nils-herrmann/clean_scirepeval huggingface dataset:
Effectiveness of Torymus sinensis: a successful long-term control of the Asian chestnut gall wasp in Italy

The biocontrol agent Torymus sinensis has been released into Japan, the USA, and Europe to suppress the Asian chestnut gall wasp, Dryocosmus kuriphilus. In this study, we provide a quantitative assessment of T. sinensis effectiveness for suppressing gall wasp infestations in Northwest Italy by annually evaluating the percentage of chestnuts infested by D. kuriphilus (infestation rate) and the number of T. sinensis adults that emerged per 100 galls (emergence index) over a 9-year period. We recorded the number of T. sinensis adults emerging from a total of 64,000 galls collected from 23 sampling sites. We found that T. sinensis strongly reduced the D. kuriphilus population, as demonstrated by reduced galls and an increased T. sinensis emergence index. Specifically, in Northwest Italy, the infestation rate was nearly zero 9 years after release of the parasitoid with no evidence of resurgence in infestation levels. In 2012, the number of T. sinensis females emerging per 100 galls was approximately 20 times higher than in 2009. Overall, T. sinensis proved to be an outstanding biocontrol agent, and its success highlights how the classical biological control approach may represent a cost-effective tool for managing an exotic invasive pest.
Deep Text Style Transfer

We propose to transfer the content of a text written in a certain style to an alternative text written in a different style, while maintaining as much as possible of the original meaning. Our work is inspired by recent progress of applying style transfer to images, as well as attempts to replicate the results to text. Our model is a deep neural network based on Generative Adversarial Networks (GAN). Our novelty is replacing the discrete next-word prediction with prediction in the embedding space, which provides two benefits (1) train the GAN without using gradient approximations and (2) provide semantically related results even for failure cases.
Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge

Skin cancer, the most common human malignancy, is primarily diagnosed visually by physicians . Classification with an automated method like CNN [2, 3] shows potential for diagnosing the skin cancer according to the medical photographs. By now, the deep convolutional neural networks can achieve the level of human dermatologist . This work is dedicated on developing a Deep Learning method for ISIC  2017 Skin Lesion Detection Competition to classify the dermatology pictures, which is aiming at improving the diagnostic accuracy rate. As an result, it will improve the general level of the human health. The challenge falls into three sub-challenges, including Lesion Segmentation, Lesion Dermoscopic Feature Extraction and Lesion Classification. We focus on the Lesion Classification task. The proposed algorithm is comprised of three steps: (1) original images preprocessing, (2) modelling the processed images using CNN [2, 3] in Caffe  framework, (3) predicting the test images and calculating the scores that represent the likelihood of corresponding classification. The models are built on the source images are using the Caffe  framework. The scores in prediction step are obtained by two different models from the source images.
Mediators and moderators of long-term effects of violent video games on aggressive behavior: practice, thinking, and action.

Although several longitudinal studies have demonstrated an effect of violent video game play on later aggressive behavior, little is known about the psychological mediators and moderators of the effect.



To determine whether cognitive and/or emotional variables mediate the effect of violent video game play on aggression and whether the effect is moderated by age, sex, prior aggressiveness, or parental monitoring.



Three-year longitudinal panel study. A total of 3034 children and adolescents from 6 primary and 6 secondary schools in Singapore (73% male) were surveyed annually. Children were eligible for inclusion if they attended one of the 12 selected schools, 3 of which were boys' schools. At the beginning of the study, participants were in third, fourth, seventh, and eighth grades, with a mean (SD) age of 11.2 (2.1) years (range, 8-17 years). Study participation was 99% in year 1.



The final outcome measure was aggressive behavior, with aggressive cognitions (normative beliefs about aggression, hostile attribution bias, aggressive fantasizing) and empathy as potential mediators.



Longitudinal latent growth curve modeling demonstrated that the effects of violent video game play are mediated primarily by aggressive cognitions. This effect is not moderated by sex, prior aggressiveness, or parental monitoring and is only slightly moderated by age, as younger children had a larger increase in initial aggressive cognition related to initial violent game play at the beginning of the study than older children. Model fit was excellent for all models.



