nils-herrmann/clean_scirepeval huggingface dataset:
Mid-air tactile stimulation using laser-induced thermoelastic effects: The first study for indirect radiation

This paper reports our recent finding that a laser that is radiated on a thin light-absorbing elastic medium attached on the skin can elicit a tactile sensation of mechanical tap. Laser radiation to the elastic medium creates inner elastic waves on the basis of thermoelastic effects, which subsequently move the medium and stimulate the skin. We characterize the associated stimulus by measuring its physical properties. In addition, the perceptual identity of the stimulus is confirmed by comparing it to mechanical and electrical stimuli by means of perceptual spaces. All evidence claims that indirect laser radiation conveys a sensation of short mechanical tap with little individual difference. To the best of our knowledge, this is the first study that discovers the possibility of using indirect laser radiation for mid-air tactile rendering.
Cadaver decomposition in terrestrial ecosystems

A dead mammal (i.e. cadaver) is a high quality resource (narrow carbon:nitrogen ratio, high water content) that releases an intense, localised pulse of carbon and nutrients into the soil upon decomposition. Despite the fact that as much as 5,000 kg of cadaver can be introduced to a square kilometre of terrestrial ecosystem each year, cadaver decomposition remains a neglected microsere. Here we review the processes associated with the introduction of cadaver-derived carbon and nutrients into soil from forensic and ecological settings to show that cadaver decomposition can have a greater, albeit localised, effect on belowground ecology than plant and faecal resources. Cadaveric materials are rapidly introduced to belowground floral and faunal communities, which results in the formation of a highly concentrated island of fertility, or cadaver decomposition island (CDI). CDIs are associated with increased soil microbial biomass, microbial activity (C mineralisation) and nematode abundance. Each CDI is an ephemeral natural disturbance that, in addition to releasing energy and nutrients to the wider ecosystem, acts as a hub by receiving these materials in the form of dead insects, exuvia and puparia, faecal matter (from scavengers, grazers and predators) and feathers (from avian scavengers and predators). As such, CDIs contribute to landscape heterogeneity. Furthermore, CDIs are a specialised habitat for a number of flies, beetles and pioneer vegetation, which enhances biodiversity in terrestrial ecosystems.
INNOVATION PRACTICE AND ITS PERFORMANCE IMPLICATIONS IN SMALL TO MEDIUM ENTERPRISES ( SMEs ) IN THE MANUFACTURING SECTOR : A RESOURCE-BASED VIEW

Small to Medium Enterprises (SMEs) in the manufacturing sector make a significant contribution to economic growth, yet most of the research into innovation management in the manufacturing sector has focused on large organizations. This article, however, identifies innovation drivers and their performance implications in manufacturing SMEs. Its study gathered survey data from a sample of 600 Australian SMEs, and found that SMEs are similar to large firms with respect to the way that innovation strategy and formal structure are the key drivers of their performance, but do not appear to utilize innovation culture in a strategic and structured manner. This study therefore concludes that SMEs’ performance is likely to improve as they increase the degree to which they mirror large manufacturing firms with respect to formal strategy and structure, and to which they recognize that innovation culture and strategy are closely aligned throughout the innovation process.
W-band, 5W solid-state power amplifier/combiner

This paper reports a W-band solid-state power amplifier with an output power of 5.2W at 95 GHz and greater than 3 watts over the 94 to 98.5 GHz band. These SOA results were achieved by combining 12 GaN MMICs in a low-loss radial-line combiner network. The 12-way combiner demonstrates an overall combining efficiency of 87.5%, and excluding combiner conductor losses (0.3 dB), exhibits a combining efficiency of 93.7% at 95GHz. The size of the 12-way amplifier/combiner is only 2.39″ dia. × 1.5″ length. This work represents the first application of the radial-line combiner configuration to applications at W-band frequencies, and establishes new levels of performance for solid-state power amplifiers operating at these frequencies.
Infrared face recognition: A literature review

Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.
Saliency Maps and Attention Selection in Scale and Spatial Coordinates: An Information Theoretic Approach

