Miracle-dz/UniFAR-FTUs huggingface dataset:
Treball final de grau

Fractal geometry was first invented by mathematicians of the late 19th and early 20th century to study problems as abstract as the existence of continuous functions non derivable at any point, the continuum hypothesis, or the existence of topological spaces with strange properties. It was not until the second half of the 20th century that fractal geometry became popular, and it was applied to the study of natural phenomena by the hand of B. Mandelbrot. The study of tumor growth using fractal geometry is relatively recent. This study is based on the fractal character of tumor contours. One of their most important features is roughness, since its increase is related to the tumor growth. Tumor growth dynamic can be described by a function that depends on both time and position, known as local width function, as well as by some power law exponents, called critical exponents. The aim of this final degree project is to study all these concepts in connection with fractals. Elementary concepts of the theory of fractals will also be studied, as the iterated function systems, the fractal dimension and the fractal interpolation functions. Since fractal dimension can only be computed accurately for some specific mathematical objects, we will study some methods that estimate fractal dimension of tumors based on its fractal characteristics. Finally, we will develop some Matlab programs which will compute the fractal dimension of tumors, as well as the roughness of their contours. Furthermore, we will compare our results with the experimental data.
Simple, biologically informed models of human target detection, but not convolutional networks generalize be-tween naturalistic and artiﬁcial images

The ﬁrst steps of visual processing are often described as a bank of oriented ﬁlters followed by divisive normalization. This approach has been tremendously successful at predicting contrast thresholds in simple visual displays. However, it is unclear to what extent this kind of architecture also supports processing in more complex visual tasks performed in naturally looking images. We used a deep generative model for natural images to embed arc segments with different curvatures in naturalistic images. These images contain the target as part of the image scene, resulting in considerable appearance variation of target as well as background. Three observers localized arc targets in these images, achieving an accuracy of 74.7% correct responses on average. Data were ﬁt by several biologically inspired models and by a 5-layer convolutional neural network. Four models were particularly good a predicting observer responses, (i) a bank of oriented ﬁlters, similar to complex cells in primate area V1, (ii) a bank of oriented ﬁlters followed by tuned gain control, incorporating knowledge about cortical surround interactions, (iii) a bank of oriented ﬁlters followed by local normalization, (iv) the 5-layer convolutional neural network. A control experiment with optimized stimuli based on these four models showed that the observers’ data were best explained by model (ii) with tuned gain control. These data suggest that standard models of early vision provide good descriptions of performance in much more complex tasks than what they were designed for, while general purpose non-linear models such as convolutional neural networks do not.
Merging path and gshare indexing in perceptron branch prediction

We introduce the hashed perceptron predictor, which merges the concepts behind the gshare, path-based and perceptron branch predictors. This predictor can achieve superior accuracy to a path-based and a global perceptron predictor, previously the most accurate dynamic branch predictors known in the literature. We also show how such a predictor can be ahead pipelined to yield one cycle effective latency. On the SPECint2000 set of benchmarks, the hashed perceptron predictor improves accuracy by up to 15.6% over a MAC-RHSP and 27.2% over a path-based neural predictor.
Accounting for 16S rRNA copy number prediction uncertainty and its implications in 1 bacterial diversity analyses 2

10 Background: 16S rRNA gene has been widely used in bacterial and archaeal diversity studies to 11 determine the community composition and structure. 16S rRNA gene copy number (16S GCN) 12 varies among bacterial species and this variation introduces biases to the relative cell abundance 13 estimated using 16S rRNA read counts. To correct the biases, methods have been developed to 14 predict 16S GCN. 16S GCN predictions come with inherent uncertainty, which is often ignored 15 in the downstream analyses. However, a recent study suggests that the uncertainty can be so 16 great that copy number correction is not justified in practice. Despite the significant implications 17 in 16S rRNA based bacterial diversity studies, the uncertainty associated with 16S GCN 18 predictions has not been well characterized and its impact on bacterial diversity studies needs to 19 be investigated. 20 Results: Here we develop RasperGade16S, a novel method and software to better model and 21 capture the inherent uncertainty in 16S rRNA GCN prediction. RasperGade16S implements a 22 maximum likelihood framework of pulsed evolution model and explicitly accounts for 23 intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross 24 validation, we show that our method provides robust confidence estimates for the GCN 25 predictions and outperforms other methods in both precision and recall. We have predicted GCN 26 for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent 27 an exhaustive and diverse list of engineered and natural environments. We found that the 28 prediction uncertainty is small enough for 99% of the communities that 16S GCN correction 29 should improve their compositional and functional profiles estimated using 16S rRNA reads. On 30 the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such 31 as PCoA, PERMANOVA and random forest test. Conclusion: We have developed a method to accurately account for uncertainty in 16S rRNA 33 GCN predictions and the downstream analyses. For almost all 16S rRNA surveyed bacterial 34 communities, correction of 16S GCN should improve the results when estimating their 35 compositional and functional profiles. However, such correction is not necessary for beta- 36 diversity analyses. 37
Domain-Specific Architectures (DSAs): Research Problems and Promising Approaches

