nils-herrmann/clean_scirepeval huggingface dataset:
Leakage resilient Attribute based Encryption with CCA2 Security
Venue: Int. J. Netw. Secur.
Year: 2019

Leakage resilient Attribute Based Encryption (ABE) is one of efficient methods to solve the side channel attacks. However, most of existing works only achieved a weak security CPA security and were not practical. Few works are focused on strong security CCA security which is left as an open problem. In this paper, we solve this problem and construct directly a CCA secure ABE. For the sake of realizing this target, a CPA secure scheme is introduced at first. Based on this basic scheme, a l leakage resilient CCA2 secure ABE is proposed in the standard model. It tolerates up to (log p o(log k) bit leakage of the private key and its leakage parameter is independent of the message length, where k is the security parameter and p is the prime order of the underlying group. Additionally, the proposed scheme is efficient and practical over the available, where the private keys are constant and independent of depth of attributes of the users. It also achieves anonymity and full security.
Sample Preparation and Analysis for Imaging Mass Spectrometry.
Venue: Methods in molecular biology
Year: 2017

Imaging mass spectrometry (IMS) is a novel quantitative technique used to investigative diverse biomolecules in tissue sections. Specifically, IMS uses analytical separation of mass spectrometry to determine the spatial distribution of certain lipids and/or proteins located directly on biological sections from a single tissue sample. Typically, IMS is combined with histological analysis to reveal additional distribution details of characterized biomolecules including cell type and/or subcellular localization. In this chapter, we describe the use of Matrix Assisted Laser Desorption/Ionization (MALDI) Time Of Flight/Time Of Flight (TOF/TOF) to analyze various cholesterol and phosphatidylcholine species in atherosclerotic plaque of murine heart aortic valves. In particular, we detail animals used, tissue collection, preparation, matrix application, spectra acquisition for generating a color coded image based on IMS spectral characteristics.
The Menace of Patent Trolls: What the World Can Learn from India
Year: 2019

First coined in the 1990s, the term 'patent troll' has no legal definition and is commonly used in the business world to describe a non performing entity that obtains the rights to a patent purely to profit from litigation without seeking to produce or further develop the said invention or method. This paper aims to discuss the techniques and modus operandi of such businesses to extract profit out of legitimate creators of products and services using cutting edge science and technology. Patent trolls have attracted the attention of law enforcement agencies worldwide. As compared to India, they have been more successful in sustaining their business models in the west where the existing legal framework is still conducive to their sustenance. The policing of patent trolls is remarkably tricky as they gain power from the strength of the very patent protection regimes they reside in. The objective of this paper is to conduct a comparative analysis of the patent systems and legal frameworks in the United States, European Union and India, and to illustrate and discuss how non Indian jurisdictions can take a leaf out of India's safeguard mechanisms to prevent the sprouting menace of such businesses and protect the interests of both large companies and emerging start ups. The methodology of comparison revolves around analysing the basic tenets of the existing patent systems, as well as the key historical cases of precedence in various jurisdictions. The study and analysis also take into consideration the interests of the various stakeholders such as individual inventors, startups, corporate businesses, governments, and the citizens involved. This paper is especially of relevance today because this continuing practice discourages innovators from investing time, effort and capital into the research and development of new technologies, without which the progress of society is hindered.
Two stage collapse of unimolecular micelles with double thermoresponsive coronas.
Venue: Langmuir the ACS journal of surfaces and colloids
Year: 2006

Phase transition behavior of unimolecular dendritic three layer nanostructures with dual thermoresponsive coronas is studied. Successive reversible addition fragmentation transfer (RAFT) polymerizations of N isopropylacrylamide (NIPAM) and 2 (dimethylamino)ethyl methacrylate (DMA) were conducted using fractionated fourth generation hyperbranched polyester (Bolton H40) based macroRAFT agent. At lower temperatures <20 degrees C) dendritic macromolecules H40 poly(N isopropylacrylamide) poly(2 (dimethylamino)ethyl methacrylate) (H40 PNIPAM PDMA) exist as unimolcular core shell corona nanostructures with hydrophobic H40 as the core, swollen PNIPAM as the inner shell, and swollen PDMA as the corona. PNIPAM and PDMA homopolymers undergo phase transitions at their lower critical solution temperatures (LCST) which are found to be 32 degrees C for PNIPAM and 40 50 degrees C for PDMA, respectively. Upon continuously heating through the LCSTs of PNIPAM and PDMA, such dendritic unimolecular micelles exhibit two stage thermally induced collapse. This process is reversible with a two stage reswelling upon cooling. Laser light scattering, micro differential scanning calorimetry, and excimer fluorescence measurements are used to investigate the double phase transitions.
Analytical solution of equivalent SEIR and agent based model of COVID 19; showing the bounds of contact tracing
Venue: medRxiv
Year: 2020

