Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel packet classification algorithm for information security firewall

The exponential growth in optical link speed has stressed the performance of routers and firewall. Consequently, a new breed of microprocessors, called Network Processors (NP), are designed and fabricated specifically to effectively process packets on firewalls and routers. Packet classification is a major function in network processors to fit requirements of next-generation Internet. The paper presents a hardware-based packet classification algorithm for NP. The innovative aspect of the proposed algorithm is to use the NP's parallel character well enough. The algorithm use hash algorithm to make six-dimension classification be three-dimension classification. Compressing the size of classification fields are used for hash input while TCAM performs the lookup of the hash value and source IP. The memory of the algorithm is the total amount of memory needed to store the rules. The search speed of our algorithm is neither sensitive to the size of the rule table. It is only relate to the collision of the hash function. The performance of the proposed algorithm test result demonstrates that the proposed scheme is a sound approach that can be implemented on NP efficiently.
Enhancement of a genetic algorithm for optical orthogonal code design using simulated annealing

The simulated annealing (SA) method is applied to the initial population of a genetic algorithm (GA) that was designed to construct optical orthogonal codes. This has improved the quality of the initial population, with an increase in the average fitness value and the number of above-average individuals. This in turn, has enabled the genetic algorithm to converge faster.
Evolving nonlinear time-series models using evolutionary programming

Different variants of evolutionary programming (EP) have been proposed recently to determine the order and parameters of time series models. Unlike conventional algorithms, the model order and coefficients are evolved simultaneously in evolutionary algorithms. In this paper, the performance of the different types of evolutionary algorithms was tested on standard problems. In particular, the algorithms considered in this paper were evolutionary programming, fast evolutionary programming and modified simulated evolutionary optimization, EP was used to evolve both the order and coefficients of the reduced parameter bilinear model and recurrent bilinear perceptron. These models were used for one-step prediction of four well investigated time series, namely the sunspot series, the Mackey-Glass series, the laser data series and the astrophysical data series. The performance of the algorithms was compared on the basis of the order of the model evolved and normalized mean square error. Fast evolutionary programming with recurrent bilinear perceptrons produced the best models with fewer parameters and lower normalized mean square error.
Effects of Different Modes of Preparatory Motion on Dart-Throwing Performance

Abstract Preparatory motion (consciously or unconsciously moving a body part just before performing a task) enhances motor performance. Repeated body movements are roughly categorized as rhythmic and discrete. However, the most effective mode of preparatory motion remains unclear. The present study utilized both modes of preparatory motion and compared subsequent performances. Twelve participants executed dart throwing after performing rhythmic, discrete, or no preparatory motion. Performance was statistically evaluated by comparing the error components, assumed contributions of each mode of preparatory motion. The results revealed that the mean value of the error component for the rhythmic mode was significantly smaller than that for the discrete mode. This suggests that the rhythmic mode of preparatory motion produced better dart-throwing performance.
Cloud Computing and the Cloud Simulation

Cloud Computing is an emerging technology that is used to provide resources as services to its users on the utility basis. The customers of Cloud Computing enjoy these services in the form of applications, platform, or infrastructure. Before deploying these services on Clouds, their allocation policies need to be tested. But testing them on real Cloud infrastructure is very difficult and expensive in terms of time, money and effort. Thus, we use the concept of simulation for this purpose. In this paper, we discuss CloudSim (Cloud Simulation toolkit). CloudSim is analysed as an efficient toolkit for simulating Cloud scheduling and allocation policies. KeywordsCloud computing, virtualisation, simulation, application scheduling.
Application of tree structured self-organizing maps in content-based image retrieval

We have developed an image retrieval system, named PicSOM, which uses tree structured self-organizing maps as the method for retrieving images similar to a given set of reference images. A novel technique introduced in the PicSOM system facilitates automatic combination of the responses from multiple TS-SOMs and their hierarchical levels. This mechanism aims at adapting to user's preferences in selecting which images resemble to each other in the particular sense interested by the user. The image queries are performed through the World Wide Web and the queries are iteratively refined as the system exposes more images to the user.
On thinning of chains in MCMC

1. Markov chain Monte Carlo (MCMC) is a simulation technique that has revolutionised the analysis of ecological data, allowing the fitting of complex models in a Bayesian framework. Since 2001, there have been nearly 200 papers using MCMC in publications of the Ecological Society of America and the British Ecological Society, including more than 75 in the journal Ecology and 35 in the Journal of Applied Ecology.
A copy network with shared buffers for large multicast ATM switches

