Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge Controlled Meminductor Emulator

Emulations of memristor-family elements are very important, since their physical realizations are very difficult to achieve with recent technologies. Although some previous studies succeeded in designing memristor and memcapacitor emulators, no significant contribution towards meminductor emulator has been presented so far. The implementation of a meminductor emulator is very important, since real meminductors are not expected to appear in near future. We designed the first meminductor emulator whose inductance can be varied by an external current source without employing any memrisitve system. The principle of our architecture and its feasibility have been verified using SPICE simulation.
CYBERBULLYING REVELATION IN TWITTER DATA USING NAÏVE BAYES CLASSIFIER ALGORITHM

Cyberbullying can be visualized as a potential issue affecting children and all categories of people. One demanding concern is effective representation for learning of content messages. The proposed system deals with cyberbullying revelation in email application using Naive Bayes Classifier Algorithm. The Classification Algorithm is a baseline method for content classification; the method of analyzing documents as relating to one classification or the other with word prevalence as features. The technique deals with the identification and filtering of spam words. The denoised messages are classified with the help of Naive Bayes Classifier Algorithm. The messages are processed under feature set extraction method. The feature probabilities are found out using Naive Bayes Classifier Algorithm . The efficiency factor is compared among the two algorithms, Naive Bayes Classifier Algorithm and Support Vector Machine and a graph is plotted. Comparison on the basis of precision factor is also done with the fact that the probabilities for each feature set are calculated independently from the twitter dataset and can evaluate the performance by predicting the output variable.
Generating Non-Linear Narratives

Stories play a crucial role in our day-to-day lives. Apart from the entertainment sector, they are also being utilized in different areas of games, education and training. The ability to generate rich stories similar to human authored ones is of significance in story generation research. Along with the content of the story the manner in which it is presented is an important factor in effective storytelling. Though a lot of development happened in automatic story generation, most of it ignored the way in which the story could be presented. This thesis deals with a presentation aspect of the story, the order in which the events could be narrated, in order to make it more engaging than that of the chronological versions generated by many story generation systems. We hypothesize that cognitive engagement is manifested when a narrative is deviated from its chronological order without affecting the ease of comprehension. Narratives with events presented in out of chronology are termed non-linear. From a computational perspective, given n events of a story, the total orderings possible are n! Generating an order in random would affect the comprehension of the material. We have studied how the comprehension of the viewers was affected upon changing the presentation order of the events, by employing a commercial feature film. Using these experimental results along with previous theory from narrative comprehension, we arrive at an algorithm that generates different presentation orders of a story by assessing the amount of processing load required for processing an order of events. Using the principles of the event-indexing model, a cognitive model of narrative comprehension, a function was devised that calculates the processing load, by taking input, the desired amount of non-linearity that could be imparted into the story. An experimental evaluation is undertaken in order to test the generated narratives by our system, corresponding to the different input levels of non-linearity, in terms of engagement. We have obtained results that, for the story employed, increasing the non-linearity up to a medium level did not produce any change in the difficulty in comprehension and thus contributing to engagement. However, it was observed that higher levels of non-linearity led to a decrease in engagement and that, character inconsistencies present in the story had a negative affect on the engagement of the material.
Analysis for the importance of mobile cloud computing in the field of mobile technology and software management

Amid the last few years, there is a progressive improvement in the field of Mobile Computing and Mobile Cloud Computing has been acquainted with be a potential innovation for Mobile Services. Also, the cell phones and their applications have high system in the service ever had, and created quickly. Once more, Mobile Cloud Computing is relied upon to produce fundamentally more creative with multi applications. Additionally, Mobile processing includes mobile hardware, mobile communication and cell programming, and right now there are numerous portable cloud applications, for example, Gmail for iPhone, Cisco’s web EX on the iPad , video playback , image editing , email access and so on. These applications are utilizing the product as an service model. Then again, this paper has clarified the Mobile Cloud Computing by a chose MCC construction modeling. Furthermore demonstrates the answer for a percentage of the issues like Process Power or Data Storage Capacity, Reliability and Battery Life. Besides, this article additionally gives the bearing for the further research
A behavioral biometric system based on human-computer interaction