Mathematical models not only forecast the possible future but also is used to find hidden parameters of the COVID 19 pandemic. Numerical estimates can inform us of both goals. Still, the interdependencies of parameters stay obscure. Many numerical solutions have been proposed so far; however, the analytical relationship between the outbreak growth, decay and equilibrium are much less studied. In this study, we have employed both an equivalent agent based model and a Susceptible Exposed Infected Recovered (SEIR) like model to prove that the growth rate can be determined analytically in terms of other model parameters, including contact tracing rate. We identify the most sensitive parameters as undocumented transmission rate and documentation ratio. Unfortunately, these are the parameters we have the least knowledge. We derived an identity that predicts the effectiveness of contact tracing in a country from observable parameters. We underline an unavoidable dilemma: that even in the case of high contact tracing, we cannot bring the outbreak to stalemate without applying substantial quarantine; however, some countries are benefiting from contact tracing. Besides, we have shown that the seemingly same parameters of the SEIR models and agent based models are not equivalent. We propose a correction to bridge both models.
Robust Optimal Control Scheme for Unknown Constrained Input Nonlinear Systems via a Plug n Play Event Sampled Critic Only Algorithm
Venue: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year: 2020

In this paper, a novel event sampled robust optimal controller is proposed for a class of continuous time constrained input nonlinear systems with unknown dynamics. In order to solve the robust optimal control problem, an online data driven identifier is established to construct the system dynamics, and an event sampled critic only adaptive dynamic programming method is developed to replace the conventional time driven actor critic structure. The designed online identification method runs during the solving process and is not applied as a priori part for the solutions, which simplifies the architecture and reduces computational load. The proposed robust optimal control algorithm tunes the parameters of critic only neural network (NN) by event triggering condition and runs in a plug n play framework without system functions, where fewer transmissions and less computation are required as all the measurements received simultaneously. Based on the novel design, the stability of system and the convergence of critic NN are demonstrated by Lyapunov theory, where the state is asymptotically stable and weight error is guaranteed to be uniformly ultimately bounded. Finally, the applications in a basic nonlinear system and the complex rotational translational actuator problem demonstrate the effectiveness of the proposed method.
A Study of the Phonological Poetic Devices of Selected Poems of Robert Browning and Alfred Tennyson
Year: 2020

This paper focuses on the phonological poetic devices found in the poetry of Robert Browning and Alfred Tennyson. It investigates five patterns of phonological poetic devices. The study is based on randomly selected poems from each poet to obtain a representative sample of the particular poetic devices and tabulates the frequency their usage. The poetic devices under investigation are onomatopoeia, assonance, consonance, alliteration, and rhyme. The paper quantitatively analyzes the occurrence of these phonological poetic devices in randomly selected poems from the works of the two poets to a clear picture of the sound patterns found in the poetry of Robert Browning and Alfred Tennyson.
A local optimization framework for addressing conservation conflicts in mosaic ecosystems
Venue: PloS one
Year: 2019

An effective strategy to resolve conservation conflicts on lands outside of nature reserves is to consider the spatial arrangement of agricultural and native vegetation parcels such that the ecological value of the landscape is improved without reducing the amount of land used for agricultural production. Global optimization methods have been used to identify the best spatial arrangement of land parcels for a given project goal, but these methods are not designed to provide pathways to reach the optimum from the initial landscape. Here we describe how local search algorithms can be used to develop land parcel rearrangement pathways to obtain a landscape that sustains greater species richness than the initial landscape without changing the amount of land used for agricultural production. To demonstrate how the local optimization framework can be applied, an ecological setting based on a forest grassland mosaic ecosystem in Rio Grande do Sul, Brazil was constructed. Plant samples collected from this region were used to construct species area curves. Multiple locally optimal solutions that improved the modeled species richness of the landscape almost to globally optimal levels were identified. To support the results, the algorithm was also applied to a 306,250 ha forest grassland region of Rio Grande do Sul. The case study results suggested that conservation polices solely based on landowners satisfying a legal reserve percentage on their property should be revised to consider landscape level connectivity. Providing multiple possible solutions for landscape configurations using local optimization methods may improve managerial flexibility for decision makers, compared to global optimization approaches providing a single solution. Furthermore, the algorithm details the parcel exchange pathways that are required to reach the optimal land state. We conclude that local and global optimization approaches can be used in combination to improve land use decision making for conservation, in mosaic ecosystems as well as other terrestrial ecosystems.
Deep and Ordinal Ensemble Learning for Human Age Estimation From Facial Images
Venue: IEEE Transactions on Information Forensics and Security
Year: 2020

