nils-herrmann/clean_scirepeval huggingface dataset:
Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis.

The Centers for Disease Control and Prevention (CDC) in the United States has declared insufficient sleep a "public health problem." Indeed, according to a recent CDC study, more than a third of American adults are not getting enough sleep on a regular basis. However, insufficient sleep is not exclusively a US problem, and equally concerns other industrialised countries such as the United Kingdom, Japan, Germany, or Canada. According to some evidence, the proportion of people sleeping less than the recommended hours of sleep is rising and associated with lifestyle factors related to a modern 24/7 society, such as psychosocial stress, alcohol consumption, smoking, lack of physical activity and excessive electronic media use, among others. This is alarming as insufficient sleep has been found to be associated with a range of negative health and social outcomes, including success at school and in the labour market. Over the last few decades, for example, there has been growing evidence suggesting a strong association between short sleep duration and elevated mortality risks. Given the potential adverse effects of insufficient sleep on health, well-being and productivity, the consequences of sleep-deprivation have far-reaching economic consequences. Hence, in order to raise awareness of the scale of insufficient sleep as a public-health issue, comparative quantitative figures need to be provided for policy- and decision-makers, as well as recommendations and potential solutions that can help tackling the problem.
Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial
A Review of Optical NDT Technologies

Optical non-destructive testing (NDT) has gained more and more attention in recent years, mainly because of its non-destructive imaging characteristics with high precision and sensitivity. This paper provides a review of the main optical NDT technologies, including fibre optics, electronic speckle, infrared thermography, endoscopic and terahertz technology. Among them, fibre optics features easy integration and embedding, electronic speckle focuses on whole-field high precision detection, infrared thermography has unique advantages for tests of combined materials, endoscopic technology provides images of the internal surface of the object directly, and terahertz technology opens a new direction of internal NDT because of its excellent penetration capability to most of non-metallic materials. Typical engineering applications of these technologies are illustrated, with a brief introduction of the history and discussion of recent progress.
Haptic Virtual Fixtures for Robot-Assisted Manipulation

Haptic virtual fixtures are software-generated force and position signals applied to human operators in order to improve the safety, accuracy, and speed of robot-assisted manipulation tasks. Virtual fixtures are effective and intuitive because they capitalize on both the accuracy of robotic systems and the intelligence of human operators. In this paper, we discuss the design, analysis, and implementation of two categories of virtual fixtures: guidance virtual fixtures, which assist the user in moving the manipulator along desired paths or surfaces in the workspace, and forbidden-region virtual fixtures, which prevent the manipulator from entering into forbidden regions of the workspace. Virtual fixtures are analyzed in the context of both cooperative manipulation and telemanipulation systems, considering issues related to stability, passivity, human modeling, and applications.
Is it really about me?: message content in social awareness streams

In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.
Taking email to task: the design and evaluation of a task management centered email tool

Email has come to play a central role in task management, yet email tool features have remained relatively static in recent years, lagging behind users? evolving practices. The Taskmaster system narrows this gap by recasting email as task management and embedding task-centric resources directly in the client. In this paper, we describe the field research that inspired Taskmaster and the principles behind its design. We then describe how user studies conducted with ?live? email data over a two-week period revealed the value of a task-centric approach to email system design and its potential benefits for overloaded users.
A 3D printed low profile magnetic dipole antenna

An electrically small, low profile magnetic dipole antenna is presented. The proposed antenna is composed of multiple small twisted loops connected in parallel. The simulated radiation efficiency is 87 % at the resonant frequency of 222.5 MHz. The corresponding electrical size ka is 0.276 with the height of 0.0016λ. The prototype is built using a selective laser sintering technology and silver paste painting. The measured result is discussed.
Applications of irradiance tensors to the simulation of non-Lambertian phenomena

We present new techniques for computing illumination from non-diffuse luminaires and scattering from non-diffuse surfaces. The methods are based on new closed-form expressions derived using a generalization of irradiance known as irradiance tensors. The elements of these tensors are angular moments, weighted integrals of the radiation field that are useful in simulating a variety of non-diffuse phenomena. Applications include the computation of irradiance due to directionally-varying area light sources, reflections from glossy surfaces, and transmission through glossy surfaces. The principles apply to any emission, reflection, or transmission distribution expressed as a polynomial over the unit sphere. We derive expressions for a simple but versatile subclass of these functions, called axial moments, and present complete algorithms their exact evaluation in polyhedral environments. The algorithms are demonstrated by simulating Phong-like emission and scattering effects.
Paddy diseases identification with texture analysis using fractal descriptors based on fourier spectrum

The efforts to increasing the quantity and quality of rice production are obstructed by the paddy disease. This research attempted to identify the four major paddy diseases in Indonesia (leaf blast, brown spot, bacterial leaf blight, and tungro) using fractal descriptors to analyze the texture of the lesions. The lesion images were extracted manually. The descriptors of `S' component of each lesion images then used in classification process using probabilistic neural networks. This techniques achieved at least 83.00% accuracy when identifying the diseases. This method has a potential to be used as one of the feature if it combined with other features, especially when two diseases with relatively same color involved.
Exploring the link between self-compassion and body image in university women.