In this paper we describe a new behavioural biometric technique based on human computer interaction. We developed a system that captures the user interaction via a pointing device, and uses this behavioural information to verify the identity of an individual. Using statistical pattern recognition techniques, we developed a sequential classifier that processes user interaction, according to which the user identity is considered genuine if a predefined accuracy level is achieved, and the user is classified as an impostor otherwise. Two statistical models for the features were tested, namely Parzen density estimation and a unimodal distribution. The system was tested with different numbers of users in order to evaluate the scalability of the proposal. Experimental results show that the normal user interaction with the computer via a pointing device entails behavioural information with discriminating power, that can be explored for identity authentication.
Simulation and modelling of digital delay locked loops

This paper discusses some results for simulation and modeling of charge-pump Delay Locked Loops (DLLs). A novel model based on a sampled-time approach is presented, and used for jitter analysis. The model is applied to input signal jitter, internally generated jitter and is further extended to handle jitter effects related with the control charge-pump. Behavior models for simulation purposes are derived from the theoretical model, and design considerations based on these are presented.
Output regulation of DC-DC switching converters using discrete-time integral control

Discrete-time integral control of the PWM DC-DC converters is considered using the general sampled-data power stage models. Either the voltage or current of the power stage can be controlled. Both state feedback and output feedback are addressed. Line and load regulation are achieved through a discrete-time integrator. By adding an analog filter, the average value of output can be regulated.
The Inclusive Classroom: Teaching Mathematics and Science to English-Language Learners. It's Just Good Teaching.

a grant and contract number RJ96006501 from the U.S. Department of Education. The content of this document does not necessarily reflect the views of the department or any other agency of the United States government. The practice of the Northwest Regional Educational Laboratory is to grant permission to reproduce this publication in whole or in part for nonprofit educational use, with the acknowledgement of the Northwest Regional Educational Laboratory as the source on all copies. Appreciation is extended to the contributors and reviewers who provided information and guidance in the development of this publication: The It's Just Good Teaching series includes publications and videos that illustrate and promote effective teaching strategies. Single copies of the publications are available free of charge to educators within the Northwest Regional Educational Laboratory's region of Alaska, Idaho, Oregon, Montana, and Washington. To request a free copy, contact NWREL's Mathematics and Science Education Center by e-mail at math_and_science@nwrel.org, by telephone at (503) 275-9500, or visit the Center's Web site, www.nwrel.org/msec/. Online versions of the publications are also available in PDF format at this Web site. Multiple copies, and copies to individuals outside of the region, may be pur-group in Northwest schools—their numbers more than doubling in Alaska, Idaho, Montana, Oregon, and Washington this decade. At the same time, mathematics and science education has been undergoing major reforms that have raised the expectations for all students. These reforms, with an emphasis on learning challenging content and developing depth of understanding through problem solving and inquiry, place high demands on students' communication skills. To enable English-language learners to participate meaningfully in the academic discourse and activities that are necessary to achieve the mathematics and science standards, teachers must help them to develop language skills that go beyond mere social fluency. Fortunately, research indicates that principles of standards-based teaching and second-language acquisition strategies are similar. The active learning central to problem solving and inquiry also promotes the development of students' communication skills. Today's inclusive classrooms provide both challenges and rich learning opportunities for teachers and students. Teaching Mathematics and Science to English-Language Learners offers ideas about how to link standards-based teaching strategies with techniques from the field of second-language acquisition. This publication is part of the Northwest Regional Educational Labora-tory's series, It's Just Good Teaching. This series of publications and videos offers teachers research-based instructional strategies with real-life examples from Northwest classrooms. Teaching Mathematics and Science to English-Language …
Simulação de Sistemas Distribuídos em Cenários com Defeitos

Este trabalho visa explorar a viabilidade de uso do ns como ambiente para simulação de SDs, particularmente em cenários sujeitos à ocorrência de defeitos. No presente artigo, são tratados apenas defeitos de colapso (crash failures); mas prevê-se a extensão da solução para outros tipos de defeitos.
Automating the Thematic Characterization of Geographic Resource Collections by Means of Topic Maps

SUMMARY Spatial Data Infrastructures at national or higher levels usually comprise the access to multiple geographic data catalogs. However, with classical search systems it is difficult to have a clear idea of the information contained in the metadata holdings of these catalogs. Topic maps are representation systems that allow the access to data with emphasis in the find-ability. This paper describes a process to generate a hierarchical topic map from a collection of geographic metadata records and the uses that the generated topic map can have in the context of a spatial data infrastructure to facilitate the access to the data.
Layout Optimization Methods for Offshore Wind Farms Using Gaussian Processes