Some recent work treats age estimation as an ordinal ranking task and decomposes it into multiple binary classifications. However, a theoretical defect lies in this type of methods: the ignorance of possible contradictions in individual ranking results. In this paper, we partially embrace the decomposition idea and propose the Deep and Ordinal Ensemble Learning with Two Groups Classification (DOEL2groups) for age prediction. An important advantage of our approach is that it theoretically allows the prediction even when the contradictory cases occur. The proposed method is characterized by a deep and ordinal ensemble and a two stage aggregation strategy. Specifically, we first set up the ensemble based on Convolutional Neural Network (CNN) techniques, while the ordinal relationship is implicitly constructed among its base learners. Each base learner will classify the target face into one of two specific age groups. After achieving probability predictions of different age groups, then we make aggregation by transforming them into counting value distributions of whole age classes and getting the final age estimation from their votes. Moreover, to further improve the estimation performance, we suggest to regard the age class at the boundary of original two age groups as another age group and this modified version is named the Deep and Ordinal Ensemble Learning with Three Groups Classification (DOEL3groups) Effectiveness of this new grouping scheme is validated in theory and practice. Finally, we evaluate the proposed two ensemble methods on controlled and wild aging databases, and both of them produce competitive results. Note that the DOEL3groups shows the state of the art performance in most cases.
The Mediating Effect of Crisis Management on Leadership Styles and Hotel Performance in Jordan
Year: 2020

Research scholars have argued that the emergence of the crisis in the Middle East have led to significant changes in business performance. This has been most notable in the hotels operating in the Jordanian tourist sector, thus, creating a need to investigate leadership styles, crisis management, and hotel performance in a holistic framework. However, this is quite lacking in the literature, and this needs to be addressed. Transformational leadership style and transactional leadership styles are the independent variables, crisis management is the mediating variable and hotel performance is the dependent variable. A quantitative study that used a self administered questionnaire was carried out on 3 star to 5 star hotels operating in Jordan. The Partial Least Squares (PLS) method as part of Structural Equation Modelling (SEM) was applied to analyse the causal relationships between constructs using the software application Smart PLS 3.0. The Partial Least Squares approach was chosen according to the research's exploratory nature. In order to analyze data two step approach was utilized; the first step was to test the reliability and validity of the constructs in the measurement models, the second step was to test research hypotheses in the structural models. The results show that transformational leadership has significant positive effects on hotel performance and crisis management. Transactional leadership had a significant positive effect on crisis management and crisis management had significant positive effects on hotel performance. Moreover, crisis management partially mediated the relationship between transformational leadership and hotel performance; while the relationship between transactional leadership and hotel performance was fully mediated by crisis management. These findings will help to strengthen the discussion in the extant literature.
Psychological context effects of participant expectation on pain pressure thresholds as an adjunct to cervicothoracic HVLA thrust manipulation: A randomised controlled trial
Year: 2020

Chronic pain is a growing global and economically costly problem leading the National Health Service (NHS) in the UK to actively search for novel strategies to improve health outcomes. Some trials have shown a benefit when practitioners use a positive communication style, however, much of the available literature investigating the use of positive language to alter patient expectation utilises subjective reports from patients.  To demonstrate whether positive and negative communication before a high velocity low amplitude (HVLA) thrust spinal manipulation of the C7 T1 spine segments, and within an osteopathic consultation setting, increases and decreases (respectively) pain pressure thresholds (PPT) to form contextual placebo and nocebo effects. Study design pre test, post test randomised controlled design. Methods 35 asymptomatic participants were recruited and randomised into three separate condition arms using a repeated measures cross over design; negative communication (NegC) neutral communication (NeuC) or positive communication (PosC) Each condition included spinal manipulation (HVLA thrust) to the C7 T1 segments. PPTs were measured by an algometer over the spinous process of C7 pre and post each condition setting.  There was a significant effect of language style on PPT for the three conditions. Post hoc tests demonstrated that positive communication had a significant effect on PPT (i.e. placebo effect) but the negative communication demonstrated no significant effect (i.e. no nocebo)  These results were discussed in the context of communication style used during an osteopathic clinical consultation to potentially improve health outcomes in NHS and other clinical settings (
Comparison of T Norms and S Norms for Interval Type 2 Fuzzy Numbers in Weight Adjustment for Neural Networks
Venue: Inf.
Year: 2017

A comparison of different T norms and S norms for interval type 2 fuzzy number weights is proposed in this work. The interval type 2 fuzzy number weights are used in a neural network with an interval backpropagation learning enhanced method for weight adjustment. Results of experiments and a comparative research between traditional neural networks and the neural network with interval type 2 fuzzy number weights with different T norms and S norms are presented to demonstrate the benefits of the proposed approach. In this research, the definitions of the lower and upper interval type 2 fuzzy numbers with random initial values are presented; this interval represents the footprint of uncertainty (FOU) The proposed work is based on recent works that have considered the adaptation of weights using type 2 fuzzy numbers. To confirm the efficiency of the proposed method, a case of data prediction is applied, in particular for the Mackey Glass time series (for t 17) Noise of Gaussian type was applied to the testing data of the Mackey Glass time series to demonstrate that the neural network using a interval type 2 fuzzy numbers method achieves a lower susceptibility to noise than other methods.
PREVENTIVE PRESERVATION OF CULTURAL HERITAGE WITHIN A HOT AND HUMID CLIMATE: A CASE STUDY OF TAINAN CONFUCIAN TEMPLE, TAIWAN
Year: 2019

