nils-herrmann/clean_scirepeval huggingface dataset:
Image Encryption Algorithm Based on Compressive Sensing and Fractional DCT via Polynomial Interpolation
Venue: Int. J. Autom. Comput.
Year: 2020

Based on compressive sensing and fractional discrete cosine transform (DCT) via polynomial interpolation (PI FrDCT) an image encryption algorithm is proposed, in which the compression and encryption of an image are accomplished simultaneously. It can keep information secret more effectively with low data transmission. Three dimensional piecewise and nonlinear chaotic maps are employed to obtain a generating sequence and the exclusive OR (XOR) matrix, which greatly enlarge the key space of the encryption system. Unlike many other fractional transforms, the output of PI FrDCT is real, which facilitates the storage, transmission and display of the encrypted image. Due to the introduction of a plain image dependent disturbance factor, the initial values and system parameters of the encryption scheme are determined by cipher keys and plain image. Thus, the proposed encryption scheme is very sensitive to the plain image, which makes the encryption system more secure. Experimental results demonstrate the validity and the reliability of the proposed encryption algorithm.
Pareto Based Multi output Model Type Selection
Venue: HAIS
Year: 2009

In engineering design the use of approximation models surrogate models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, Kriging models, and splines. An engineering simulation typically involves multiple response variables that must be approximated. With many approximation methods available, the question of which method to use for which response consistently arises among engineers and domain experts. Traditionally, the different responses are modeled separately by independent models, possibly involving a comparison among model types. Instead, this paper proposes a multi objective approach can benefit the domain expert since it enables automatic model type selection for each output on the fly without resorting to multiple runs. In effect the optimal model complexity and model type for each output is determined automatically. In addition a multi objective approach gives information about output correlation and facilitates the generation of diverse ensembles. The merit of this approach is illustrated with a modeling problem from aerospace.
Cognitive vs. generative construction grammar: The case of coercion and argument structure
Year: 2015

One of the most salient hallmarks of construction grammar is its approach to argument structure and coercion: rather than positing many different verb senses in the lexicon, the same lexical construction may freely interact with multiple argument structure constructions. This view has however been criticized from within the construction grammar movement for leading to overgeneration. This paper argues that this criticism falls flat for two reasons: (1) lexicalism, which is the alternative solution proposed by the critics, has already been proven to overgenerate itself, and (2) the argument of overgeneration becomes void if grammar is implemented as a problem solving model rather than as a generative competence model; a claim that the paper substantiates through a computational operationalization of argument structure and coercion in Fluid Construction Grammar. The paper thus shows that the current debate on argument structure is hiding a much more fundamental rift between practitioners of construction grammar that touches upon the role of grammar itself.
Discriminative Learning Under Covariate Shift
Venue: J. Mach. Learn. Res.
Year: 2009

We address classification problems for which the training instances are governed by an input distribution that is allowed to differ arbitrarily from the test distribution problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. The problem of learning under covariate shift can be written as an integrated optimization problem. Instantiating the general optimization problem leads to a kernel logistic regression and an exponential model classifier for covariate shift. The optimization problem is convex under certain conditions; our findings also clarify the relationship to the known kernel mean matching procedure. We report on experiments on problems of spam filtering, text classification, and landmine detection.
Sistem Pendukung Keputusan Pendeteksian Kerusakan Mesin Filling Sachet Wolf Vci 180 dengan Menggunakan Metode Forward Chaining Berbasis Web di PT. Sanghiang Perkasa Jakarta
Year: 2017

Perkembangan industri di dunia semakin pesat seiring dengan perkembangan ilmu pengetahuan dan teknologi. Hal ini mendorong Perusahaan yang berbasis manufacturing untuk bisa membuat produknya dengan output yang besar dengan akurasi kualitas yang tinggi, untuk itu banyak digunakan mesin mesin produksi dengan teknologi yang canggih, sebagai contoh penggunaan mesin filling sachet wolf VCI 180. Dengan kondisi tersebut Perusahaan harus merekrut tenaga ahli untuk bisa merawat performance mesin agar dalam kondisi yang optimal, hal ini memaksa Perusahaan untuk mengeluarkan dana yang tidak sedikit karena harus mendatangkan tenaga ahli dari luar karena keterbatasan dari sumber daya team engineering dan maintenance. Sehingga perlunya sebuah sistem yang dapat membantu pengguna untuk menangani masalah yang ringan maka bisa diselesaikan sendiri dengan bantuan sistem tersebut. Ide sederhana berdasarkan latar belakang tersebut adalah tentang bagiamana caranya membuat suatu sistem yang dapat memberikan solusi kepada pengguna mesin dalam mendeteksi kerusakan mesin saat sedang bermasalah. Pengembangan aplikasi berbasis web menggunakan konsep sistem pakar dengan metode inferensi forward chaining yang dapat membantu pengguna dalam menyelesaikan permasalahan terkait dengan mesin tanpa perlu mendatangkan tenaga ahli atau team maintenance yang berpengalaman. Kata kunci Sistem Pakar, Forward Chaining, Maintenance, Troubleshoot Mesin
Rod shape inorganic biomimetic mutual reinforcing MnO2 Au nanozymes for catalysis enhanced hypoxic tumor therapy
Venue: Nano Research
Year: 2020

