nils-herrmann/clean_scirepeval huggingface dataset:
Fear and anxiety: animal models and human cognitive psychophysiology.

The aim of this paper is to explicate what is special about emotional information processing, emphasizing the neural foundations that underlie the experience and expression of fear. A functional, anatomical model of defense behavior in animals is presented and applications are described in cognitive and physiological studies of human affect. It is proposed that unpleasant emotions depend on the activation of an evolutionarily primitive subcortical circuit, including the amygdala and the neural structures to which it projects. This motivational system mediates specific autonomic (e.g., heart rate change) and somatic reflexes (e.g., startle change) that originally promoted survival in dangerous conditions. These same response patterns are illustrated in humans, as they process objective, memorial, and media stimuli. Furthermore, it is shown how variations in the neural circuit and its outputs may separately characterize cue-specific fear (as in specific phobia) and more generalized anxiety. Finally, again emphasizing links between the animal and human data, we focus on special, attentional features of emotional processing: The automaticity of fear reactions, hyper-reactivity to minimal threat-cues, and evidence that the physiological responses in fear may be independent of slower, language-based appraisal processes.
Wafer-level bonding/stacking technology for 3D integration

Enhanced transmission speeds, lower power consumption, better performance, and smaller form factors are reported as advantages in many devices and applications when using 3D integration. One core technique for performing 3D interconnection is stacked bonding. In this paper, wafer-level bonding technologies are reviewed and described in detail, including bonding materials and bonding conditions. The corresponding 3D integration technologies and platforms developed world-wide are also organized
Is Generator Conditioning Causally Related to GAN Performance?

If you were to just glance at the spectra figures provided in the main text, using the log-determinant might seem like a reasonable thing to do. However, we note that (at least for the MNIST experiments) the largest singular values for the ‘well behaved’ runs are distinctly lower than those for the ‘poorly behaved’ ones. This suggests that the conditioning might be more pertinent than the determinant.
Fast and High Quality Fusion of Depth Maps

Reconstructing the 3D surface from a set of provided range images – acquired by active or passive sensors – is an important step to generate faithful virtual models of real objects or environments. Since several approaches for high quality fusion of range images are already known, the runtime efficiency of the respective methods are of increased interest. In this paper we propose a highly efficient method for range image fusion resulting in very accurate 3D models. We employ a variational formulation for the surface reconstruction task. The global optimal solution can be found by gradient descent due to the convexity of the underlying energy functional. Further, the gradient descent procedure can be parallelized, and consequently accelerated by graphics processing units. The quality and runtime performance of the proposed method is demonstrated on wellknown multi-view stereo benchmark datasets.
Integration of RFID and sensor in agriculture using IOT

Agriculture is the backbone of the Indian economy. As all know that demand of agricultural products are increasing day by day as the population is ever increasing, so there is a need to minimize labor, limit the use of water and increase the production of crops. So there is a need to switch from traditional agriculture to the modern agriculture. The introduction of internet of things into agriculture modernization will help solve these problems. This paper presents the IOT based agriculture production system which will monitor or analyze the crop environment like temperature humidity and moisture content in soil. This paper uses the integration of RFID technology and sensors. As both have different objective sensors are for sensing and RIFD technology is for identification This will effectively solve the problem of farmer, increase the yield and saves his time, power, money.
Enhancing Health and Emotion : Mindfulness as a Missing Link Between Cognitive Therapy and Positive Psychology

Mindfiilness meditation is an increasingly popular intervention for the treatment of physical illnesses and psychological difficulties. Using intervention strategies with mechanisms familiar to cognitive behavioral therapists, the principles and practice of mindfijlness meditation offer promise for promoting many of the most basic elements of positive psychology. It is proposed that mindfulness meditation promotes positive adjustment by strengthening metacognitive skills and by changing schemas related to emotion, health, and illness. Additionally, the benefits of yoga as a mindfulness practice are explored. Even though much empirical work is needed to determine the parameters of mindfulness meditation's benefits, and the mechanisms by which it may achieve these benefits, theory and data thus far clearly suggest the promise of mindfulness as a link between positive psychology and cognitive behavioral therapies.
Information security awareness and behavior : a theory-based literature review

