nils-herrmann/clean_scirepeval huggingface dataset:
Super Resolution of Images and Video

This book focuses on the super resolution of images and video. The authors’ use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial and temporal RE, but also to hyperspectral image enhancement. This book concentrates on motion based spatial RE, although the authors also describe motion free and hyperspectral image SR problems. Also examined is the very recent research area of SR for compression, which consists of the intentional downsampling, during pre-processing, of a video sequence to be compressed and the application of SR techniques, during post-processing, on the compressed sequence. It is clear that there is a strong interplay between the tools and techniques developed for SR and a number of other inverse problems encountered in signal processing (e.g., image restoration, motion estimation). SR techniques are being applied to a variety of fields, such as obtaining improved still images from video sequences (video printing), high definition television, high performance color Liquid Crystal Display (LCD) screens, improvement of the quality of color images taken by one CCD, video surveillance, remote sensing, and medical imaging. The authors believe that the SR/RE area has matured enough to develop a body of knowledge that can now start to provide useful and practical solutions to challenging real problems and that SR techniques can be an integral part of an image and video codec and can drive the development of new coder-decoders (codecs) and standards.
Aspects in Effectiveness of Glass-and Polyethylene-Fibre Reinforced Composite Resin in Periodontal Splinting

EDWIN SEVER BECHIR1, MARIANA PACURAR1, TUDOR ALEXANDRU HANTOIU1, ANAMARIA BECHIR2*, OANA SMATREA2, ALEXANDRU BURCEA2, CHERANA GIOGA2, MONICA MONEA1 1 Medicine and Pharmacy University of Tirgu-Mures, Faculty of Dentistry, 38 Gheorghe Marinescu Str., 540142,Tirgu-Mures, Romania 2 Titu Maiorescu University of Bucharest, Faculty of Dentistry, Department of Dental Specialties, 67A Gheorghe Petrascu Str., 031593, Bucharest, Romania
Directed attention and perception of temporal order.

The present research examined the effects of directed attention on speed of information transmission in the visual system. Ss judged the temporal order of 2 stimuli while directing attention toward 1 of the stimuli or away from both stimuli. Perception of temporal order was influenced by directed attention: Given equal onset times, the attended stimulus appeared to occur before the unattended stimulus. Direction of attention also influenced the perception of simultaneity. The findings support the notion that attention affects the speed of transmission of information in the visual system. To account for the pattern of temporal order and simultaneity judgments, a model is proposed in which the temporal profile of visual responses is affected by directed attention.
Employees ’ Perceptions of Corporate Social Responsibility and Job Performance : A Sequential Mediation Model

In spite of the increasing importance of corporate social responsibility (CSR) and employee job performance, little is still known about the links between the socially responsible actions of organizations and the job performance of their members. In order to explain how employees’ perceptions of CSR influence their job performance, this study first examines the relationships between perceived CSR, organizational identification, job satisfaction, and job performance, and then develops a sequential mediation model by fully integrating these links. The results of structural equation modeling analyses conducted for 250 employees at hotels in South Korea offered strong support for the proposed model. We found that perceived CSR was indirectly and positively associated with job performance sequentially mediated first through organizational identification and then job satisfaction. This study theoretically contributes to the CSR literature by revealing the sequential mechanism through which employees’ perceptions of CSR affect their job performance, and offers practical implications by stressing the importance of employees’ perceptions of CSR. Limitations of this study and future research directions are discussed.
COMMUNICATING HIERARCHICAL NEURAL CONTROLLERS FOR LEARNING ZERO-SHOT TASK GENERALIZATION

The ability to generalize from past experience to solve previously unseen tasks is a key research challenge in reinforcement learning (RL). In this paper, we consider RL tasks defined as a sequence of high-level instructions described by natural language and study two types of generalization: to unseen and longer sequences of previously seen instructions, and to sequences where the instructions themselves were previously not seen. We present a novel hierarchical deep RL architecture that consists of two interacting neural controllers: a meta controller that reads instructions and repeatedly communicates subtasks to a subtask controller that in turn learns to perform such subtasks. To generalize better to unseen instructions, we propose a regularizer that encourages to learn subtask embeddings that capture correspondences between similar subtasks. We also propose a new differentiable neural network architecture in the meta controller that learns temporal abstractions which makes learning more stable under delayed reward. Our architecture is evaluated on a stochastic 2D grid world and a 3D visual environment where the agent should execute a list of instructions. We demonstrate that the proposed architecture is able to generalize well over unseen instructions as well as longer lists of instructions.
Turning Equations into Latex : Pipeline vs . End-to-End

