Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy dynamic path planning using genetic algorithms

Fuzzy control concepts are useful in both global and local path planning tasks for autonomous mobile objects. The search for an optimal decision table to be used as the inference engine in fuzzy-based planning and navigation algorithms is highly important. The authors address the problem using genetic algorithms as a search and optimisation tool. The approach yields better planned paths compared to those obtained from random decision tables generated using rules of thumb.
Right-and-Left Tree Join for Less Redundant Candidate Generation in Tree Mining

Frequent substructure mining on semi-structured data has been getting important in recent years. In this thesis we focus on the frequent ordered tree discovery problem. The main procedure in frequent tree mining is to determine whether a candidate tree generated from smaller frequent trees appears extensively or not by scanning the set of data trees. Avoiding the duplication of same candidate trees and reducing the enumeration of redundant infrequent candidate trees are effective for fast discovery of all frequent trees. Previous algorithms are based on an enumeration method called rightmost expansion which appends a new node to the rightmost branch of a frequent tree to generate a candidate tree. The method enumerates the candidate trees including all frequent trees without duplication. Rightmost expansion, however, has a problem that it generates a number of infrequent candidate trees. Therefore we propose right-and-left tree join, a less redundant candidate generation method. It enumerates only candidate tree of size k+1 with high possibility of being frequent by combining two frequent trees of size k and results in the reduction of most redundant candidate trees. In fact, right-and-left tree join never generates a larger number of infrequent candidate trees than rightmost expansion. We present an algorithm named AMIOT which applies right-and-left tree join for candidate generation in frequent tree mining and evaluate the performance of it in comparison with FREQT, a typical algorithm based on rightmost expansion. The experiments on synthetic dataset and XML database show that AMIOT outperforms FREQT by from 2.5 to 6 times in execution time.
Learner perspectives regarding device type in technology-assisted language learning

ABSTRACT This study compared learner perspectives regarding using PCs and smartphones as learning devices. A total of 167 Korean university students completed online assignments designed to maximize interaction with these devices. The assignments required students to submit their responses regarding assigned reading passages for a semester. Then they completed an online survey of five questions: (a) most frequently used device; (b) reasons for favoring a particular device; (c) limitations of a particular device; (d) better device for facilitating cognitive effort; (e) places where they used a particular device. The results showed that half the participants used PCs and the other half used smartphones. PC users favored ‘comfortable use,’ whereas smartphone users favored ‘ubiquitous learning. ’ PC users reported ‘not easily portable’ was the most prominent limitation, whereas smartphone users pointed out ‘using smartphone's costly data’ and ‘uncomfortable to drag/move’ were noticeable limitations. In general however, the majority of both PC and smartphone users reported no limitations in using these devices as learning tools. Regarding facilitating cognitive effort, around 38.0% of participants indicated that there was no difference between these two devices. Most PC users did online assignments either at home or in a dormitory. Smartphone users completed tasks in several different places.
Modeling and Simulation of a Tracked Mobile Inspection Robot in Matlab and V-Rep Software

: This paper presents modeling and simulation of a tracked mobile robot for pipe inspection with usage of MATLAB and V-REP software. Mechanical structure of the robot is described with focus on pedipulators, used to change pose of track drive modules to adapt to different pipe sizes and shapes. Modeling of the pedipulators is shown with application of MATLAB environment. The models are verified using V-REP and MATLAB co-simulations. Finally, operation of a prototype is shown on a test rig. The robot utilizes joint space trajectories, computed with usage of the mathematical models of the pedipulators.
Orthogonal combination of local binary patterns for dynamic texture recognition

Dynamic texture (DT) is an extension of texture to the temporal domain. Recognizing DTs has received increasing attention. Volume local binary pattern (VLBP) is the most widely used descriptor for DTs. However, it is time consuming to recognize DTs using VLBP due to the large scale of data and the high dimensionality of the descriptor itself. In this paper, we propose a new operator called orthogonal combination of VLBP (OC-VLBP) for DT recognition. The original VLBP is decomposed both longitudinally and latitudinally, and then combined to constitute the OC-VLBP operator, so that the dimensionality of the original VLBP descriptor is lowered. The experimental results show that the proposed operator significantly reduces the computational costs of recognizing DTs without much loss in recognizing accuracy.
Multidimensional spectral analysis and the decomposition of ionospheric mode structures