Information security awareness and behavior: a theory-based literature review Benedikt Lebek, Jörg Uffen, Markus Neumann, Bernd Hohler, Michael H. Breitner, Article information: To cite this document: Benedikt Lebek, Jörg Uffen, Markus Neumann, Bernd Hohler, Michael H. Breitner, (2014) "Information security awareness and behavior: a theory-based literature review", Management Research Review, Vol. 37 Issue: 12, pp.1049-1092,  Permanent link to this document:
Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos

Automated polyp detection in colonoscopy videos has been demonstrated to be a promising way for colorectal cancer prevention and diagnosis. Traditional manual screening is time consuming, operator dependent, and error prone; hence, automated detection approach is highly demanded in clinical practice. However, automated polyp detection is very challenging due to high intraclass variations in polyp size, color, shape, and texture, and low interclass variations between polyps and hard mimics. In this paper, we propose a novel offline and online three-dimensional (3-D) deep learning integration framework by leveraging the 3-D fully convolutional network (3D-FCN) to tackle this challenging problem. Compared with the previous methods employing hand-crafted features or 2-D convolutional neural network, the 3D-FCN is capable of learning more representative spatio-temporal features from colonoscopy videos, and hence has more powerful discrimination capability. More importantly, we propose a novel online learning scheme to deal with the problem of limited training data by harnessing the specific information of an input video in the learning process. We integrate offline and online learning to effectively reduce the number of false positives generated by the offline network and further improve the detection performance. Extensive experiments on the dataset of MICCAI 2015 Challenge on Polyp Detection demonstrated the better performance of our method when compared with other competitors.
Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study

Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the great instruments in customer retention process and inferring the future behavior of the customers. However, the performances of predictive models are greatly affected when the real-world data set is highly imbalanced. A data set is called imbalanced if the samples size from one class is very much smaller or larger than the other classes. The most commonly used technique is over/under sampling for handling the class-imbalance problem (CIP) in various domains. In this paper, we survey six well-known sampling techniques and compare the performances of these key techniques, i.e., mega-trend diffusion function (MTDF), synthetic minority oversampling technique, adaptive synthetic sampling approach, couples top-N reverse k-nearest neighbor, majority weighted minority oversampling technique, and immune centroids oversampling technique. Moreover, this paper also reveals the evaluation of four rules-generation algorithms (the learning from example module, version 2 (LEM2), covering, exhaustive, and genetic algorithms) using publicly available data sets. The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.
The explicit dynamic model and inertial parameters of the PUMA 560 arm

To provide COSMOS, a dynamic model baaed manipulator control system, with an improved dynamic model, a PUMA 560 arm waa  diaaaaembled; the inertial propertiea of the individual links were meaaured; and an ezplicit model incorporating all ofthe non-zero meaaured parametera waa deriued. The ezplicit model of the PUMA arm has been obtained with a derivation procedure comprised of aeveral heuristic rulea  for simplification. A aimplijied model, abbreviated from the full ezplicit model with a 1% aignijicance criterion, can be evaluated with 305 calculationa, one fifth the number required by the recuraive Newton-Euler method. The procedure used to derive the model i a laid out; the meaaured inertial parametera are preaented, and the model ia included in an appendiz.
Digital disease detection--harnessing the Web for public health surveillance.

