nils-herrmann/clean_scirepeval huggingface dataset:
Classroom Misbehavior in the Eyes of Students: A Qualitative Study

Using individual interviews, this study investigated perceptions of classroom misbehaviors among secondary school students in Hong Kong (N = 18). Nineteen categories of classroom misbehaviors were identified, with talking out of turn, disrespecting teacher, and doing something in private being most frequently mentioned. Findings revealed that students tended to perceive misbehaviors as those actions inappropriate in the classroom settings and even disrupting teachers' teaching and other students' learning. Among various misbehaviors, talking out of turn and disrespecting teacher were seen as the most disruptive and unacceptable. These misbehaviors were unacceptable because they disturbed teaching and learning, and violated the values of respect, conformity, and obedience in the teacher-student relationship within the classroom. The frequency and intensity of misbehaviors would escalate if students found it fun, no punishment for such misbehaviors, or teachers were not authoritative enough in controlling the situations. Implications for further research and classroom management are discussed.
Neighborhood Mixture Model for Knowledge Base Completion

Knowledge bases are useful resources for many natural language processing tasks, however, they are far from complete. In this paper, we define a novel entity representation as a mixture of its neighborhood in the knowledge base and apply this technique on TransE—a well-known embedding model for knowledge base completion. Experimental results show that the neighborhood information significantly helps to improve the results of the TransE, leading to better performance than obtained by other state-of-the-art embedding models on three benchmark datasets for triple classification, entity prediction and relation prediction tasks.
Representation Tradeoffs for Hyperbolic Embeddings

Hyperbolic embeddings offer excellent quality with few dimensions when embedding hierarchical data structures. We give a combinatorial construction that embeds trees into hyperbolic space with arbitrarily low distortion without optimization. On WordNet, this algorithm obtains a meanaverage-precision of 0.989 with only two dimensions, outperforming existing work by 0.11 points. We provide bounds characterizing the precisiondimensionality tradeoff inherent in any hyperbolic embedding. To embed general metric spaces, we propose a hyperbolic generalization of multidimensional scaling (h-MDS). We show how to perform exact recovery of hyperbolic points from distances, provide a perturbation analysis, and give a recovery result that enables us to reduce dimensionality. Finally, we extract lessons from the algorithms and theory above to design a scalable PyTorch-based implementation that can handle incomplete information.
The debate over dopamine’s role in reward: the case for incentive salience

Debate continues over the precise causal contribution made by mesolimbic dopamine systems to reward. There are three competing explanatory categories: ‘liking’, learning, and ‘wanting’. Does dopamine mostly mediate the hedonic impact of reward (‘liking’)? Does it instead mediate learned predictions of future reward, prediction error teaching signals and stamp in associative links (learning)? Or does dopamine motivate the pursuit of rewards by attributing incentive salience to reward-related stimuli (‘wanting’)? Each hypothesis is evaluated here, and it is suggested that the incentive salience or ‘wanting’ hypothesis of dopamine function may be consistent with more evidence than either learning or ‘liking’. In brief, recent evidence indicates that dopamine is neither necessary nor sufficient to mediate changes in hedonic ‘liking’ for sensory pleasures. Other recent evidence indicates that dopamine is not needed for new learning, and not sufficient to directly mediate learning by causing teaching or prediction signals. By contrast, growing evidence indicates that dopamine does contribute causally to incentive salience. Dopamine appears necessary for normal ‘wanting’, and dopamine activation can be sufficient to enhance cue-triggered incentive salience. Drugs of abuse that promote dopamine signals short circuit and sensitize dynamic mesolimbic mechanisms that evolved to attribute incentive salience to rewards. Such drugs interact with incentive salience integrations of Pavlovian associative information with physiological state signals. That interaction sets the stage to cause compulsive ‘wanting’ in addiction, but also provides opportunities for experiments to disentangle ‘wanting’, ‘liking’, and learning hypotheses. Results from studies that exploited those opportunities are described here. In short, dopamine’s contribution appears to be chiefly to cause ‘wanting’ for hedonic rewards, more than ‘liking’ or learning for those rewards.
Radiation Characteristics of Tunable Graphennas in the Terahertz Band

