Miracle-dz/UniFAR-FTUs huggingface dataset:
An Algorithm for Generating Quantifiers

Quantifiers, and their associated scoping phenomena are ubiquitous in English and other natural languages, and a great deal of attention has been paid to their treatment in the context of natural language analysis. Rather less attention, however, has been paid to their treatment in the context of language generation. This paper describes an algorithm for generating quantifiers in English sentences which describe small models containing collections of individuals which are inter-related in various ways. The input to the algorithm is, i) a model represented as a collection of facts and ii) an abstract description of the target sentence with gaps where the quantifiers should be.
Skyline Query Processing over Encrypted Data: An Attribute-Order-Preserving-Free Approach

Making co-existent and convergent the need for efficiency of relational query processing over Clouds and the security of data themselves is figuring-out how one of the most challenging research problems in the Big Data era. Indeed, in actual analytics-oriented engines, such as Google Analytics and Amazon S3, where key-value storage-representation and efficient-management models are employed as to cope with the simultaneous processing of billions of transactions, querying encrypted data is becoming one of the most annoying problem, which has also attracted a great deal of attention from the research community. While this issue has been applied to a large variety of data formats, e.g. relational, RDF and multidimensional data, very few initiatives have pointed-out skyline query processing over encrypted data, which is, indeed, relevant for database analytics. In order to fulfill this methodological and technological gap, in this paper we present eSkyline, a prototype system and query interface that enables the processing of skyline queries over encrypted data, even without preserving the order on each attribute as order-preserving encryption would do. Our system comprises of an encryption scheme that facilitates the evaluation of domination relationships, hence allows for state-of-the-art skyline processing algorithms to be used. In order to prove the effectiveness and the reliability of our system, we also provide the details of the underlying encryption scheme, plus a suitable GUI that allows a user to interact with a server, and showcases the efficiency of computing skyline queries and decrypting the results.
Creating a Foundation for a Snowboarding Video Game

In this paper, I describe a basic set of data structures and algorithms for creating a snowboarding video game. I use a simple model for the terrain and the snowboarder, and the physical laws that govern the interactions between them. For the terrain, I create a heightmap that is displayed. The heightmap can report interpolated height values and normal values at any point on the map. The snowboarder is defined by its mass, position, and velocity. Gravity and ground forces are applied to the snowboarder in such a way that results in a fast, realistic, and fun snowboarding simulation. The methods described here could be used in a wide range of video games that involve objects interacting with terrain including surfing games, marble games, motorcycle games, and many others.
Soft constrained leading voice separation with music score guidance

Separating leading voice from a music mixture remains challenging for automatic systems. Competing harmonics from music accompaniment severely interfere the leading voice estimation. To properly extract the leading voice, separation algorithms based on source-filter modeling of human voice and non-negative matrix factorization have been introduced. This paper extends this approach with a statistical weighting scheme to rank various pitch candidates with music score information. It imposes a soft constraint on the likelihood of these pitch candidates, so the interference from music accompaniment on leading voice estimation is reduced. Our experiments showed that this soft-constrained separation with score guidance provides accurate inference about the leading vocal pitch with reliable score and remains robust for erroneous score.
Biochemically and histopathologically comparative review of thiamine’s and thiamine pyrophosphate’s oxidative stress effects generated with methotrexate in rat liver

Summary Background Oxidative liver injury occurring with methotrexate restricts its use in the desired dose. Therefore, whether or not thiamine and thiamine pyrophosphate, whose antioxidant activity is known, have protective effects on oxidative liver injury generated with methotrexate was comparatively researched in rats using biochemical and histopathological approaches. Material/Methods Thiamine pyrophosphate+methotrexate, thiamine+methotrexate, and methotrexate were injected intraperitoneally in rats for 7 days. After this period, all animals’ livers were excised, killing them with high-dose anesthesia, and histopathologic and biochemical investigations were made. Result Biochemical results demonstrated a significant elevation in level of oxidant parameters such as MDA and MPO, and a reduction in antioxidant parameters such as GSH and SOD in the liver tissue of the methotrexate group. Also, the quantity of 8-OHdG/dG, a DNA injury product, was higher in the methotrexate group with high oxidant levels and low antioxidant levels, and the quantity of 8-OHdG/dG was in the thiamine pyrophosphate group with low oxidant levels and high antioxidant levels. In the thiamine and control groups, the 8-OHdG/dG rate was 1.48±0.35 pmol/L (P>0.05) and 0.55±0.1 pmol/L (P<0.0001). Thiamine pyrophosphate significantly decreased blood AST, ALT and LDH, but methotrexate and thiamine did not decrease the blood levels of AST, ALT and LDH. Histopathologically, although centrilobular necrosis, apoptotic bodies and inflammation were monitored in the methotrexate group, the findings in the thiamine pyrophosphate group were almost the same as in the control group. Conclusions Thiamine pyrophosphate was found to be effective in methotrexate hepatotoxicity, but thiamine was ineffective.
Sex Differences in Psychoneuroendocrine Reactions to Examination Stress