Process technology-driven performance and energy eiciency improvements have slowed down as we approach physical design limits. General-purpose manycore architectures attempt to circumvent this challenge, but they have a signiicant performance and energy-eicient gap compared to special-purpose solutions. Domain-speciic architectures (DSAs), an instance of heterogeneous architectures, eiciently combine general-purpose cores and specialized hardware accelerators to boost energy eiciency and provide programming lexibility. Indeed, the hardware, software, and systems aspects in DSAs are highly tailored to maximize the energy eiciency of applications in a target domain. As DSAs and their conceptualization advance rapidly, there is a strong need to understand the research problems that need immediate attention. This paper discusses the primary research directions in the design and runtime management of DSAs. Then, it surveys some promising approaches and highlights the outstanding research needs.
Pattern Spectrum and Multiscale Shape Representation

The results of a study on multiscale shape description, smoothing and representation are reported. Multiscale nonlinear smoothing filters are first developed, using morphological opening and closings. G. Matheron (1975) used openings and closings to obtain probabilistic size distributions of Euclidean-space sets (continuous binary images). These distributions are used to develop a concept of pattern spectrum (a shape-size descriptor). A pattern spectrum is introduced for continuous graytone images and arbitrary multilevel signals, as well as for discrete images, by developing a discrete-size family of patterns. Large jumps in the pattern spectrum at a certain scale indicate the existence of major (protruding or intruding) substructures of the signal at the scale. An entropy-like shape-size complexity measure is also developed based on the pattern spectrum. For shape representation, a reduced morphological skeleton transform is introduced for discrete binary and graytone images. This transform is a sequence of skeleton components (sparse images) which represent the original shape at various scales. It is shown that the partially reconstructed images from the inverse transform on subsequences of skeleton components are the openings of the image at a scale determined by the number of eliminated components; in addition, two-way correspondences are established among the degree of shape smoothing via multiscale openings or closings, the pattern spectrum zero values, and the elimination or nonexistence of skeleton components at certain scales. >
Taking Things at Face Value : How Stance Informs Politeness of Virtual Agents 1

How police officers approach and try to reason with civilians and offenders can determine how certain situations are resolved. Due to monetary and time costs, training of such social skills is currently not available to all police officers. To assist in the training curriculum of the Dutch police, we are developing a serious game prototype in which police officers interact with virtual agents to improve their social awareness.
Mobile to Server Face Recognition : A System Overview

Server Face Recognition, which is a face recognition application developed specifically for mobile phones. Images taken from mobile phone cameras lack of quality due to the low resolution of the cameras. Thus, a prototype is developed to experiment the chosen method. However, this paper shows a result of system backbone without the face recognition functionality. The result demonstrated in this paper indicates that the interaction between mobile phones and server is successfully working. The result shown before the database is completely ready. The system testing is currently going on using real images and a mock-up database to test the functionality of the face recognition algorithm used in this system. An overview of the whole system including screenshots and system flow-chart are presented in this paper. This paper also presents the inspiration or motivation and the justification in developing this system. overview I. INTRODUCTION ACE detection and recognition is one of the most difficult problems in computer vision area. This is the reason why this field receives a huge attention in medical field and research communities including biometric, pattern recognition and computer vision communities [1][2][3][4]. The first step for face recognition is face detection or can generally be regarded as face localization. It is to identify and localize the face. Face detection technology is imperative in order to support applications such as automatic lip reading, facial expression recognition and face recognition [5][6].
Exploring the Use of Enterprise Content Management Systems in Unification Types of Organizations