Because wave interference effects due to ionospheric multipath and undesired signals within the receiver bandwidth may cause large bearing estimation errors, it is necessary to resolve the constitutent wavefield incident upon an array of sensors. This paper considers the comparative performance of five multidimensional spectral analysis algorithms in the decomposition of complex radio wavefields. Computer simulations were conducted for small angle of arrival separations, additive noise, time-bandwidth product, mutual coherence and cochannel interference. The eigenstructure method exhibited best overall performance.
Color Reproduction from Noisy CFA Data of Single Sensor Digital Cameras

Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate Denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based Denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.
AI Approaches for Classiﬁcation and Attribute Extraction in Text

As the amount of data online grows, the urge to use this data for different applications grows as well. Machine learning can be used with the intent to reconstruct and validate the data you are interested in. Although the problem is very domain speciﬁc, this report will attempt to shed some light on what we call strategies for classiﬁcation, which in broad terms mean, a set of steps in a process where the end goal is to have classiﬁed some part of the original data. As a result, we hope to introduce clarity into the classiﬁcation process in detail as well as from a broader perspective. The report will investigate two classiﬁcation objectives, one of which is dependent on many variables found in the input data and one that is more literal and only dependent on one or two variables. Speciﬁcally, the data we will classify are sales-objects. Each sales-object has a text describing the object and a related image. We will attempt to place these sales-objects into the correct product category. We will also try to derive the year of creation and it’s dimensions such as height and width. Different approaches are presented in the aforementioned strategies in order to classify such attributes. The results showed that for broader attributes such as a product category, supervised learning is indeed an appropriate approach, while the same can not be said for narrower attributes, which instead had to rely on entity recognition. Experiments on image analytics in conjunction with supervised learning proved image analytics to be a good addition when requiring a higher precision score.
Customer Value Creation in Multichannel Systems: The Interactive Effect of Integration Quality and Multichannel Complexity

Abstract This research examines factors that influence consumer perceptions of value created by a multichannel system of service delivery. The literature suggests that multichannel integration quality allows firms to benefit from the effect of synergy and complementarity between channels. We investigate the perceived value of multichannel service delivery in the context of retail banking services, where such multichannel systems are omnipresent. We propose and test a model in which multichannel integration quality is an important value driver, such that higher multichannel integration quality leads to greater value perceptions of not only the multichannel system, but also the overall value of the bank as perceived by the customer. Importantly, the complexity of the multichannel system of service delivery, as perceived by customers, moderates the direct effect of channel integration quality on perceived value of the multichannel system, so that in highly complex multichannel systems, channel integration quality will have a stronger effect on the perceived value to customer. Our findings also shed light on the specific factors that contribute to consumer value perceptions of multichannel retail banking services, which has important implications to managers and researchers.
Predictive Wireless Antenna Selection for High Mobility Conditions

Accurate channel knowledge is indispensable to the practical success of channel-aware wireless communication technologies. However, channel estimates obtained from pilot symbols rapidly become outdated due to fast time variations of multipath fading channels. To reduce system cost, antenna subset selection reduces radio frequency (RF) chain components. For systems selecting a subset of a plurality of antennas for reception, this outdated channel information is a significant impediment to selection and data decoding reliability. In this thesis, training-based schemes for antenna selection (AS) for time-varying channels which account for practical constraints such as training, packetization and antenna switching time are proposed based on discrete prolate spheroidal sequences. They only operate with knowledge or estimates of the Doppler frequency and the channel signal-to-noise ratio (SNR), but do not require detailed statistical correlation knowledge. A pilot-based AS scheme for time-varying frequency-flat channels for single input– multiple output (SIMO) systems selecting one of a plurality of antennas using packet or symbol-rate antenna switching is first proposed. It is demonstrated that the presented scheme provides significant performance gain over AS methods using Fourierbased orthogonal training as well as over single antenna systems with perfect channel knowledge. Analytical expressions for the symbol error probability (SEP) of M-ary
Design of a data warehouse model for decision support at higher education

