Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing DBN structure based on BOA

An optimal algorithm for dynamic Bayesian networks(DBN) based on Bayesian optimal algorithm(BOA) is developed for learning and constructing DBN structure.Firstly,some basic theories and concepts of the probability model evolutionary algorithm are introduced.Secondly,the basic mode for constructing DBN diagram are described and the mechanism of DBN structure learning based on BOA is clarified.The BOA includes two parts of main technique,one is to gain the structure and parameter of DBN in term of good solutions,the another is to produce new group according to DBN.The learning of DBN is done by genetic algorithm based on the greed mechanism.The inference of DBN is done by a forward-simulation algorithm. Matlab simulation result demonstrates the proposed algorithm is effective.
A WIDEBAND GATE MIXER USING 0.15 μM GAAS ENHANCEMENT-MODE PHEMT TECHNOLOGY

This paper presents a wideband gate mixer using 0.15 μm GaAs enhancement-mode pseudomorphic high electron mobility transistor (E-mode PHEMT) process. The proposed mixer is based on a single-ended gate mixer topology. Proper input matching networks are used to ensure good conversion gain as well as a wide frequency band. A λ/4 open stub at local oscillator (LO) frequency and a low-pass filter at the drain terminal do great help to enhance LO-IF and RF-IF isolation performance. A Lange coupler is used to maintain LO-RF isolation in a wide frequency band. The measured results show that the mixer operates in wide RF frequency of 17–26 GHz and IF frequency of 0.8–1.7 GHz with a conversion gain of 5–8 dB. The 1 dB compression point (P1 dB) is −1 ∼ 1 dBm, and the needed LO power is only 1 dBm. The LO-IF, RF-IF, and LO-RF isolations are about 45, 45, and 20 dB, respectively. This represents excellent performance for GaAs PHEMT mixer in terms of frequency bandwidth, conversion gain, isolation, and P1 dB performance.
Predictive processing of aspectual information: evidence from event-related brain potentials

ABSTRACT We conducted an event-related potential experiment using aspectual coercion to examine whether aspectual information is predictively processed prior to verb input in Japanese. In the experiment, experimental sentences were presented with two presentation durations (500 ms vs. 800 ms) to manipulate the temporal predictability of a verb. Aspectually coerced sentences elicited an early anterior negativity (AN) only when participants had a sufficient time to generate an aspectual prediction for a verb. A late AN was observed in aspectual coercion irrespective of the manipulation of the duration. This result indicates that early AN is elicited by a prediction error pertaining to aspectual information, suggesting that aspectual information is computed predictively based on adverbials without waiting for verb information.
Evaluation of measurement uncertainty from a nonstationary process

In metrology, when repeated measurements are autocorrelated, it is not appropriate to use the traditional approach to evaluate the uncertainty of the average of repeated measurements. In literature, methodologies were developed to evaluate the uncertainty of measurements from a stationary process, which demonstrated ‘statistical equilibrium’. In this paper, we discuss approaches to evaluate the measurement uncertainty when the data are from a nonstationary process. In particular, for some nonstationary processes with a time-dependent mean and a constant variance, we may be able to assess the uncertainty based on one realization. Specifically, after the effect of the time-dependent mean is removed, the residuals may show equilibrium behavior and thus can be treated as being from a stationary process. We propose approaches to evaluate the uncertainty of measurements based on one realization of such a process with practical examples presented for illustration.
DTM:A Dynamic Trust Management Model for Internet Computing Environments

Nowadays,many novel computing technologies such as Pervasive Computing and Grid Computing have emerged to empower resource sharing and collaboration over Internet.However,trust establishment across multiple autonomous domains has become an important issue because resources are dynamic and behaviours are uncontrollable over Internet.Firstly,existing trust management solutions and systems lack of a unified model,specially the definitions of trust in several security mechanisms are inconsistent,moreover there is no formal proof on trust transitive property referring to the trust management model.Additionally,dynamic short-lived collaboration among entities frequently happens,which may require the trust relationship among collaborating entities to be changed on demand.Therefore,this paper proposes a dynamic trust management model(DTM) to support flexible trust establishment between unfamiliar entities,in which the concept of trust is formally defined based on a belief formula,and the transitive property of trust(trust chain) is proved.In this model,an event is used to describe the cause of trust relationship evolution between principals.A regular event sequence is employed to describe the resource authorization process,and a dependent sequence on regular event sequences is designed for the resource authorization process of trust negotiation.Finally,a trust management system in CROWN middleware,and some preliminary experiments are conducted and the experimental results are given and analysed.
Experimental and simulative investigation of the oil distribution during a deep-hole drilling process and comparing of the RANS kω-SST and RANS hybrid SAS-SST turbulence model

