Miracle-dz/UniFAR-FTUs huggingface dataset:
InfoVis 2003 Contest Submission: Visualization and Pair Wise Comparison of Trees Treemap, Radial Tree, and 3D Tree Visualizations

This paper presents and discusses data analysis and visualization results as part of our InfoVis 2003 contest submission. Two inhouse developed approaches – a coupled dual radial tree layout interfaces and a three-dimensional tree viewer (Stewart, Hart et al. 2001) – were employed to compare phylogenetic trees. An inhouse developed radial tree layout was applied to visualize tree topology. The treemap algorithm (Bederson, Shneiderman et al. 2002) developed at HCIL, University of Maryland was utilized to visualize tree attributes.
Bayesian analysis of dynamic correlation -multivariate stochastic volatility (DC-MSV) model

The forex exchange rate is the most volatile aspect in financial studies. This study uses a bayesian approach to estimate dynamic correlation multivariate stochastic volatility, a case of the Kenya stocks. Data was obtained from the Central Bank of Kenya website depicting the daily exchange rates for a period of 12 years (2003-2015). Multivariate Stochastic Volatility (MSV) model was fitted and its residuals exhibited volatility clustering hence the use Dynamic Correlation Multivariate Stochastic Volatility (DC-MSV) was applied to address these characteristics. Using Akaike Information Criterion (AIC) and Deviance Information Criterion (DIC) found that the returns are leptokurtic and have fat tails. The study estimated posterior parameters of the model by using of Markovian chain Monte Carlo (MCMC) iterations that worked well. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients. The dynamic correlation multivariate stochastic volatility was found ideal in estimating volatility in stock exchanges.
A neural network approach to the interpretation of ground penetrating radar data

A neural procedure for the analysis of ground penetrating radar images is presented. The method works by adapting the input image to the search of some objects' signatures, that are successively identified by means of a recognition step using a back-propagation neural network. The results on actual data showing buried pipe signatures present the same degree of accuracy than the analyses performed by a human operator.
Numerical Simulation Tools for the Design and the Analysis of Acoustofluidic Devices

The research domain of acoustofluidics is concerned with the effects of acoustic fields inside fluidic devices. The arising time-averaged phenomena called acoustic streaming and acoustic radiation forces can be used to manipulate fluid-suspended micro-particles in a contactless fashion. I has been shown that acoustofluidic particle handling has some clear advantages over competing technologies like hydrodynamic, dielectrophoretic, magnetophoretic, or optical manipulation strategies. Acoustofluidic particle manipulation does not require specific electric, magnetic, or optical particle properties and it is known for excellent cell viability when processing living biological samples like cells, bacteria, or larger organisms. Massively parallel particle manipulation can be achieved easily because the acoustic force fields spread over the volume of the fluid cavity, enabling the simultaneous manipulation of hundreds or thousands of particles at a time. During the past years, a number of promising applications in the miniaturization of process steps in the life sciences has been demonstrated by experimental acoustofluidic setups. In this context, miniaturization is key for the development of novel products such as medical diagnostic rapid tests since it allows to drastically reduce the sample size and the processing time. Despite the significant economic potential of these applications, acoustofluidic devices did not have their large-scale commercial break through yet. This is mainly due to the fact that commercial process standards are challenging to meet in terms of reliability, integrability, and efficiency. The complexity of the underlying physics is high because the interactions between the piezo-electric transducer, the structural device design, the device support, and the loading with different fluid-particle suspensions give rise to a large variety of acoustofluidic effects. At first glance, most experimental setups look simple but the interdependencies are too involved to be understood intuitively. This limits the success of classical design approaches based on "design-experiment-redesignexperiment-. ". Due to the microscale of the device and the acoustofluidic effects, experimental analysis techniques can also not provide sufficient insight to develop a good sense of cause and effect. This is an important problem for both the analysis of existing devices and the design of new devices. As the field of acoustofluidics is moving toward commercial applications, the problem will become even more crucial. An increased control over the device design and the acoustofluidic processes is required to meet commercial standards. Numerical modeling can play a key role in meeting theses goals. In this thesis, the numerical modeling of acoustofluidic devices and processes is reviewed and augmented to gain a more detailed understanding of acoustofluidic devices, the underlying physical effects, and their interplay. A second goal is to advance simulation models to a degree of accuracy that allows a quantitative pre-
Hummingbird maneuvering performance : aerodynamic mechanisms and physiological constraints