n engl j med 360;21 nejm.org may 21, 2009 2153 online announcements by government agencies but also through informal channels, ranging from press reports to blogs to chat rooms to analyses of Web searches (see box). Collectively, these sources provide a view of global health that is fundamentally different from that yielded by the disease reporting of the traditional public health  Over the past 15 years, Internet technology has become integral to public health surveillance. Systems using informal electronic information have been credited with reducing the time to recognition of an outbreak, preventing governments from suppressing outbreak information, and facilitating public health responses to outbreaks and emerging diseases. Because Web-based sources frequently contain data not captured through traditional government communication channels, they are useful to public health agencies, including the Global Outbreak Alert and Response Network of the World Health Organization (WHO), which relies on such sources for daily surveillance activities. Early efforts in this area were made by the International Society for Infectious Diseases’ Program for Monitoring Emerging Diseases, or ProMED-mail, which was founded in 1994 and has grown into a large, publicly available reporting system, with more than 45,000 subscribers in 188 countries.2 ProMED uses the Internet to disseminate information on outbreaks by e-mailing and posting case reports, including many gleaned from readers, along with expert commentary. In 1997, the Public Health Agency of Canada, in collaboration with the WHO, created the Global Public Health Intelligence Network (GPHIN), whose software retrieves relevant articles from news aggregators every 15 minutes, using extensive search queries. ProMED and GPHIN played critical roles in informing public health officials of the outbreak of SARS, or severe acute respiratory syndrome, in Guangdong, China, as early as November 2002, by identifying informal reports on the Web through news media and chat-room discussions. More recently, the advent of openly available news aggregators and visualization tools has spawned a new generation of disDigital Disease Detection — Harnessing the Web for Public Health Surveillance
P2P Lending Survey: Platforms, Recent Advances and Prospects

P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

This letter presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we exploit deep learning to handle wireless OFDM channels in an end-to-end manner. Different from existing OFDM receivers that first estimate channel state information (CSI) explicitly and then detect/recover the transmitted symbols using the estimated CSI, the proposed deep learning-based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from simulation based on channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach can address channel distortion and detect the transmitted symbols with performance comparable to the minimum mean-square error estimator. Furthermore, the deep learning-based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix is omitted, and nonlinear clipping noise exists. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortion and interference.
Design and optimization of broadband asymmetrical multisection wilkinson power divider

In this paper, a numerical algorithm based on the method of least squares (MLS) is presented for the design and optimization of a Wilkinson power divider for arbitrary power division between its outputs in a specified frequency bandwidth incorporating source and load impedance matching. At first, its transmission, admittance, and scattering matrices are determined successively by considering the dispersion and dissipation models of substrate and metallic strips. An error function is then constructed for the desired output power division, maximum level of isolation between them, and the least amount of reflected power. Minimization of the error function with respect to the widths and lengths of strips and values of resistors provides their optimum values. A procedure is provided for the initial design and determination of initial values of parameters of a Wilkinson power divider based on the method of even- and odd-mode analysis. The optimum design of the Wilkinson power divider by the proposed MLS procedure is validated by available full-wave analysis software and fabrication and measurement of two samples.
The REA-DSL: A Domain Specific Modeling Language for Business Models

In the discipline of accounting, the resource-event-agent (REA) ontology is a well accepted conceptual accounting framework to analyze the economic phenomena within and across enterprises. Accordingly, it seems to be appropriate to use REA in the requirements elicitation to develop an information architecture of accounting and enterprise information systems. However, REA has received comparatively less attention in the field of business informatics and computer science. Some of the reasons may be that the REA ontology despite of its well grounded core concepts is (1) sometimes vague in the definition of the relationships between these core concepts, (2) misses a precise language to describe the models, and (3) does not come with an easy to understand graphical notation. Accordingly, we have started developing a domain specific modeling language specifically dedicated to REA models and corresponding tool support to overcome these limitations. In this paper we present our REA DSL which supports the basic set of REA concepts.
An ankle-foot emulation system for the study of human walking biomechanics

Although below-knee prostheses have been commercially available for some time, today's devices are completely passive, and consequently, their mechanical properties remain fixed with walking speed and terrain. A lack of understanding of the ankle-foot biomechanics and the dynamic interaction between an amputee and a prosthesis is one of the main obstacles in the development of a biomimetic ankle-foot prosthesis. In this paper, we present a novel ankle-foot emulator system for the study of human walking biomechanics. The emulator system is comprised of a high performance, force-controllable, robotic ankle-foot worn by an amputee interfaced to a mobile computing unit secured around his waist. We show that the system is capable of mimicking normal ankle-foot walking behaviour. An initial pilot study supports the hypothesis that the emulator may provide a more natural gait than a conventional passive prosthesis
Introspective Neural Networks for Generative Modeling