Helical deep hole drilling is a process frequently used in industrial applications to produce bores with a large length to diameter ratio. For better cooling and lubrication, the deep drilling oil is fed directly into the bore hole via two internal cooling channels. Due to the inaccessibility of the cutting area, experimental investigations that provide information on the actual machining and cooling behavior are difficult to carry out. In this paper, the distribution of the deep drilling oil is investigated both experimentally and simulatively and the results are evaluated. For the Computational Fluid Dynamics (CFD) simulation, two different turbulence models, i.e. the RANS k-ω-SST and hybrid SAS-SST model, are used and compared. Thereby, the actual used deep drilling oil is modelled instead of using fluid dynamic parameters of water, as is often the case. With the hybrid SAS-SST model, the flow could be analyzed much better than with the RANS k-ω-SST model and thus the processes that take place during helical deep drilling could be simulated with realistic details. Both the experimental and the simulative results show that the deep drilling oil movement is almost exclusively generated by the tool rotation. At the tool’s cutting edges and in the flute, the flow velocity drops to zero for the most part, so that no efficient cooling and lubrication could take place there. In addition, cavitation bubbles form and implode, concluding in the assumption that the process heat is not adequately dissipated and the removal of chips is adversely affected, which in turn can affect the service life of the tool and the bore quality. The carried out investigations show that the application of CFD simulation is an important research instrument in machining technology and that there is still great potential in the area of tool and process optimization.
Stochastic Geometry for Modeling, Analysis and Design of Future Wireless Networks

This thesis focuses on the modeling, analysis and design of future wireless networks with smart devices, i.e., devices with intelligence and ability to communicate with one another with/without the control of base stations (BSs). Using stochastic geometry, we develop realistic yet tractable frameworks to model and analyze the performance of such networks, while incorporating the intelligence features of smart devices. In the first half of the thesis, we develop stochastic geometry tools to study arbitrarily shaped network regions. Current techniques in the literature assume the network regions to be infinite, while practical network regions tend to be arbitrary. Two well-known networks are considered, where devices have the ability to: (i) communicate with others without the control of BSs (i.e., ad-hoc networks), and (ii) opportunistically access spectrum (i.e., cognitive networks). First, we propose a general algorithm to derive the distribution of the distance between the reference node and a random node inside an arbitrarily shaped ad-hoc network region, which helps to compute the outage probability. We then study the impact of boundary effects and show that the outage probability in infinite regions may not be a meaningful bound for arbitrarily shaped regions. By extending the developed techniques, we further analyze the performance of underlay cognitive networks, where different secondary users (SUs) activity protocols are employed to limit the interference at a primary user. Leveraging the information exchange among SUs, we propose a cooperation-based protocol. We show that, in the short-term sensing scenario, this protocol improves the network's performance compared to the existing threshold-based protocol. In the second half of the thesis, we study two recently emerged networks, where devices have the ability to: (i) communicate directly with nearby devices under the control of BSs (i.e., device-to-device (D2D) communication), and (ii) harvest radio frequency energy (i.e., energy harvesting networks). We first analyze the intra-cell interference in a finite cellular region underlaid with D2D communication, by incorporating a mode selection scheme to reduce the interference. We derive the outage probability at the BS and a D2D receiver, and propose a spectrum reuse ratio metric to assess the overall D2D communication performance. We demonstrate that, without impairing the performance at the BS, if the path-loss exponent on cellular link is slightly lower than that on D2D link, the spectrum reuse ratio can have negligible decrease while the average number of successful D2D transmissions increases with the increasing D2D node density. This indicates that an increasing level of D2D communication is beneficial in future networks. Then we study an ad-hoc network with simultaneous wireless…
Variations in MTF and NPS between CT scanners with two different IR algorithms and detectors