Biomimetic nanozymes possessing natural enzyme mimetic activities have been extensively applied in nanocatalytic tumor therapy. However, engineering hybrid biomimetic nanozymes to achieve superior nanozyme activity remained to be an intractable challenge in hypoxic tumors. Herein, a rod like biomimetic hybrid inorganic MnO2 Au nanozymes are developed, where MnO2 and ultrasmall Au nanoparticles (NPs) are successively deposited on the mesoporous silica nanorod to cooperatively improve the O2 content and thermal sensitivity of hypoxic solid tumors guided by multi modal imaging. Under the catalyzing of MnO2, the intratumoral H2O2 is decomposed to greatly accelerate O2 generation, which could boost the curative effect of radiation therapy (RT) and further enhance the Au catalyzed glucose oxidation. Mutually, the Au NPs can steadily and efficiently catalyze the oxidation of glucose in harsh tumor microenvironment, thus sensitizing tumor cells to thermal ablation for mild photothermal therapy and further promoting the catalytic efficiency of MnO2 with the self supplied H2O2/H+ As a result, this mutual reinforcing cycle can endow the nanoplatform with accelerated O2 generation, thus alleviating hypoxic environment and further boosting RT effect. Furthermore, acute glucose consuming can induce downregulation expression of heat shock protein (HSP) achieving starvation promoted mild photothermal therapy. This synthesized hybrid nanozymes proves to be a versatile theranostic agent for nanocatalytic cancer therapy.
Pathogens and host immunity in the ancient human oral cavity
Venue: Nature Genetics
Year: 2014

Calcified dental plaque (dental calculus) preserves for millennia and entraps biomolecules from all domains of life and viruses. We report the first, to our knowledge, high resolution taxonomic and protein functional characterization of the ancient oral microbiome and demonstrate that the oral cavity has long served as a reservoir for bacteria implicated in both local and systemic disease. We characterize (i) the ancient oral microbiome in a diseased state, (ii) 40 opportunistic pathogens, (iii) ancient human associated putative antibiotic resistance genes, (iv) a genome reconstruction of the periodontal pathogen Tannerella forsythia, (v) 239 bacterial and 43 human proteins, allowing confirmation of a long term association between host immune factors, 'red complex' pathogens and periodontal disease, and (vi) DNA sequences matching dietary sources. Directly datable and nearly ubiquitous, dental calculus permits the simultaneous investigation of pathogen activity, host immunity and diet, thereby extending direct investigation of common diseases into the human evolutionary past.
OMERACT Development of a Core Domain Set of Outcomes for Shared Decision making Interventions
Venue: The Journal of Rheumatology
Year: 2019

The Outcome Measures in Rheumatology (OMERACT) Shared Decision Making (SDM) Working Group aims to determine the core outcome domain set for measuring the effectiveness of SDM interventions in rheumatology trials.  A white paper was developed to clarify the draft core domain set. It was then used to prepare for interviews to investigate reasons for lack of consensus on it and to suggest further improvements.  OMERACT scientists/clinicians (n 13) and patients (n 10) suggested limiting the core domain set to outcome domains, removing process domains, and clarifying remaining domains.  A revised core domain set will undergo further consensus building.
A Unified Solution for IoT Device Management
Venue: 2019 22nd International Symposium on Wireless Personal Multimedia Communications (WPMC)
Year: 2019

Sensing and actuating tasks in the Internet of Things (IoT) increasingly involve a variety of sensors, comprising not only physical, electronic based devices, but also virtual sensors (i.e. software agents that abstract one or more physical sensors) and even human sensors, such as, humanoriginated data collected from social networks. In this context, the main objective of this paper is to propose and present a unified IoT device management solution based on the LightWeight Machine to Machine (LwM2M) protocol that deals with the heterogeneity of sensing and actuating mechanisms in IoT. We start by presenting the underlying concepts and then proceed to present the proposed unified management model. Subsequently, proof of concept implementations of the proposed model using physical sensors, virtual sensors, and human sensors are presented. The outcome of these implementations, which use off the shelf technology, shows that the proposed management approach is feasible and leads to a unified view of all types of sensing.
Design and Field Implementation of Blockchain Based Renewable Energy Trading in Residential Communities
Venue: 2019 2nd International Conference on Smart Grid and Renewable Energy (SGRE)
Year: 2019