Image-to-LaTeX conversion is an important but challenging task. Learning a function from rendered LaTeX back to the original markup would enable students to more quickly complete their homework. The function from rendered LaTeX to the markup is challenging to learn because the vocabulary of valid LaTeX tokens is large (about 500 symbols) and the available data sets are not huge (in the low 100,000s of labelled examples). A clear winner in this domain has not yet emerged. As a consequence, academics around the world struggle.
Filtering Corrupted Image and Edge Detection in Restored Grayscale Image Using Derivative Filters

In this paper, different first and second derivative filters are investigated to find edge map after denoising a corrupted gray scale image. We have proposed a new derivative filter of first order and described a novel approach of edge finding with an aim to find better edge map in a restored gray scale image. Subjective method has been used by visually comparing the performance of the proposed derivative filter with other existing first and second order derivative filters. The root mean square error and root mean square of signal to noise ratio have been used for objective evaluation of the derivative filters. Finally, to validate the efficiency of the filtering schemes different algorithms are proposed and the simulation study has been carried out using MATLAB 5.0.
Effects of magnetized water application on soil and maize growth indices under different amounts of salt in the water

Application of low quality water for irrigation is compulsive in facing water scarcity. Use of a magnetic field is an approach to overcome this challenge. This study examined the impact of magnetic field technology on improving germination under water of different salinity levels. An experiment was conducted to determine the effects of saline water levels, i.e. (S1):0.5, (S2):2, (S3):4 and (S4):6 dS/m combined with magnetized technology (with or without) on maize growth. Thus, magnetic treatment was applied by passing the irrigation water through a 1,500 mT magnetic field at 3 litres per minute (lpm) flow rate. Some emergence indices, such as emergence index, emergence rate index (ERI) and mean emergence time, were used to evaluate the germination of maize seed. As for soil properties after plant harvest, the use of magnetically treated irrigation water reduced soil pH but increased soil electrical conductivity and available N and P. ERI increased from 7.6 to 10.2, 9.1 to 11.1, 10.3 to 13.3, and 11.8 to 13.3 when applying the magnetized field for S1, S2, S3 and S4, respectively. Overall, the growth parameters of maize were improved by using magnetic technology with saline water, while the opposite trend was shown for increasing salinity without magnetic treatment.
Quantitative Analysis of Penalty Kicks and Yellow Card Referee Decisions in Soccer

Soccer referees are required to make instant decisions during the game under non-optimal conditions such as imperfect view of the incident and substantial pressure from the crowd, the teams, and the media. Some of the decisions can be subjective, such as a yellow card decision after a foul is called, where different referees might make different decisions. Here we perform quantitative analysis of factors related to the reputation of the team such as the team’s rank, budget, and crowd attendance in home games, and correlate these factors with referee decisions such as penalty kicks and yellow cards. The calls were normalized by dividing the number of yellow cards by the number of fouls, and the number of penalty kicks by the number of shot attempts from the penalty box. Application of the analysis to the four major soccer leagues shows that certain referee decisions have significant correlation with factors such as the team’s rank, budget, and audience in home games, while for other decisions the Pearson correlation is not statistically significant. For budget, or audience in home games. On the other hand, a significant Pearson correlation has been identified between the chance of a foul call to result in a yellow card and the rank or budget of the team in the Bundesliga. The strongest correlation has been observed between the chance of a tackle to result in a foul call, and the budget and rank of the team.
Spreading dynamics in complex networks

Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from the epidemic control, innovation diffusion, viral marketing, social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community – LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in LiveJournal social network, only a small fraction of them involve in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with large influence. Our results should provide useful information for designing efficient spreading strategies in reality.
Systemic functional linguistics and code theory

