nils-herrmann/clean_scirepeval huggingface dataset:
Kernel Density Estimation of traffic accidents in a network space

A standard planar Kernel Density Estimation (KDE) aims to produce a smooth density surface of spatial point events over a 2-D geographic space. However the planar KDE may not be suited for characterizing certain point events, such as traffic accidents, which usually occur inside a 1-D linear space, the roadway network. This paper presents a novel network KDE approach to estimating the density of such spatial point events. One key feature of the new approach is that the network space is represented with basic linear units of equal network length, termed lixel (linear pixel), and related network topology. The use of lixel not only facilitates the systematic selection of a set of regularly spaced locations along a network for density estimation, but also makes the practical application of the network KDE feasible by significantly improving the computation efficiency. The approach is implemented in the ESRI ArcGIS environment and tested with the year 2005 traffic accident data and a road network in the Bowling Green, Kentucky area. The test results indicate that the new network KDE is more appropriate than standard planar KDE for density estimation of traffic accidents, since the latter covers space beyond the event context (network space) and is likely to overestimate the density values. The study also investigates the impacts on density calculation from two kernel functions, lixel lengths, and search bandwidths. It is found that the kernel function is least important in structuring the density pattern over network space, whereas the lixel length critically impacts the local variation details of the spatial density pattern. The search bandwidth imposes the highest influence by controlling the smoothness of the spatial pattern, showing local effects at a narrow bandwidth and revealing " hot spots " at larger or global scales with a wider bandwidth. More significantly, the idea of representing a linear network by a network system of equal-length lixels may potentially 3 lead the way to developing a suite of other network related spatial analysis and modeling methods.
An Empirical Comparison of Parsing Methods for Stanford Dependencies

Stanford typed dependencies are a widely desired representation of natural language sentences, but parsing is one of the major computational bottlenecks in text analysis systems. In light of the evolving definition of the Stanford dependencies and developments in statistical dependency parsing algorithms, this paper revisits the question of Cer et al. (2010): what is the tradeoff between accuracy and speed in obtaining Stanford dependencies in particular? We also explore the effects of input representations on this tradeoff: part-of-speech tags, the novel use of an alternative dependency representation as input, and distributional representaions of words. We find that direct dependency parsing is a more viable solution than it was found to be in the past. An accompanying software release can be found at: http://www.ark.cs.cmu.edu/TBSD
The architecture of emergent self-organizing maps to reduce projection errors

There are mainly two types of Emergent Self-Organizing Maps (ESOM) grid structures in use: hexgrid (honeycomb like) and quadgrid (trellis like) maps. In addition to that, the shape of the maps may be square or rectangular. This work investigates the effects of these different map layouts. Hexgrids were found to have no convincing advantage over quadgrids. Rectangular maps, however, are distinctively superior to square maps. Most surprisingly, rectangular maps outperform square maps for isotropic data, i.e. data sets with no particular primary direction.
ArikIturri: An Automatic Question Generator Based on Corpora and NLP Techniques

Knowledge construction is expensive for Computer Assisted Assessment. When setting exercise questions, teachers use Test Makers to construct Question Banks. The addition of Automatic Generation to assessment applications decreases the time spent on constructing examination papers. In this article, we present ArikIturri, an Automatic Question Generator for Basque language test questions, which is independent from the test assessment application that uses it. The information source for this question generator consists of linguistically analysed real corpora, represented in XML markup language. ArikIturri makes use of NLP tools. The influence of the robustness of those tools and the used corpora is highlighted in the article. We have proved the viability of ArikIturri when constructing fill-in-the-blank, word formation, multiple choice, and error correction question types. In the evaluation of this automatic generator, we have obtained positive results as regards the generation process and its usefulness.
Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria

The NINCDS-ADRDA and the DSM-IV-TR criteria for Alzheimer's disease (AD) are the prevailing diagnostic standards in research; however, they have now fallen behind the unprecedented growth of scientific knowledge. Distinctive and reliable biomarkers of AD are now available through structural MRI, molecular neuroimaging with PET, and cerebrospinal fluid analyses. This progress provides the impetus for our proposal of revised diagnostic criteria for AD. Our framework was developed to capture both the earliest stages, before full-blown dementia, as well as the full spectrum of the illness. These new criteria are centred on a clinical core of early and significant episodic memory impairment. They stipulate that there must also be at least one or more abnormal biomarkers among structural neuroimaging with MRI, molecular neuroimaging with PET, and cerebrospinal fluid analysis of amyloid beta or tau proteins. The timeliness of these criteria is highlighted by the many drugs in development that are directed at changing pathogenesis, particularly at the production and clearance of amyloid beta as well as at the hyperphosphorylation state of tau. Validation studies in existing and prospective cohorts are needed to advance these criteria and optimise their sensitivity, specificity, and accuracy.
Long-term memory, sleep, and the spacing effect.

