nils-herrmann/clean_scirepeval huggingface dataset:
A sex difference in features that elicit genital response

Previous research suggests that women's genital arousal is an automatic response to sexual stimuli, whereas men's genital arousal is dependent upon stimulus features specific to their sexual interests. In this study, we tested the hypothesis that a nonhuman sexual stimulus would elicit a genital response in women but not in men. Eighteen heterosexual women and 18 heterosexual men viewed seven sexual film stimuli, six human films and one nonhuman primate film, while measurements of genital and subjective sexual arousal were recorded. Women showed small increases in genital arousal to the nonhuman stimulus and large increases in genital arousal to both human male and female stimuli. Men did not show any genital arousal to the nonhuman stimulus and demonstrated a category-specific pattern of arousal to the human stimuli that corresponded to their stated sexual orientation. These results suggest that stimulus features necessary to evoke genital arousal are much less specific in women than in men.
Survey on Image Steganography and its Techniques

In present Scenario of the world, Internet has almost reached to every aspect of our lives. Due to this, most of the information sharing and communication is carried out using web. With such rapid development of Internet technology, a big issue arises of unauthorized access to confidential data, which leads to utmost need of information security while transmission. Cryptography and Steganography are two of the popular techniques used for secure transmission. Steganography is more reliable over cryptography as it embeds secret data within some cover material. Unlike cryptography, Steganography is not for keeping message hidden from intruders but it does not allow anyone to know that hidden information even exist in communicated material, as the transmitted material looks like any normal message which seem to be of no use for intruders. Although, Steganography covers many types of covers to hide data like text, image, audio, video and protocols but recent developments focuses on Image Steganography due to its large data hiding capacity and difficult identification, also due to their greater scope and bulk sharing within social networks. A large number of techniques are available to hide secret data within digital images such as LSB, ISB, and MLSB etc. In this paper, a detailed review will be presented on Image Steganography and also different data hiding and security techniques using digital images with their scope and features.
A complete over-current/short-circuit protection system for Low-Drop Out regulators

A complete over-current and short-circuit protection system for Low-Drop Out (LDO) regulator applications is presented. The system consists of a current-sense circuit, a current comparator, a D Flip-Flop, an OR logic gate and the short-circuit sense topology. The protection circuit is able to shut down the LDO rapidly by producing a control signal when an over-current event occurs while during the normal operation of the LDO, the protection circuit is idle. The restart of the LDO has to be made manually and a master Reset signal is, also, available. The proposed protection system was designed by using a standard 0.18u CMOS technology using high-voltage transistors.
Job Satisfaction in Agile Development Teams: Agile Development as Work Redesign

Agile software-development advocates claim that an important value proposition of agile methods is that they make people more motivated and satisfied with their jobs. While several studies present anecdotal evidence that agile methods increase motivation and satisfaction, research has not theoretically explained or empirically examined how agile development practices relate to team members’ feelings about their work. Drawing on the management and software-development literature, we articulate a model of job design that connects agile development practices to perceptions of job characteristics and, thereby, improve agile team members’ job satisfaction. Using data collected from 252 software-development professionals, we tested the model and found a positive relationship between agile projectmanagement and software-development practices and employees’ perceptions of job characteristics. Further, we found direct effects between agile development-practice use and job satisfaction. Finally, we found interaction effects between the use of agile project-management and software-development approaches and the perception of job autonomy. With this study, we contribute to the literature by theoretically explaining and directly evaluating agile development practices’ impact on individuals’ perceptions about their job characteristics and on their job satisfaction.
A place-based model for understanding community resilience to natural disasters

There is considerable research interest on the meaning and measurement of resilience from a variety of research perspectives including those from the hazards/disasters and global change communities. The identification of standards and metrics for measuring disaster resilience is one of the challenges faced by local, state, and federal agencies, especially in the United States. This paper provides a new framework, the disaster resilience of place (DROP) model, designed to improve comparative assessments of disaster resilience at the local or community level. A candidate set of variables for implementing the model are also presented as a first step towards its implementation.
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent (SGD) from random initialization. In the overparameterized setting, when the data comes from mixtures of well-separated distributions, we prove that SGD learns a network with a small generalization error, albeit the network has enough capacity to fit arbitrary labels. Furthermore, the analysis provides interesting insights into several aspects of learning neural networks and can be verified based on empirical studies on synthetic data and on the MNIST dataset.
Equivariant Transformer Networks

How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonical coordinate systems, ETs incorporate functions that are equivariant by construction with respect to these transformations. We show empirically that ETs can be flexibly composed to improve model robustness towards more complicated transformation groups in several parameters. On a real-world image classification task, ETs improve the sample efficiency of ResNet classifiers, achieving relative improvements in error rate of up to 15% in the limited data regime while increasing model parameter count by less than 1%.
THE PERSON – SITUATION DEBATE REVISITED : EFFECT OF SITUATION STRENGTH AND TRAIT ACTIVATION ON THE VALIDITY OF THE BIG FIVE PERSONALITY TRAITS IN PREDICTING JOB PERFORMANCE

