nils-herrmann/clean_scirepeval huggingface dataset:
Image Inpainting via Generative Multi-column Convolutional Neural Networks

In this paper, we propose a generative multi-column network for image inpainting. This network synthesizes different image components in a parallel manner within one stage. To better characterize global structures, we design a confidence-driven reconstruction loss while an implicit diversified MRF regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our method produces visual compelling results even without previously common post-processing.
Technology acceptance model: analyse of the value build through the user experience

The working environment of managers can be viewed as a technological ecosystem with more and more convergent information and communication technologies (ICT) and social and collaborative systems. Our objective is to study how the process of technology acceptance occurs in this population. By using various models and theories, we propose a systemic and iterative model of the process of technology acceptance in the professional context. It is characterized by consideration of the user experience (UX), formed during the use of ICT and social and collaborative systems, according to the degree to which fundamental needs are satisfied or not satisfied. We use the model to analyse acceptance in the case of a sample of 1768 managers. This study presents empirical values of the use of various ICTs by this population, the importance of eight UX criteria, and their impacts on the general process of technology acceptance.
Restorability of Teeth: A Numerical Simplified Restorative Decision-Making Chart

Nermin Kamal., et al. “Restorability of Teeth: A Numerical Simplified Restorative Decision-Making Chart”. EC Dental Science 17.6 (2018): 961-967.  A decision to extract or to keep a tooth was always a debatable matter in dentistry. Each dental specialty has its own perspective in that regards. Although, real life in the dental clinic showed that the decision is always multi-disciplinary, and that full awareness of all aspects should be there in order to reach to a reliable outcome. This article presents a simple evidence-based clinical chart for the judgment of restorability of teeth for better treatment planning.
Vibration Based Condition Monitoring : A Review

Vibration based condition monitoring refers to the use of in situ non-destructive sensing and analysis of system characteristics – in the time, frequency or modal domains – for the purpose of detecting changes, which may indicate damage or degradation. In the field of civil engineering, monitoring systems have the potential to facilitate the more economical management and maintenance of modern infrastructure. This paper reviews the state of the art in vibration based condition monitoring with particular emphasis on structural engineering applications.
Necessary and Sufficient Conditions for Limit Theorems in Gibbs-markov Maps

We investigate limit theorems for Birkhoff sums of locally Hölder functions under the iteration of Gibbs-Markov maps. Aaronson and Denker have given sufficient conditions to have limit theorems in this setting. We show that these conditions are also necessary: there is no exotic limit theorem for Gibbs-Markov maps. Our proofs, valid under very weak regularity assumptions, involve weak perturbation theory and interpolation spaces. For L 2 observables, we also obtain necessary and sufficient conditions to control the speed of convergence in the central limit theorem.
Present and Future Trends in Filters and Multiplexers

In the last 10 or 15 years, exciting new developments have occurred in the field of filters and multiplexers. The two words that best explain this phenomenon are “topology” and “technology.” Thus, this paper will cover new connections, new materials, and new processes. This paper summarizes what is new, describes the new devices, and avoids simply surveying the entire field. It is not possible to cover everything, and regrettably, some new and possibly exciting areas are omitted (such as filters fabricated using liquid crystal polymers, active element inclusion, extreme power devices used in accelerators), and others, ..., because something has to be left for the next paper! The paper does cover bandstop filters, intrinsically switched film bulk acoustic resonator, and barium-strontium-titanate filters, brand new derivations of lossy filter synthesis and dual-band filter synthesis, multimode networks, new work on optimal multiplexer (multiport network) configurations, and substrate integrated waveguide filters. In each of these areas significant new work has been recently performed and is presented in a summary format suitable for those thinking that the area of filters and multiplexers has become mature (stagnant) with nothing interesting to learn. Nothing could be further from the truth!
Customer ' Loyalty Forming Mechanism of O 2 O