Many studies have shown that memory is enhanced when study sessions are spaced apart rather than massed. This spacing effect has been shown to have a lasting benefit to long-term memory when the study phase session follows the encoding session by 24 hours. Using a spacing paradigm we examined the impact of sleep and spacing gaps on long-term declarative memory for Swahili-English word pairs by including four spacing delay gaps (massed, 12 hours same-day, 12 hours overnight, and 24 hours). Results showed that a 12-hour spacing gap that includes sleep promotes long-term memory retention similar to the 24-hour gap. The findings support the importance of sleep to the long-term benefit of the spacing effect.
Efficient Third-Order Dependency Parsers

We present algorithms for higher-order dependency parsing that are “third-order” in the sense that they can evaluate substructures containing three dependencies, and “efficient” in the sense that they require only O(n4) time. Importantly, our new parsers can utilize both sibling-style and grandchild-style interactions. We evaluate our parsers on the Penn Treebank and Prague Dependency Treebank, achieving unlabeled attachment scores of 93.04% and 87.38%, respectively.
WEB 2 . 0 in the tourism industry : A new tourism generation and new e-business models

During the last years, we are experiencing the mushrooming and increased use of web tools enabling Internet users to both create and distribute content (multimedia information). These tools referred to as Web 2.0 technologies-applications can be considered as the tools of mass collaboration, since they empower Internet users to actively participate and simultaneously collaborate with other Internet users for producing, consuming and diffusing the information and knowledge being distributed through the Internet. In other words, Web 2.0 tools do nothing more than realising and exploiting the full potential of the genuine concept and role of the Internet (i.e. the network of the networks that is created and exists for its users). The content and information generated by users of Web 2.0 technologies are having a tremendous impact not only on the profile, expectations and decision making behaviour of Internet users, but also on e-business model that businesses need to develop and/or adapt. The tourism industry is not an exception from such developments. On the contrary, as information is the lifeblood of the tourism industry the use and diffusion of Web 2.0 technologies have a substantial impact of both tourism demand and supply. Indeed, many new types of tourism cyber-intermediaries have been created that are nowadays challenging the e-business model of existing cyberintermediaries that only few years ago have been threatening the existence of intermediaries!. In this vein, the purpose of this article is to analyse the major applications of Web 2.0 technologies in the tourism and hospitality industry by presenting their impact on both demand and supply.
Targeted genome modification technologies and their applications in crop improvements

Recent advances in genome engineering indicate that innovative crops developed by targeted genome modification (TGM) using site-specific nucleases (SSNs) have the potential to avoid the regulatory issues raised by genetically modified organisms. These powerful SSNs tools, comprising zinc-finger nucleases, transcription activator-like effector nucleases, and clustered regulatory interspaced short palindromic repeats/CRISPR-associated systems, enable precise genome engineering by introducing DNA double-strand breaks that subsequently trigger DNA repair pathways involving either non-homologous end-joining or homologous recombination. Here, we review developments in genome-editing tools, summarize their applications in crop organisms, and discuss future prospects. We also highlight the ability of these tools to create non-transgenic TGM plants for next-generation crop breeding.
Automatic Decoding of Facial Movements Reveals Deceptive Pain Expressions

In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1-3]. Two motor pathways control facial movement [4-7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8-11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55%. However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling.
Online aggressor/targets, aggressors, and targets: a comparison of associated youth characteristics.

While most youth report positive experiences and activities online, little is known about experiences of Internet victimization and associated correlates of youth, specifically in regards to Internet harassment.



The Youth Internet Safety Survey is a cross-sectional, nationally representative telephone survey of young regular Internet users in the United States. Interviews were conducted between the fall of 1999 and the spring of 2000 and examined characteristics of Internet harassment, unwanted exposure to sexual material, and sexual solicitation that had occurred on the Internet in the previous year. One thousand, five hundred and one regular Internet users between the ages of 10 and 17 years were interviewed, along with one parent or guardian. To assess the characteristics surrounding Internet harassment, four groups of youth were compared: 1) targets of aggression (having been threatened or embarrassed by someone; or feeling worried or threatened by someone's actions); 2) online aggressors (making rude or nasty comments; or harassing or embarrassing someone with whom the youth was mad at); 3) aggressor/targets (youth who report both being an aggressor as well as a target of Internet harassment); and 4) non-harassment involved youth (being neither a target nor an aggressor online).