Derived from two theoretical concepts—situation strength and trait activation—we develop and test an interactionist model governing the degree to which five-factor model personality traits are related to job performance. One concept—situation strength—was hypothesized to predict the validities of all of the “Big Five” traits, while the effects of the other—trait activation—were hypothesized to be specific to each trait. Based on this interactionist model, personality–performance correlations were located in the literature, and occupationally homogeneous jobs were coded according to their theoretically relevant contextual properties. Results revealed that all five traits were more predictive of performance for jobs in which the process by which the work was done represented weak situations (e.g., work was unstructured, employee had discretion to make decisions). Many of the traits also predicted performance in job contexts that activated specific traits (e.g., extraversion better predicted performance in jobs requiring social skills, agreeableness was less positively related to performance in competitive contexts, openness was more strongly related to performance in jobs with strong innovation/ creativity requirements). Overall, the study’s findings supported our interactionist model in which the situation exerts both general and specific effects on the degree to which personality predicts job performance.
Exploiting Equivalence to Infer Type Subsumption in Linked Graphs

Open Knowledge Graphs (KGs) such as DBpedia and Wikidata have been recognized as the foundations for diverse applications in the field of data mining and information retrieval. Each of these KGs follows a different knowledge organization as well as is based on differently structured ontologies. Moreover, it has been observed that type information are often noisy, incomplete or even incorrect. In general, there is a need for well defined and comparable type information for the entities of the KGs. In this paper, we propose an isomorphism-based approach to infer subsumption relations to RDF type information in Wikidata by exploiting the RDF type information from DBpedia.
ISSARS: An integrated software environment for structure-specific earthquake ground motion selection

motion selection DTU Orbit (12/12/2018)  ISSARS: An integrated software environment for structure-specific earthquake ground motion selection Current practice enables the design and assessment of structures in earthquake prone areas by performing time history analysis with the use of appropriately selected strong ground motions. This study presents a Matlab-based software environment, which is integrated with a finite element analysis package, and aims to improve the efficiency of earthquake ground motion selection by accounting for the variability of critical structural response quantities. This additional selection criterion, which is tailored to the specific structure studied, leads to more reliable estimates of the mean structural response quantities used in design, while fulfils the criteria already prescribed by the European and US seismic codes and guidelines. To demonstrate the applicability of the software environment developed, an existing irregular, multi-storey, reinforced concrete building is studied for a wide range of seismic scenarios. The results highlight the applicability of the software developed and the benefits of applying a structure-specific criterion in the process of selecting suites of earthquake motions for the seismic design and assessment.
A Survey on Automatic Detection of Hate Speech in Text

The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

We present Fashion-MNIST, a new dataset comprising of 28 × 28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category. The training set has 60, 000 images and the test set has 10, 000 images. Fashion-MNIST is intended to serve as a direct dropin replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist.
Analyzing Factors, Construction of Dataset, Estimating Importance of Factor, and Generation of Association Rules for Indian Road Accident

Convenience and safety of road is very important and crucial in society. Statistics of accidental deaths has shown a growing drift in India. Accidents on road network with vehicle collision can lead to injuries or even deaths and causes economy loss. Reasons for accident includes over speeding, drunken driver and defects in vehicle as well as bad road condition. Places near residential areas, pedestrian crossing, around schools, college and other educational institutions are the major zone of accidents. Mega cities traffic in India is increasing day by day due to increase in the population. In this paper we have figure out the factor influencing the accidents and identified which factor is more accident prone with Info Gain Attribute Evaluator function using WEKA tool. We have also performed association classification using Apriori algorithm and identified best rule for accident dataset.
Weaponizing Femtocells: The Effect of Rogue Devices on Mobile Telecommunications

Mobile phones and carriers trust the traditional base stations which serve as the interface between the mobile devices and the fixed-line communication network. Femtocells, miniature cellular base stations installed in homes and businesses, are equally trusted yet are placed in possibly untrustworthy hands. By making several modifications to a commercially available femtocell, we evaluate the impact of attacks originating from a compromised device. We show that such a rogue device can violate all the important aspects of security for mobile subscribers, including tracking phones, intercepting communication and even modifying and impersonating traffic. The specification also enables femtocells to directly communicate with other femtocells over a VPN and the carrier we examined had no filtering on such communication, enabling a single rogue femtocell to directly communicate with (and thus potentially attack) all other femtocells within the carrier’s network.
Applying the balanced scorecard in healthcare provider organizations.