We took the example of the Tainan Confucian Temple to explore preventive preservation of Taiwanese cultural assets within a hot and humid climate. We mainly used microclimate monitoring data and computational fluid dynamics simulation to analyze the overall environment and identify problem areas, which were shown by the results to be the Ritual Implement Storeroom and Musical Instrument Storeroom. The latter had the highest humidity due to a mango tree located at the rear with a canopy stretching 4 6 m across the roof, which blocked the sunlight during the day and prevented any reduction in humidity. This storeroom includes the original calligraphic wall panels and central beam of the Minglun Hall; therefore we propose measures to improve the current environment without modifying the building materials or structures. First, we suggest either changing the display items or shifting the exhibition area, and transplanting or pruning the mango tree. Second, dehumidification or ventilation equipment should be placed not only in the Musical Instrument Storeroom, but also in the Chung Sheng Shrine and Ta Cheng Palace at night during the hot season, and in the Ritual Appliance Storeroom in the day during the cold season, to improve environmental problems.
Privacy Preserving Compressive Model for Enhanced Deep Learning Based Service Provision System in Edge Computing
Venue: IEEE Access
Year: 2019

Advancements in the Internet of Things promote edge computing, which provides enhanced IoT services. IoT applications remarkably benefit from deep learning (DL) based on neural networks. Considering edge nodes with the limited ability of processing, the small scale compressive DL model is applicatively deployed into them. However, the model can expose the privacy of training dataset. Thus, the model should be regarded as confidential due to the sensitive training dataset when applied in commercial and security fields. In this paper, we design a deep learning based service provision system for protecting the privacy and enhancing services in edge computing. We propose a practical approach for building the private compressive DL model by incorporating differential privacy mechanism on the cloud server side. For protecting the privacy of training dataset, the approach is composed of the private dense training step and the private compressive training step, where differential privacy is used to construct the private pre trained dense model and the private compressive model, respectively. The private compressive model with 1/9 of the dense model's size can be practically embedded into edge servers. Edge servers provide enhanced services to the near IoT devices. Finally, we mainly execute a set of experiments on MNIST dataset. Compared to the dense model with no privacy constraints and with differential privacy (train and test accuracies of 98.0% and 98.2% 95.5% and 97.2% respectively) the private compressive model perturbation with (6, 10 5) differential privacy (train and test accuracies of 96.3% and 95.8% can achieve high utility while upholding tight privacy.
CRESCIMENTO, DESENVOLVIMENTO E PRODUTIVIDADE DO GERGELIM BRS SEDA NA AGROVILA DE CANUDOS, EM CEARA MIRIM (RN)
Year: 2013

The sesame crop, today little known in Brazil, may become an alternative to grain production by small and medium farmers in Northeast Brazil, viewed as a functional food, has good market prospects. The field experiment was conducted at Agrovila Canudos distant 23 Km from the city of Ceara Mirim/RN, in an area of 0.7 ha, irrigated with groundwater, using 80,000 plants of BRS Silk. The present study aimed to evaluate the growth and development of that crop given that these parameters are indicators of productivity, besides being a source of information to deploy and expand the crop in the region. At the end of the production cycle, the plants had, on average, 95.70 g of capsules and 20.40 g of grains per plant. These values are correlated to grain yield amounted to 1,600 kg/ha. The result of this productivity indicates that this sesame cultivar should be inserted in productive ways of that community and can thus ensure an alternative source of income to the families of small producers.
Commentary on the recent FSH collection: known knowns and known unknowns.
Venue: Endocrinology
Year: 2019

Follicle stimulating hormone (FSH) is a dimeric glycoprotein secreted by the anterior pituitary gonadotrope, which is necessary for reproductive function in mammals. FSH primarily regulates granulosa cells and follicular growth in females, and Sertoli cell function in males. Since its identification in 1930s and sequencing in 1970s, significant progress has been made in elucidating its regulation and downstream function. Recent advances provide deeper insight into FSH synthesis and effects in the gonads, suggest potential roles in extragonadal tissues, and examine pharmacological approaches and clinical applications in infertility treatment, that now affects 18% of couples. These advances were discussed in detail in a number of reviews published in the last two years in Endocrinology. In this brief commentary, we summarize these reviews and point to the outstanding questions that should be answered in the near future to bridge a gap in our understanding of this hormone.