nils-herrmann/clean_scirepeval huggingface dataset:
Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning.

A comprehensive mission for schools is to educate students to be knowledgeable, responsible, socially skilled, healthy, caring, and contributing citizens. This mission is supported by the growing number of school-based prevention and youth development programs. Yet, the current impact of these programs is limited because of insufficient coordination with other components of school operations and inattention to implementation and evaluation factors necessary for strong program impact and sustainability. Widespread implementation of beneficial prevention programming requires further development of research-based, comprehensive school reform models that improve social, health, and academic outcomes; educational policies that demand accountability for fostering children's full development; professional development that prepares and supports educators to implement programs effectively; and systematic monitoring and evaluation to guide school improvement.
Ecoenzymatic Stoichiometry and Ecological Theory

The net primary production of the biosphere is consumed largely by microorganisms; whose metabolism creates the trophic base for detrital foodwebs, drives element cycles, and mediates atmospheric composition. Biogeochemical constraints on microbial catabolism, relative to primary production, create reserves of detrital organic carbon in soils and sediments that exceed the carbon content of the atmosphere and biomass. The production of organic matter is an intracellular process that generates thousands of compounds from a small number of precursors drawn from intermediary metabolism. Osmotrophs generate growth substrates from the products of biosynthesis and diagenesis by enzyme-catalyzed reactions that occur largely outside cells. These enzymes, which we define as ecoenzymes, enter the environment by secretion and lysis. Enzyme expression is regulated by environmental signals, but once released from the cell, ecoenzymatic activity is determined by environmental interactions, represented as a kinetic cascade, that lead to multiphasic kinetics and large spatiotemporal variation. At the ecosystem level, these interactions can be viewed as an energy landscape that directs the availability and flow of resources. Ecoenzymatic activity and microbial metabolism are integrated on the basis of resource demand relative to environmental availability. Macroecological studies show that the most widely measured ecoenzymatic activities have a similar stoichiometry for all microbial communities. Ecoenzymatic stoichiometry connects the elemental stoichiometry of microbial biomass and detrital organic matter to microbial nutrient assimilation and growth. We present a model that combines the kinetics of enzyme activity and community growth under conditions of multiple resource limitation with elements of metabolic and ecological stoichiometry theory. This biogeochemical equilibrium model provides a framework for comparative studies of microbial community metabolism, the principal driver of biogeochemical cycles.
Contagion dynamics of extremist propaganda in social networks

Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified. By using a computational tool known as dynamic activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters. We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion. Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.
A Study of Efficient Power Consumption Wireless Communication Techniques/ Modules for Internet of Things (IoT) Applications

A study of wireless technologies for IoT applications in terms of power consumption has been presented in this paper. The study focuses on the importance of using low power wireless techniques and modules in IoT applications by introducing a comparative between different low power wireless communication techniques such as ZigBee, Low Power Wi-Fi, 6LowPAN, LPWA and their modules to conserve power and longing the life for the IoT network sensors. The approach of the study is in term of protocol used and the particular module that achieve that protocol. The candidate protocols are classified according to the range of connectivity between sensor nodes. For short ranges connectivity the candidate protocols are ZigBee, 6LoWPAN and low power Wi-Fi. For long connectivity the candidate is LoRaWAN protocol. The results of the study demonstrate that the choice of module for each protocol plays a vital role in battery life due to the difference of power consumption for each module/protocol. So, the evaluation of protocols with each other depends on the module used.
Recent developments in human motion analysis

Visual analysis of human motion is currently one of the most active research topics in computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human motion analysis concerns the detection, tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. This paper provides a comprehensive survey of research on computer-vision-based human motion analysis. The emphasis is on three major issues involved in a general human motion analysis system, namely human detection, tracking and activity understanding. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are discussed. ? 2002 Pattern Recognition Society.
Model-Based Reinforcement Learning via Meta-Policy Optimization

Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve the same asymptotic performance as model-free methods. We propose Model-Based MetaPolicy-Optimization (MB-MPO), an approach that foregoes the strong reliance on accurate learned dynamics models. Using an ensemble of learned dynamic models, MB-MPO meta-learns a policy that can quickly adapt to any model in the ensemble with one policy gradient step. This steers the meta-policy towards internalizing consistent dynamics predictions among the ensemble while shifting the burden of behaving optimally w.r.t. the model discrepancies towards the adaptation step. Our experiments show that MB-MPO is more robust to model imperfections than previous model-based approaches. Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.
Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction

The growth of fingerprint databases creates a need for strategies to reduce the identification time. Fingerprint classification reduces the search penetration rate by grouping the fingerprints into several classes. Typically, features describing the visual patterns of a fingerprint are extracted and fed to a classifier. The extraction can be time-consuming and error-prone, especially for fingerprints whose visual classification is dubious, and often includes a criterion to reject ambiguous fingerprints. In this paper, we propose to improve on this manually designed process by using deep neural networks, which extract implicit features directly from the images and perform the classification within a single learning process. An extensive experimental study assesses that convolutional neural networks outperform all other tested approaches by achieving a very high accuracy with no rejection. Moreover, multiple copies of the same fingerprint are consistently classified. The runtime of convolutional networks is also lower than that of combining feature extraction procedures with classification algorithms.
A Method for Text Localization and Recognition in Real-World Images