The ability of a bird to maneuver in flight can determine its success at avoiding predators, catching prey, and other critical behaviors. Highly maneuverable animals, such as hummingbirds, are capable of diverse behaviors but it is unknown how their maneuvering is constrained by wing motion, wing morphology, and muscle capacity. The purpose of this dissertation was to determine: 1) if hummingbird wings create independent wakes; 2) if independent wingbeat kinematics are used to control maneuvers; and 3) how maneuverability is limited by intrinsic features, such as wing morphology, body mass, and physical properties of the air, versus facultative capacity, such as muscle power. The goal of chapter two was to determine if hummingbirds produce single or bilateral vortex wakes using flow visualization. The goal of chapter three was to determine if sustained maneuvers can be controlled by orienting the wings independently of the body. I tested this hypothesis by filming the three dimensional kinematics of a hummingbird feeding from a translating feeder. The goal of chapter four was to determine if the ability to perform voluntary maneuvers was associated with intrinsic or facultative features. I addressed this question using a tracking system to record a large data set of voluntary flight trajectories, with independent measurements of individual morphology and maximum muscle capacity. The goal of chapter five was to determine if maneuvering performance declines with increasing elevation and, if so, whether changes in oxygen availability or air density are most responsible. I addressed these questions by measuring maneuvering performance across elevation and in an airtight chamber with gas manipulations. Collectively, my results indicate that hummingbirds have wings that operate with a high degree of independence and that this feature influences their precision and control. Voluntary maneuvers at low elevation are primarily
Design of A Web-Services Based Spam Filtering

A board filtering system is a software to protect spam words being written to the web bulletin boards. Since a filtering system needs sophisticated administrative works and sometimes requires modification of server pages, existing board filtering systems are not applicable for web servers for a small and medium businesses who cannot support a dedicated site manager. In this paper, we propose a new architecture for the board filtering systems that is based on a web-services [1] oriented architecture. Proposed architecture exploits the distributed processing such that it can provide ease of maintenance and high scalability.
Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms

The unimodal normal distribution crossover (UNDX) for the real-coded genetic algorithms (RCGA) proposed by Ono et al. (1997, 1998) shows an excellent performance in optimization problems of multi-modal and highly epistatic fitness functions in continuous search space. Further, theoretical analysis of the UNDX shows that the UNDX is a crossover operator that preserves the statistics such as the mean vector and the covariance matrix of the population well. The present paper proposes some design guidelines for crossover operators for RCGA. Then, based on these guidelines, a multi-parental extension of the UNDX is proposed so as to enhance its exploration ability. Performance of the extended UNDX is evaluated by numerical experiments.
Enabling User-Oriented Data Access in a Satellite Portal

This paper presents the design and implementation of a webenabled platform for enhancing user data access experience in a satellite data portal. The platform integrates a number of useroriented capabilities including the subscription, production, dissemination, and visualization of custom satellite remote sensing data hosted at the Purdue Terrestrial observatory (PTO). Different from a traditional remote sensing system, the PTO data portal aims at enabling access to real-time satellite remote sensing data products for a large, diverse user community. Central to this system is a user-driven publish/subscribe model that empowers users with the ability to specify, control, and receive satellite data products without having to go through the time-consuming and error-prone manual process of configuring the system to generate the requested data. The user-oriented capabilities are implemented on top of a service-oriented architecture (SOA) backed by an existing satellite data receiving and processing backend. To control the processing of user data subscription requests, a workflow is created by leveraging the SOA interfaces and mechanisms. The user-oriented platform also embraces the recent Web 2.0 technologies in bringing the rich access, sharing, and collaboration capabilities available in the general cyberspace to the scientific user communities.
Guest Editorial: Ethics and Privacy in Learning Analytics

The European Learning Analytics Community Exchange (LACE) project is responsible for an ongoing series of workshops on ethics and privacy in learning analytics (EP4LA), which have been responsible for driving and transforming activity in these areas. Some of this activity has been brought together with other work in the papers that make up this special issue. These papers cover the creation and development of ethical frameworks, as well as tools and approaches that can be used to address issues of ethics and privacy. This editorial suggests that it is worth taking time to consider the often intertangled issues of ethics, data protection and privacy separately. The challenges mentioned within the special issue are summarised in a table of 22 challenges that are used to identify the values that underpin work in this area. Nine ethical goals are suggested as the editors’ interpretation of the unstated values that lie behind the challenges raised in this paper.
Preferential protein depolymerization as a preservation mechanism for vascular litter decomposing in Sphagnum peat