Information measures with respect to spatial locations and scales of objects in an image are important to image processing and interpretation. It allows us to focus attention on relevant data, saving eeort and reducing false positives. In particular, the information content of a man-made scene is typically conned to a small set of scales. We devise a scale space based measure of image information. Kullback contrasts between successive resolution lengths gives the diierential information gain. Experiments show that this measure gives a clear indication of characteristic lengths in a variety of real world images and is superior to power spectrum based measurements. Decomposing the expected information gain into spatial coordinates gives us a saliency map for use by an attention selector. Last we combine the scale and spatial decompositions into a single information measure, giving both the spatial extent and scale range of interest. The information measure has an eecient implementation , and thus can be used routinely in early vision processing.
The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems

We present an analysis of how the generalization performance (expected test set error) relates to the expected training set error for nonlinear learning systems, such as multilayer perceptrons and radial basis functions. The principal result is the following relationship (computed to second order) between the expected test set and tlaining set errors: (1) Here, n is the size of the training sample e, u;f f is the effective noise variance in the response variable( s), ,x is a regularization or weight decay parameter, and Peff(,x) is the effective number of parameters in the nonlinear model. The expectations ( ) of training set and test set errors are taken over possible training sets e and training and test sets e' respectively. The effective number of parameters Peff(,x) usually differs from the true number of model parameters P for nonlinear or regularized models; this theoretical conclusion is supported by Monte Carlo experiments. In addition to the surprising result that Peff(,x) ;/; p, we propose an estimate of (1) called the generalized prediction error (GPE) which generalizes well established estimates of prediction risk such as Akaike's F P E and AI C, Mallows Cp, and Barron's PSE to the nonlinear setting.! lCPE and Peff(>") were previously introduced in Moody (1991).
DFSTT-MRAM: Dual Functional STT-MRAM Cell Structure for Reliability Enhancement and 3-D MLC Functionality

Spin transfer torque-based magnetic random access memory (STT-MRAM) is considered as one of the most promising candidates for the next generation of nonvolatile memories; however, its storage density and reliability are currently uncompetitive compared with other nonvolatile memories (e.g., NAND flash). In this paper, a dual-functional memory cell structure, named DFSTT-MRAM, is proposed by stacking multiple magnetic tunnel junctions (MTJs) on top of the CMOS access transistor. In such a structure, the cell can be dynamically configured between two possible functional modes, i.e., high-reliability mode (HR-mode) and multilevel cell mode (MLC-mode), based on the data requirements of the addressed applications. The DFSTT-MRAM cell was electrically modeled based on the perpendicular magnetic anisotropy CoFeB/MgO/CoFeB MTJ integrating the STT stochastic switching behaviors. Transient and Monte Carlo simulations were then performed to evaluate its MLC functionality and reliability performance. Our evaluation results show that the DFSTT-MRAM cell can indeed realize MLC capability providing proper write/read control at the MLC-mode and enhance the intrinsic cell reliability by several orders of magnitude when operating at the HR-mode. This cell structure provides a flexible memory cell design for future advanced applications, such as high-density nonvolatile memory and neuromorphic circuits.
Using Eye Tracking to Trace a Cognitive Process : Gaze Behaviour During Decision Making in a Natural Environment

The advent of more sophisticated mobile eye-tracking equipment has lead in recent years to a growth of research into the deployment of visual attention and eye movements in natural environments. Car driving (Shinoda, Hayhoe & Shrivastava, 2001), playing cricket (Land & McLeod, 2000), walking (Jovanevic-Misic & Hayhoe, 2009), and the everyday task of making a cup of tea (Land, Mennie & Rusted, 1999), all require the tight coordination of attentional, cognitive, and motor abilities. Despite the research amassed from laboratory settings (e.g. Findlay, Brown & Gilchrist, 2001; McSorley & Findlay, 2003; Mulckhuyse, Van Zoest & Theeuwes, 2008; Born, Kerzel & Theeuwes, 2011), it is evident that the mechanisms of attentional deployment differ considerably in the lab compared to the real-world (e.g. Hayhoe & Ballard, 2005; Smilek, Eastwood, Reynolds & Kingstone, 2007; Kingstone, Smilek, Eastwood, 2008; Foulsham, Walker & Kingstone, 2011). Although we know from the seminal studies of Yarbus (1967), and more recent laboratory based work (e.g. Castelhano, Mack, & Henderson, 2009), that eye movements are highly task dependent and are linked to our cognitive goals, research is yet to uncover the eye movement repertoires associated with higher level tasks we encounter on a day-to-day basis. One such avenue is decision making. Almost all decisions we make involve acquisition of visual information but decision-making is a special kind of task where the information is valued very differently in each case. For each case, the kind of information needed to complete the task might differ largely due to different preferences or goals. One piece of information might be crucial for one person but not at all interesting to another. This calls for a new set of eye tracking measures that can be used to compare one cognitive process to another without relying on exactly what is being visually attended to. A prime example of the many choices we make in everyday life is the supermarket, and this setting provides the ideal scenario to investigate the eye-movement repertoires of decision-making in the real world. This is the focus of this paper. Using Eye Tracking to Trace a Cognitive Process: Gaze Behaviour During Decision Making in a Natural Environment
A Data-Driven Framework for Identifying High School Students at Risk of Not Graduating on Time [ Extended