The purpose of the present research was to examine the relationships between self-compassion and women's body image. In Study 1, female undergraduates (N=142) completed three measures of body image and measures of self-esteem and self-compassion. Results showed that high self-compassion predicted fewer body concerns independently of self-esteem. Moreover, when both self-compassion and self-esteem were included as predictors, self-compassion accounted for unique variance in body preoccupation and weight concerns whereas self-esteem did not. In Study 2, this finding was partially replicated with one component (self-judgment) of self-compassion uniquely predicting body preoccupation in undergraduate women (N=187). High scores on self-compassion also predicted less eating guilt independent of self-esteem. Additionally, self-compassion was shown to partially mediate the relationship between body preoccupation and depressive symptoms. The findings highlight the possibility that a consideration of self-compassion for body image may contribute to identifying who is most at risk for body/shape concerns.
Matrix factorization and neighbor based algorithms for the netflix prize problem

Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item selection using known user ratings of items. This subfield of machine learning has gained a lot of popularity with the Netflix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization (MF) and the neighbor based approach (NB). In this work, we propose various variants of MF and NB that can boost the performance of the usual ensemble based scheme. First, we investigate various regularization scenarios for MF. Second, we introduce two NB methods: one is based on correlation coefficients and the other on linear least squares. At the experimentation part, we show that the proposed approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. We present results of blending the proposed methods.
EAF2- A Framework for Categorizing Enterprise Architecture Frameworks

What constitutes an enterprise architecture framework is a contested subject. The contents of present enterprise architecture frameworks thus differ substantially. This paper aims to alleviate the confusion regarding which framework contains what by proposing a meta framework for enterprise architecture frameworks. By using this meta framework, decision makers are able to express their requirements on what their enterprise architecture framework must contain and also to evaluate whether the existing frameworks meets these requirements. An example classification of common EA frameworks illustrates the approach.
Open Set Learning with Counterfactual Images

In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, we introduce a dataset augmentation technique that we call counterfactual image generation. Our approach, based on generative adversarial networks, generates examples that are close to training set examples yet do not belong to any training category. By augmenting training with examples generated by this optimization, we can reformulate open set recognition as classification with one additional class, which includes the set of novel and unknown examples. Our approach outperforms existing open set recognition algorithms on a selection of image classification tasks.
Decoronation - a conservative method to treat ankylosed teeth for preservation of alveolar ridge prior to permanent prosthetic reconstruction: literature review and case presentation.

Avulsed teeth that are stored extraorally in a dry environment for >60 min generally develop replacement root resorption or ankylosis following their replantation due to the absence of a vital periodontal ligament on their root surface. One negative sequelae of such ankylosis is tooth infra-positioning and the local arrest of alveolar bone growth. Removal of an ankylosed tooth may be difficult and traumatic leading to esthetic bony ridge deformities and optimal prosthetic treatment interferences. Recently a treatment option for ankylosed teeth named 'de-coronation' gained interest, particularly in pediatric dentistry that concentrated in dental traumatology. This article reviews the up-to-date literature that has been published on decoronation with respect to its importance for future prosthetic rehabilitation followed by a case presentation that demonstrates its clinical benefits.
A Sketching Interface for Procedural City Generation

In this paper, we present an application which uses a sketch and gesture interface to facilitate the procedural creation of three dimensional city environments. City boundaries, roads and region allocations are drawn directly onto a 3D terrain, and a city is generated using these inputs. Aspects of the city that can be modified include the population density, allocation of parks, and variances in building height. Once generated, the city is rendered in a non-photorealistic sketching style, to correspond with the nature of its creation.
A solution to the simultaneous localization and map building (SLAM) problem

The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in , , , this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including sub-optimal map-building algorithms and map management.
Perceptual Adversarial Networks for Image-to-Image Transformation

In this paper, we propose perceptual adversarial networks (PANs) for image-to-image transformations. Different from existing application driven algorithms, PAN provides a generic framework of learning to map from input images to desired images (<xref ref-type="fig" rid="fig1">Fig. 1</xref>), such as a rainy image to its de-rained counterpart, object edges to photos, and semantic labels to a scenes image. The proposed PAN consists of two feed-forward convolutional neural networks: the image transformation network <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> and the discriminative network <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>. Besides the generative adversarial loss widely used in GANs, we propose the perceptual adversarial loss, which undergoes an adversarial training process between the image transformation network <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> and the hidden layers of the discriminative network <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>. The hidden layers and the output of the discriminative network <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> are upgraded to constantly and automatically discover the discrepancy between the transformed image and the corresponding ground truth, while the image transformation network <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is trained to minimize the discrepancy explored by the discriminative network <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula>. Through integrating the generative adversarial loss and the perceptual adversarial loss, <inline-formula> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> can be trained alternately to solve image-to-image transformation tasks. Experiments evaluated on several image-to-image transformation tasks (e.g., image de-raining and image inpainting) demonstrate the effectiveness of the proposed PAN and its advantages over many existing works.
Memristor for computing: Myth or reality?