A general method for text localization and recognition in real-world images is presented. The proposed method is novel, as it (i) departs from a strict feed-forward pipeline and replaces it by a hypothesesverification framework simultaneously processing multiple text line hypotheses, (ii) uses synthetic fonts to train the algorithm eliminating the need for time-consuming acquisition and labeling of real-world training data and (iii) exploits Maximally Stable Extremal Regions (MSERs) which provides robustness to geometric and illumination conditions. The performance of the method is evaluated on two standard datasets. On the Char74k dataset, a recognition rate of 72% is achieved, 18% higher than the state-of-the-art. The paper is first to report both text detection and recognition results on the standard and rather challenging ICDAR 2003 dataset. The text localization works for number of alphabets and the method is easily adapted to recognition of other scripts, e.g. cyrillics.
High School Students’ Perceptions of Motivations for Cyberbullying: An Exploratory Study

Internet usage has increased in recent years resulting in a growing number of documented reports of cyberbullying. Despite the rise in cyberbullying incidents, there is a dearth of research regarding high school students' motivations for cyberbullying. The purpose of this study was to investigate high school students' perceptions of the motivations for cyberbullying.



We undertook an exploratory qualitative study with 20 high school students, conducting individual interviews using a semi-structured interview protocol. Data were analyzed using Grounded Theory.



The developed coding hierarchy provides a framework to conceptualize motivations, which can be used to facilitate future research about motivations and to develop preventive interventions designed to thwart the negative effects of cyberbullying. The findings revealed that high school students more often identified internally motivated reasons for cyberbullying (e.g., redirect feelings) than externally motivated (no consequences, non-confrontational, target was different).



Uncovering the motivations for cyberbullying should promote greater understanding of this phenomenon and potentially reduce the interpersonal violence that can result from it. By providing a framework that begins to clarify the internal and external factors motivating the behavior, there is enhanced potential to develop effective preventive interventions to prevent cyberbullying and its negative effects.
OrthoNoC: A Broadcast-Oriented Dual-Plane Wireless Network-on-Chip Architecture

On-chip communication remains as a key research issue at the gates of the manycore era. In response to this, novel interconnect technologies have opened the door to new Network-on-Chip (NoC) solutions towards greater scalability and architectural flexibility. Particularly, wireless on-chip communication has garnered considerable attention due to its inherent broadcast capabilities, low latency, and system-level simplicity. This work presents OrthoNoC, a wired-wireless architecture that differs from existing proposals in that both network planes are decoupled and driven by traffic steering policies enforced at the network interfaces. With these and other design decisions, OrthoNoC  seeks to emphasize the ordered broadcast advantage offered by the wireless technology. The performance and cost of OrthoNoC are first explored using synthetic traffic, showing substantial improvements with respect to other wired-wireless designs with a similar number of antennas. Then, the applicability of OrthoNoC in the multiprocessor scenario is demonstrated through the evaluation of a simple architecture that implements fast synchronization via ordered broadcast transmissions. Simulations reveal significant execution time speedups and communication energy savings for 64-threaded benchmarks, proving that the value of OrthoNoC goes beyond simply improving the performance of the on-chip interconnect.
Romantic love conceptualized as an attachment process.

This article explores the possibility that romantic love is an attachment process--a biosocial process by which affectional bonds are formed between adult lovers, just as affectional bonds are formed earlier in life between human infants and their parents. Key components of attachment theory, developed by Bowlby, Ainsworth, and others to explain the development of affectional bonds in infancy, were translated into terms appropriate to adult romantic love. The translation centered on the three major styles of attachment in infancy--secure, avoidant, and anxious/ambivalent--and on the notion that continuity of relationship style is due in part to mental models (Bowlby's "inner working models") of self and social life. These models, and hence a person's attachment style, are seen as determined in part by childhood relationships with parents. Two questionnaire studies indicated that relative prevalence of the three attachment styles is roughly the same in adulthood as in infancy, the three kinds of adults differ predictably in the way they experience romantic love, and attachment style is related in theoretically meaningful ways to mental models of self and social relationships and to relationship experiences with parents. Implications for theories of romantic love are discussed, as are measurement problems and other issues related to future tests of the attachment perspective.
Latent Class Models for Collaborative Filtering

This paper presents a statistical approach to collaborative ltering and investigates the use of latent class models for predicting individual choices and preferences based on observed preference behavior. Two models are discussed and compared: the aspect model, a probabilistic latent space model which models individual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for di erent variants of the aspect model and derive an approximate EM algorithmbased on a variational principle for the two-sided clustering model. The bene ts of the di erent models are experimentally investigated on a large movie data set.
Using Bayes to get the most out of non-significant results

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory's predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.
Deep Predictive Models in Interactive Music