Some students, for a variety of factors, struggle to complete high school on time. To address this problem, school districts across the U.S. use intervention programs to help struggling students get back on track academically. Yet in order to best apply those programs, schools need to identify off-track students as early as possible and enroll them in the most appropriate intervention. Unfortunately, identifying and prioritizing students in need of intervention remains a challenging task. This paper describes work that builds on current systems by using advanced data science methods to produce an extensible and scalable predictive framework for providing partner U.S. public school districts with individual early warning indicator systems. Our framework employs machine learning techniques to identify struggling students and describe features that are useful for this task, evaluating these techniques using metrics important to school administrators. By doing so, our framework, developed with the common need of several school districts in mind, provides a common set of tools for identifying struggling students and the factors associated with their struggles. Further, by integrating data from disparate districts into a common system, our framework enables cross-district analyses to investigate common early warning indicators not just within a single school or district, but across the U.S. and beyond.
Artificial Neural Networks in Medical Diagnosis

Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; data is the disease symptoms. The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: infected and non-infected. Classification is an important tool in medical diagnosis decision support. Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases. The results of applying the artificial neural networks methodology to acute nephritis diagnosis based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person. In this study, the data were obtained from UCI machine learning repository in order to diagnosed diseases. The data is separated into inputs and targets. The targets for the neural network will be identified with 1's as infected and will be identified with 0's as non-infected. In the diagnosis of acute nephritis disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 99 percent while in the diagnosis of heart disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 95 percent.
Object Recognition in Aerial Images Using Convolutional Neural Networks

There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.
Nightmares and bad dreams: their prevalence and relationship to well-being.

This study, for the first time, distinguishes between nightmares and bad dreams, measures the frequency of each using dream logs, and separately assesses the relation between nightmares, bad dreams, and well-being. Eighty-nine participants completed 7 measures of well-being and recorded their dreams for 4 consecutive weeks. The dream logs yielded estimated mean annual nightmare and bad-dream frequencies that were significantly (ps < .01) greater than the mean 12-month and 1-month retrospective estimates. Nightmare frequency had more significant correlations than bad-dream frequency with well-being, suggesting that nightmares are a more severe expression of the same basic phenomenon. The findings confirm and extend evidence that nightmares are more prevalent than was previously believed and underscore the need to differentiate nightmares from bad dreams.
Default-mode brain dysfunction in mental disorders: A systematic review

In this review we are concerned specifically with the putative role of the default-mode network (DMN) in the pathophysiology of mental disorders. First, we define the DMN concept with regard to its neuro-anatomy, its functional organisation through low frequency neuronal oscillations, its relation to other recently discovered low frequency resting state networks, and the cognitive functions it is thought to serve. Second, we introduce methodological and analytical issues and challenges. Third, we describe putative mechanisms proposed to link DMN abnormalities and mental disorders. These include interference by network activity during task performance, altered patterns of antagonism between task specific and non-specific elements, altered connectively and integrity of the DMN, and altered psychological functions served by the network DMN. Fourth, we review the empirical literature systematically. We relate DMN dysfunction to dementia, schizophrenia, epilepsy, anxiety and depression, autism and attention deficit/hyperactivity disorder drawing out common and unique elements of the disorders. Finally, we provide an integrative overview and highlight important challenges and tasks for future research.
Using Deep Learning to Predict Customer Churn in a Mobile Telecommunication Network