CMOS technology and its sustainable scaling have been the enablers for the design and manufacturing of computer architectures that have been fuelling a wider range of applications. Today, however, both the technology and the computer architectures are suffering from serious challenges/ walls making them incapable to deliver the right computing power at pre-defined constraints. This motivates the need of exploring new architectures and new technologies; not only to maintain the economic benefit of scaling, but also to enable the solutions of emerging computer power and data storage hungry applications such as big-data and data-intensive applications. This paper discusses the emerging memristor device as complementary (or alternative) to CMOS device and shows how this device can enable new ways of computing that will at least solve the challenges of today's architectures for some applications. The paper shows not only the potential of memristor devices in enabling new memory technologies and new logic design styles, but also their potential in enabling memory intensive architectures as well as neuromorphic computing due to their unique properties such as the tight integration with CMOS and the ability to learn and adapt.
Speed-accuracy tradeoff in Fitts' law tasks-on the equivalency of actual and nominal pointing precision

Pointing tasks in human–computer interaction obey certain speed–accuracy tradeoff rules. In general, the more accurate the task to be accomplished, the longer it takes and vice versa. Fitts’ law models the speed–accuracy tradeoff effect in pointing as imposed by the task parameters, through Fitts’ index of difficulty (Id) based on the ratio of the nominal movement distance and the size of the target. Operating with different speed or accuracy biases, performers may utilize more or less area than the target specifies, introducing another subjective layer of speed–accuracy tradeoff relative to the task specification. A conventional approach to overcome the impact of the subjective layer of speed–accuracy tradeoff is to use the a posteriori ‘‘effective’’ pointing precision W e in lieu of the nominal target width W. Such an approach has lacked a theoretical or empirical foundation. This study investigates the nature and the relationship of the two layers of speed–accuracy tradeoff by systematically controlling both Id and the index of target utilization Iu in a set of four experiments. Their results show that the impacts of the two layers of speed–accuracy tradeoff are not fundamentally equivalent. The use of W e could indeed compensate for the difference in target utilization, but not completely. More logical Fitts’ law parameter estimates can be obtained by the W e adjustment, although its use also lowers the correlation between pointing time and the index of difficulty. The study also shows the complex interaction effect between see front matter r 2004  1112; fax: +1 408 927 3033. dress: zhai@us.ibm.com (S. Zhai).
A layout-similarity-based approach for detecting phishing pages

Phishing is a current social engineering attack that results in online identity theft. In a phishing attack, the attacker persuades the victim to reveal confidential information by using web site spoofing techniques. Typically, the captured information is then used to make an illegal economic profit by purchasing goods or undertaking online banking transactions. Although simple in nature, because of their effectiveness, phishing attacks still remain a great source of concern for organizations with online customer services. In previous work, we have developed AntiPhish, a phishing protection system that prevents sensitive user information from being entered on phishing sites. The drawback is that this system requires cooperation from the user and occasionally raises false alarms. In this paper, we present an extension of our system (called DOMAntiPhish) that mitigates the shortcomings of our previous system. In particular, our novel approach leverages layout similarity information to distinguish between malicious and benign web pages. This makes it possible to reduce the involvement of the user and significantly reduces the false alarm rate. Our experimental evaluation demonstrates that our solution is feasible in practice.
Electricity generation from model organic wastewater in a cassette-electrode microbial fuel cell

A new highly scalable microbial fuel cell (MFC) design, consisting of a series of cassette electrodes (CE), was examined for increasing power production from organic matter in wastewater. Each CE chamber was composed of a box-shaped flat cathode (two air cathodes on both sides) sandwiched in between two proton-exchange membranes and two graphite-felt anodes. Due to the simple design of the CE-MFC, multiple cassettes can be combined to form a single unit and inserted into a tank to treat wastewater. A 12-chamber CE-MFC was tested using a synthetic wastewater containing starch, peptone, and fish extract. Stable performance was obtained after 15 days of operation in fed-batch mode, with an organic removal efficiency of 95% at an organic loading rate of 2.9 kg chemical oxygen demand (COD) per cubic meter per day and an efficiency of 93% at 5.8 kg COD per cubic meter per day. Power production was stable during this period, reaching maximum power densities of 129 W m−3 (anode volume) and 899 mW m−2 (anode projected area). The internal resistance of CE-MFC decreased from 2.9 (day 4) to 0.64 Ω (day 25). These results demonstrate the usefulness of the CE-MFC design for energy production and organic wastewater treatment.