nils-herrmann/clean_scirepeval huggingface dataset:
Content-Centric Networking in the Internet of Things

According to an estimate several billion smart devices will be connected to the Internet by year 2020. This exponential increase in devices is a challenge to the current Internet architecture, where connectivity is based on host-to-host communication. Information-Centric Networking is a novel networking paradigm in which data is addressed by its name instead of location. Several ICN architecture proposals have emerged from research communities to address challenges introduced by the current Internet Protocol (IP) regarding e.g. scalability. Content-Centric Networking (CCN) is one of the proposals. In this paper we present a way to use CCN in an Internet of Things (IoT) context. We quantify the benefits from hierarchical content naming, transparent in-network caching and other information-centric networking characteristics in a sensor environment. As a proof of concept we implemented a presentation bridge for a home automation system that provides services to the network through CCN.
Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language

Lenneberg (1967) hypothesized that language could be acquired only within a critical period, extending from early infancy until puberty. In its basic form, the critical period hypothesis need only have consequences for first language acquisition. Nevertheless, it is essential to our understanding of the nature of the hypothesized critical period to determine whether or not it extends as well to second language acquisition. If so, it should be the case that young children are better second language learners than adults and should consequently reach higher levels of final proficiency in the second language. This prediction was tested by comparing the English proficiency attained by 46 native Korean or Chinese speakers who had arrived in the United States between the ages of 3 and 39, and who had lived in the United States between 3 and 26 years by the time of testing. These subjects were tested on a wide variety of structures of English grammar, using a grammaticality judgment task. Both correlational and t-test analyses demonstrated a clear and strong advantage for earlier arrivals over the later arrivals. Test performance was linearly related to age of arrival up to puberty; after puberty, performance was low but highly variable and unrelated to age of arrival. This age effect was shown not to be an inadvertent result of differences in amount of experience with English, motivation, self-consciousness, or American identification. The effect also appeared on every grammatical structure tested, although the structures varied markedly in the degree to which they were well mastered by later learners. The results support the conclusion that a critical period for language acquisition extends its effects to second language acquisition.
Event related potentials to emotional adjectives during reading.

We investigated to what extent emotional connotation influences cortical potentials during reading. To this end, event-related potentials (ERPs) were recorded during reading of high arousal pleasant and unpleasant and low arousal neutral adjectives that were presented at rates of 1 Hz and 3 Hz. Enhanced processing of both pleasant and unpleasant emotional compared to neutral adjectives was first reflected in an amplified early posterior negativity (EPN) starting from 200 ms after word onset. Later potentials (>300 ms), as analyzed in the slower 1 Hz condition, revealed facilitated processing selectively for pleasant adjectives that were associated with a reduced N400 and an enhanced late positive potential (LPP). Pleasant adjectives were also better remembered in an incidental memory test. Thus, emotionally relevant adjectives are processed spontaneously and selectively. Initially, emotional arousal drives attention capture (EPN). Healthy subjects may have a natural bias toward pleasant information facilitating late ERPs (N400, LPP) to pleasant adjectives as well as their superior recall.
A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through artificial neural networks, that has been shown to well cope with several of the well known issues for background maintenance. Here, we propose a spatial coherence variant to such approach to enhance robustness against false detections and formulate a fuzzy model to deal with decision problems typically arising when crisp settings are involved. We show through experimental results and comparisons that higher accuracy values can be reached for color video sequences that represent typical situations critical for moving object detection.
An ensemble of neural networks for weather forecasting