The aim of this paper is to better understand how highly standardized and integrated businesses known as unification types of organizations use Enterprise Content Management Systems (ECMS) to support their business processes. Multiple case study approach was used to study the ways two unification organizations use their ECMS in their daily work practices. Arising from these case studies are insights into the differing ways in which ECMS is used to support businesses. Based on the comparisons of the two cases, this study proposed that unification organizations may use ECMS in four ways, for: (1) collaboration, (2) information sharing that supports a standardized process structure, (3) building custom workflows that support integrated and standardized processes, and (4) providing links and access to information systems. These findings may guide organizations that are highly standardized and integrated in fashion, to achieve their intended ECMS-use, to understand reasons for ECMS failures and underutilization and to exploit technologies investments.
AIS Electronic Library (AISeL)

Internet based collaboration tools are increasingly being used in information systems courses. This study reports on the results of one such implementation at Lawrence Technological University. Using a sample of 51 students, the researcher explores three hypotheses. First, students in a class with electronic collaboration will work on course material more days during the week than those without. Second, students will perceive that quiet peers can more freely express themselves. Third, students in a course with electronic collaboration will perceive a greater gain in their Internet skills compared to those in a course without collaboration. The researcher supported his second hypothesis.
Guest Editorial for Special Issue on Southern Africa Growing communities of practice among educational technology researchers and practitioners in development-oriented contexts: Linking local and global debates

This editorial starts from the context of disparities in access between the North and the South and within the Southern African region. The authors then explore the origins of the papers in the e/merge 2004 online conference which was designed to support the growth of communities of practice of educational technology researchers and practitioners across Southern Africa. This special issue represents a shift from a time-bound community of practice event to publication within an ongoing community focussed on the use of educational technology within and across developing countries. SOUTHERN AFRICA AS A GLOBAL ICT MICROCOSM This special issue of IJEDICT concerns the application of information and communication technologies (ICT) in education in Southern Africa. This is a region in which global disparities between technologically well-endowed and economically restricted uses of ICT in education are starkly apparent. The articles in this issue point to many of the challenges facing educators and researchers concerned with finding and building contextually appropriate and flexible approaches to the uses of ICT in development-oriented settings. The focus on Southern Africa in this issue is the outcome of an earlier regional online conference, e/merge 2004, which we saw as providing rich material for a wider ICT and education audience. Economic conditions frame access to ICT nationally and regionally, and play an enormous role in determining how ICT is situated and contextualised in less developed countries. They often mask innovations and local initiatives which are important illustrations of what it is possible to conceptualise and realise. It is crucial that the work of early adopters, of devoted educators and of centres, units and research groups is seen against the backdrop of prevailing issues of access to ICT. Again and again, access is a primary factor which enables and yet also restricts our students’ changing literacies, of which ICT is now a part. In order to provide context for the articles in this special issue we will:
Evidence of Volcanism and Glaciation from the Sausar Group, Central India

Palaeoproterozoic rocks of the Sausar Group, central India, are exposed on the southern flank of the Satpura Mountain Belt. Mapping in different parts of this belt indicates complex evolutionary history. The presence of a volcanosedimentary unit in the basal part of the Sausar Group is reported here. The sedimentological characters of the overlying unit indicate its glaciogenic origin. Association of volcanogenic and glaciogenic rocks in the Sausar Group could be used as tectonostratigraphic tool for supercontinent assembly during Palaeoproterozoic.
Evaluation Methods in Biomedical Informatics

Chapter 1 : Challenges of Evaluation in Biomedical Informatics Chapter 2: Evaluation as a Field Chapter 3: Determining What to Study Chapter 4: The Structure of Objectivist Studies Chapter 5: Measurement Fundamentals Chapter 6: Developing and Improving Measurement Methods Chapter 7 Chapter 8 Chapter 9: Subjectivist Approaches to Evaluation Chapter 10: Performing Subjectivist Studies in the Qualitative Traditions Responsive to Users Chapter 11: Economic Aspects of Evaluation Chapter 12
2 Informal Description and Comparisons