Given that more than 90% of youths play video games, understanding the psychological mechanisms by which they can influence behaviors is important for parents and pediatricians and for designing interventions to enhance or mitigate the effects.
LCARS: a location-content-aware recommender system

Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history.
 In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item co-occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency.
Identifying Languages at the Word Level in Code-Mixed Indian Social Media Text

Language identification at the document level has been considered an almost solved problem in some application areas, but language detectors fail in the social media context due to phenomena such as utterance internal code-switching, lexical borrowings, and phonetic typing; all implying that language identification in social media has to be carried out at the word level. The paper reports a study to detect language boundaries at the word level in chat message corpora in mixed EnglishBengali and English-Hindi. We introduce a code-mixing index to evaluate the level of blending in the corpora and describe the performance of a system developed to separate multiple languages.
Attention and Augmented Recurrent Neural Networks

The basic RNN design struggles with longer sequences, but a special variant — “long short-term memory” networks — can even work with these. Such models have been found to be very powerful, achieving remarkable results in many tasks including translation, voice recognition, and image captioning. As a result, recurrent neural networks have become very widespread in the last few years. As this has happened, we’ve seen a growing number of attempts to augment RNNs with new properties. Four directions stand out as particularly exciting:
Automatic time series forecasting: the forecast package for R

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.
Discrimination of Word Senses with Hypernyms

Languages are inherently ambiguous. Four out of five words in English have more than one meaning. Nowadays there is a growing number of small proprietary thesauri used for knowledge management for different applications. In order to enable the usage of these thesauri for automatic text annotations, we introduce a robust method for discriminating word senses using hypernyms. The method uses collocations to induce word senses and to discriminate the thesaural sense from the other senses by utilizing hypernym entries taken from a thesaurus. The main novelty of this work is the usage of hypernyms already at the stage sense induction. The hypernyms enable us to cast the task to a binary scenario, namely teasing apart thesaural senses from all the rest. The introduced method outperforms the baseline and has indicates accuracy above 80%.
A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy.

The rapid advancement of robotics technology in recent years has pushed the development of a distinctive field of robotic applications, namely robotic exoskeletons. Because of the aging population, more people are suffering from neurological disorders such as stroke, central nervous system disorder, and spinal cord injury. As manual therapy seems to be physically demanding for both the patient and therapist, robotic exoskeletons have been developed to increase the efficiency of rehabilitation therapy. Robotic exoskeletons are capable of providing more intensive patient training, better quantitative feedback, and improved functional outcomes for patients compared to manual therapy. This review emphasizes treadmill-based and over-ground exoskeletons for rehabilitation. Analyses of their mechanical designs, actuation systems, and integrated control strategies are given priority because the interactions between these components are crucial for the optimal performance of the rehabilitation robot. The review also discusses the limitations of current exoskeletons and technical challenges faced in exoskeleton development. A general perspective of the future development of more effective robot exoskeletons, specifically real-time biological synergy-based exoskeletons, could help promote brain plasticity among neurologically impaired patients and allow them to regain normal walking ability.
Statistical Fraud Detection : A Review

Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.
Search Based Path and Input Data Generation for Web Application Testing

Test case generation for web applications aims at ensuring full coverage of the navigation structure. Existing approaches resort to crawling and manual/random input generation, with or without a preliminary construction of the navigation model. However, crawlers might be unable to reach some parts of the web application and random input generation might not receive enough guidance to produce the inputs needed to cover a given path. In this paper, we take advantage of the navigation structure implicitly specified by developers when they write the page objects used for web testing and we define a novel set of genetic operators that support the joint generation of test inputs and feasible navigation paths. On a case study, our tool Subweb was able to achieve higher coverage of the navigation model than crawling based approaches, thanks to its intrinsic ability of generating inputs for feasible paths and of discarding likely infeasible paths.
Mastering Structured Data on the Semantic Web

Mastering Structured Data on the Semantic Web explains the practical aspects and the theory behind the Semantic Web and how structured data, such as HTML5 Microdata and JSON-LD annotations, can be used to improve your site’s performance on next-generation Search Engine Result Pages and be displayed on Google Knowledge Panels. You will learn how to represent data in a machineinterpretable form, using the Resource Description Framework (RDF), the cornerstone of the Semantic Web. You will see how to store and manipulate RDF data in ways that benefit Big Data applications, such as the Google Knowledge Graph, Wikidata, or Facebook’s Social Graph. The book also covers the most important tools for manipulating RDF data, including, but not limited to, Protégé, TopBraid Composer, Sindice, Apache Marmotta, Callimachus, and Tabulator. You will learn to use the Apache Jena and Sesame APIs for rapid Semantic Web application development. Mastering Structured Data on the Semantic Web demonstrates how to create and interlink five-star Linked Open Data to reach a wider audience, encourage data reuse, and provide content that can be automatically processed with full certainty. The book is for web developers and search engine optimization (SEO) experts who want to learn state-of-the-art SEO methods. The book will also benefit researchers interested in automatic knowledge discovery.
Toward Characteristic-Preserving Image-Based Virtual Try-On Network