This study presents the applicability of an ensemble of artificial neural networks (ANNs) and learning paradigms for weather forecasting in southern Saskatchewan, Canada. The proposed ensemble method for weather forecasting has advantages over other techniques like linear combination. Generally, the output of an ensemble is a weighted sum, which are weight-fixed, with the weights being determined from the training or validation data. In the proposed approach, weights are determined dynamically from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight. The proposed ensemble model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN), radial basis function network (RBFN), Hopfield model (HFM) predictive models and regression techniques. The data of temperature, wind speed and relative humidity are used to train and test the different models. With each model, 24-h-ahead forecasts are made for the winter, spring, summer and fall seasons. Moreover, the performance and reliability of the seven models are then evaluated by a number of statistical measures. Among the direct approaches employed, empirical results indicate that HFM is relatively less accurate and RBFN is relatively more reliable for the weather forecasting problem. In comparison, the ensemble of neural networks produced the most accurate forecasts.
Cross-Domain Recommendation via Cluster-Level Latent Factor Model

Recommender systems always aim to provide recommendations for a user based on historical ratings collected from a single domain (e.g., movies or books) only, which may suffer from the data sparsity problem. Recently, several recommendation models have been proposed to transfer knowledge by pooling together the rating data from multiple domains to alleviate the sparsity problem, which typically assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. In practice, however, the related domains do not necessarily share such a common rating pattern, and diversity among the related domains might outweigh the advantages of such common pattern, which may result in performance degradations. In this paper, we propose a novel cluster-level based latent factor model to enhance the cross-domain recommendation, which can not only learn the common rating pattern shared across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns of users in each domain that involve the discriminative information propitious to performance improvement. To this end, the proposed model is formulated as an optimization problem based on joint nonnegative matrix tri-factorization and an efficient alternating minimization algorithm is developed with convergence guarantee. Extensive experiments on several real world datasets suggest that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.
Sexism in online video games: The role of conformity to masculine norms and social dominance orientation

Sexism toward women in online video game environments has become a pervasive and divisive issue in the gaming community. In this study, we sought to determine what personality traits, demographic variables, and levels of game play predicted sexist attitudes towards women who play video games. Male and female participants (N = 301) who were players of networked video games were invited to participate in an anonymous online survey. Social dominance orientation and conformity to some types of masculine norms (desire for power over women and the need for heterosexual self-presentation) predicted higher scores on the Video Game Sexism Scale (i.e., greater sexist beliefs about women and gaming). Implications for the social gaming environment and female gamers are discussed.
Mind Wandering Behind the Wheel: Performance and Oculomotor Correlates

An experiment studied the frequency and correlates of driver mind wandering.



Driver mind wandering is associated with risk for crash involvement. The present experiment examined the performance and attentional changes by which this effect might occur.



Participants performed a car-following task in a high-fidelity driving simulator and were asked to report any time they caught themselves mind wandering. Vehicle control and eye movement data were recorded.



As compared with their attentive performance, participants showed few deficits in vehicle control while mind wandering but tended to focus visual attention narrowly on the road ahead.



Data suggest that mind wandering can engender a failure to monitor the environment while driving.



Results identify behavioral correlates and potential risks of mind wandering that might enable efforts to detect and mitigate driver inattention.
Forecasting Social Unrest Using Activity Cascades

Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen "on the ground." Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.
Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges

The unabated flurry of research activities to augment various mobile devices by leveraging heterogeneous cloud resources has created a new research domain called Mobile Cloud Computing (MCC). In the core of such a non-uniform environment, facilitating interoperability, portability, and integration among heterogeneous platforms is nontrivial. Building such facilitators in MCC requires investigations to understand heterogeneity and its challenges over the roots. Although there are many research studies in mobile computing and cloud computing, convergence of these two areas grants further academic efforts towards flourishing MCC. In this paper, we define MCC, explain its major challenges, discuss heterogeneity in convergent computing (i.e. mobile computing and cloud computing) and networking (wired and wireless networks), and divide it into two dimensions, namely vertical and horizontal. Heterogeneity roots are analyzed and taxonomized as hardware, platform, feature, API, and network. Multidimensional heterogeneity in MCC results in application and code fragmentation problems that impede development of cross-platform mobile applications which is mathematically described. The impacts of heterogeneity in MCC are investigated, related opportunities and challenges are identified, and predominant heterogeneity handling approaches like virtualization, middleware, and service oriented architecture (SOA) are discussed. We outline open issues that help in identifying new research directions in MCC.
Toward robust early-warning models: A horse race, ensembles and model uncertainty