The expansion of higher education (HE) institutions and their increased emphasis on strategic planning have raised the demand for integrated information systems that can support strategic analysis. A data warehouse (DW) provides decision data for managers in a form that facilitates their access, using business intelligence (BI) tools to enhance their decision making process. However, available DW models and design methods do not address the HE industry’s specific needs. In this paper, we aim to fill this gap by providing a methodological framework that encompasses methods tailored to meet the needs of HE. Also, a prototype has been developed using actual student data to validate the framework. It shows the efficacy of the framework in supporting strategic analysis at universities, leading them to develop effective strategies and policies. The paper further provides guidelines for researchers on the important issues to be considered for designing efficient DWs.
Apontamentos sobre guias turísticos e design da informação

guia turistico, design da informacao, design grafico, Este trabalho parte da importância que os guias turisticos exercem sobre as escolhas e decisoes de viajantes/usuarios, e todas as suas implicacoes. Propoe relacionar os principios basicos/diretrizes de design da informacao de Redig (2004) como forma de organizacao e estruturacao no projeto de guias turisticos. Tais principios referem-se a quantidade e conteudo (concisao, clareza, coloquialidade e cordialidade), organizacao e apresentacao (analogia, consistencia e enfase) e acesso a informacao (senso de oportunidade e estabilidade). Assim, tendo como premissa que todo projeto da area de design da informacao e focado no usuario, buscou-se analisar os tipos de guias e seus leitores para apontar os aspectos pertinentes ao projeto especifico de guias turisticos. tourist guidebook, information design, graphic design This article originates on the influence that tourist guidebooks have over the traveling decisions of its users. It proposes to relate the basic information design principles from Redig (2004) as means of organizing and structuring guidebook’s projects. These principles refer to the amount of content (conciseness, clarity, colloquiality, cordiality); the manner of organizing and presenting information (analogy, consistency, emphasis); and finally to information access (opportunity sense and stability). Therefore, assuming that every information design project is focused on the user, an analysis of different types of guidebooks and its readers was made on the effort of pointing out relevant aspects for designing a tourist guidebook.
A Novel Measure for Semantic Similarity Computation of Gene Ontology Terms Using Weighted Aggregation of Information Contents

Background: Gene ontology (GO) is a well-structured knowledge of biological terms that describes roles of genes and their products in a standardized and organized controlled vocabulary format. Over the last decade, many measures are developed to exploit GOadvantagestodeterminesemanticsimilaritiesbetweenbiologicalentities. UsingGOontologies,therearesomeconstraintsthat existing GO-based semantic similarity measures try to address them. For instance, (1) edges in a GO graph, do not indicate uniform distances and also have diﬀerent densities, and (2) ignoring term levels in an ontology makes “shallow annotation” drawback, i.e., two terms with a certain distance near the root of GO graph have equal semantic similarity with two terms with the same distance but far from the root. Methods: Here, we present wAIC, a two-stage hybrid semantic similarity measure using weighted aggregation of information contents. In wAIC, the impact of each common ancestor on semantic similarity value is determined according to the location of the ancestor in the ontology graph. wAIC, also, ﬁlters (from annotating term set) terms that are in upper levels of the graph ontology to reduce shallow annotation constraints. Results: Experimental results conﬁrm that the proposed measure is more consistent with major related constraints, such that, wAIC semantic similarity values have more correlation with both sequence similarity values and gene expression based similarity values than state-of-the-art semantic similarity measures. Conclusions: WAICshowusingaweightedaggregationof commonancestorsiscompletelyconsistentwiththehumanperception and can improve accuracy of gene similarity measurement.
Analytical Control Grid Registration for Efficient Application of Optical Flow

Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edgedirected interpolation of a single image as an intensity-based registration problem. 2) Providing an analytical framework for intensity-based registration using control grid constraints. 3) Quantitative assessment of the new, single-image enlargement algorithm based on analytical intensity-based registration. In addition to single image resizing, the new methods and analytical approaches were extended to address a wide range of applications including volumetric (multi-slice) image interpolation, video deinterlacing, motion detection, and atmospheric distortion correction. Overall, the new approaches generate results that more accurately reflect the underlying signals than less computationally demanding approaches and with lower processing requirements and fewer restrictions than methods with comparable accuracy.
Signaling and Adhesive Mechanisms in Acute Pancreatitis