Musical performance requires prediction to operate instruments, to perform in groups and to improvise. We argue, with reference to a number of digital music instruments (DMIs), including two of our own, that predictive machine learning models can help interactive systems to understand their temporal context and ensemble behaviour. We also discuss how recent advances in deep learning highlight the role of prediction in DMIs, by allowing data-driven predictive models with a long memory of past states. We advocate for predictive musical interaction, where a predictive model is embedded in a musical interface, assisting users by predicting unknown states of musical processes. We propose a framework for characterising prediction as relating to the instrumental sound, ongoing musical process, or between members of an ensemble. Our framework shows that different musical interface design configurations lead to different types of prediction. We show that our framework accommodates deep generative models, as well as models for predicting gestural states, or other high-level musical information. We apply our framework to examples from our recent work and the literature, and discuss the benefits and challenges revealed by these systems as well as musical use-cases where prediction is a necessary component.
Keynote: small neural nets are beautiful: enabling embedded systems with small deep-neural- network architectures

Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications. As a result, Deep Neural Networks have supplanted other approaches to solving problems in these areas, and enabled many new applications. While the design of Deep Neural Nets is still something of an art form, in our work we have found basic principles of design space exploration used to develop embedded microprocessor architectures to be highly applicable to the design of Deep Neural Net architectures. In particular, we have used these design principles to create a novel Deep Neural Net called SqueezeNet that requires only 480KB of storage for its model parameters. We have further integrated all these experiences to develop something of a playbook for creating small Deep Neural Nets for embedded systems.
Recreational marijuana legalization and college student use: Early evidence☆

We analyze marijuana use by college undergraduates before and after legalization of recreational marijuana. Using survey data from the National College Health Assessment, we show that students at Washington State University experienced a significant increase in marijuana use after legalization. This increase is larger than would be predicted by national trends. The change is strongest among females, Black students, and Hispanic students. The increase for underage students is as much as for legal-age students. We find no corresponding changes in the consumption of tobacco, alcohol, or other drugs.
A survey on context awareness

We have reviewed the research of context awareness of recent years in this paper. First of all, we describe the definitions of context and context awareness which are made by different scholars in different fields, and propose our own definition. In addition, different classifications of context and context awareness are analyzed. We focus on context acquisition and sensing, context modeling and representation, context filtering and fusion, context storage and retrieval in context awareness computing. Finally, the context awareness application of domestic and foreign are discussed, and the development of the context awareness is prospected.
Thai car license plate recognition using essential-elements-based method

There has been several research on car license plate recognition (LPR). However, the number of research on Thai LPR is limited, besides, most of which are published in Thai. The existing work on Thai LPR have faced problems caused by low- quality license plates and a great number of similar characters that exist in Thai alphabets. Generally, car license plates in Thailand come in different conditions, ranging from new license plates of excellent quality to low-quality ones with screws on or even some paint already peeled off. Thai characters that appear on Thai license plates are also generally shape-complicated. Area-based methods, such as conventional template matching, are ineffective to low-quality or resembling characters. To cope with these problems, this paper presents a new method, which recognizes the character patterns relying only on essential elements of characters. This method lies on the concept that Thai characters can be distinguished by essential elements that form up different shapes. Similar characters are handled by directly focusing on their obvious differences among them. The overall success rate of the proposed method, tested on 300 actual license plates of various qualities, is 85.33%.
What leads to romantic attraction: similarity, reciprocity, security, or beauty? Evidence from a speed-dating study.

Years of attraction research have established several "principles" of attraction with robust evidence. However, a major limitation of previous attraction studies is that they have almost exclusively relied on well-controlled experiments, which are often criticized for lacking ecological validity. The current research was designed to examine initial attraction in a real-life setting-speed-dating. Social Relations Model analyses demonstrated that initial attraction was a function of the actor, the partner, and the unique dyadic relationship between these two. Meta-analyses showed intriguing sex differences and similarities. Self characteristics better predicted women's attraction than they did for men, whereas partner characteristics predicted men's attraction far better than they did for women. The strongest predictor of attraction for both sexes was partners' physical attractiveness. Finally, there was some support for the reciprocity principle but no evidence for the similarity principle.
Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases

Translating natural language questions to semantic representations such as SPARQL is a core challenge in open-domain question answering over knowledge bases (KB-QA). Existing methods rely on a clear separation between an offline training phase, where a model is learned, and an online phase where this model is deployed. Two major shortcomings of such methods are that (i) they require access to a large annotated training set that is not always readily available and (ii) they fail on questions from before-unseen domains. To overcome these limitations, this paper presents NEQA, a continuous learning paradigm for KB-QA. Offline, NEQA automatically learns templates mapping syntactic structures to semantic ones from a small number of training question-answer pairs. Once deployed, continuous learning is triggered on cases where templates are insufficient. Using a semantic similarity function between questions and by judicious invocation of non-expert user feedback, NEQA learns new templates that capture previously-unseen syntactic structures. This way, NEQA gradually extends its template repository. NEQA periodically re-trains its underlying models, allowing it to adapt to the language used after deployment. Our experiments demonstrate NEQA’s viability, with steady improvement in answering quality over time, and the ability to answer questions from new domains.