The rise and development of O2O e-commerce has brought new opportunities for the enterprise, and also proposed the new challenge to the traditional electronic commerce. The formation process of customer loyalty of O2O e-commerce environment is a complex psychological process. This paper will combine the characteristics of O2O e-commerce, customer's consumer psychology and consumer behavior characteristics to build customer loyalty formation mechanism model which based on the theory of reasoned action model. The related factors of the model including the customer perceived value, customer satisfaction, customer trust and customer switching costs. By exploring the factors affecting customer’ loyalty of O2O e-commerce can provide reference and basis for enterprises to develop e-commerce and better for O2O e-commerce enterprises to develop marketing strategy and enhance customer loyalty. At the end of this paper will also put forward some targeted suggestions for O2O e-commerce enterprises.
Psychosocial risk factors associated with cyberbullying among adolescents: a population-based study.

To our knowledge, no population study examining psychosocial and psychiatric risk factors associated with cyberbullying among adolescents exists.



To study cross-sectional associations between cyberbullying and psychiatric and psychosomatic problems among adolescents.



Population-based cross-sectional study.



Finland.



The sample consists of 2215 Finnish adolescents aged 13 to 16 years with complete information about cyberbullying and cybervictimization.



Self-reports of cyberbullying and cybervictimization during the past 6 months.



In the total sample, 4.8% were cybervictims only, 7.4% were cyberbullies only, and 5.4% were cyberbully-victims. Cybervictim-only status was associated with living in a family with other than 2 biological parents, perceived difficulties, emotional and peer problems, headache, recurrent abdominal pain, sleeping difficulties, and not feeling safe at school. Cyberbully-only status was associated with perceived difficulties, hyperactivity, conduct problems, low prosocial behavior, frequent smoking and drunkenness, headache, and not feeling safe at school. Cyberbully-victim status was associated with all of these risk factors. Among cybervictims, being cyberbullied by a same-sex or opposite-sex adult, by an unknown person, and by a group of people were associated with fear for safety, indicating possible trauma.



Both cyberbullying and cybervictimization are associated with psychiatric and psychosomatic problems. The most troubled are those who are both cyberbullies and cybervictims. This indicates the need for new strategies for cyberbullying prevention and intervention.
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only

We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the first step towards development of effective CLIR models for language pairs and domains where parallel data are scarce or non-existent.
Feeding the world: genetically modified crops versus agricultural biodiversity

The growing demand for food poses major challenges to humankind. We have to safeguard both biodiversity and arable land for future agricultural food production, and we need to protect genetic diversity to safeguard ecosystem resilience. We must produce more food with less input, while deploying every effort to minimize risk. Agricultural sustainability is no longer optional but mandatory. There is still an on-going debate among researchers and in the media on the best strategy to keep pace with global population growth and increasing food demand. One strategy favors the use of genetically modified (GM) crops, while another strategy focuses on agricultural biodiversity. Here, we discuss two obstacles to sustainable agriculture solutions. The first obstacle is the claim that genetically modified crops are necessary if we are to secure food production within the next decades. This claim has no scientific support, but is rather a reflection of corporate interests. The second obstacle is the resultant shortage of research funds for agrobiodiversity solutions in comparison with funding for research in genetic modification of crops. Favoring biodiversity does not exclude any future biotechnological contributions, but favoring biotechnology threatens future biodiversity resources. An objective review of current knowledge places GM crops far down the list of potential solutions in the coming decades. We conclude that much of the research funding currently available for the development of GM crops would be much better spent in other research areas of plant science, e.g., nutrition, policy research, governance, and solutions close to local market conditions if the goal is to provide sufficient food for the world’s growing population in a sustainable way.
Multi-modal Variational Encoder-Decoders