A new architecture for the copy network which is an integral part of multicast asynchronous transfer mode (ATM) switches is proposed. The new architecture makes use of the property that the Broadcast Banyan Network (BBN) is non-blocking if the active inputs are cyclically concentrated and the outputs are monotone. In the new architecture, by employing a token ring reservation scheme, the outputs of the copy network are reserved before a multicast cell is replicated. By the new copy principle, the number of copies requested by a multicast call is not limited by the size of the copy network, so that very large multicast switches can be configured in a modular fashion. The sequence of cells is preserved in the new structure. Though physically separated, buffers within the copy network are completely shared, so that the throughput can reach 100%, and the cell delay and the cell loss probability can be made very small. <<ETX>>
Classificação de dados em modelos com resposta binária via algoritmo boosting e regressão logística

Classify something is a natural human task, but there are situations where it is not best suited to perform this function. The need for automatic methods for classification arises in several areas, ranging from voice recognition, tumors recognition by x-ray films, email classification as spam or legitimate, among others. Due to the increasing complexity and importance of problems such as these, there is still a need for methods which provide greater accuracy and interpretability of the results. Among these methods Boosting, which operates sequentially applying a classification algorithm to reweighted versions of the training data set. Recently it was shown that Boosting may also be viewed as a method for estimating functional. Currently the logistic regression models with its parameters estimated by maximum likelihood (henceforth called LRMML) are very used to this kind of situation. In this sense, the present study was to compare the LRMML and Boosting algorithm, specifically Binomial Boosting algorithm (henceforth called LRMBB), logistic regression model, and select the model with the best fit and suitability of higher discrimination capacity in the situation of presence / absence of coronary heart disease (CHD) as a function of various biological variables in patients in order to provide the most accurate response to situations which is binary. To adjust the model, the data set was randomly partitioned into two subsets, one subset equivalent to 70 % of the original set (called training sample) and the remainder (called test set). The results show lower values of AIC and BIC for the LRMBB model compared to LRMML and the Hosmer-Lemeshow test shows both models (LRMLM and LRMBB) present no evidence of bad fit. The LRMBB model presented higher values of AUC, sensitivity, specificity and accuracy and lower values for the rate of false positives and false negatives, being therefore a model with better discrimination power in relation to the LRMML model. Observing the odds ratios, the LRMBB model showed more reliable results about the chance of a patient having CHD. Based on these results, the LRMBB model is best suited to describe the problem of presence / absence of coronary heart disease in patients because it provides more accurate information about the problem exposed.
Rapid Prototyping for Mobile Robots Embedded Control Systems

One of the main motivations of this work is to propitiate a virtual environment that facilitates development of archetypes of embedded systems, emphasizing implementation of tools that allow the simulation of kinematic, dynamic and control conditions, with real time monitoring of all important points of the system. In this way, the proposal of a virtual simulator of mobile robotic systems is presented together with techniques of rapid prototyping. The use of the rapid prototyping technique in mobile robotic systems differs from the traditional target used in mechanics engineering and enters in a new field of research and development for projects of mobile robots mechatronics systems. In this way, the rapid prototyping of these systems is associated not only with the project of the physical system, but mainly with the experimental implementations in the fields of hardware and software of the robotic system. In the context of this work, the rapid prototyping is the technology that allows, in conjunction with a simulation virtual environment, the development of a mobile robots controller project. After tested and validated in the simulator, the control system is programmed in the control onboard memory of the mobile robot. In this way, a economy of time and material are obtained, validating first all the model virtually for later operating the physical implementation of the system. It is fundamental that the architecture of hardware of the considered system be opened and flexible in the way of effecting the necessary modifications for system optimization. A proposal of open architecture system was presented in Melo et al. (2005) and summarized in this work. The software of the embedded control system of the mobile robot, in the context of the rapid prototyping, can be elaborated in simulators and have all the parameters tested for adjustments that makes necessary in accordance with the physical system to be implemented, the hardware architecture, the actuators and the sensors. In this way, in the context of this work, the rapid prototyping is then the methodology that allows the creation of a virtual environment of simulation for the project of a controller for mobile robots. After being tested and validated in the simulator, the control system is programmed in the control board memory of the mobile robot. In this way, an economy of time and material are obtained, firstly validating all the model virtually and later operating the physical implementation of the system (Dudek & Jekin, 2000). The choice of DSP TMS320C6474 multicore digital signal processor as main onboard manager device falls into two main factors. First, its great information and instructions processing 13
Code-notes-music: an epistemological investigation of algorithmic music