Of the 19% of young regular Internet users involved in online aggression, 3% were aggressor/targets, 4% reported being targets only, and 12% reported being online aggressors only. Youth aggressor/targets reported characteristics similar to conventional bully/victim youth, including many commonalities with aggressor-only youth, and significant psychosocial challenge.



Youth aggressor/targets are intense users of the Internet who view themselves as capable web users. Beyond this, however, these youth report significant psychosocial challenge, including depressive symptomatology, problem behavior, and targeting of traditional bullying. Implications for intervention are discussed.
Mid-level Visual Element Discovery as Discriminative Mode Seeking

Recent work on mid-level visual representations aims to capture information at the level of complexity higher than typical “visual words”, but lower than full-blown semantic objects. Several approaches [5, 6, 12, 23] have been proposed to discover mid-level visual elements, that are both 1) representative, i.e., frequently occurring within a visual dataset, and 2) visually discriminative. However, the current approaches are rather ad hoc and difficult to analyze and evaluate. In this work, we pose visual element discovery as discriminative mode seeking, drawing connections to the the well-known and well-studied mean-shift algorithm [2, 1, 4, 8]. Given a weakly-labeled image collection, our method discovers visually-coherent patch clusters that are maximally discriminative with respect to the labels. One advantage of our formulation is that it requires only a single pass through the data. We also propose the Purity-Coverage plot as a principled way of experimentally analyzing and evaluating different visual discovery approaches, and compare our method against prior work on the Paris Street View dataset of . We also evaluate our method on the task of scene classification, demonstrating state-of-the-art performance on the MIT Scene-67 dataset.
Energy Efficient Computational Offloading Framework for Mobile Cloud Computing

The latest developments in mobile computing technology have changed user preferences for computing. However, in spite of all the advancements in the recent years, Smart Mobile Devices (SMDs) are still low potential computing devices which are limited in memory capacity, CPU speed and battery power lifetime. Therefore, Mobile Cloud Computing (MCC) employs computational offloading for enabling computationally intensive mobile applications on SMDs. However, state-of-the-art computational offloading frameworks lack of considering the additional overhead of components migration at runtime. Therefore resources intensive and energy consuming distributed application execution platform is established. This paper proposes a novel distributed Energy Efficient Computational Offloading Framework (EECOF) for the processing of intensive mobile applications in MCC. The framework focuses on leveraging application processing services of cloud datacenters with minimal instances of computationally intensive component migration at runtime. As a result, the size of data transmission and energy consumption cost is reduced in computational offloading for MCC. We evaluate the proposed framework by benchmarking prototype application in the real MCC environment. Analysis of the results show that by employing EECOF the size of data transmission over the wireless network medium is reduced by 84 % and energy consumption cost is reduced by 69.9 % in offloading different components of the prototype application. Hence, EECOF provides an energy efficient application layer solution for computational offloading in MCC.
Skeletal Strokes

A skeletal stroke is a kind of general brush stroke for changing the shape of pictures as if by bending, shearing, twisting, while conservating the aspect ratio of selected features on the picture. It is neither a simple warping nor texture mapping technique, but a new method for controlling the deformation of a picture. A deformation model of a coordinate system has been proposed taking into account cases of discontinuous or extreme bending. Complicated pictures can be built up hierarchically by defining higher order strokes and recursive strokes. It is therefore a powerful general drawing tool and extended image transformation instrument. The use of skeletal strokes as a replacement for affine transformations in IFS coding has been explored. A novel general anchoring mechanism is proposed, which allows arbitrary control of any point in the picture. This control flexibility is particularly desirable in computer animation and digital typography. As a result, virtual ‘2-D models’ of cartoon characters as well as pseudo 3-D objects can be created and manipulated with ease.
Neural Trojans