This paper proposes a peer to peer (P2P) blockchain based energy trading market platform for residential communities with the objective of reducing overall community peak demand and household electricity bills. Smart homes within the community place energy bids for its available distributed energy resources (DERs) for each discrete trading period during a day, and a double auction mechanism is used to clear the market and compute the market clearing price (MCP) The marketplace is implemented on a permissioned blockchain infrastructure, where bids are stored to the immutable ledger and smart contracts are used to implement the MCP calculation and award service contracts to all winning bids. Utilizing the blockchain obviates the need for a trusted, centralized auctioneer, and eliminates vulnerability to a single point of failure. Simulation results show that the platform enables a community peak demand reduction of 46% as well as a weekly savings of 6% The platform is also tested at a real world Canadian microgrid using the Hyperledger Fabric blockchain framework, to show the end to end connectivity of smart home DERs to the platform.
Bootstrap resampling for voxel wise variance analysis of three dimensional density maps derived by image analysis of two dimensional crystals.
Venue: Journal of structural biology
Year: 2007

Difference density maps are commonly used in structural biology for identifying conformational changes in macromolecular complexes. For interpretation of the results, it is essential to estimate the variance or standard deviation of the difference density and the distribution of errors in space. In order to compare three dimensional density maps of gap junction channels with and without the C terminal regulatory domain, we developed a bootstrap resampling method for estimation of the voxel wise standard deviation. The bootstrap approach has been successfully used for estimating the sampling distribution from a limited data set and for estimating the statistical properties of the derived quantities [Efron, B. 1979.  another look at the jackknife. ] In our application, the standard deviation map can be estimated by bootstrapping the images. Our results show that, apart from the symmetry axes and small regions bordering the lumen of the extracellular vestibule, difference maps normalized by the mean of the standard deviation map can be used as a good approximation of the t test map of the gap junction crystals.
Cyber Security Awareness Among Secondary School Students in Malaysia
Year: 2020

It is people's choice to go online without worrying about their presence being at the stake. However, there are too many unknown cyber risks and threats of attack in different forms coming ahead. These cause their presence along with personal data are dangerously at risk. Moreover, the access to the cyber spaces are unlimited and unrestricted to all levels of ages. If this situation is ignored, it will create more unpredicted and new cybercrimes as well as more increasing the existing ones. This paper explores on understanding the level of cyber security situational awareness among secondary school students, their teachers as well as their parents in Malaysia. Both physical and online survey methods were deployed to administer the data collection exercise. The target groups were divided into three categories: students (secondary students aged 13 16 years old) teachers and parents. A different questionnaire set was designed for each category. The survey topics/areas included Internet and digital citizenship knowledge. Respondents were selected from certain areas throughout the Klang Valley area in Malaysia. The results show most of the respondents are aware of the cyber threats and risks of being in cyber space, but a very few of them take actions on security measures of being online. The findings and recommendations from this awareness study are fundamental to develop a model for secondary school students to understand the security risks and threats associated with the Internet throughout their years in school. Early exposure and awareness will help ensure healthy cyber habits among millennials and their environment in Malaysia.
A Systematic Review: The Role of Resident Memory T Cells in Infectious Diseases and Their Relevance for Vaccine Development
Venue: Front. Immunol.
Year: 2018

Resident memory T cells have emerged as key players in the immune response generated against a number of pathogens. Their ability to take residence in non lymphoid peripheral tissues allows for the rapid deployment of secondary effector responses at the site of pathogen entry. This ability to provide enhanced regional immunity has gathered much attention, with the generation of resident memory T cells being the goal of many novel vaccines.  This review aimed to systematically analyze published literature investigating the role of resident memory T cells in human infectious diseases. Known effector responses mounted by these cells are summarized and key strategies that are potentially influential in the rational design of resident memory T cell inducing vaccines have also been highlighted.  A Boolean search was applied to Medline, SCOPUS, and Web of Science. Studies that investigated the effector response generated by resident memory T cells and/or evaluated strategies for inducing these cells were included irrespective of published date. Studies must have utilized an established technique for identifying resident memory T cells such as T cell phenotyping.  While over 600 publications were revealed by the search, 147 articles were eligible for inclusion. The reference lists of included articles were also screened for other eligible publications. This resulted in the inclusion of publications that studied resident memory T cells in the context of over 25 human pathogens. The vast majority of studies were conducted in mouse models and demonstrated that resident memory T cells mount protective immune responses.  Although the role resident memory T cells play in providing immunity varies depending on the pathogen and anatomical location they resided in, the evidence overall suggests that these cells are vital for the timely and optimal protection against a number of infectious diseases. The induction of resident memory T cells should be further investigated and seriously considered when designing new vaccines.
Influence of isokinetic inspiratory muscle training at antero cervical temperature in individuals with obstructive sleep apnea
Venue: Sleep and control of breathing
Year: 2019