This paper deals with the role of the composer in algorithmic music. This role departs from traditional models because of the way computers are used in the compositional process, particularly when signal processing techniques are being integrated with sophisticated formal models to generate musical compositions. We shall examine several types of ‘musical formalism’ in order to bring out the active role of the composer in the compositional process.
The usage of an online discussion forum for the facilitation of case-based learning in an intermediate accounting course: a New Zealand case

This paper describes the implementation of an online discussion forum as a means of facilitating case-based learning in an intermediate financial accounting course. The paper commences with a review of case-based learning literature and the use of online discussions as a delivery platform, linking these pedagogical approaches to the emerging needs and learning styles of the current generation of ‘digital’ students. The paper then describes the context in which the research takes place, including the nature of the course, its learning objectives and its educational philosophy, and describes the rationale for the implementation and usage of the online discussion forum. Survey data were collected and analysed from a single student cohort to assess student perceptions of the benefits and limitations of case studies and the usage of online discussion groups to augment case-based learning. The findings suggest that students perceive numerous benefits associated with case-based online discussions, including being exposed to other students’ opinions, improving their ability to critically review case information, motivation to consult additional non-course related material and the convenience of the technology utilised.
The use of SystemC for design verification and integration test of IP-cores

The current trend of systems on silicon is leading to System-on-Chips with embedded software and hardware components. Design simplification is becoming necessary to respect the target time-to-market of SoCs, and this goal can be obtained by using predesigned IP-cores. However, their correct integration in a design implies more complex verification problems. The SystemC language allows one to create and integrate accurate models of software algorithms, hardware architectures and interfaces for SoCs. In this paper, characteristics of the language are exploited to define a design verification framework for integration-test of IP-cores. Intellectual property of cores is guaranteed by adopting-a client/server simulation architecture and by allowing functional test generation on faulty IP-core models without disclosing their internal structure. Moreover, the methodology can be applied to mixed descriptions based on VHDL and SystemC, since an abstraction layer has been defined allowing clients and/or servers to be indifferently described in VHDL or SystemC.
Lyapunov stability analysis for self-learning neural model with application to semi-active suspension control system

A supervised training algorithm and an unsupervised learning algorithm for neural models are derived using Lyapunov stability theory and the concept of model reference adaptive control (MRAC). The practicability of the algorithm is demonstrated by its successful application to a semiactive suspension system. The neurocontroller establishes its own control laws for improving the suspension performance without requiring a complete knowledge of the system dynamics. It also makes it possible to evaluate the roles of simple sensors in defining the control laws. Simulation results illustrate the self-improving suspension performance and show that superior suspension performance can be achieved using the neurocontrol. It is found that the discretized version of the developed training and learning algorithms corresponds to certain well-known existing algorithms. <<ETX>>
Yoking of Algorithms for Effective Clustering

Cluster plays a vital and very important in data mining. Cluster is a main and absolute part of real time applications. Grouping an object with its own class is known as Cluster. It has two different segments, Similar and Dissimilar objects. K Mean (KM) is one of the exclusive clustering algorithms. K Mean algorithm is introduced by cluster, which forms an easier and simpler way of classifying a given set of data. This paper is clearly based on Gravitational Search Algorithm (GSA) and KM algorithm. The main advantage of GSA and KM algorithm is to escape local optima and make convergence motions in rapid progression. A main five data sets in an UCI repository is used to bring the results and solutions in an excellent way using these algorithms. This paper aims to bring an exclusive and efficient result from both the algorithms compared to other algorithm and also gives perfect solution for the existing set of data.
Un Método Eficiente para la Sustracción de Fondo en Videos Usando GPU

La sustraccion de fondo es una tecnica ampliamente utilizada para detectar objetos en movimiento a partir de camaras estaticas. En el campo de la video-vigilancia, es una tecnica indispensable, ya que permite discriminar rapidamente cuando ha surgido un evento y permite a su vez realizar algun tipo de estudio de movilidad. En los ultimos anos, se han propuesto muchos metodos con diferentes fortalezas y requerimientos para realizar la sustraccion de fondo. Uno de los metodos mas novedosos es el Visual Background Extractor (ViBe), el cual utilizando analisis estocastico multimodelo permite discriminar objetos del fondo con una alta tasa de confianza. En este trabajo se propone una variante de este algoritmo para el estudio de objetos en situaciones particulares a partir de videos de vigilancia. Dado que se desea trabajar con videos de alta resolucion, se presenta una implementacion en GPU utilizando OpenCL para lograr un analisis en tiempo real. Presentamos la funcionalidad en casos de estudios tipicos en para este area.
Composing Music with Computers