No theory is an island. In the case of systemic functional linguistics (SFL), its most long-standing and intense relationship has been with code theory, a sociological framework originated by Basil Bemstein that has recently been developed into Legitimation Code Theory (LCT) (Maton2014b). From their beginnings, scholars developing these approaches have engaged in exchanges over a wide range of issues, sparking advances in both fi'ameworks, posing questions to each other and providing fresh insights on persistent concerns. Indeed, in recent years, this dialogue has intensified as SFL and LCT have become increasingly used together in joint analyses of shared data (Martin and Maton 2013; Vidal Lizama 2014; Hood 2016). These genuinely interdisciplinary projects are leading to a growing number of fundamental innovations in both approaches and the emergence of a generation of scholars who are theoretically 'bilingual'. With such intertwined biographies, then, to understand SFL, one must understand its exchanges with code theory. This chapter explores relations between the two theories, the forms that these exchanges have taken and how they have shaped each other. We consider the past, present and possible future natures ofthis dialogue. First, we trace the evolving history ofexchanges, outline the advances that they encouraged and highlight the foundations each phase laid for future exchanges. Second, we explore current close collaborations between SFL and LCT in which research is productively utilising both approaches in interdisciplinary analyses of shared data. Finally, we consider possible paths for future collaboration, and offer insights into why SFL and code theory are working so effectively together across an ever-widening range ofissues, topics and contexts.
Assessing Security and Performances of Consensus algorithms for Permissioned Blockchains

Blockchain is a novel technology that is rising a lot of interest in the industrial and research sectors because its properties of decentralisation, immutability and data integrity. Initially, the underlying consensus mechanism has been designed for permissionless blockchain on trustless network model through the proof-of-work, i.e. a mathematical challenge which requires high computational power. This solution suffers of poor performances, hence alternative consensus algorithms as the proof-of-stake have been proposed. Conversely, for permissioned blockchain, where participants are known and authenticated, variants of distributed consensus algorithms have been employed. However, most of them comes out without formal expression of security analysis and trust assumptions because the absence of an established knowledge. Therefore the lack of adequate analysis on these algorithms hinders any cautious evaluation of their effectiveness in a real-world setting where systems are deployed over trustless networks, i.e. Internet. In this thesis we analyse security and performances of permissioned blockchain. Thus we design a general model for such a scenario in a way to propose a general benchmark for the experimental evaluations. This work brings two main contributions. The first contribution concern the analysis of Proof-of-Authority, a Byzantine FaultTolerant consensus protocol. We compare two of the main algorithms, named Aura and Clique, with respect the well-established Practical Byzantine Fault-Tolerant, in terms of security and performances. We refer the CAP theorem for the consistency, availability and partition tolerance guarantees and we describe a possible attack scenario in which one of the algorithms loses consistency. The analysis advocates that Proof-of-Authority for permissioned blockchains deployed over WANs experimenting Byzantine nodes, do not provide adequate consistency guarantees for scenarios where data integrity is essential. We claim that actually the Practical Byzantine Fault-Tolerant can fit better for permissioned blockchain, despite a limited loss in terms of performance. The second contribution is the realisation of a benchmark for practical evaluations. We design a general model for permissioned blockchain under which benchmarking performances and security guarantees. However, because no experiment can verify all the possible security issues permitted by the model, we prototype an adversarial model which simulate three attacks, feasible for a blockchain system. We then integrate this attacker model in a real blockchain client to evaluate the resiliency of the system and how much the attacks impact performances and security guarantees.
Extinction risks from climate change

Biologists worry that the rapid rates of warming projected for the planet (1) will doom many species to extinction. Species could face extinction with climate change if climatically suitable habitat disappears or is made inaccessible by geographic barriers or species' inability to disperse (see the figure, panels A to E). Previous studies have provided region- or taxon-specific estimates of biodiversity loss with climate change that range from 0% to 54%, making it difficult to assess the seriousness of this problem. On page 571 of this issue, Urban (2) provides a synthetic and sobering estimate of climate change–induced biodiversity loss by applying a model-averaging approach to 131 of these studies. The result is a projection that up to one-sixth of all species may go extinct if we follow “business as usual” trajectories of carbon emissions.
BIM-BASED SCHEDULING OF CONSTRUCTION – A COMPARATIVE ANALYSIS OF PREVAILING AND BIM- BASED SCHEDULING PROCESSES