&NA; Sex differences in adaptation and coping were studied by comparing neuroendocrine and psychological functions in male and female high‐school students during 2‐3 hr of routine school work (control condition) and a 6‐hr matriculation examination (stress condition). In the control condition sex differences were slight and nonsignificant. During examination stress, the urinary excretion of cortisol, adrenaline, noradrenaline, and 3‐methoxy‐4‐hydroxy‐phenylethylene glycol (MOPEG or MHPG) increased in both sexes, but to a consistently greater extent in the male group, significantly so for adrenaline and MOPEG. Both sexes performed equally well in the examination, but self‐reports showed that feelings of success and confidence were common among males, whereas feelings of discomfort and failure dominated in the female group. High discomfort correlated with poor performance in the males but with good performance in the females.
The origin of large-scale H i structures in the Magellanic Bridge

We investigate the formation of a number of key large-scale Hi features in the ISM of the Magellanic Bridge using dissipationless numerical simulation techniques. This study comprises the first direct comparison between detailed Hi maps of the Bridge and numerical simulations. We confirm that the SMC forms two tidal filaments: a near arm, which forms the connection between the SMC and LMC, and a counterarm. We show that the Hi of the most dense part of the Bridge can become arranged into a bimodal configuration, and that the formation of a ’loop’ of Hi, located off the North-Eastern edge of the SMC can be reproduced simply as a projection of the counter-arm, and without invoking localised energy-deposition processes such as SNe or stellar winds.
Schedulability Analysis of Periodic Tasks Implementing Synchronous Finite State Machines

Model-based design of embedded systems using Synchronous Reactive (SR) models is among the best practices for software development in the automotive and aeronautics industry. The correct implementation of an SR model must guarantee the synchronous assumption, that is, all the system reactions complete before the next event. This assumption can be verified using schedulability analysis, but the analysis can be quite challenging when the system also consists of blocks implementing finite state machines, as in modern modeling tools like Simulink and SCADE. In this paper, we discuss the schedulability analysis of such systems, including the applicability of traditional task analysis methods and an algorithmic solution to compute the exact demand and request bound functions. In addition, we define conditions for computing these functions using a periodic recurrent term, even when there is no cyclic recurrent behavior in the model.
Deficiency of NOX1 or NOX4 Prevents Liver Inflammation and Fibrosis in Mice through Inhibition of Hepatic Stellate Cell Activation

Reactive oxygen species (ROS) produced by nicotinamide adenine dinucleotide phosphate oxidase (NOX) play a key role in liver injury and fibrosis. Previous studies demonstrated that GKT137831, a dual NOX1/4 inhibitor, attenuated liver fibrosis in mice as well as pro-fibrotic genes in hepatic stellate cells (HSCs) as well as hepatocyte apoptosis. The effect of NOX1 and NOX4 deficiency in liver fibrosis is unclear, and has never been directly compared. HSCs are the primary myofibroblasts in the pathogenesis of liver fibrosis. Therefore, we aimed to determine the role of NOX1 and NOX4 in liver fibrosis, and investigated whether NOX1 and NOX4 signaling mediates liver fibrosis by regulating HSC activation. Mice were treated with carbon tetrachloride (CCl4) to induce liver fibrosis. Deficiency of either NOX1 or NOX4 attenuates liver injury, inflammation, and fibrosis after CCl4 compared to wild-type mice. NOX1 or NOX4 deficiency reduced lipid peroxidation and ROS production in mice with liver fibrosis. NOX1 and NOX4 deficiency are approximately equally effective in preventing liver injury in the mice. The NOX1/4 dual inhibitor GKT137831 suppressed ROS production as well as inflammatory and proliferative genes induced by lipopolysaccharide (LPS), platelet-derived growth factor (PDGF), or sonic hedgehog (Shh) in primary mouse HSCs. Furthermore, the mRNAs of proliferative and pro-fibrotic genes were downregulated in NOX1 and NOX4 knock-out activated HSCs (cultured on plastic for 5 days). Finally, NOX1 and NOX4 protein levels were increased in human livers with cirrhosis compared with normal controls. Thus, NOX1 and NOX4 signaling mediates the pathogenesis of liver fibrosis, including the direct activation of HSC.
Atomic diffusion in metal-poor stars - II. Predictions for the Spite plateau