Graphene-enabled wireless communications constitute a novel paradigm which has been proposed to implement wireless communications among nanosystems. Indeed, graphene-based plasmonic nano-antennas, or graphennas, just a few micrometers in size have been predicted to radiate electromagnetic waves at the terahertz band. In this work, the important role of the graphene conductivity in the characteristics of graphennas is analyzed, and their radiation performance both in transmission and reception is numerically studied. The resonance frequency of graphennas is calculated as a function of their length and width, both analytically and by simulation. Moreover, the influence of a dielectric substrate with a variable size, and the position of the patch with respect to the substrate is also evaluated. Further, important properties of graphene, such as its chemical potential or its relaxation time, are found to have a profound impact in the radiation properties of graphennas. Finally, the radiation pattern of a graphenna is compared to that of an equivalent metallic antenna. These results will prove useful for designers of future graphennas, which are expected to enable wireless communications
Helping Students Make Appropriate English Verb Tense-Aspect Choices

b. general, timeless truths, such as physical laws or customs The earth revolves around the sun. c. states It is cloudy. d. subordinate clauses of time or condition when the main clause contains a future-time verb When she comes, we’ll find out. e. events or actions in the present, such as in sporting events The goal counts! f. speech acts in the present I nominate Chris. g. conversational historical present (in narration) “So he enters the room and crosses over to the other side without looking at anyone.” h. events scheduled in the future My flight departs at 9 a.m. tomorrow.
policy association 1 Belonging nowhere : Marginalization & radicalization risk among Muslim Immigrants

In the last 15 years, the threat of Muslim violent extremists emerging within Western countries has grown. Terrorist organizations based in the Middle East are recruiting Muslims in the United States and Europe via social media. Yet we know little about the factors that would drive Muslim immigrants in a Western country to heed this call and become radicalized, even at the cost of their own lives. Research into the psychology of terrorism suggests that a person’s cultural identity plays a key role in radicalization, so we surveyed 198 Muslims in the United States about their cultural identities and attitudes toward extremism. We found that immigrants who identify with neither their heritage culture nor the culture they are living in feel marginalized and insignificant. Experiences of discrimination make the situation worse and lead to greater support for radicalism, which promises a sense of meaning and life purpose. Such insights could be of use to policymakers engaged in efforts against violent extremism, including terrorism.
Intelligent Anti-Money Laundering System

Criminal elements in today's technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. While many anti-money laundering solutions have been in place for some time within the financial community, they cannot adapt to the ever-changing risk and methods in relation to money laundering. In order for a more adaptive, intelligent and flexible solution for anti-money laundering, the intelligent agent technology is applied in this research. Intelligent agents with their properties of autonomy, reactivity and proactivity are well suited for money laundering prevention controls. Several types of agents are proposed and a novel and open multi-agent architecture is presented for anti-money laundering. A prototype system for money laundering detection is also developed to demonstrate the advances of the proposed system architecture and business
Internet of things: Survey on security

The Internet of Things (IoT) is intended for ubiquitous connectivity among different entities or “things”. While its purpose is to provide effective and efficient solutions, security of the devices and network is a challenging issue. The number of devices connected along with the ad-hoc nature of the system further exacerbates the situation. Therefore, security and privacy has emerged as a significant challenge for the IoT. In this paper, we aim to provide a thorough survey related to the privacy and security challenges of the IoT. This document addresses these challenges from the perspective of technologies and architecture used. This work focuses also in IoT intrinsic vulnerabilities as well as the security challenges of various layers based on the security principles of data confidentiality, integrity and availability. This survey analyzes articles published for the IoT at the time and relates it to the security conjuncture of the field and its projection to the future.
Alcohol advertising in magazines and adolescent readership.

Adolescent drinking is a major public health concern. The federal government does not restrict alcohol advertising to adolescents, but relies on the alcohol industry for self-regulation.



To investigate recent alcohol advertising in magazines and to determine whether advertising frequency is associated with adolescent readership.



All alcohol advertisements were counted that appeared from 1997-2001 in 35 of 48 major US magazines, which tracked their adolescent readership (3 refused all alcohol advertisements; and advertisement counts were unavailable for 10). Variation was assessed in the advertisement placement frequency for each major category of alcohol (beer, wine and wine coolers, and distilled liquor) by a magazine's adolescent readership (age 12-19 years), young adult readership (age 20-24 years), and older adult readership (age > or =25 years); readership demographics (sex, race, and income); year; frequency of publication; and cost per advertisement.



Variation in alcohol advertising frequency by adolescent readership.



Adolescent readership ranged from 1.0 to 7.1 million. The alcohol industry placed 9148 advertisements at a cost of 696 million dollars. Of the 9148 advertisements, 1201 (13%) were for beer, 443 (5%) for wine, and 7504 (82%) for liquor. After adjustment for other magazine characteristics, the advertisement rate ratio was 1.6 times more for beer (95% confidence interval [CI], 1.0-2.6; P =.05) and liquor (95% CI, 1.1-2.3; P =.01) for every additional million adolescent readers. Wine industry advertising was not associated with adolescent readership.