Image-based virtual try-on systems for fitting a new in-shop clothes into a person image have attracted increasing research attention, yet is still challenging. A desirable pipeline should not only transform the target clothes into the most fitting shape seamlessly but also preserve well the clothes identity in the generated image, that is, the key characteristics (e.g. texture, logo, embroidery) that depict the original clothes. However, previous image-conditioned generation works fail to meet these critical requirements towards the plausible virtual try-on performance since they fail to handle large spatial misalignment between the input image and target clothes. Prior work explicitly tackled spatial deformation using shape context matching, but failed to preserve clothing details due to its coarse-to-fine strategy. In this work, we propose a new fully-learnable Characteristic-Preserving Virtual Try-On Network (CP-VTON) for addressing all real-world challenges in this task. First, CP-VTON learns a thin-plate spline transformation for transforming the in-shop clothes into fitting the body shape of the target person via a new Geometric Matching Module (GMM) rather than computing correspondences of interest points as prior works did. Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness. Extensive experiments on a fashion dataset demonstrate our CP-VTON achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
Attention to Head Locations for Crowd Counting

Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a novel method using an attention model to exploit head locations which are the most important cue for crowd counting. The attention model estimates a probability map in which high probabilities indicate locations where heads are likely to be present. The estimated probability map is used to suppress nonhead regions in feature maps from several multi-scale feature extraction branches of a convolutional neural network for crowd density estimation, which makes our method robust to complex backgrounds, scale variations and non-uniform distributions. In addition, we introduce a relative deviation loss to compensate a commonly used training loss, Euclidean distance, to improve the accuracy of sparse crowd density estimation. Experiments on ShanghaiTech, UCF CC 50 and WorldExpo’10 datasets demonstrate the effectiveness of our method.
A smart home application to eldercare: current status and lessons learned.

To address an aging population, we have been investigating sensor networks for monitoring older adults in their homes. In this paper, we report ongoing work in which passive sensor networks have been installed in 17 apartments in an aging in place eldercare facility. The network under development includes simple motion sensors, video sensors, and a bed sensor that captures sleep restlessness and pulse and respiration levels. Data collection has been ongoing for over two years in some apartments. This longevity in sensor data collection is allowing us to study the data and develop algorithms for identifying alert conditions such as falls, as well as extracting typical daily activity patterns for an individual. The goal is to capture patterns representing physical and cognitive health conditions and then recognize when activity patterns begin to deviate from the norm. In doing so, we strive to provide early detection of potential problems which may lead to serious health events if left unattended. We describe the components of the network and show examples of logged sensor data with correlated references to health events. A summary is also included on the challenges encountered and the lessons learned as a result of our experiences in monitoring aging adults in their homes.
Ten Years of Search Based Software Engineering: A Bibliometric Analysis

Despite preceding related publications, works dealing with the resolution of software engineering problems by search techniques has especially risen since 2001. By its first decade, the Search Based Software Engineering (SBSE) approach has been successfully employed in several software engineering contexts, using various optimization techniques. Aside the relevance of such applications, knowledge regarding the publication patterns on the field plays an important role to its understanding and identity. Such information may also shed light into SBSE trends and future. This paper presents the first bibliometric analysis to SBSE publications. The study covered 677 publications of the SBSE community from 2001 through 2010. The performed bibliometric analysis concerned mainly in four categories: Publication, Sources, Authorship, and Collaboration. Additionally, estimates for the next years of several publication metrics are given. The study also analyzed the applicability of bibliometric laws in SBSE, such as Bradfords and Lotka.
Towards active detection of identity clone attacks on online social networks

Online social networks (OSNs) are becoming increasingly popular and Identity Clone Attacks (ICAs) that aim at creating fake identities for malicious purposes on OSNs are becoming a significantly growing concern. Such attacks severely affect the trust relationships a victim has built with other users if no active protection is applied. In this paper, we first analyze and characterize the behaviors of ICAs. Then we propose a detection framework that is focused on discovering suspicious identities and then validating them. Towards detecting suspicious identities, we propose two approaches based on attribute similarity and similarity of friend networks. The first approach addresses a simpler scenario where mutual friends in friend networks are considered; and the second one captures the scenario where similar friend identities are involved. We also present experimental results to demonstrate flexibility and effectiveness of the proposed approaches. Finally, we discuss some feasible solutions to validate suspicious identities.
OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.
End-to-end Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.
Modified 0.6–50 GHz ultra-wideband Double-Ridged horn antenna design for parameters improvement

Design of the 0.6–50 GHz ultra-wideband (UWB) double-ridged horn antenna (DRHA) is presented in this paper. This work focuses on several upgrades in the model to improve its performance: by adding absorber and perforations in coaxial to waveguide launcher, Luneburg dielectric lens at the aperture of the horn radiation pattern at upper end of the band and voltage standing wave ratio (VSWR) are improved. Radiation pattern and VSWR of new design are compared with antenna before modifications. The improved DRHA has VSWR less than 1.5 at the band from 1 GHz and the main lobe remains along the antenna axis at high frequencies of the band.