We have computed a grid of up-to-date stellar evolutionary models including atomic diusion, in order to study the evolution with time of the surface Li abundance in low-mass metal-poor stars. We discuss in detail the dependence of the surface Li evolution on the initial metallicity and stellar mass, and compare the abundances obtained from our models with the available Li measurements in Pop II stars. While it is widely accepted that the existence of the Spite Li-plateau for these stars is a strong evidence that diusion is inhibited, we show that, when taking into account observational errors, uncertainties in the Li abundance determinations, in the Te scale, and in particular the size of the observed samples of stars, the Spite plateau and the Li abundances in subgiant branch stars can be reproduced also by models including fully ecient diusion, provided that the most metal-poor eld halo objects are between 13.5 and 14 Gyr old. We provide the value of the minimum number of plateau stars to observe, for discriminating between ecient or inhibited diusion. From our models with diusion we derive that the average Li abundance along the Spite plateau is about a factor of 2 lower than the primordial one. As a consequence, the derived primordial Li abundance would be consistent with a high helium and low deuterium Big Bang Nucleosynthesis; this implies a high cosmological baryon density as inferred from the analyses of the cosmic microwave background.
Discrete data-based state feedback model of human operator

Modeling human operator's dynamic characteristics has been an active research area for several decades. Previously the author has proposed data-based state feedback model (DBSFM) concept whose structure is based on the controlled system but the parameters can be identified from experimental data by using the frequency domain approach. This paper proposes a discrete version of DBSFM (DDBSFM) whose parameters can be effectively identified in the time domain using the gradient-based optimization algorithms. The method is implemented for identification of the dynamics of a human operator in trolley crane control.
Improving the visual quality of secret fragment visible mosaic image

For security of digital images, an emerging technique is the use of mosaics of an image to construct an entirely different image. This is done by modifying the colour properties of a secret image in accordance with some reference image, i.e., transferring colour between mosaics of images. These mosaics are placed together to give an image that is similar to the reference image. This method is significant because it preserves all the 8 bits of all 3 colour planes and hence, can be useful for hiding highly detailed images. Here, there are two ultimate goals in view- make the final mosaic image more and more similar to the reference image; and make only reversible modifications to the secret image to achieve this desired similarity. This paper is focussed on improving the results of existing method to obtain a better final mosaic image. This will reduce the chances of drawing an attacker's attention. Also, different image sizes are studied in this work. The improvement in final image quality increases the scope of mosaic technique to smaller images as well. Details about this improvement are also presented here.
Model Predictive Control of Buoy Type Wave Energy Converter

Abstract The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible. The straight forward solution to this maximization problem is achieved by maximizing the instantaneous range of motion of the buoy. The buoy as a single degree of freedom oscillator will undergo its maximum movements when it is in resonance with the sea state. Hence the best solution to the problem is achieved by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement. This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller.
Psychological benefits of greenspace increase with biodiversity

The world's human population is becoming concentrated into cities, giving rise to concerns that it is becoming increasingly isolated from nature. Urban public greenspaces form the arena of many people's daily contact with nature and such contact has measurable physical and psychological benefits. Here we show that these psychological benefits increase with the species richness of urban greenspaces. Moreover, we demonstrate that greenspace users can more or less accurately perceive species richness depending on the taxonomic group in question. These results indicate that successful management of urban greenspaces should emphasize biological complexity to enhance human well-being in addition to biodiversity conservation.
Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
Automatically Classifying Students in Need of Support by Detecting Changes in Programming Behaviour