Generalized Multitext Grammar (GMTG) is a synchronous grammar formalism that is weakly equivalent to Linear Context-Free Rewriting Systems (LCFRS), but retains much of the notational and intuitive simplicity of Context-Free Grammar (CFG). GMTG allows both synchronous and independent rewriting. Such flexibility facilitates more perspicuous modeling of parallel text than what is possible with other synchronous formalisms. This paper investigates the generative capacity of GMTG, proves that each component grammar of a GMTG retains its generative power, and proposes a generalization of Chomsky Normal Form, which is necessary for synchronous CKY-style parsing.
Topological evolution in the ordered and isotropic phases of a lyotropic system

In this review, after a brief overview of fundamental concepts involved in describing the geometrical properties of surfactant aggregates I summarize some results obtained recently by indirect studies on the topology changes in the well-known non-ionic lyotropic system C12EO6/H2O by measuring the diffusion constants of tracer molecules in the mesophases and by high-frequency rheology in the isotropic phase.
Practical privacy preserving content based retrival in cloud image repositories

- Due to the increasing quality of cloud computing, additional and additional data homeowners are motivated to supply their data to cloud servers for nice convenience and reduced worth in data management. However, sensitive data got to be encrypted before outsourcing for privacy wants that obsoletes data utilization like keyword-based document retrieval. Throughout this paper, we tend to gift a secure multi-keyword stratified search theme over encrypted cloud data, that at a similar time supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and so the widely-used TFIDF model square measure combined within the index construction and question generation. we tend to construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to supply economical multi-keyword stratified search. The secure kNN algorithm is utilized to cipher the index and question vectors, and within the meanwhile guarantee correct association score calculation between encrypted index and question vectors. Therefore on resist applied mathematics attacks, phantom terms square measure extra to the index vector for bright search results . due to the use of our special tree-based index structure, the planned theme will do sub-linear search time and upset the deletion and insertion of documents flexibly. extensive experiments are conducted to demonstrate the efficiency of the planned theme.
I''''d be overwhelmed, but it''''s just one more thing to do

An apparatus for cleaning a photosensitive member comprising a cleaning roller formed of foamed resin which contacts the member and a pressure plate held in compressing abutment against the roller so as to extend in the direction of its forward rotation at the point of contact therebetween. Any residual powder on the photosensitive member is removed by the roller and retained in the pores formed in the roller surface. The powder retained in the pores is expelled by the resilience of the compressed surface of the roller as it is restored upon release from the pressure plate. The cleaning apparatus also comprises a system for recovering powder that is expelled from the roller surface, and a rotating helical coil disposed in a conveyance path for preventing solidification of the powder and for facilitating its conveyance.
Polarization and Belief Dynamics in the Black and White Communities: An Agent-Based Network Model from the Data

Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated with positions in that social network. With simple assumptions regarding belief change and social reinforcement, we use those modeled networks to build dynamic agent-based models of how information can be expected to flow and how beliefs can be expected to change across each community. With contrasting information from governmental and religious sources, the results show importantly different dynamic patterns of belief polarization within the two communities.
Benchmarking model-free and model-based optimal control I

Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an explicitly given mathematical model of the system. In this article, we provide a comprehensive comparison of the performance of reinforcement learning and nonlinear model predictive control for an ideal system as well as for a system with parametric and structural uncertainties. The comparison is based on two different criteria, namely the similarity of trajectories and the resulting rewards. The evaluation of both methods is performed on a standard benchmark problem: a cart-pendulum swing-up and balance task. We first find suitable mathematical formulations and discuss the effect of the differences in the problem formulations. Then, we investigate the robustness of reinforcement learning and nonlinear model predictive control against uncertainties. The results demonstrate that nonlinear model predictive control has advantages over reinforcement learning if uncertainties can be eliminated through identification of the system parameters. Otherwise, there exists a break-even point after which model-free reinforcement learning performs better than nonlinear model predictive control with an inaccurate model. These findings suggest that benefits can be obtained by combining these methods for real systems being subject to such uncertainties. In the future, we plan to develop a hybrid controller and evaluate its performance on a real seven-degree-of-freedom walking robot.