Recent advances in neural variational inference have facilitated efficient training of powerful directed graphical models with continuous latent variables, such as variational autoencoders. However, these models usually assume simple, unimodal priors — such as the multivariate Gaussian distribution — yet many realworld data distributions are highly complex and multi-modal. Examples of complex and multi-modal distributions range from topics in newswire text to conversational dialogue responses. When such latent variable models are applied to these domains, the restriction of the simple, uni-modal prior hinders the overall expressivity of the learned model as it cannot possibly capture more complex aspects of the data distribution. To overcome this critical restriction, we propose a flexible, simple prior distribution which can be learned efficiently and potentially capture an exponential number of modes of a target distribution. We develop the multi-modal variational encoder-decoder framework and investigate the effectiveness of the proposed prior in several natural language processing modeling tasks, including document modeling and dialogue modeling.
VideoCapsuleNet: A Simplified Network for Action Detection

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input. The 3D generalization drastically increases the number of capsules in the network, making capsule routing computationally expensive. We introduce capsule-pooling in the convolutional capsule layer to address this issue and make the voting algorithm tractable. The routing-by-agreement in the network inherently models the action representations and various action characteristics are captured by the predicted capsules. This inspired us to utilize the capsules for action localization and the class-specific capsules predicted by the network are used to determine a pixel-wise localization of actions. The localization is further improved by parameterized skip connections with the convolutional capsule layers and the network is trained end-to-end with a classification as well as localization loss. The proposed network achieves state-of-the-art performance on multiple action detection datasets including UCF-Sports, J-HMDB, and UCF-101 (24 classes) with an impressive ∼20% improvement on UCF-101 and ∼15% improvement on J-HMDB in terms of v-mAP scores.
Biomass gasification technology: The state of the art overview

The reduction of imported forms of energy, and the conservation of the limited supply of fossil fuels, depends up on the utilization of all other available fuel energy sources. Biomass is a renewable energy source and represents a valid alternative to fossil fuels. The abundance of biomass ranks it as the third energy resource after oil and coal. Moreover, when compared to fossil fuels, biomass fuels possess negligible sulphur concentrations, produce less ash, and generate far less emissions in to the air. In other words, biomass can deliver significant greenhouse gas reductions in electricity, heat and transport fuel supply. The energy in biomass may be realized by different thermochemical technologies of which gasification is most promising alternative routes to convert biomass to power/heat generation and production of transportation fuels and chemical feedstock. This paper deals with the state of the art biomass gasification technologies, evaluating advantages and disadvantages, the potential use of the syngas and the application of the biomass gasification. Also, this paper provides short overview of the current status of the biomass gasification in Serbia.
Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks

Many shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this problem, the original Stanford Dependencies representation also defines two dependency graph representations which contain additional and augmented relations that explicitly capture otherwise implicit relations between content words. In this paper, we revisit and extend these dependency graph representations in light of the recent Universal Dependencies (UD) initiative and provide a detailed account of an enhanced and an enhanced++ English UD representation. We further present a converter from constituency to basic, i.e., strict surface structure, UD trees, and a converter from basic UD trees to enhanced and enhanced++ English UD graphs. We release both converters as part of Stanford CoreNLP and the Stanford Parser.
Extracting domain models from natural-language requirements: approach and industrial evaluation

Domain modeling is an important step in the transition from natural-language requirements to precise specifications. For large systems, building a domain model manually is a laborious task. Several approaches exist to assist engineers with this task, whereby candidate domain model elements are automatically extracted using Natural Language Processing (NLP). Despite the existing work on domain model extraction, important facets remain under-explored: (1) there is limited empirical evidence about the usefulness of existing extraction rules (heuristics) when applied in industrial settings; (2) existing extraction rules do not adequately exploit the natural-language dependencies detected by modern NLP technologies; and (3) an important class of rules developed by the information retrieval community for information extraction remains unutilized for building domain models.
 Motivated by addressing the above limitations, we develop a domain model extractor by bringing together existing extraction rules in the software engineering literature, extending these rules with complementary rules from the information retrieval literature, and proposing new rules to better exploit results obtained from modern NLP dependency parsers. We apply our model extractor to four industrial requirements documents, reporting on the frequency of different extraction rules being applied. We conduct an expert study over one of these documents, investigating the accuracy and overall effectiveness of our domain model extractor.
Structured Generative Adversarial Networks