While neural networks demonstrate stronger capabilities in pattern recognition nowadays, they are also becoming larger and deeper. As a result, the effort needed to train a network also increases dramatically. In many cases, it is more practical to use a neural network intellectual property (IP) that an IP vendor has already trained. As we do not know about the training process, there can be security threats in the neural IP: the IP vendor (attacker) may embed hidden malicious functionality, i.e neural Trojans, into the neural IP. We show that this is an effective attack and provide three mitigation techniques: input anomaly detection, re-training, and input preprocessing. All the techniques are proven effective. The input anomaly detection approach is able to detect 99.8% of Trojan triggers although with 12.2% false positive. The re-training approach is able to prevent 94.1% of Trojan triggers from triggering the Trojan although it requires that the neural IP be reconfigurable. In the input preprocessing approach, 90.2% of Trojan triggers are rendered ineffective and no assumption about the neural IP is needed.
Disentangling Motion, Foreground and Background Features in Videos

This paper introduces an unsupervised framework to extract semantically rich features for video representation. Inspired by how the human visual system groups objects based on motion cues, we propose a deep convolutional neural network that disentangles motion, foreground and background information. The proposed architecture consists of a 3D convolutional feature encoder for blocks of 16 frames, which is trained for reconstruction tasks over the first and last frames of the sequence. A preliminary supervised experiment was conducted to verify the feasibility of proposed method by training the model with a fraction of videos from the UCF-101 dataset taking as ground truth the bounding boxes around the activity regions. Qualitative results indicate that the network can successfully segment foreground and background in videos as well as update the foreground appearance based on disentangled motion features. The benefits of these learned features are shown in a discriminative classification task, where initializing the network with the proposed pretraining method outperforms both random initialization and autoencoder pretraining. Our model and source code are publicly available at https: //allenovo.github.io/cvprw17_webpage/ .
Data augmentation using synthetic data for time series classification with deep residual networks

Data augmentation in deep neural networks is the process of generating artificial data in order to reduce the variance of the classifier with the goal to reduce the number of errors. This idea has been shown to improve deep neural network’s generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike in image recognition problems, data augmentation techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted. In this paper, we fill this gap by investigating the application of a recently proposed data augmentation technique based on the Dynamic Time Warping distance, for a deep learning model for TSC. To evaluate the potential of augmenting the training set, we performed extensive experiments using the UCR TSC benchmark. Our preliminary experiments reveal that data augmentation can drastically increase deep CNN’s accuracy on some datasets and significantly improve the deep model’s accuracy when the method is used in an ensemble approach.
Validation of the Spanish versions of the long (26 items) and short (12 items) forms of the Self-Compassion Scale (SCS)

Self-compassion is a key psychological construct for assessing clinical outcomes in mindfulness-based interventions. The aim of this study was to validate the Spanish versions of the long (26 item) and short (12 item) forms of the Self-Compassion Scale (SCS).



The translated Spanish versions of both subscales were administered to two independent samples: Sample 1 was comprised of university students (n = 268) who were recruited to validate the long form, and Sample 2 was comprised of Aragon Health Service workers (n = 271) who were recruited to validate the short form. In addition to SCS, the Mindful Attention Awareness Scale (MAAS), the State-Trait Anxiety Inventory-Trait (STAI-T), the Beck Depression Inventory (BDI) and the Perceived Stress Questionnaire (PSQ) were administered. Construct validity, internal consistency, test-retest reliability and convergent validity were tested.



The Confirmatory Factor Analysis (CFA) of the long and short forms of the SCS confirmed the original six-factor model in both scales, showing goodness of fit. Cronbach's α for the 26 item SCS was 0.87 (95% CI = 0.85-0.90) and ranged between 0.72 and 0.79 for the 6 subscales. Cronbach's α for the 12-item SCS was 0.85 (95% CI = 0.81-0.88) and ranged between 0.71 and 0.77 for the 6 subscales. The long (26-item) form of the SCS showed a test-retest coefficient of 0.92 (95% CI = 0.89-0.94). The Intraclass Correlation (ICC) for the 6 subscales ranged from 0.84 to 0.93. The short (12-item) form of the SCS showed a test-retest coefficient of 0.89 (95% CI: 0.87-0.93). The ICC for the 6 subscales ranged from 0.79 to 0.91. The long and short forms of the SCS exhibited a significant negative correlation with the BDI, the STAI and the PSQ, and a significant positive correlation with the MAAS. The correlation between the total score of the long and short SCS form was r = 0.92.



The Spanish versions of the long (26-item) and short (12-item) forms of the SCS are valid and reliable instruments for the evaluation of self-compassion among the general population. These results substantiate the use of this scale in research and clinical practice.