The potential of BIM is generally recognized in the construction industry, but the practical application of BIM for management purposes is, however, still limited among contractors. The objective of this study is to review the current scheduling process of construction in light of BIM-based scheduling, and to identify how it should be incorporated into current practice. The analysis of the current scheduling processes identifies significant discrepancies between the overall and the detailed levels of scheduling. The overall scheduling process is described as an individual endeavor with limited and unsystematic sharing of knowledge within and between projects. Thus, the reuse of scheduling data and experiences are inadequate, preventing continuous improvements of the overall schedules. Besides, the overall scheduling process suffers from lack of information, caused by uncoordinated and unsynchronized overlap of the design and construction processes. Consequently, the overall scheduling is primarily based on intuition and personal experiences, rather than well founded figures of the specific project. Finally, the overall schedule is comprehensive and complex, and consequently, difficult to overview and communicate. Scheduling on the detailed level, on the other hand, follows a stipulated approach to scheduling, i.e. the Last Planner System (LPS), which is characterized by involvement of all actors in the construction phase. Thus, the major challenge when implementing BIM-based scheduling is to improve overall scheduling, which in turn, can secure a better starting point of the LPS. The study points to the necessity of involving subcontractors and manufactures in the earliest phases of the project in order to create project specific information for the overall schedule. In addition, the design process should be prioritized and coordinated with each craft, a process library should be introduced to promote transfer of knowledge and continuous improvements, and information flow between design and scheduling processes must change from push to pull.
Meditation practices for health: state of the research.

To review and synthesize the state of research on a variety of meditation practices, including: the specific meditation practices examined; the research designs employed and the conditions and outcomes examined; the efficacy and effectiveness of different meditation practices for the three most studied conditions; the role of effect modifiers on outcomes; and the effects of meditation on physiological and neuropsychological outcomes.



Comprehensive searches were conducted in 17 electronic databases of medical and psychological literature up to September 2005. Other sources of potentially relevant studies included hand searches, reference tracking, contact with experts, and gray literature searches.



A Delphi method was used to develop a set of parameters to describe meditation practices. Included studies were comparative, on any meditation practice, had more than 10 adult participants, provided quantitative data on health-related outcomes, and published in English. Two independent reviewers assessed study relevance, extracted the data and assessed the methodological quality of the studies.



Five broad categories of meditation practices were identified (Mantra meditation, Mindfulness meditation, Yoga, Tai Chi, and Qi Gong). Characterization of the universal or supplemental components of meditation practices was precluded by the theoretical and terminological heterogeneity among practices. Evidence on the state of research in meditation practices was provided in 813 predominantly poor-quality studies. The three most studied conditions were hypertension, other cardiovascular diseases, and substance abuse. Sixty-five intervention studies examined the therapeutic effect of meditation practices for these conditions. Meta-analyses based on low-quality studies and small numbers of hypertensive participants showed that TM(R), Qi Gong and Zen Buddhist meditation significantly reduced blood pressure. Yoga helped reduce stress. Yoga was no better than Mindfulness-based Stress Reduction at reducing anxiety in patients with cardiovascular diseases. No results from substance abuse studies could be combined. The role of effect modifiers in meditation practices has been neglected in the scientific literature. The physiological and neuropsychological effects of meditation practices have been evaluated in 312 poor-quality studies. Meta-analyses of results from 55 studies indicated that some meditation practices produced significant changes in healthy participants.



Many uncertainties surround the practice of meditation. Scientific research on meditation practices does not appear to have a common theoretical perspective and is characterized by poor methodological quality. Firm conclusions on the effects of meditation practices in healthcare cannot be drawn based on the available evidence. Future research on meditation practices must be more rigorous in the design and execution of studies and in the analysis and reporting of results.
Data-dependent Initializations of Convolutional Neural Networks

Convolutional Neural Networks spread through computer vision like a wildfire, impacting almost all visual tasks imaginable. Despite this, few researchers dare to train their models from scratch. Most work builds on one of a handful of ImageNet pre-trained models, and fine-tunes or adapts these for specific tasks. This is in large part due to the difficulty of properly initializing these networks from scratch. A small miscalibration of the initial weights leads to vanishing or exploding gradients, as well as poor convergence properties. In this work we present a fast and simple data-dependent initialization procedure, that sets the weights of a network such that all units in the network train at roughly the same rate, avoiding vanishing or exploding gradients. Our initialization matches the current state-of-the-art unsupervised or self-supervised pre-training methods on standard computer vision tasks, such as image classification and object detection, while reducing the pre-training time by three orders of magnitude. When combined with pre-training methods, our initialization significantly outperforms prior work, narrowing the gap between supervised and unsupervised pre-training.