Magazine advertising by the beer and liquor industries is associated with adolescent readership. Industry and federal policymakers should examine ways to regulate advertising that reaches large numbers of adolescents.
Voting Using Face Recognition and Password Based Security System

In the online voting system, people can vote through the internet. In order to prevent voter frauds, we use two levels of security. In the first level of security, the face of the voter is captured by a web camera and sent to the database. Later, the face of the person is verified with the face present in the database and validated using Matlab. The comparison of the two faces is done using Local Binary Pattern algorithm. The scheme is based on a merging an image and assigns a value of a central pixel. These central pixels are labeled either 0 or 1. If the value is a lower pixel, a histogram of the labels is computed and used as a descriptor. LBP results are combined together to create one vector representing the entire face image. A password (OTP) is used as the second level of security, after entering the one time password generated to their mail it is verified and allow to vote. It should be noted that with this system in place, the users, in this case, shall be given an ample time during the voting period. They shall also be trained on how to vote online before the election time.
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly

We describe a method to produce a network where current methods such as DeepFool have great difficulty producing adversarial samples. Our construction suggests some insights into how deep networks work. We provide a reasonable analyses that our construction is difficult to defeat, and show experimentally that our method is hard to defeat with both Type I and Type II attacks using several standard networks and datasets. This SafetyNet architecture is used to an important and novel application SceneProof, which can reliably detect whether an image is a picture of a real scene or not. SceneProof applies to images captured with depth maps (RGBD images) and checks if a pair of image and depth map is consistent. It relies on the relative difficulty of producing naturalistic depth maps for images in post processing. We demonstrate that our SafetyNet is robust to adversarial examples built from currently known attacking approaches.
ProjE: Embedding Projection for Knowledge Graph Completion

With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dimensional graph embeddings. Although researchers continue to improve these models using an increasingly complex feature space, we show that simple changes in the architecture of the underlying model can outperform state-of-the-art models without the need for complex feature engineering. In this work, we present a shared variable neural network model called ProjE that fills-in missing information in a knowledge graph by learning joint embeddings of the knowledge graph’s entities and edges, and through subtle, but important, changes to the standard loss function. In doing so, ProjE has a parameter size that is smaller than 11 out of 15 existing methods while performing 37% better than the current-best method on standard datasets. We also show, via a new fact checking task, that ProjE is capable of accurately determining the veracity of many declarative statements.
Automatic Rank Selection for High-Speed Convolutional Neural Network

Low-rank decomposition plays a central role in accelerating convolutional neural network (CNN), and the rank of decomposed kernel-tensor is a key parameter that determines the complexity and accuracy of a neural network. In this paper, we define rank selection as a combinatorial optimization problem and propose a methodology to minimize network complexity while maintaining the desired accuracy. Combinatorial optimization is not feasible due to search space limitations. To restrict the search space and obtain the optimal rank, we define the space constraint parameters with a boundary condition. We also propose a linearly-approximated accuracy function to predict the fine-tuned accuracy of the optimized CNN model during the cost reduction. Experimental results on AlexNet and VGG-16 show that the proposed rank selection algorithm satisfies the accuracy constraint. Our method combined with truncated-SVD outperforms state-of-the-art methods in terms of inference and training time at almost the same accuracy.
AN ADAPTIVE FUZZY SLIDING MODE CONTROL SCHEME FOR ROBOTIC SYSTEMS

In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been replaced by a fuzzy system and the uncertainties are estimated online. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.
REAL-TIME PARAMETER ESTIMATION IN THE FREQUENCY DOMAIN

