting mail, assigning grades to homeworks, are all examples of assigning a class or category to an input. The potential challenges of this task are highlighted by the fabulist Jorge Luis Borges (1964), who imagined classifying animals into: (a) those that belong to the Emperor, (b) embalmed ones, (c) those that are trained, (d) suckling pigs, (e) mermaids, (f) fabulous ones, (g) stray dogs, (h) those that are included in this classification, (i) those that tremble as if they were mad, (j) innumerable ones, (k) those drawn with a very fine camel's hair brush, (l) others, (m) those that have just broken a flower vase, (n) those that resemble flies from a distance. While many language processing tasks can be productively viewed as tasks of classification, the classes are luckily far more practical than those of Borges. In this chapter we present two general algorithms for classification, demonstrated on one important set of classification problems: text categorization, the task of classifying text categorization an entire text by assigning it a label drawn from some set of labels. We focus on one common text categorization task, sentiment analysis, the ex-sentiment analysis traction of sentiment, the positive or negative orientation that a writer expresses toward some object. A review of a movie, book, or product on the web expresses the author's sentiment toward the product, while an editorial or political text expresses sentiment toward a candidate or political action. Automatically extracting consumer sentiment is important for marketing of any sort of product, while measuring public sentiment is important for politics and also for market prediction. The simplest version of sentiment analysis is a binary classification task, and the words of the review provide excellent cues. Consider, for example, the following phrases extracted from positive and negative reviews of movies and restaurants,. Words like great, richly, awesome, and pathetic, and awful and ridiculously are very informative cues: + ...zany characters and richly applied satire, and some great plot twists − It was pathetic. The worst part about it was the boxing scenes... + ...awesome caramel sauce and sweet toasty almonds. I love this place! − ...awful pizza and ridiculously overpriced... …
Design of 4–5GHz transition from double-ridge waveguide to coaxial line

This paper presents a transition from double-ridge waveguide to coaxial line, working at 4-5 GHz. The size of the double-ridge waveguide is WRD350. The design of the transition is based on the theory of mode conversion and step-impedance matching. The transition between the high and low impedance is achieved by employing the Chebyshev type of quarter-wavelength step-impedance matching. Using lead-inserted transition for leading the coaxial probe insert the waveguide, as we know, the dominant mode of the ridge waveguide is different from the coaxial line. If the inner conductor of the connector contacts the down-ridge directly, the VSWR of two ports is very bad. However, when the probe has a cylinder bottom and increasing impedance matching transitions, a good performance can be got. Using simulation software HFSS, the simulation results show that the VSWR in the frequency range 4-5 GHz is less than 1.1. The test results show that the adapter has a good performance.
UWB Bowtie Slot Antenna for Breast Cancer Detection

UWB is a very attractive technology for many applications. It provides many advantages such as fine resolution and high power efficiency. Our interest in the current study is the use of UWB radar technique in microwave medical imaging systems, especially for early breast cancer detection. The Federal Communications Commission FCC allowed frequency bandwidth of 3.1 to 10.6 GHz for this purpose. In this paper we suggest an UWB Bowtie slot antenna with enhanced bandwidth. Effects of varying the geometry of the antenna on its performance and bandwidth are studied. The proposed antenna is simulated in CST Microwave Studio. Details of antenna design and simulation results such as return loss and radiation patterns are discussed in this paper. The final antenna structure exhibits good UWB characteristics and has surpassed the bandwidth requirements. Ultra Wide Band (UWB), microwave imaging system, Bowtie antenna, return loss, impedance bandwidth enhancement.
Novel Design of a Soft Lightweight Pneumatic Continuum Robot Arm with Decoupled Variable Stiffness and Positioning

Soft robot arms possess unique capabilities when it comes to adaptability, flexibility, and dexterity. In addition, soft systems that are pneumatically actuated can claim high power-to-weight ratio. One of the main drawbacks of pneumatically actuated soft arms is that their stiffness cannot be varied independently from their end-effector position in space. The novel robot arm physical design presented in this article successfully decouples its end-effector positioning from its stiffness. An experimental characterization of this ability is coupled with a mathematical analysis. The arm combines the light weight, high payload to weight ratio and robustness of pneumatic actuation with the adaptability and versatility of variable stiffness. Light weight is a vital component of the inherent safety approach to physical human-robot interaction. To characterize the arm, a neural network analysis of the curvature of the arm for different input pressures is performed. The curvature-pressure relationship is also characterized experimentally.
Anti-debugging scheme for protecting mobile apps on android platform

The Android application package file, APK file, can be easily decompiled using Android reverse engineering tools. Thus, general apps can be easily transformed into malicious application through reverse engineering and analysis. These repacked apps could be uploaded in general android app market called Google Play Store and redistributed. To prevent theses malicious behaviors such as malicious code injection or code falsifications, many techniques and tools were developed. However, these techniques also can be analyzed using debuggers. Also, analyzed apps can be tampered easily. For example, when applying anti-analysis techniques to android apps using Dexprotector which is commercial tool for protecting android app, it can be seen that these techniques can also be analyzed using debugger. In this paper, to protect the android app from the attack using debugger, we propose anti-debugging techniques for native code debugging and managed code debugging of android apps.
AdaOUBoost: adaptive over-sampling and under-sampling to boost the concept learning in large scale imbalanced data sets

Automatic concept learning from large scale imbalanced data sets is a key issue in video semantic analysis and retrieval, which means the number of negative examples is far more than that of positive examples for each concept in the training data. The existing methods adopt generally under-sampling for the majority negative examples or over-sampling for the minority positive examples to balance the class distribution on training data. The main drawbacks of these methods are: (1) As a key factor that affects greatly the performance, in most existing methods, the degree of re-sampling needs to be pre-fixed, which is not generally the optimal choice; (2) Many useful negative samples may be discarded in under-sampling. In addition, some works only focus on the improvement of the computational speed, rather than the accuracy. To address the above issues, we propose a new approach and algorithm named AdaOUBoost (Adaptive Over-sampling and Under-sampling Boost). The novelty of AdaOUBoost mainly lies in: adaptively over-sample the minority positive examples and under-sample the majority negative examples to form different sub-classifiers. And combine these sub-classifiers according to their accuracy to create a strong classifier, which aims to use fully the whole training data and improve the performance of the class-imbalance learning classifier. In AdaOUBoost, first, our clustering-based under-sampling method is employed to divide the majority negative examples into some disjoint subsets. Then, for each subset of negative examples, we utilize the borderline-SMOTE (synthetic minority over-sampling technique) algorithm to over-sample the positive examples with different size, train each sub-classifier using each of them, and get the classifier by fusing these sub-classifiers with different weights. Finally, we combine these classifiers in each subset of negative examples to create a strong classifier. We compare the performance between AdaOUBoost and the state-of-the-art methods on TRECVID 2008 benchmark with all 20 concepts, and the results show the AdaOUBoost can achieve the superior performance in large scale imbalanced data sets.