The use of iterative reconstruction (IR) in computed tomography (CT) has become a useful tool for improved image quality or for reduced dose in CT examinations. In this study, the image quality of the two different Siemens IR algorithms, sinogram affirmed iterative reconstruction (SAFIRE) and advanced modelled iterative reconstruction (ADMIRE), were evaluated and compared. Differences between the Stellar detector and the conventional detector and similarities between scanners with the same detector and IR algorithm were also examined. The objective image quality parameters modulation transfer function (MTF) and noise power spectrum (NPS) were measured on a Catphan 600 phantom (The Phantom Laboratory Incorporated, NY) for five different Siemens CT scanners. MTF was calculated from the circular edge of the sensitometry targets in Catphan to facilitate measurements at different contrasts and dose levels. The results showed that the image quality was improved by using ADMIRE compared to SAFIRE on the same CT scanner equipped with the Stellar detector for certain specific tasks. The MTF was higher for the three low contrast objects when the sharp reconstruction kernel was applied. The CT scanners equipped with the Stellar detector showed an improved image quality compared to the CT scanners equipped with the conventional detector. Larger differences in MTF and NPS were observed between the detectors than between SAFIRE and ADMIRE.
IMAGE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS: AN EXPERIMENTAL STUDY ON COREL DATABASE

In this paper high-level image classes are inferred from low-level image features like color and shape features with the help of artificial neural network. Back propagation neural network algorithm is used for integrating knowledge from low- level image features and classify the images into high level concepts / semantic classes. The classifier is evaluated on a database of 1000 images from COREL database. The experimental results show that the accuracy using back propagation neural network algorithm to classify COREL images ranges between 80.5% to 88.6%.
Automatic Entity Relation Extraction

Entity Relation Extraction is an important research field in Information Extraction. Two kinds of machine learning algorithms, Winnow and Support Vector Machine (SVM), were used to extract entity relation from the training data of ACE (Automatic Content Extraction) Evaluation 2004 automatically. Both of the algorithms need appropriate feature selection. When two words around an entity were selected, the performance of the both algorithms got the peak. The average weighted F Score of Winnow and SVM algorithms were 73 08% and 73 27% respectively. We can conclude that when the same feature set is used, the performance of different machine learning algorithms get little difference. So we should pay more attention to find better features when we use the automatic learning methods to extract the entity relation.
DIFFICULTY IN HANDLING HIGH DIMENSIONAL DATA- PLEASE STAND UP

With the advancement of technology huge amount of high dimensional data are getting generated. This paper present’s the high dimensional data as a serious problem for computational analysis. Broadly high dimensional data could be handled in two ways, firstly with dimensionality reduction, secondly without dimensionality reduction using classical machine learning. Two main categories of dimensionality reduction are feature selection and extraction. Feature selection and feature extraction. Feature selection selects a subset of features based on some criteria while feature extraction method transforms the data into the lower dimensional space. Experiment result of the high dimensional data shows the performance improvement of classifier before and after dimensionality reduction.
Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments

While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a difficult task. The benefit of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques.
An improvement on recommender systems by exploring more relationships

Recommender systems are systems that can filter a great number of pieces of data and suggest mostly similar interested items of the user’s preference. A variety of approaches have been proposed to perform recommendation, including content-based, collaborative filtering and association-based, etc. A potential problem existing in a recommender system is cold start [1]; simply defined that a system cannot draw any inference for users. In this paper, we deal with one of cold start problems by proposing a hybrid approach which combines two distinct features to solve the problem. While a user is related to other users in product purchase behaviors or preference, an item is connected to different items by its inside information. Our recommender system utilizes both these relations instead of each individual one to ameliorate the quality of output suggestion. This procedure will be revealed and discussed through this paper.
Trade-off between automated and manual testing: A production possibility curve cost model

Testing is always important for Software Quality Assurance (SQA) activities and key cost multiplier in software development. The decision to automate or not to automate a test case is critical. In this paper we discuss the possibility of test automation and in relation to the trade-off between manual and automated test cases. We purpose a Production cost frontier based technique to distinguish the point of automation and manual test within the cost constraints. Our objective is to identify the facts that up to what extant a testing process can be automated. In this paper a cost model is proposed for deciding the proportion of automated and manual testing. The objective is to find best possible combination of these two and production possibility in one type by eliminating the other type of testing
Empowering Teachers to Design Learning Resources with Metacognitive Interface Elements