Obstructive sleep apnea (OSA) results in mechanical stress induced by pharyngeal collapse during sleep, with increase of inflammatory mediators, can be evaluated through Infrared Thermography (IT) (Jarnalo, 2018) Therapies such as isokinetic inspiratory muscle training (Herkenrath, 2018) have been applied as an effective method to minimize respiratory deficit resulting from chronic diseases, impacting on the improvement of the quality of life. The aime of study was to evaluate the influence of isokinetic inspiratory muscle training on airway thermal imaging in subjects with OSA. The study was composed of 20 participants with OSA (polysomnography diagnosed) approved by the research ethics committee, of a sample for convenience, divided into two groups: Group Training (TG) (n 10) and control group (CG) (n 10) Both patients was acclimation in a room with temperature of 23 degrees, and the images were performed (FlirONE Pro camera) in a standard way, with a distance of one meter to the participant, who remained seated in a chair with a cervical hyperextension.  The thermal images recording in two moments (M1= initial) and (M2=10 minutes after M1) and the CG they were at rest and thermal image recording in M1 and M2. Acute responses in the temperature reduction after the stimulation of IMT in the TG were demonstrated (p=0,087) and satble on CG. The isokinetic inspiratory muscle training could impact on the reduction of antero cervical region temperature in individuals with OSA.
Which numbers do you have in mind? Number generation is influenced by reading direction
Venue: Cognitive Processing
Year: 2015

In Western participants, small numbers are associated with left and larger numbers with right space. A biological account proposes that brain asymmetries lead to these attentional asymmetries in number space. In contrast, a cultural account proposes that the direction of this association is shaped by reading direction. We explored whether number generation is influenced by reading direction in participants from a left to right (UK) and a right to left (Arab) reading culture. Participants generated numbers randomly while lying on their left and right side. The mean number generated by participants from a left to right reading culture was smaller when they lay on their left than on their right side, and the opposite was found for participants from a right to left reading culture. Asymmetries in number space observed in number generation are more compatible with a cultural than biological account.
Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes
Venue: Comput. Vis. Image Underst.
Year: 2018

We present an efficient method for detecting and localizing anomalies in videos showing crowded scenes. Research on \it fully convolutional neural networks} (FCNs) has shown the potentials of this technology for object detection and localization, especially in images. We investigate how to involve temporal data, and how to transform a supervised FCN into an unsupervised one such that the resulting FCN ensures anomaly detection. Altogether, we propose an FCN based architecture for anomaly detection and localization in crowded scenes videos. For reducing computations and, consequently, improving performance both with respect to speed and accuracy, we investigate the use of cascaded out layer detection. Our architecture includes two main components, one for feature representation, and one for cascaded out layer detection. Experimental results on Subway and UCSD benchmarks confirm that the detection and localization accuracy of our method is comparable to state of the art methods, but at a significantly increased speed of 370 fps.
Correlation between fecal egg count, presence of Strongylus vulgaris, and body score of feral horses on Fort Polk, Louisiana.
Venue: Veterinary parasitology, regional studies and reports
Year: 2018

Approximately 700 feral horses, dubbed "trespass horses" by the United States Army, occupy Fort Polk, Louisiana and the surrounding Kisatchie National Forest. These horses are considered a nuisance and hazard, and the military is seeking to remove the horses via adoption. The aim of this research was to evaluate the fecal egg count (FEC) body condition score (BCS) and the presence of Strongylus vulgaris within this previously unstudied horse population prior to removal. The feral horse data was compared to domestic horses living on a single farm in the same area. A modified McMaster FEC, Henneke body scoring via photography, and PCR were used to evaluate 10 domestic horses and 28 feral horses. A significantly higher FEC was identified for feral horses when compared to domestic horses (p 0.004) and 69.2% of feral horses were positive for S. vulgaris while all domestic horses tested negative. Additionally, no correlation was found between FEC and BCS for domestic (p 0.213) or feral (p 0.099) horses, and no association was found between FEC and S. vulgaris presence (p 0.21) or BCS and S. vulgaris presence (p 0.52) This study provides insight into S. vulgaris and strongyle prevalence in a previously unstudied group of horses and indicates a need for anthelmintic treatment and monitoring of the feral horses once they are adopted.