Foreword by Daniel V. Oppenheim Preface Computer music: facing the facts Preparing the ground Probabilities, grammars and automata Iterative algorithms: chaos and fractals Neural computation and music Evolutionary music: breaking new ground Case studies Music composition software on the CD-ROM Epilogue Appendix 1: Excerpt of J. S. Bach's Choral BWV 668 Appendix 2: Musical clip Appendix 3: Formant chart Appendix 4: A primer in Lisp programming References CD-ROM Instructions
Notice of Retraction Controlling Peacemakers by Accelerometers

Presently is used for controlling pacemakers measuring by one vibration sensor or a movement sensor, which is placed on a pacemaker's case or on a tip of stimulating electrode. As movement sensor is used a piezoelectric crystal or an accelerometer. Suggested solution uses two accelerometers placed on patient's body so it is possible to extend a set of detected movements and also we can detect patient's position (sitting, lying, standing position etc.). On the basis of suitable processing of measured data is pointed out patient's physical activity, which is one of the main input parameters in algorithm for controlling pacemakers. Usage physical activity would significantly reduce an amount of parameters, which are entered by setting pacemakers.
Imágenes post-matern en instagram: el selfie como narrativa personal

In the present paper I present a case study developed within the framework of the Selfiestories and personal data research project (financed by the BBVA Foundation, 2014-2017). Using a hybrid methodology that combines a quantitative and a qualitative approach, some Instagram profiles belonging to women who had published selfies shortly after giving birth are analyzed, in order to understand the different narratives about motherhood that emerge from these profiles.
Good to great: a framework for improving instruction In a ninth grade academy

Good to Great: A Framework for Improving Instruction in a Ninth Grade Academy by Donna L. Marable Rowan University: 2008 This participatory action research project explores the leadership of a female African American high school principal in an urban district who applies the principles in Jim Collins’ Good to Great (2001, 2005) to improve academic performance in a ninth grade academy. Participatory Action Research (PAR) is defined as planning, acting, observing, reflecting, and repeating each cycle in a spiral of self-reflective cycles. The data was captured through qualitative and quantitative data collection strategies. The study is written as an autoethnographic self-narrative and is a self-reflective study of my leadership. The findings of this study suggest that applying the principles in Jim Collins’ (2001, 2005) Good to Great Framework as a second-order change initiative can increase academic performance in an urban school district by improving classroom instruction. This study although not statistically significant, allows for the preponderance of inferences and is significant and germane to female urban school administrators.
Sensor-driven associative random optimization (SARO) for control

A novel mechanism for adaptation of synaptic weights of a neural control net, using a separate 'training net' with sensor-driven associative random optimization (SARO), is proposed. SAROnet adjusts all weights of the control net in parallel, as defined by a scalar 'Critic', which evaluates the performance error of the controlled system. The sensory input for SAROnet may be composed of the control net input and the output of the controlled system. The learning performance of SARO for global and stepwise error minimization was successfully tested with simulations for several n-bit parity tasks and the 8-3-8 encoding problem. Speed of convergence for n-bit parity tasks was found to be considerably higher as compared to error-backpropagation or a more recent random optimization method. Based on these encouraging benchmark tests, SAROnet will be applied to control tasks. <<ETX>>
A ROM based fractional-N frequency synthesizer for wireless communications

The wireless market has experienced an exponential growth over the past few years. To sustain this growth along with the increasing demands of new wireless standards the cost, battery lifetime, and performance of wireless devices must all be enhanced. With the advancement of radio frequency (RF) technology and requirement for more integration, new RF wireless architectures are needed. One of the most critical components in a wireless transceiver is the frequency synthesizer. It largely affects all three dimensions of a wireless transceiver design: cost, battery lifetime, and performance. The common approach to frequency synthesis design for wireless communication is to design an analog-compensated fractional-N phaselocked loop (PLL). However, this technique lacks of adequate fractional spurs suppression for third generation wireless standards. In this paper, a new ROM based sigma-delta PLL architecture is reported to enhance the above mentioned limitations. This architecture has all the benefits of the sigma-delta architecture in terms of fractional spurs reduction without its drawbacks like speed, power, cost, and stability. This aids in fully integrating a high-performance PLL frequency synthesizer, and hence reducing cost. The use of this architecture is examined to give a close match to the regular sigma-delta architecture in terms of noise shaping and measured spectrum at the VCO output.