Customer churn is defined as the loss of customers because they move out to competitors. It is an expensive problem in many industries since acquiring new customers costs five to six times more than retaining existing ones [1-4]. In particular, in telecommunication companies, churn costs roughly $10 billion per year . A wide range of supervised machine learning classifiers have been developed to predict customer churn [6-9]. In general, these models base their effectiveness in the feature engineering process which is usually time consuming and thus tailored to specific datasets. Since deep learning automatically comes up with good features and representation for the input data; we investigated the application of autoencoders, deep belief networks and multi-layer feedforward networks with different configurations. We report results for predicting customer churn using a four-layer feedforward architecture. To scale the model to full-sized high dimensional customer data, like the social graph of a customer, we introduced a data representation architecture that allows efficient learning across multiple layers of detailed user behavior representations. In this article, we use billions of call records from an enterprise business intelligence system and present our current work towards using deep learning for predicting churn in a prepaid mobile telecommunication network. To the best of our knowledge this is the first work reporting the use of deep learning for predicting customer churn. On average, our model achieves 77.9% AUC on validation data, significantly better than our prior best performance of 73.2% obtained with random forests and an extensive custom feature engineering applied to the same datasets.
Customer Knowledge Management Framework in E-commerce

This paper aims to present a framework for describing Customer Knowledge Management in online purchase process using two models from literature including consumer online purchase process and ECKM. Since CKM is a recent concept and little empirical research is available, we will first present the theories from which CKM derives. In the first stage we discuss about e-commerce trend and increasing importance of customer loyalty in today’s business environment. Then some related concepts about Knowledge Management, Customer Relationship Management and CKM are presented, in order to provide the reader with a better understanding and clear picture regarding CKM. Finally, providing models representing e-CKM and online purchasing process, we propose a comprehensive procedure to manage customer data and knowledge in e-commerce.
Graph classification based on pattern co-occurrence

Subgraph patterns are widely used in graph classification, but their effectiveness is often hampered by large number of patterns or lack of discrimination power among individual patterns. We introduce a novel classification method based on pattern co-occurrence to derive graph classification rules. Our method employs a pattern exploration order such that the complementary discriminative patterns are examined first. Patterns are grouped into co-occurrence rules during the pattern exploration, leading to an integrated process of pattern mining and classifier learning. By taking advantage of co-occurrence information, our method can generate strong features by assembling weak features. Unlike previous methods that invoke the pattern mining process repeatedly, our method only performs pattern mining once. In addition, our method produces a more interpretable classifier and shows better or competitive classification effectiveness in terms of accuracy and execution time.
To Put That in Perspective: Generating Analogies that Make Numbers Easier to Understand

Laypeople are frequently exposed to unfamiliar numbers published by journalists, social media users, and algorithms. These figures can be difficult for readers to comprehend, especially when they are extreme in magnitude or contain unfamiliar units. Prior work has shown that adding "perspective sentences" that employ ratios, ranks, and unit changes to such measurements can improve people's ability to understand unfamiliar numbers (e.g., "695,000 square kilometers is about the size of Texas"). However, there are many ways to provide context for a measurement. In this paper we systematically test what factors influence the quality of perspective sentences through randomized experiments involving over 1,000 participants. We develop a statistical model for generating perspectives and test it against several alternatives, finding beneficial effects of perspectives on comprehension that persist for six weeks. We conclude by discussing future work in deploying and testing perspectives at scale.
A mobile robot for inspection of overhead transmission lines

A new mobile robot prototype for inspection of overhead transmission lines is proposed. The mobile platform is composed of 3 arms. And there is a motorized rubber wheel on the end of each arm. On the two end arms, a gripper is designed to clamp firmly onto the conductors from below to secure the robot. Each arm has a motor to achieve 2 degrees of freedom which is realized by moving along a curve. It could roll over some obstacles (compression splices, vibration dampers, etc). And the robot could clear other types of obstacles (spacers, suspension clamps, etc).