Surrogate modeling is a family of engineering techniques that attracts great interest today and can be applied in many challenging fields. A big advantage of it is that surrogate models (models based on these techniques) offer reliable results by being computationally cheaper than other candidate models. The savings in computational time is usually paramount for problems that involve a lot of variables and parameters and many iterative processes. In the wind energy industry in particular, the design of the best layout of the wind farm is a problem that has been presented in the literature as an optimization problem; that is, a problem to optimize the wind farm layout in respect to some objective the modeler deems appropriate. More often than not, maximizing the expected power of the layout is mainly considered as this objective. The layout's expected power is -- among other things -- heavily dependent on the layout and the wake interactions between the turbines. The iterative search among many layouts to find the best one can be done with the help of a well-known optimization tool, the binary genetic algorithm. However, this tool cannot work alone, it solely facilitates the search over an adequate number of candidate solutions. To make it work, the modeler should provide it with some model that assesses how good in terms of the objective that has been set. In this thesis therefore, the theory, the development and the use of two models of interest are investigated: Gaussian Process Regression (a surrogate model) and the Monte Carlo Method (a method based on random sampling). Great care was given to compile the theoretical basis of these models in order to be a good reference point for the non-experienced reader. The nature of these two models differs quite a bit, but they both can be used by the modeler to yield interesting results. These results will be compared to each other and against a third model's results, a specific wake model. This third model is the Original Model which the Gaussian Process Regression model and the Monte Carlo Method model utilize and compare against. The reliability of the results and computational speed will be the measure of success and ranking for these three models. Finally, the comparison of the three models continues in how potent they are to propose an optimized layout for a wind farm. Each of the three models is coupled with the binary genetic algorithm that is developed specifically to connect with them. Afterwards, the proposed best layouts are discussed. The results show that the Gaussian Process Regression model performs reliably and very fast in comparison to the Original model. On the other hand, the Monte Carlo model, although also fast when it is used to find an optimized layout, could not be verified that it performs reliably and therefore, its results cannot be trusted without going into further investigation. After the comparison, further discussion follows with some recommendations for future research.
Improving the reliability of the jpHMM recombination prediction in HIV

Accurate classification of HIV and the identification of recombinants, including precise breakpoint definitions, is of crucial importance for epidemiological monitoring and the design of potential drugs. Recently we developed jpHMM, a new method to detect recombinations in HIV-l genomes. jpHMM predicts phylogenetic recombination breakpoints in a query sequence and assigns to each segment of the sequence one of the major HIV-l subtypes. For the user the reliability of the predicted breakpoint positions and parental subtypes is most important. For this reason we extended the output of jpHMM to include the information on regions where the model is 'uncertain' about the parental subtype and an interval estimate of the breakpoint. This information is determined using the posterior probabilities of the subtypes at each query sequence position.
Cloud Provisioning for Work ow Application with Deadline using Discrete PSO

The need of cloud consumers to optimize all options offered by cloud provider has been rapidly arisen during the recent years. The consideration involves the appropriate number of VMs must be purchased along with the allocation of supporting resources. Moreover, commercial clouds may have many different purchasing options. Finding optimal provisioning solutions is thus an NP-hard problem. Currently, there are many research works discussing the cloud provisioning cost optimization. However, most of the works mainly concerned with task scheduling. In this paper, we proposed a new framework where number of purchased instance, instance type, purchasing options, and task scheduling are considered within an optimization process. The Particle Swarm Optimization (PSO) technique is used to find the optimal solution. The initial results show a promising performance in both the perspectives of the total cost and fitness convergence. The designed system provides the solutions of purchasing options with optimum budget for any specified workflow-based application based on the required performance.
Creating and enforcing access control policies using description logic techniques

The quantity of generated information we store and need to access is colossal. Security of this information is becoming an issue of greater importance as the techniques and granularity with which it can be accessed become more advanced. Availability of information is a key component of any security system, although the information must be protected, it must also be available to the people who need it as and when they request it. However, increasing the methods by which it is accessible automatically increases the chance it maybe compromised. Security systems are now using advanced levels of encryption, digital signatures containing biometric data and highly complex access control policies. We are proposing an access control system which reduces the complexity involved in defining authorisation permissions, particularly in structured documents such as XML where the user may be granted restricted access. Our solution employs techniques usually reserved for intelligent systems and the semantic web.
Associative memories based on lattice algebra