We study unsupervised learning by developing a generative model built from progressively learned deep convolutional neural networks. The resulting generator is additionally a discriminator, capable of "introspection" in a sense — being able to self-evaluate the difference between its generated samples and the given training data. Through repeated discriminative learning, desirable properties of modern discriminative classifiers are directly inherited by the generator. Specifically, our model learns a sequence of CNN classifiers using a synthesis-by-classification algorithm. In the experiments, we observe encouraging results on a number of applications including texture modeling, artistic style transferring, face modeling, and unsupervised feature learning.
Development of future short range radar technology

First cars equipped with 24 GHz short range radar (SRR) systems in combination with 77 GHz long range radar (LRR) system enter the market in autumn 2005 enabling new safety and comfort functions. In Europe the 24 GHz ultra wideband (UWB) frequency band is temporally allowed only till end of June 2013 with a limitation of the car pare penetration of 7%. From middle of 2013 new cars have to be equipped with SRR sensors which operate in the frequency band of 79 GHz (77 GHz to 81 GHz). The development of the 79 GHz SRR technology within the German government (BMBF) funded project KOKON is described
Dimensions and Relations of the Dentogingival Junction in Humans

IN 1921, Gottlieb's discovery of the epithelial attachment of the gingiva opened new horizons which served as the basis for a better understanding of the biology of the dental supporting tissues in health and disease. Three years later his pupils, Orban and Kohler (1924), undertook the task of measuring the epithelial attachment as well as the surrounding tissue relations during the four phases of passive eruption of the tooth. Gottlieb and Orban's descriptions of the epithelial attachment unveiled the exact morphology of this epithelial structure, and clarified the relation of this
Characteristics and internet marketing strategies of online auction sellers

Internet marketing is a field that is continuing to grow, and the online auction concept may be defining a totally new and unique distribution alternative. Very few studies have examined auction sellers and their internet marketing strategies. This research examines the internet auction phenomenon as it relates to the marketing mix of online auction sellers. The data in this study indicate that, whilst there is great diversity among businesses that utilise online auctions, distinct cost leadership and differentiation marketing strategies are both evident. These two approaches are further distinguished in terms of the internet usage strategies employed by each group.
Computer vision for microscopy diagnosis of malaria

This paper reviews computer vision and image analysis studies aiming at automated diagnosis or screening of malaria infection in microscope images of thin blood film smears. Existing works interpret the diagnosis problem differently or propose partial solutions to the problem. A critique of these works is furnished. In addition, a general pattern recognition framework to perform diagnosis, which includes image acquisition, pre-processing, segmentation, and pattern classification components, is described. The open problems are addressed and a perspective of the future work for realization of automated microscopy diagnosis of malaria is provided.
Biometric Authentication and Identification using Keystroke Dynamics: A Survey

Dependence on computers to store and process sensitive information has made it necessary to secure them from intruders. A behavioral biometric such as keystroke dynamics which makes use of the typing cadence of an individual can be used to strengthen existing security techniques effectively and cheaply. Due to the ballistic (semi-autonomous) nature of the typing behavior it is difficult to impersonate, making it useful as a biometric. Therefore in this paper, we provide a basic background of the psychological basis behind the use of keystroke dynamics. We also discuss the data acquisition methods, approaches and the performance of the methods used by researchers on standard computer keyboards. In this survey, we find that the use and acceptance of this biometric could be increased by development of standardized databases, assignment of nomenclature for features, development of common data interchange formats, establishment of protocols for evaluating methods, and resolution of privacy issues.
A Review of the Optical Properties of Biological Tissues

A comprehensive compilation of published optical properties (absorption, scattering, total attenuation, effective attenuation, andlor anisotropy coefficients) of various biological tissues at a variety of wavelengths is presented. The theoretical foundations for most experimental approaches are outlined. Relations between Kubelka-Munk parameters and transport coefficients are listed. The optical properties of aorta, liver, and muscle at 633 nm are discussed in detail.