This paper presents first steps toward robust early-warning models. We conduct a horse race of conventional statistical methods and more recent machine learning methods. As early-warning models based upon one approach are oftentimes built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods. Further, we test various ensemble approaches to aggregating the information products of the built early-warning models, providing a more robust basis for measuring country-level vulnerabilities. Finally, we provide approaches to estimating model uncertainty in early-warning exercises, particularly model performance uncertainty and model output uncertainty. The approaches put forward in this paper are shown with Europe as a playground.
Learning of Human-like Algebraic Reasoning Using Deep Feedforward Neural Networks

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals.
AN EXPLORATORY STUDY ON THE DETERMINANTS OF SMARTPHONE APP PURCHASE

Along with the growth of the smartphone market, the furnishing of diverse and plentiful mobile Apps is surfacing as the key competitiveness of smartphones. The mobile App market has been appraised as a competitive new market carrying huge potential. Although many users download and use paid and free Apps from App Store and App Market, relevant research regarding consumer app buying is virtually non-existent. This study aims to examine the key determinants in deciding the purchase of Smartphone Apps. Because customers may have different consideration factors in deciding the purchase depending on the App type, we first classified Apps into 4 types: Productivity, Entertainment, Information, and Networking, With interviews with 30 App buyers, we identified the antecedents in the purchase of App in each type and compared them across the four types. This study has several implications for research and practice. Especially, the findings provide guidance to App developers and marketers in promoting the sales of App.
Enhancing LambdaMART Using Oblivious Trees

Learning to rank is a machine learning technique broadly used in many areas such as document retrieval, collaborative filtering or question answering. We present experimental results which suggest that the performance of the current state-of-the-art learning to rank algorithm LambdaMART, when used for document retrieval for search engines, can be improved if standard regression trees are replaced by oblivious trees. This paper provides a comparison of both variants and our results demonstrate that the use of oblivious trees can improve the performance by more than 2.2%. Additional experimental analysis of the influence of a number of features and of a size of the training set is also provided and confirms the desirability of properties of oblivious decision trees.
A survey: Several technologies of non-orthogonal transmission for 5G

One key advantage of 4G OFDM system is the relatively simple receiver implementation due to the orthogonal resource allocation. However, from sum-capacity and spectral efficiency points of view, orthogonal systems are never the achieving schemes. With the rapid development of mobile communication systems, a novel concept of non-orthogonal transmission for 5G mobile communications has attracted researches all around the world. In this trend, many new multiple access schemes and waveform modulation technologies were proposed. In this paper, some promising ones of them were discussed which include Non-orthogonal Multiple Access (NOMA), Sparse Code Multiple Access (SCMA), Multi-user Shared Access (MUSA), Pattern Division Multiple Access (PDMA) and some main new waveforms including Filter-bank based Multicarrier (FBMC), Universal Filtered Multi-Carrier (UFMC), Generalized Frequency Division Multiplexing (GFDM). By analyzing and comparing features of these technologies, a research direction of guiding on future 5G multiple access and waveform are given.
Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides job awareness and avoids stragglers by a new technique, called Sticky Batch Probing (SBP).
 The dynamic partitioning of the data center nodes is accomplished by a technique called Succinct State Sharing (SSS), in which the distributed schedulers are informed of the locations where long jobs are executing. SSS is particularly easy to implement with a hybrid scheduler, in which the centralized scheduler places long jobs.
 With SBP, when a distributed scheduler places a probe for a job on a node, the probe stays there until all tasks of the job have been completed. When finishing the execution of a task corresponding to probe P, rather than executing a task corresponding to the next probe P' in its queue, the node may choose to execute another task corresponding to P. We use SBP in combination with a distributed approximation of Shortest Remaining Processing Time (SRPT) with starvation prevention.
 We have implemented Eagle as a Spark plugin, and we have measured job completion times for a subset of the Google trace on a 100-node cluster for a variety of cluster loads. We provide simulation results for larger clusters, different traces, and for comparison with other scheduling disciplines. We show that Eagle outperforms other state-of-the-art scheduling solutions at most percentiles, and is more robust against mis-estimation of task duration.
Software clones in scratch projects: on the presence of copy-and-paste in computational thinking learning