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer of neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Application of a nonlinear activation function usually follows the linear operation in order to provide for nonlinearity of the network. In this paper, we discuss a novel class of artificial neural networks, based on lattice algebra, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, computation is nonlinear before the application of a nonlinear activation function. As a consequence, the properties of these networks, which are also known as morphological neural networks, are drastically different than those of traditional neural network models. The main emphasis of the results presented in this paper is on morphological associative memories. We define the basic computational model of a morphological neuron and examine some differences between these novel network models and traditional models.
Unsupervised Classification Learning from Cross-Modal Environmental Structure

This dissertation addresses the problem of unsupervised learning for pattern classification or category learning. A model that is based on gross cortical anatomy and implements biologically plausible computations is developed and shown to have classification power approaching that of a supervised discriminant algorithm. pp The advantage of supervised learning is that the final error metric is available during training. Unfortunately, when modeling human category learning, or in constructing classifiers for autonomous robots, one must deal with not having an omniscient entity labeling all incoming sensory patterns. We show that we can substitute for the labels by making use of structure between the pattern distributions to different sensory modalities. For example the co-occurrence of a visual image of a cow with a ``moo'''' sound can be used to simultaneously develop appropriate visual features for distinguishing the cow image and appropriate auditory features for recognizing the moo. pp We model human category learning as a process of minimizing the disagreement between outputs of sensory modalities processing temporally coincident patterns. We relate this mathematically to the optimal goal of minimizing the number of misclassifications in each modality and apply the idea to derive an algorithm for piecewise linear classifiers in which each network uses the output of the other networks as a supervisory signal. pp Using the Peterson-Barney vowel dataset we show that the algorithm finds appropriate placement for the classification boundaries. The algorithm is then demonstrated on the task of learning to recognize acoustic and visual speech from images of lips and their emanating sounds Performance on these tasks is within 1-7\% of the related supervised algorithm (LVQ2.1). pp Finally we compare the algorithm to Becker''s IMAX algorithm and give suggestions as to how the algorithm may be implemented in the brain using physiological results concerning the relationship between two types of neural plasticity, LTP and LTD, observed in visual cortical cells. We also show how the algorithm can be used as an efficient method for dealing with learning from data with missing values.
BUILDING FUZZY GOAL PROGRAMMING WITH FUZZY RANDOM LINEARPROGRAMMING FOR MULTI-LEVEL MULTI-OBJECTIVE PROBLEM

This paper proposes a fuzzy goal programming that is developed by fuzzy random regression approach, to solve a multi-level multi-objective problem. Fuzzy random regression enables us to deal with fuzzy random circumstances and approximate the coefficients for the developed model. A numerical example of the production planning problem illustrates the proposed solution approach. The proposed method is important where the fuzzy random data is dealt in the mathematical model to solve the multi-level multi-objective decision making problem which can attain a satisfactory solution.
Enhanced wavelet based audio coder

Several enhancements on the wavelet based audio coder proposed by Sinha and Tewfik (see. Proceedings of the Int. Conf. of Acoust., Speech and Signal Proc. Society (ICASSP), vol.1, p.197-200, 1993) are discussed. These enhancements include use of temporal information in masking for audio segments with sudden change of amplitudes in them, efficient quantization of the variances and run-length coding for the zeros. Experiments suggest that these enhancements give almost transparent compression of monophonic CD quality audio signals (sampled at 44.1 kHz) at bit rates of 55-63 kbits/sec. We also discuss a real time implementation of a simplified version of the wavelet based audio coder at somewhat higher bit rates (64-78 kbits/sec). <<ETX>>
What do acquisition activities really cost? A case study in Estonian university libraries

Purpose – The purpose of this paper is to mainly find out how well is time-driven activity-based costing (TDABC) suits for a university library setting in Estonia. For this purpose, all activities related to acquisitions process were identified and recorded in detail, and the cost of all these activities related to acquisition process in Estonian university libraries based on the example of the TDABC method were anlyzed. Design/methodology/approach – The data used in this paper is based on a review of relevant literature to provide an overview of the concept of the different cost accounting methods suitable for the measurement of the acquisition process. Through a case study, conducted among Estonian university libraries, the TDABC approach was used to analyze the acquisition process in university libraries. More specifically, the acquisition process studied concerned print format books, audiovisual documents and sheet music, and covered acquisition processes such as receipt of orders, ordering documents,.