Abstract. Nitrogen (N) dynamics in Phragmites australis litter due to anaerobic decomposition in three anoxic wetland substrates were analyzed by elemental analyses and infrared spectroscopy (FTIR). After 75 days of decomposition, a relative accumulation of bulk N was detected in most litters, but N accumulated less when decomposition took place in a more N-poor environment. FTIR was used to quantify the relative content of proteins in litter tissue and revealed a highly linear relationship between bulk N content and protein content. Changes in bulk N content thus paralleled and probably were governed by changes in litter protein content. Such changes are the result of two competing processes within decomposing litter: enzymatic protein depolymerization as a part of the litter breakdown process and microbial protein synthesis as a part of microbial biomass growth within the litter. Assuming microbial homeostasis, DNA signals in FTIR spectra were used to calculate the amount of microbial N in decomposed litter which ranged from 14 to 42 % of the total litter N for all leaf samples. Microbial carbon (C) content and resultant calculated carbon-use efficiencies (CUEs) indicate that microbial N in litter accumulated according to predictions of the stoichiometric decomposition theory. Subtracting microbial C- and N-contributions from litter, however, revealed decomposition site dependent variations in the percentual amount of remaining, still unprocessed plant N compared to remaining plant C, an indicator for preferential protein depolymerization. For all leaf litters, the coefficient of preferential protein depolymerization (α), which relates N-compound depolymerization to C-compound depolymerization, ranged from 0.74–0.88 in a nutrient-rich detritus mud to 1.38–1.82 in Sphagnum peat, the most nutrient-poor substrate in this experiment. Preferential protein depolymerization leads to a gradual N depletion of decomposing litter which we propose as a preservation mechanism for vascular litter decomposing in Sphagnum peat.
Mental Disorders Detection by means of Online Social Media Mining

Social Medias have turned into a piece of our lives. The broad utilization of social Medias may expedite an adverse effect in individuals' lives. In any case, our dependence via web-based networking media can detrimentally affect our psychological well-being. An expanding number of social media mental disorders (SMMD) have been observed recently. Mining on the social media gives a chance to effectively recognize SMMDs at a beginning period. In order to precisely recognize potential instances of SMMDs a machine learning framework is proposed, which exploits features extracted from social network data. The results show that the framework is promising for distinguishing the users with potential SNMDs at an early stage.
RSDC (RELIABLE SCHEDULING DISTRIBUTED IN CLOUD COMPUTING )

In this paper we will present a reliable scheduling algorithm in cloud computing environment. In this algorithm we create a new algorithm by means of a new technique and with classification and considering request and acknowledge time of jobs in a qualification function. By evaluating the previous algorithms, we understand that the scheduling jobs have been performed by parameters that are associated with a failure rate. Therefore in the proposed algorithm, in addition to prev ious parameters, some other important parameters are used so we can gain the jobs with different scheduling based on these parameters. This work is associated with a mechanism. The major job is divided to sub jobs. In order to balance the jobs we should calculate the request and acknowledge time separately. Then we create the scheduling of each job by calculating the request and acknowledge time in the form of a shared job. Finally efficiency of the system is increased. So the real time of this algorithm wi ll be improved in comparison with the other algorithms. Finally by the mechanism presented, the total time of processing in cloud computing is improved in comparison with the other algorithms.
Data association for multiple target tracking using Hopfield neural network

Data association consists of assigning measurements to the predicted track positions in the multiple target tracking application. The data association problem can be structured in a basic framework very similar to that of the classic travelling salesman problem (TSP). The derivation of the energy function is presented, and the solution is based on a modified Hopfield network which uses the Runge-Kutta method and Aiyer (1990) network's structure. We demonstrate the feasibility of applying neural network technology to multitarget tracking using simulated and real-life radar data. The modified Hopfield tracker is also observed to have better performance than the original Hopfield network. <<ETX>>
On the Mixed-Integer Linear-Quadratic Optimal Control With Switching Cost

This letter concerns the optimal control of a continuous-time dynamical system via continuous and discrete-valued control variables, where the objective functional also accounts for state-independent switching costs. The class of mixed-integer optimal control problems (OCPs) is interpreted as a bilevel problem, involving both switching times optimization, for a given sequence of modes, and purely continuous optimal control. Additionally, an original nonconvex formulation for the switching costs is introduced, in terms of cardinality, inspired by sparse optimization and compressed sensing techniques. We then adopt proximal algorithms to solve the resulting bilevel OCP with composite objective function. An efficient routine for evaluating the proximal operator is developed. Two examples are numerically solved via a proximal gradient method, discussed and compared with the literature. Although this letter focuses on switched linear time-varying dynamics and quadratic cost functionals with a specific formulation of the switching costs, the proposed approach may also apply to more general mixed-integer OCPs.
Review Paper on Video Watermarking Techniques