We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labeled instances as supervision or are unable to accurately control the semantics of generated samples. We propose structured generative adversarial networks (SGANs) for semi-supervised conditional generative modeling. SGAN assumes the data x is generated conditioned on two independent latent variables: y that encodes the designated semantics, and z that contains other factors of variation. To ensure disentangled semantics in y and z, SGAN builds two collaborative games in the hidden space to minimize the reconstruction error of y and z, respectively. Training SGAN also involves solving two adversarial games that have their equilibrium concentrating at the true joint data distributions p(x, z) and p(x,y), avoiding distributing the probability mass diffusely over data space that MLE-based methods may suffer. We assess SGAN by evaluating its trained networks, and its performance on downstream tasks. We show that SGAN delivers a highly controllable generator, and disentangled representations; it also establishes start-of-the-art results across multiple datasets when applied for semi-supervised image classification (1.27%, 5.73%, 17.26% error rates on MNIST, SVHN and CIFAR-10 using 50, 1000 and 4000 labels, respectively). Benefiting from the separate modeling of y and z, SGAN can generate images with high visual quality and strictly following the designated semantic, and can be extended to a wide spectrum of applications, such as style transfer.
ENHANCING READING COMPREHENSION THROUGH TASK-BASED WRITING ACTIVITIES: AN EXPERIMENTAL STUDY

_______________ Task-based learning is a popular topic in ELT/EFL circles nowadays. It is accepted by its proponents as a flourishing method that may replace Communicative Language Learning. However, it can also be seen as an adventure just because there are almost no experimental studies to tackle questions concerning applicability of Task-based Learning. In this paper we try to find out whether or not task-based writing activities have a positive effect upon reading comprehension in English as a foreign language. An experimental study was conducted in order to scrutinize implications of Task-based Learning. Two groups of 28 students were chosen through random cluster sampling. Both groups were given a pre-test and a post-test. The pre-test and post-test mean scores of the experimental group, which got treatment through task-based writing activities, were compared with those of the control group, which was taught English through traditional methods. The effect of the treatment upon reading comprehension was analyzed through two-way ANOVA. The results provide a theoretical justification for the claims of the proponents of Task-based Learning. Theoretical  Researchers have been discussing and asserting that the Communicative Language Teaching, a method which has a worldwide use nowadays, has some important drawbacks. Having been based on principles of first language acquisition, it lacks a proper theoretical basis about language learning as a cognitive process of skill acquisition and a clear research about second language acquisition (Klapper, 2003:33-34). It puts much emphasis on ‘communication’, pair work, information-gap activities, and intensive target language use (Pica, 2000; Richards and Rodgers, 1996). However, teachers and practitioners have encountered some problems while applying it. One of the most important problems was the demotivation of students because of intensive target language use. Task-based Learning is a flourishing method which can compensate for the weaknesses of the Communicative Language Teaching mentioned above and which is seen as an alternative to it by researchers (Klapper, 2003:35-36). ‘Task’ is taken as a goal-oriented activity which has a clear purpose and which involves achieving an outcome, creating a final
Single Feed Stacked Patch Circular Polarized Antenna for Triple Band GPS Receivers

A novel design of a circular polarized antenna for multiband GPS receivers is presented. The design employs the concept of multistacked patches fed through a single coaxial probe. Three patches being stacked together with a slit and symmetry I-slot are used to achieve triple operating frequency bands for GPS including L1 (1.575 GHz), L2 (1.227 GHz) and L5 (1.176 GHz). The proposed antenna has achieved a bandwidth of 2.0%, 1.5%, and 1.7% at GPS L1, L2, and L5 bands, respectively. It exhibits a minimum axial ratio of 0.51 dB with broad beamwidth in the upper hemisphere required for the GPS applications. The design of the proposed antenna is verified in the experiment. In addition, a detailed analysis has been carried out to study the effects of different geometrical parameters on the performance of the antenna.
Cloud Computing: Survey on Energy Efficiency

Cloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions.