Miracle-dz/UniFAR-FTUs huggingface dataset:
Uma revisao de abordagens geneticodifusas para descoberta de conhecimento em banco de dados

O processo geral de descoberta de conhecimento em banco de dados e composto por diversas etapas, destacando-se a de Mineracao de Dados (MD). As principais tarefas de MD sao associacao, agrupamento e descoberta de regras de classificacao. A tarefa de classificacao pode ser realizada por algoritmos convencionais ( e.g ., estatisticos) ou por metodos de inteligencia artificial ( e.g ., redes neurais, algoritmos evolucionarios, etc.). Nesta pesquisa o interesse e revisar algumas abordagens que utilizam algoritmos geneticos (AG) em combinacao com conjuntos difusos (CD) de forma hibrida para realizar busca em espacos grandes e complexos. Este artigo mostra diversas abordagens hibridas (AG+CD), desenvolvidas para descoberta de regras de classificacao, disponiveis na literatura e indica a possibilidade de adaptacao das mesmas na descoberta de conhecimento em banco de dados de Ciencia e Tecnologia (C&T)
Dopant profiling in p-i-n GaN structures using secondary electrons

We show that secondary electrons in a scanning electron microscope can provide important information about spatial dopant distribution in p-i-n GaN structures, with the highest contrast observed for a primary electron beam accelerating voltage in the range of 1 to 2 kV. The current through the specimen is used to determine the total backscattered and secondary electron yield. We establish a correlation between the secondary electron emission intensity and the doping characteristics of the material. The secondary electron emission intensity was found to be highest for p-type GaN, intermediate for n-type GaN, and lowest for undoped GaN. Specimen currents are found to have a strong correlation with the Mg concentration in p-GaN films. The contrast associated with dopants is shown to depend on experimental parameters such as primary electron beam voltage, total electron beam exposure, and specimen surface history. This technique can serve as a powerful tool for the development and characterization of thin films for GaN power electronics.We show that secondary electrons in a scanning electron microscope can provide important information about spatial dopant distribution in p-i-n GaN structures, with the highest contrast observed for a primary electron beam accelerating voltage in the range of 1 to 2 kV. The current through the specimen is used to determine the total backscattered and secondary electron yield. We establish a correlation between the secondary electron emission intensity and the doping characteristics of the material. The secondary electron emission intensity was found to be highest for p-type GaN, intermediate for n-type GaN, and lowest for undoped GaN. Specimen currents are found to have a strong correlation with the Mg concentration in p-GaN films. The contrast associated with dopants is shown to depend on experimental parameters such as primary electron beam voltage, total electron beam exposure, and specimen surface history. This technique can serve as a powerful tool for the development and characterization of thin fil.
Parity Horizons, Black Holes and Chronology Protection in Shape Dynamics

I introduce the notion of a parity horizon, and show that many simple solutions of shape dynamics possess them. I show that the event horizons of the known asymptotically at black hole solutions of shape dynamics are parity horizons and that this notion of parity implies that these horizons possess a notion of CPT invariance that can in some cases be extended to the solution as a whole. I present three new solutions of shape dynamics with parity horizons and nd that not only event horizons become parity horizons in shape dynamics, but observerdependent horizons and Cauchy horizons do as well. The fact that Cauchy horizons become (singular) parity horizons suggests a general chronology protection mechanism in shape dynamics that prevents the formation of closed time-like curves.
Bayesian Nonparametric Joint Mixture Model for Clustering Spatially Correlated Time Series

Abstract We develop a Bayesian nonparametric joint mixture model for clustering spatially correlated time series based on both spatial and temporal similarities. In the temporal perspective, the pattern of a time series is flexibly modeled as a mixture of Gaussian processes, with a Dirichlet process (DP) prior over mixture components. In the spatial perspective, the spatial location is incorporated as a feature for clustering, like a time series being incorporated as a feature. Namely, we model the spatial distribution of each cluster as a DP Gaussian mixture density. For the proposed model, the number of clusters does not need to be specified in advance, but rather is automatically determined during the clustering procedure. Moreover, the spatial distribution of each cluster can be flexibly modeled with multiple modes, without determining the number of modes or specifying spatial neighborhood structures in advance. Variational inference is employed for the efficient posterior computation of the proposed model. We validate the proposed model using simulated and real-data examples. Supplementary materials for the article are available online.
Review of cloud based trust model

Combining the concepts of trust and cloud,the trust evaluation model of security application was discussed from the perspective of subjective.The fuzziness and randomness subjective description to network objects and the reli-ability of the information evaluation could be provided by cloud based trust model(CTM),and it also,improving the se-curity of network applications,determined the trust level of entire information system.By reviewing the implement prin-ciple,classic improvement and existing problems,the mechanism of CTM could be analyzed,and the development direc-tions for CTM were pointed out.
Research on Combat Command Intelligence Activities and Information Model Based on IDEF3

IDEF3 modeling analysis and design method can describe well the process flow scene and the object of the state transition process. The operation command information analysis method is compared,the operational command information describing model is defined,based on this,the process of the operation command intelligence activities is analyzed. The process model is astablished by the use of PFN,analyzing the flow of information process based on the intelligence activities. The intelligence information model is astablished by the use of OSTN,suggesting intelligence activities information mapping table according to the operation command information describing model,model validation method is given.
Approximate Solutions of Singularly Perturbed Nonlinear Ill-posed and Sixth-order Boussinesq Equations with Hybrid Method

The aim of this paper is to obtain the approximate solution of singularly perturbed ill-posed and sixth-order Bouissnesq equation by hybrid method (differential transform and finite difference method) as a different alternative method. Differential transform method is applied for time variable and the finite difference method (central difference approach) is applied for position variable. Two examples are presented to demonstrate the efficiency and reliability of the hybrid method. Numerical results are given and compared with exact solution and in literature RDTM solution. The numerical data show that hybrid method is a powerful, quite efficient and is practically well suited for solving nonlinear singular perturbed Boussinesq equations.
Routing Protocol Simulation Research Based on SUMO

This paper refers TIGER database,builds a realistic road map as simulation scene.It evaluates the applicability of three routing protocols including AODV,DSR and DSDV in Vehicular Ad-hoc NETwork(VANET) by the joint simulation of Simulation of Urban MObility(SUMO) traffic simulator and NS2 network simulation platform.Experimental results show that existing routing protocols exist the shortages of low packet transmission success,high normalized routing load and big average end-to-end delay under urban VANET environment,it can not meet the needs of existing VANET communications in urban scenarios,and needs developing new routing protocol.
Combining neighborhood based collaborative filtering with tag informationfor personalized recommendation

Collaborative filtering recommendation is one of the most effective recommending techniques, which provide customers with suggestions according to their interests. However, neighborhood based collaborative filtering methods confront great challenges of data sparsity and lack of accessorial information in the context of big data. To address these problems, we propose a hybrid model combining tag information and neighborhood based collaborative filtering. A folksonomy network model based on tag information is proposed to analyze the tag relevance between different items. And tag relevance is incorporated into rating prediction of neighborhood based collaborative filtering for improving the recommendation accuracy. Experiments on MovieLens and Netflix datasets are carried out to evaluate the performance of our method. The results show that our method outperforms other methods and can improve recommending quality effectively.
Image processing and analysis methods in quantitative endothelial cell biology

This thesis details the development of computerised image processing and analysis pipelines for quantitative evaluation of microscope image data acquired in endothelial vascular biology experimentation. The overarching objective of this work was to advance our understanding of the cell biology of cardiovascular processes; principally involving haemostasis, thrombosis, and inflammation. Bioinformatics techniques are increasingly necessary to extract and evaluate information from biological experimentation. In cell biology advances in microscopy and the increased acquisition of large scale digital image data sets have created a need for automated image processing and data analysis. The development, testing, and evaluation of three computerised workflows for analysis of microscopy images investigating cardiovascular cell biology are described here. The first image analysis pipeline extracts morphometric features from high-throughput experiments imaging endothelial cells and organelles. Segmentation of endothelial cells and their organelles followed by extraction of morphometric features provides a rich quantitative data set to investigate haemostatic mechanisms. A second image processing workflow was applied to platelet images obtained from super-resolution microscopy, and used in a proof-of-principle study of a new platelet dense-granule deficiency diagnostic method. The method was able to efficiently differentiate between healthy volunteers and three patients with Hermansky-Pudlak syndrome. This was achieved by segmenting and counting the number of CD63-positive structures per platelet, allowing for the differentiation of patients from control volunteers with 99\% confidence. The final workflow described is a video analysis method that quantifies interactions of leukocytes with an endothelial monolayer. Phase contrast microscopy videos were analysed with a Haar-like features object detection and custom tracking method to quantify the dynamic interaction of rolling leukocytes. This technique provides much more information than a manual evaluation and was found to give a tracking accuracy of 92\%. These three methodologies provide a toolkit to further biological understanding of multiple facets of cardiovascular behaviour.
AN EXPERIMENTAL STUDY ON BANK FORECASTING USING REGRESSION DYNAMIC LINIER MODEL

Saat ini, peramalan dikembangkan lebih cepat karena keputusan yang lebih sistematis membuat proses di perusahaan. Salah satu karakteristik peramalan yang baik adalah akurasi, yaitu mendapatkan error sekecil mungkin. Banyak metode peramalan saat ini banyak digunakan data historis untuk mendapatkan error minimal. Selain itu, mereka tidak memperhatikan dipengaruhi faktor. Pada tugas akhir ini, salah satu metode peramalan akan diusulkan. Metode ini disebut Regression Dynamic Linear Model (RDLM). Metode ini adalah Ekspansi dari metode Dynamic Linear Model (DLM), yang memodelkan data berdasarkan variabel yang mempengaruhinya. Dalam RDLM, variabel yang mempengaruhi suatu data disebut regresi variabel. Jika data memiliki lebih dari satu variabel regresi, maka akan ada begitu banyak Model kandidat RDLM. Hal ini akan membuat hal sulit untuk menentukan yang paling optimal model. Karena itu, salah satu metode Bayesian Model Averaging (BMA) akan diterapkan untuk menentukan model yang paling optimal dari sekumpulan model kandidat RDLM. IniMetode ini disebut Akaike Information Criteria (AIC). Dengan menggunakan metode AIC ini, pemilihan model proses akan lebih mudah, dan model RDLM yang optimal dapat digunakan untuk meramalkan data. BMA- Metode Akaike Information Criteria (AIC) mampu menentukan model RDLM secara optimal.Model RDLM yang optimal memiliki akurasi yang tinggi untuk peramalan. Itu bisa disimpulkan dari hasil estimasi kesalahan, nilai MAPE adalah 0,62897% dan nilai U 0,20262.
Cloud Forensics: Drawbacks in Current Methodologies and Proposed Solution

Cloud Computing is a heavily evolving domain in technology. Many public and private entities are shifting their workstations on the cloud due to its robust, remote, virtual environment. Due to the enormity of this domain, it has become increasingly easier to carry out any sort of malicious attacks on such cloud platforms. There is a very low research done to develop the theory and practice of cloud forensics. One of the main challenges includes the inability to collect enough evidence from each and every subscriber of a Cloud Service Provider(CSP) and thus not being able to trace out the roots of the malicious activity committed. In this paper we compare past research done in this field and address the gaps and loopholes in the frameworks previously suggested. Overcoming these, our system/framework facilitates the collection, organization, and thereby the analysis of the evidence sought, hence preserving the essential integrity of the sensitive and volatile data.
Investigating The Three-Click Rule: A Pilot Study

Human-computer interaction is a part of our everyday lives. A component of HCI is usability, often used as a measure of ease-of use. People have been studying usability for a long time and a standard scale, the System Usability Scale, has been created to measure the usability of systems. To increase ease of use in websites, designers have been following the ThreeClick Rule. As technology evolves, user expectations can be expected to evolve as well. Therefore, a pilot study was created and conducted to evaluate measures for testing the relevancy of this rule.
An Empirical Investigation of Word Representations for Parsing the Web

Parsing web text is progressively becoming important for many applications in natural language processing, such as machine translation, information retrieval, and sentiment analysis. Current syntactic parsing has been focused on canonical data such as newswires. When evaluated on standard benchmarks such as Wall Street Journal data set, current state-of-the-art parsers achieve accuracies well above 90%. However the accuracy drops dramatically when they are applied to new domains such as web data, barely over 80%. In order to make progress in many applications that rely on parsing, we need robust parsers that can handle such texts. One approach that is becoming popular recently is to use unsupervised word representations as extra features. Koo et al. [1] has shown that unsupervised clustering features are effective to improve dependency parsing. Turian et al. [2] examined clustering and unsupervised word embedding features on chunking and named entity recognition tasks. Unsupervised word embeddings are dense, low-dimensional and real-value vectors representing words, often induced by neural language models. They have shown that these word representation features lead to improvement in the performances. These word representations are induced by unsupervised methods, thus they are good for new domains such as the web, which has enormous amount of unlabeled data but little labeled data. In this paper we investigate the effect of unsupervised word representation features on dependency parsing with web texts. We consider two different kinds of word representations, namely Brown clustering and word embeddings induced from a neural language model. To the best of our knowledge, this is the first work that systematically examines these word representations on the task of dependency parsing on web text.
Inelastic scattering imaging system for Pseudocercospora fijiensis detection

This work sought to develop an inelastic scattering imaging system based on Raman spectroscopy for the detection of the fungal phytopathogen, Pseudocercospora fijiensis, which causes Black Sigatoka disease in banana crops, very important in Colombian agro-industrial economy. This system consists of a modified stereoscope with an optical setup able to simultaneously capture spectral images together with its Raman spectra. The camera has two different bandpass filters attached, centered in the spectral region of C=O stretching of Chitin and the equatorial bending vibration of beta-1,3-glucan, molecules of the fungal cell wall. In this way, the system can get images with unique spectral features, suitable for training a convolutional neural network in order to get a recognition pattern of the fungal strain growing in the PDA agar. As a result, the instrument was able to detect the presence of P.fijiensis over the culture media.
Early science with the Large Millimetre Telescope: new mm-wave detections of circumstellar discs in IC 348 from LMT/AzTEC