The protection and illegal redistribution of digital media has become an important issue in the digital era. This is due to the popularity and accessibility of the Internet now a days by people. This results in recording, editing and replication of multimedia contents. Digital watermarking can be used to protect digital information against illegal manipulations and distributions. Digital watermarking technique is the process of embedding noise-tolerant signal such as audio or image data in the carrier signal. This technique provides a robust solution to the problem of intellectual property rights for online contents. This paper reviews different aspects and techniques of digital watermarking for protecting digital contents.
Scalable Cluster Based Cloud Storage

We consider a cloud system that has to save lots of files and has to use hundreds of computers. The existing cloud storage designs are not scalable enough to support such a huge number of nodes. In this paper, we propose a novel cloud storage system containing thousands of virtual file servers on hundreds of computers. We group these virtual servers into clusters. This system is perfectly scalable because the system load is divided among the clusters. Our simulation experiments show that our cloud storage system achieves smaller file read/write latency and traffic/processing overhead than the existing systems.
The Effects of Learners' Differences on Variable Manipulation Behaviors in Simulation-Based Learning

The present study examined the relationship among learners' differences, behaviors in manipulating variables, and learning achievements in a simulation-based program that supports discovery learning in the subject of C-programming algorithm. Participants (n = 66) took the Group Embedded Figures Test, Action Control Scale, and Computer Self-Efficacy Scale to determine their field dependence, action-state orientation, and computer self-efficacy before interacting with the simulation program. Results from this study have shown that reading domain information supported learners to manipulate more simulation variables systematically. In addition, manipulating simulation variables in a systematic manner positively affects learning post-test achievement. In the context of individual differences, failure-related action control and computer self-efficacy were found to affect learners' behaviors in manipulating variables, while field dependence was shown to affect learning post-test achievement. Implications for both theoretical and instructional designs are discussed in terms of how various support types can accommodate the needs of different learners in simulation programs.
Embedding of orthogonal Buekenhout-Metz unitals in the Desarguesian plane of order q2

A unital, that is a 2 - ( q 3 + 1, q + 1, 1) block-design, is embedded in a projective plane π of order q 2 if its points are points of π and its blocks are subsets of lines of π , the point-block incidences being the same as in π . Regarding unitals U which are isomorphic, as a block-design, to the classical unital, T. Szőnyi and the authors recently proved that the natural embedding is the unique embedding of U into the Desarguesian plane of order q 2 . In this paper we extend this uniqueness result to all unitals which are isomorphic, as block-designs, to orthogonal Buekenhout-Metz unitals.
Monkey on the Roof: Researching creative practice, music consumption, social change and the online environment

ABSTRACT The process of practice-led research within the arts is most often experienced and expressed as a mode of enquiry through the performance of an art form or creative expression. This article instead reveals the methodologies and theoretical bases of a research project that uses exploratory experiments in mediation and meaning, as well as the application of professional practices and new media tools to an existing arts participation project. An explanation of the process and the framework reveals opportunities for further application of the methodology, which is both adaptable to the object of analysis and is in itself an object worthy of analysis. Comparisons are made with related projects in performance arts mediation and the methodology's potential for knowledge exchange — the creation and dissemination of ‘useful, transferable knowledge’ — as well as research, is presented.
Other Things Being Equal: Comparing Literacies in the Czech and Slovak Republics

Large-scale international assessments, such as PIAAC (Programme for the International Assessment of Adult Competencies) or PISA (Programme for International Student Assessment), have heightened interest in the comparative analysis of factors influencing the formation of skills and their change over time. However, such comparison has been hindered by substantial differences in contexts that can only be partially controlled in statistical analysis (e.g., cultural, social, or geographical factors particular to jurisdictions). In this article, we analyze a “natural experiment,” trends in the development of learning outcomes in the Czech and Slovak Republics following the dissolution of the joint state in 1993. The analysis is theoretically based on an age cohort-period framework. We find that in both countries, PISA results are closely related to the future abilities of the particular age cohort (as measured in PIAAC), which supports the thesis that education has a long-lasting effect. Although both countries followed similar trajectories, we find that some noticeable trends can be explained by different policies following dissolution of the joint state.
A Guide to CoCo

CoCo is a program for estimation, test and model search among hierarchical interaction models for large complete contingency tables. The name CoCo is derivated of "Co"mplete "Co"ntingency tables, since the initial program could only handle complete tables, but the program has been enhanced to handle incomplete tables. CoCo works especially efficiently on graphical models, and some of the commands are designed to handle graphical models.