Educational research has established that learning can be defined as an enduring change in behaviour, which results from practice or other forms of experience. In introductory programming courses, proficiency is typically approximated through relatively small but frequent assignments and tests. Scaling these assessments to track significant behavioural change is challenging due to the subtle and complex metrics that must be collected from large student populations. Based on a four-semester study, we present an analysis of learning tool interaction data collected from 514 students and 38,796 solutions to practice programming exercises. We first evaluate the effectiveness of measuring workflow patterns to detect students at-risk of failure within the first three weeks of the semester. Our early predictor analysis accurately detects 81% of the students who struggle throughout the course. However, our early predictor also captures transient struggling, as 43% of the students who ultimately did well in the course were classified as at-risk. In order to better differentiate sustained versus transient struggling, we further propose a trajectory metric which measures changes in programming behaviour. The trajectory metric detects 70% of the students who exhibit sustained struggling, and mis-classifies only 11% of students who go on to succeed in the course. Overall, our results show how detecting changes in programming behaviour can help us differentiate between learning and struggling in CS1.
Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network

Effective forecasting of future prevalent topics plays animportant role in social network business development.It involves two challenging aspects: predicting whethera topic will become prevalent, and when. This cannotbe directly handled by the existing algorithms in topicmodeling, item recommendation and action forecasting.The classic forecasting framework based on time seriesmodels may be able to predict a hot topic when a seriesof periodical changes to user-addressed frequency in asystematic way. However, the frequency of topics discussedby users often changes irregularly in social networks.In this paper, a generic probabilistic frameworkis proposed for hot topic prediction, and machine learningmethods are explored to predict hot topic patterns.Two effective models, PreWHether and PreWHen, areintroduced to predict whether and when a topic will becomeprevalent. In the PreWHether model, we simulatethe constructed features of previously observed frequencychanges for better prediction. In the PreWHen model,distributions of time intervals associated with the emergenceto prevalence of a topic are modeled. Extensiveexperiments on real datasets demonstrate that ourmethod outperforms the baselines and generates moreeffective predictions.
Performance Comparison of AODV , DSR and AntHocNet Protocols

There are several standard protocols for Mobile Ad hoc Networks (MANET) that have been developed for devices with higher computing features. In this paper, the performance of the two common MANET protocols, namely AODV and DSR has been shown . The Average end-to-end delay and the Packet Delivery Ratio have been considered as the two performance parameters . Finally, a new protocol has been introduced, known as AntHocNet, which is the implementation of AntNet protocol for MANET. AntNet is a distributed shortest-path algorithm based on the principles of Ant Colony Optimization that takes care of load balancing in a very natural way. The comparison of AntHocNet against AODV and DSR, taking into consideration the above performance parameters was then done. I. Introduction Wireless technologies are becoming important in the world of communications. It is mainly due to three reasons: mobility, low cost and good bandwidth. MANETs are self-organized networks. MANET networks [1] do not have infrastructure or any access point. Communication in an ad-hoc network does not require existence of a central base station or a fixed network infrastructure. Each node of an ad-hoc network is both a host and a router. This is a feature that makes difficult the communication between the participants of the network. Moreover, the allocation of IP addresses to build routes where the information will travel from a source node to a destination node is so difficult. A number of algorithms have been proposed, and can be categorized as either proactive or reactive protocols. The former ones are constantly scanning the network to build and maintain routes from and to every node, even if there are no packets to be sent. The main idea behind this behavior is to have always a path available on which to send an eventual flow of data packets. They are efficient if routes are frequently used. The latter ones, on the other hand, use an on-demand approach. They establish a multihop path between a pair of nodes only if there are packets to be transmitted. Thus, saving bandwidth and reducing overhead. They are efficient if routes are sporadically used. The structure of the paper is as follows. Related works are presented in the next section. Section 3 describes the MANET protocols that are going to be used in this paper. The simulations, analysis and comparison are shown in section 4. Finally, in section 5, the paper concludes and gives the future …