Acute pancreatitis (AP) is an inflammatory disease with variable severity ranging from mild interstitial edematous to severe necrotizing disease. The overall mortality rate of AP is 8-9%. Specific treatment of AP is lacking which is partly related to an incomplete understanding of the basic pathophysiology behind the disease. It is widely held that premature intra-cellular trypsinogen activation and leukocyte recruitment play key roles in the pathophysiology of the AP. However, the signaling and adhesive mechanisms remain elusive. The aim of this thesis was to investigate the signaling and adhesive mechanisms in AP. In this thesis, biliary pancreatitis was induced by retrograde infusion of taurocholate into the pancreatic duct in different mice strains to elucidate the role of toll-like receptor 2 (TLR2), TLR4, lymphocyte function antigen-1 (LFA-1), matrix metalloproteinases (MMPs) and Rho-kinase as well as nuclear factor of activated T-cell (NFAT) signaling in AP. We found that TLR4 but not TLR2 plays a role in AP. LFA-1 adhesive mechanisms play a role in tissue damage and leukocyte recruitment but not trypsinogen activation. Furthermore, neutrophil-derived MMP-9 mediates tissue damage and neutrophil-dependent trypsinogen activation. Rho-kinase signaling regulates trypsinogen activation, leukocyte recruitment and tissue damage in AP. Moreover, NFATc3 is activated and translocated to the acinar cell nucleus and regulates trypsinogen activation, leukocyte recruitment and tissue damage in AP. Taken together, the results of this thesis demonstrate that signaling and adhesive mechanisms are of particular importance in the pathophysiology of AP and could be used as useful targets in the management of patients with AP.
Robust digital image hashing algorithms for image identification

Image hashing has been a popular alternative of digital watermarking for copyright protection and content authentication of digital images, due to its two critical properties – robustness and security. Also, its uniqueness and compactness make image hashing attractive for efficient image indexing and retrieval applications. In this thesis, novel image hashing algorithms are proposed to improve the robustness of digital image hashing against various perceptually insignificant manipulations and distortions on image content. Furthermore, image hashing concept is extended to the content-based fingerprinting concept, which combines various hashing schemes efficiently to achieve superior robustness and higher identification accuracy. The first contribution of the thesis is the novel FJLT image hashing, which applies a recently proposed low-distortion dimension reduction technique, referred as Fast Johnson-Lindenstrauss Transform (FJLT), into image hashing generation. FJLT shares the low distortion characteristics of random projections, but requires less computational cost, which are desirable properties to generate robust and secure image hashes. The Fourier-Mellin transform can also be incorporated into FJLT hashing to improve its performances under rotation attacks. Further, the content-based fingerprinting concept is proposed, which combines different FJLT-based hashes to achieve better overall robustness and identification capability. The second contribution of the thesis is the novel shape contexts based image hashing (SCH) using robust local feature points. The robust SIFTHarris detector is proposed to select the most stable feature points under various content-preserving distortions, and compact and robust image hashes are generated by embedding the detected feature points into the shape contexts based descriptors. The proposed SCH approach yields better identi-
Real-Time Screen Space Cartoon Water Rendering with the Iterative Separated Bilateral Filter

We present the improvements and new results of our method for rendering particle-based liquid simulations that runs in real-time and includes an adjustable performance/quality trade-off. Our approach smooths the fluid surface with an iterative version of the separated bilateral filter, and introduces a new method for generating foam and droplets that is appropriate for non-photorealistic styles. The entire method occurs in screen space, which avoids the usual artifacts of polygonization techniques. All the steps are implemented directly in the graphics hardware. Improvements include a new method to generate foam and droplets, visual enhancements in the optical effects generated by the interaction between water and light, and tests in a environment with support to collision of water particles with rigid bodies. Performance and visual analysis that includes comparisons with previous methods shows the applicability of our approach.