Several innovative healthcare executives have recently introduced a new business strategy implementation tool: the Balanced Scorecard. The scorecard's measurement and management system provides the following potential benefits to healthcare organizations: It aligns the organization around a more market-oriented, customer-focused strategy It facilitates, monitors, and assesses the implementation of the strategy It provides a communication and collaboration mechanism It assigns accountability for performance at all levels of the organization It provides continual feedback on the strategy and promotes adjustments to marketplace and regulatory changes. We surveyed executives in nine provider organizations that were implementing the Balanced Scorecard. We asked about the following issues relating to its implementation and effect: 1. The role of the Balanced Scorecard in relation to a well-defined vision, mission, and strategy 2. The motivation for adopting the Balanced Scorecard 3. The difference between the Balanced Scorecard and other measurement systems 4. The process followed to develop and implement the Balanced Scorecard 5. The challenges and barriers during the development and implementation process 6. The benefits gained by the organization from adoption and use. The executives reported that the Balanced Scorecard strategy implementation and performance management tool could be successfully applied in the healthcare sector, enabling organizations to improve their competitive market positioning, financial results, and customer satisfaction. This article concludes with guidelines for other healthcare provider organizations to capture the benefits of the Balanced Scorecard performance management system.
Hematopoiesis: An Evolving Paradigm for Stem Cell Biology

Establishment and maintenance of the blood system relies on self-renewing hematopoietic stem cells (HSCs) that normally reside in small numbers in the bone marrow niche of adult mammals. This Review describes the developmental origins of HSCs and the molecular mechanisms that regulate lineage-specific differentiation. Studies of hematopoiesis provide critical insights of general relevance to other areas of stem cell biology including the role of cellular interactions in development and tissue homeostasis, lineage programming and reprogramming by transcription factors, and stage- and age-specific differences in cellular phenotypes.
Big Data as the Big Game Changer

Big Data is the phenomenon of the Information era. Big Data is a new dimension to explore, collecting Big Data we fix the time. Big Data has some functions, including impact on society, form spatio-temporal structures, change the world and future, and integration society with IT technologies. Most important aspect is risk in Cloud computing. To leverage risks, secure Cloud services and get additional benefits an Integrated Approach should be applied. It is important to separate the various kinds of “Security” needs when considering Cloud computing issues. Also Security Analyst should be included into Data Science Team. Data-driven economy is based on three points: open data, legislation for Big Data, and education. For students is very important practical training that engages students into the culture of Big Data Analytics. This opportunity provides the EMC Academic Alliance Russia & CIS through the establishment of ad-hoc Big Data Analytics Teams among universities. The results of the first stage of launched in 2015 the Big Data Analytics Multicenter Study are presented.
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks

Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization. In this work we suggest a novel complexity measure based on unit-wise capacities resulting in a tighter generalization bound for two layer ReLU networks. Our capacity bound correlates with the behavior of test error with increasing network sizes, and could potentially explain the improvement in generalization with over-parametrization. We further present a matching lower bound for the Rademacher complexity that improves over previous capacity lower bounds for neural networks.
Psychological characteristics and perceptions of stuttering of adults who stutter with and without support group experience.

To compare adults who stutter with and without support group experience on measures of self-esteem, self-efficacy, life satisfaction, self-stigma, perceived stuttering severity, perceived origin and future course of stuttering, and importance of fluency.



Participants were 279 adults who stutter recruited from the National Stuttering Association and Board Recognized Specialists in Fluency Disorders. Participants completed a Web-based survey comprised of various measures of well-being including the Rosenberg Self-Esteem Scale, Generalized Self-Efficacy Scale, Satisfaction with Life Scale, a measure of perceived stuttering severity, the Self-Stigma of Stuttering Scale, and other stuttering-related questions.



Participants with support group experience as a whole demonstrated lower internalized stigma, were more likely to believe that they would stutter for the rest of their lives, and less likely to perceive production of fluent speech as being highly or moderately important when talking to other people, compared to participants with no support group experience. Individuals who joined support groups to help others feel better about themselves reported higher self-esteem, self-efficacy, and life satisfaction, and lower internalized stigma and perceived stuttering severity, compared to participants with no support group experience. Participants who stutter as an overall group demonstrated similar levels of self-esteem, higher self-efficacy, and lower life satisfaction compared to averages from normative data for adults who do not stutter.



Findings support the notion that self-help support groups limit internalization of negative attitudes about the self, and that focusing on helping others feel better in a support group context is linked to higher levels of psychological well-being.



At the end of this activity the reader will be able to: (a) describe the potential psychological benefits of stuttering self-help support groups for people who stutter, (b) contrast between important aspects of well-being including self-esteem self-efficacy, and life satisfaction, (c) summarize differences in self-esteem, self-efficacy, life satisfaction, self-stigma, perceived stuttering severity, and perceptions of stuttering between adults who stutter with and without support group experience, (d) summarize differences in self-esteem, self-efficacy, and life satisfaction between adults who stutter and normative data for adults who do not stutter.
UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges

There could be no smart city without a reliable and efficient transportation system. This necessity makes the ITS a key component of any smart city concept. While legacy ITS technologies are deployed worldwide in smart cities, enabling the next generation of ITS relies on effective integration of connected and autonomous vehicles, the two technologies that are under wide field testing in many cities around the world. Even though these two emerging technologies are crucial in enabling fully automated transportation systems, there is still a significant need to automate other road and transportation components. To this end, due to their mobility, autonomous operation, and communication/processing capabilities, UAVs are envisaged in many ITS application domains. This article describes the possible ITS applications that can use UAVs, and highlights the potential and challenges for UAV-enabled ITS for next-generation smart cities.
Introduction to Combinators and Lambda-Calculus

Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the introduction to combinators and lambda calculus book. You can open the device and get the book by on-line.