We present the most complete sample of mm measurements of protoplanetary discs in the star-forming region IC 348 to date. New observations from the Large Millimetre Telescope and the 1.1 mm camera AzTEC are combined with literature results in order to characterize the disc population as relating to both stellar properties within the IC 348 region and across other star-forming regions. In addition to detecting 28 of 116 observed known infrared-excess sources, we detected emission from two previously unknown candidate transition discs in the region. When combined with literature results, we find evidence for a steeper-than-expected slope, on average, in disc spectral energy distributions at millimetre wavelengths in the IC 348 region. We show that the presence or absence of high-mass discs is a sensitive indicator of regional evolution, both among star-forming regions and within IC 348. In contrast, low-mass discs exhibit almost no apparent evolution within the first ∼5 Myr when compared among regions.
The Influence of Simulation-Based Information Systems Education on Enterprise Systems Knowledge and User Satisfaction

Enterprise systems implementations are often high cost and high risk with many companies failing to achieve their targeted business objectives. At the individual level, negative user reactions to enterprise systems lead to resistance to IT use, counterproductive adaptations, and reduced job satisfaction. Factors that contribute to these individual level effects include a lack of understanding of reengineered business processes and a lack of understanding of economic interdependencies. In this research, a computer-based business simulation educational intervention is investigated to determine its effectiveness in facilitating enterprise systems knowledge acquisition and promoting job satisfaction. Simulation-based education utilizes an experiential learning model, immersing participants in a realistic, dynamic business environment. This dissertation consists of three related essays. Two essays compare simulationbased education against traditional education to determine their relative effectiveness at imparting enterprise business process knowledge (essay 1), business motivational knowledge of economic interdependencies (essay 2), and at influencing job satisfaction. In essays 1 and 2, user knowledge structures are assessed using concept relatedness measures and analyzed using established Pathfinder network structure techniques. Results support the importance of accurate business process knowledge and understanding of enterprise economic interdependencies as antecedents to improved job satisfaction but the two types of knowledge appear to operate differently. Business process knowledge has a relatively stronger effect on perceptions of opportunities and control over the technology; whereas business motivational knowledge resulted in stronger reductions to perceptions
Fractional variable order anti-windup control strategy

In this paper, a novel anti-windup strategy is presented. It is based on using fractional variable order integrator instead of integer order one in PID controller. It is shown that among four different types of variable order derivative definitions, only one gives satisfactory results – comparable, and even slightly better than the classical back-calculation anti-windup algorithm. Results are also presented in the form of simulation plots.
Models of Web Services Composition Based on Timed Color Petri Nets

BPEL is often used to describe the composition of Web Services,but it is lack of sound formal semantic.Web services were prone errors.Based on Timed Color Petri Net,transitions from Web Services which was described using BPEL to Timed Color Petri Net models were proposed,while BPEL activities execute ways and environments were considered.The models we get are more exacter and can be used to verify and test Web Service.An instance of this transition were given.
Study on mimitype embedded monitoring system of rolling bearing

To meet the real time request of monitoring system of rolling bearing,in this paper,an embedded monitoring system of rolling bearing based on Windows CE is proposed.The implement include the hardware selection,the operation system selection and customization and application implement,main technology is studied,such as realtime data sampling,realtime data analyzing,data transport and backup,monitoring system will analyze the sampling data,and supply the friendly interface to technic-man to decision-making.
Fisher Vector Encoding of Supervoxel-Based Features for Airborne LiDAR Data Classification

Point cloud feature extraction as a classification task is crucial in maximizing the efficient downstream applicability of raw point clouds. With the goal of learning optimum features for efficient classification of a multi-class point cloud for downstream applications, this letter presents a supervoxel-Fisher vector (FV)-based approach for airborne light detection and ranging (LiDAR) data classification. In our approach, FV encoding is implemented to deduce compact global descriptors from aggregated supervoxels to establish a more descriptive and discriminative representation, transforming the low-level visual features into high-level semantic features. As a result, the proposed approach combines local and global feature properties through the quantization and aggregation of higher order statistics to harnesses their combined advantages for producing good classification results. Experiments were conducted on the international society for photogrammetry and remote sensing 3-D semantic labeling benchmark data set. Results indicate that the proposed approach is robust and efficient, attained the third best position with an overall accuracy of 81.79%, and ranked first with an $\text{F}_{{1}}$ -score of 72.31%.