A method for real-time estimation of parameters in a linear dynamic state space model was developed and studied. The application is aircraft dynamic model parameter estimation from measured data in flight for indirect adaptive or reconfigurable control. Equation error in the frequency domain was used with a recursive Fourier transform for the real-time data analysis. Linear and nonlinear simulation examples and flight test data from the F-18 High Alpha Research Vehicle (HARV) were used to demonstrate that the technique produces accurate model parameter estimates with appropriate error bounds. Parameter estimates converged in less than 1 cycle of the dominant dynamic mode natural frequencies, using control surface inputs measured in flight during ordinary piloted maneuvers. The real-time parameter estimation method has low computational requirements, and could be implemented aboard an aircraft in real time. Nomenclature A,B,C,D system matrices E ; @ expectation operator g acceleration due to gravity, ft/sec h altitude, ft j imaginary number = -1 M Mach number N total number of samples Re real part R measurement noise covariance matrix t time  No copyright is asserted in the United States under Title 17, U.S. Code. The U.S. Government has a royaltyfree license to exercise all rights under the copyright claimed herein for Governmental purposes. All other rights are reserved by the copyright owner. Vt true airspeed, ft/sec x, u, y state, input, and output vectors zi measured output vector at time i t D α angle of attack, rad β sideslip angle, rad δ δ a r , aileron, rudder deflections, rad δ δ e s , elevator, stabilator deflections, rad δ ij Kronecker delta ν i discrete measurement noise vector σ 2 variance ω angular frequency, rad/sec θ p-dimensional parameter vector superscripts T transpose † complex conjugate transpose ~ discrete Fourier transform $ estimate –1 matrix inverse subscripts i value at time i t D o trim or initial value  Real-time identification of dynamic models is a requirement for indirect adaptive or reconfigurable control. One approach for satisfying this requirement is to assume the dynamic model has a linear structure with time-varying parameters to account for changes in the flight condition, stores, configuration, remaining fuel, or from various types of failures, wear, or damage. The task is then to identify accurate linear model parameter estimates from measured data in real time, so that the adaptive control logic can make the necessary https://ntrs.nasa.gov/search.jsp?R=20040087105 2017-09-13T22:15:14+00:00Z
Formalization, User Strategy And Interaction Design: Users? Behaviour With Discourse Tagging Semantics

When authors publish their interpretations of the ideas, opinions, claims or rebuttals in the literature, they are drawing on a repertoire of well understood moves, contributing to an extended discourse. Readers also bring their own perspective to documents, interpreting them in the light of their own research interests, and initiating, for instance, new connections that may not have been intended by authors. Collaborative, social, tagging holds promise as an approach to mediating these processes via the Web, but may lack the discourse dimension that is fundamental to the articulation of interpretations. We therefore take a hybrid semiformal approach to add structure to freeform folksonomies. Our experience demonstrates that this particular brand of tagging requires tools designed specifically for this sensemaking task by providing enough support to initiate the annotation, while not overwhelming users with suggestions. We describe a tool called ClaimSpotter that aims at supporting this tradeoff, through a novel combination of system-initiated tag recommendations, Web interface design, and an expanded conception of how tags can be both expressed, and semantically linked. We then report a detailed study which analysed the tool’s usability and the tag structures created, contributing to our understanding of the implications of adding structure to collaborative tagging.
Fraud detection in E-banking by using the hybrid feature selection and evolutionary algorithms

Nowadays, discovering knowledge from the mass set of data is considered by researchers. In this regard, data mining as one of the most efficient tools of data analysis has attracted the attention of many people. The use of different techniques and algorithms of this tool in various fields like customer relationship management, fraud management and detection, medical sciences, sport and etc. proves this claim. It is one of the areas that can be considered as one of the fields of data mining. In today’s world, financial and banking systems and services have been developed with the advancement of information technology and communication infrastructure. Banks and financial institutions have invested the field of modern technologies to provide more updated and efficient products and services. Thus, the variety of relevant products and services and also the number and value of transactions have increased. In addition to this development, securing transactions, detection of new ways of fraud and abuse in financial documents, discovery of finished and unfinished frauds, detection and discovery of processes and operations of money laundering and etc. are among the most challenging issues inj this area. The present study provides a new method for fraud detection in e-banking that is based on a hybrid feature selection and genetic algorithm.
Undertreatment of people with major depressive disorder in 21 countries.

Major depressive disorder (MDD) is a leading cause of disability worldwide.



To examine the: (a) 12-month prevalence of DSM-IV MDD; (b) proportion aware that they have a problem needing treatment and who want care; (c) proportion of the latter receiving treatment; and (d) proportion of such treatment meeting minimal standards.



Representative community household surveys from 21 countries as part of the World Health Organization World Mental Health Surveys.



Of 51 547 respondents, 4.6% met 12-month criteria for DSM-IV MDD and of these 56.7% reported needing treatment. Among those who recognised their need for treatment, most (71.1%) made at least one visit to a service provider. Among those who received treatment, only 41.0% received treatment that met minimal standards. This resulted in only 16.5% of all individuals with 12-month MDD receiving minimally adequate treatment.



Only a minority of participants with MDD received minimally adequate treatment: 1 in 5 people in high-income and 1 in 27 in low-/lower-middle-income countries. Scaling up care for MDD requires fundamental transformations in community education and outreach, supply of treatment and quality of services.