It is becoming increasingly easy for teachers to create many forms of digital learning resources, such as web-based materials, quizzes and games. Extensive research in metacognition provides strong evidence that metacognitive interface elements could make such materials more effective. This chapter argues for the creation of such a set of metacognitive interface elements so a teacher can easily incorporate these into the learning resources they create. We illustrate this in two ways: a series of examples of a simple, but valuable and broadly applicable, interface element which asks learners to assess their confidence; and an overview of a rich and sophisticated example, the Metatutor system. We conclude with a research agenda for creating a toolkit of metacognitive interface elements.
A Multi-Criteria Collaborative Filtering Recommender System Using Clustering and Regression Techniques

Traditional Collaborative Filtering (CF) recommender systems recommend the items to users based on their single ratings which are used to match similar users. In multi-criteria CF recommender systems, however, multi-criteria ratings are used instead of single ratings which can significantly improve the accuracy of traditional CF algorithms. This research proposes a new recommendation method using Classification and Regression Tree (CART) and Expectation Maximization (EM) for accuracy improvement of multi-criteria recommender systems. We also apply Principal Component Analysis (PCA) for dimensionality reduction and to address multi-collinearity induced from the interdependencies among criteria in multi-criteria CF datasets. Experimental results on Yahoo! Movies and TripAdvisor datasets demonstrated that the proposed method significantly improve recommendation accuracy in case of precision.
Análisis sobre el uso de Twitter en las administraciones locales de la provincia de Castellón

The important impact of the usage of digital technologies in the social relationships, specially in social networks, has made that even politics and administration have joined to be represented on them. The appeareance of the 2.0 web facilitates the access to the population of the administration�s paperwork and enables interaction, something that converts this sites in politic communication platforms that change the concept of public space and question the hegemony of the traditional means of communication. Due to this fact, since 2011 the communication cabinets from la Diputacion de Castellon and the city councils with a higher socio-economic impact in the province have decided to fit together the emission of announcements in the institutional web with the creation of profiles in social networks, such as Twitter. This essay has as a final objective the study of the analysis of the evolution during these past years and the changes of the relations strategies from the communications cabinets to move closer to the population and also to recover their confidence. This is done starting with a secondary data research such as the emission of press announcements from 2011 until 2013 and the analysis of institutional messages on Twitter with the help of the tools Metricspot and Brandtweet. As a result, it is verified the increase of institutional information in their websites and social networks without any chance of reaching to a public discussion with the population.
La caída de las cajas de ahorros españolas. Cuestión de rentabilidad, tamaño y estructura de propiedad=The Fall of Spanish Savings Banks. Question of Profitability, Size and Ownership Structure

En Espana entre 2010 y 2011 se ha producido una gran restructuracion financiera, que ha llevado a la practica desa¬paricion de las Cajas de Ahorros mediante fusiones y posterior cambio de estructura de la propiedad convirtiendose en bancos. Esta gran restructuracion ha sido promovida por el Banco de Espana, como salida para que las entidades financieras subsistieran a la crisis que les afectaba. Este trabajo analiza los bancos y cajas del sistema financiero espanol durante los cuatro anos previos al comienzo de la reestructuracion financiera del 2010. El objetivo es comprobar si existen diferencias en cuanto a rentabilidad por causas de tamano y tipo de gobierno de la entidad (cajas de ahorros, bancos). Los resultados muestran que el incre¬mento de tamano y la bancarizacion auspiciadas por el Banco de Espana es una estrategia adecuada para resolver la actual crisis financiera. In Spain between 2010 and 2011 there has been a major financial restructuring, which has led to the virtual disap¬pearance of savings banks through mergers and subsequent change in ownership structure becoming banks. This major restructuring has been promoted by the Bank of Spain, as an outlet for that financial institutions subsisted to the crisis that affected them. This paper analyzes the banks and savings banks the Spanish financial system during the four years prior to the beginning given the financial restructuring of 2010. The aim is to check whether there are differences in profitability for reasons of size and form of ownership of the entity (savings banks, banks). The results show that the increase in size and bankarization under the auspices by the Bank of Spain is an appropriate strategy for resolving the current financial crisis.