Computer programming is being introduced in schools worldwide as part of a movement that promotes Computational Thinking (CT) skills among young learners. In general, learners use visual, block-based programming languages to acquire these skills, with Scratch being one of the most popular ones. Similar to professional developers, learners also copy and paste their code, resulting in duplication. In this paper we present the findings of correlating the assessment of the CT skills of learners with the presence of software clones in over 230,000 projects obtained from the Scratch platform. Specifically, we investigate i) if software cloning is an extended practice in Scratch projects, ii) if the presence of code cloning is independent of the programming mastery of learners, iii) if code cloning can be found more frequently in Scratch projects that require specific skills (as parallelism or logical thinking), and iv) if learners who have the skills to avoid software cloning really do so. The results show that i) software cloning can be commonly found in Scratch projects, that ii) it becomes more frequent as learners work on projects that require advanced skills, that iii) no CT dimension is to be found more related to the absence of software clones than others, and iv) that learners -even if they potentially know how to avoid cloning- still copy and paste frequently. The insights from this paper could be used by educators and learners to determine when it is pedagogically more effective to address software cloning, by educational programming platform developers to adapt their systems, and by learning assessment tools to provide better evaluations.
Virtual AoA and AoD estimation for sparse millimeter wave MIMO channels

In this paper, we investigate an angle of arrival (AoA) and angle of departure (AoD) estimation algorithm for sparse millimeter wave multiple-input multiple-output (MIMO) channels. The analytical channel model whose use we advocate here is the beam space (or virtual) MIMO channel representation. By leveraging the beam space MIMO concept, we characterize probabilistic channel priors under an analog precoding and combining constraints. This investigation motivates Bayesian inference approaches to virtual AoA and AoD estimation. We divide the estimation task into downlink sounding for AoA estimation and uplink sounding for AoD estimation. A belief propagation (BP)-type algorithm is adopted, leading to computationally efficient approximate message passing (AMP) and approximate log-likelihood ratio testing (ALLRT) algorithms. Numerical results demonstrate that the proposed algorithm outperforms the conventional AMP in terms of the AoA and AoD estimation accuracy for the sparse millimeter wave MIMO channel.
Speech Feature Extraction Techniques : A Review

This paper provides a survey on speech recognition and discusses the techniques and system that enables computers to accept speech as input. This paper shows the major developments in the field of speech recognition. This paper highlights the speech recognition techniques and provides a brief description about the four stages in which the speech recognition techniques are classified. In addition, this paper gives a description of four feature extraction techniques: Linear Predictive Coding (LPC), Mel-frequency cepstrum (MFFCs), RASTA filtering and Probabilistic Linear Discriminate Analysis (PLDA). The objective of this paper is to summarize the feature extraction techniques used in speech recognition system.
Adaptive document image binarization

A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type-related degradations are addressed. Two new algorithms are applied to determine a local threshold for each pixel. The performance evaluation of the algorithm utilizes test images with ground-truth, evaluation metrics for binarization of textual and synthetic images, and a weight-based ranking procedure for the "nal result presentation. The proposed algorithms were tested with images including di!erent types of document components and degradations. The results were compared with a number of known techniques in the literature. The benchmarking results show that the method adapts and performs well in each case qualitatively and quantitatively. ( 1999 Pattern Recognition Society.